Marc Andreessen on AI Winters and Agent Breakthroughs

This episode originally aired on the Latent Space podcast.

Marc Andreessen has watched AI cycle through summers and winters for more than 35 years, from coding in LISP in 1989 to backing the foundation model companies today. He argues that the current moment is not another false start, but the payoff from eight decades of foundational research, catalyzed by four distinct breakthroughs,

• large language models
• reasoning
• agents
• self-improvement.

He also makes the case that the combination of a language model, a Unix shell, and a file system represent one of the most important software architectures in a generation. Swix and Alessio Fanelli speak with Marc Andreessen, co-founder and general partner at A16Z.

Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.

Having said that, I think what’s actually happened is an enormous amount of technical progress that built up over time. For example, we now know the neural network is the correct architecture. I will tell you, there was a 60-year run where that was 70 years where that was controversial.

I call it 80-year overnight success.

Which is an overnight success because it’s bam, ChatGPT hits and then 01 hits and then open call hits. And these are open, overnight, radical, overnight transformative successes, but they’re drawing on an 80-year sort of wellspring backlog of ideas and thinking. It’s not just that it’s all brand new. It’s that it’s an unlock of all of these decades of very serious hardcore research.

If I were 18, this is 100, this is what I would be spending all of my time on. This is such an incredible conceptual breakthrough.

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Now let’s get into it.

Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I’m joined by Swyx, editor of Latent Space. Hello, and we’re in A16Z with A, Mark and Jason. Welcome.

Yes. Yes. A and what? Half of 16? Half of the one. A1. Exactly.

Apparently, this is the final few days in your current office. You’re moving across the road. We have a limit of some projects underway. But yeah, this is actually, this is the original. We’re in actually the original office. We’re in the, we’re in the, we’re in the, we’re in the whole thing. It’s beautiful.

Yeah. Great. Thank you.

So I have to come out. This is a, I wanted to pick a spicy start. In October, 2022, I just made friends with Rune and I wanted to give him something to sort of be spicy about. And I said, it’ll never not be funny that A16Z was constantly going, the future is where the smart people choose to spend their time and then going deep into crypto and not in AI. And that was in October, 2022, and Rune says there was an internal meeting in A16Z to reorient around Gen AI. Obviously you have, but was there a meeting? What, what was that?

I mean, I don’t look, I’ve been doing AI since the late eighties. Yeah. So I don’t know, all that, as far as I’m concerned, this stuff is all Johnny come lately. Yeah. I mean, look, we’ve been doing AI our entire existence. I mean, we’ve been doing AI machine learning deep, deep, but we’ve been doing this stuff way from the beginning, obviously AI is just core to computer science. I actually view them as quite continuous.

Ben and I both have computer science degrees. We both, Ben and I actually both are old enough to remember the actual AI boom in the 1980s. There was a big AI boom at the time, and there was one of their names expert systems, and they were of Lisp and Lisp machines. I coded at Lisp. I was coding a Lisp in 1989 when that was the language of the AI future. Yeah. So this is something that we’re completely comfortable with and Been doing the whole time and are very enthusiastic about.

Is there a strong, this time is different because, my closest analog was 2016, 17. It was an AI boom. And it petered out very, very quickly. It’s just, it’s just in terms of investing, sort of investment, investment, excitement. Although that’s really when the, the, the NVIDIA phenomenon really, it was, it was, I would say it was in that period when it was very clear that at the time it, the vocabulary was more machine learning, but it was very clear at that time that machine learning was hitting some sort of takeoff point.

Well, and as you guys, you guys have talked about this at length on your, on your thing, but if you really track what happened, I think the real story is it was, it was the AlexNet basically breakthrough in 2013. That was the, that was the real knee in the curve. And then it was obviously the transformer breakthrough in 17.

And then everything that followed, but, but, machine learning, there were, I mean, I’ve been working, I’ve been working with one of my, kind of projects working with Facebook since 2004, and on the board since 2007. And of course that, they started using machine learning very early. And I’ve used it basically for 20 years for content, feed optimization and advertising optimization, and obviously many financial services, many, many, many companies, many different sectors have been doing this.

And so it’s like one of these things, it’s like, it’s not a single thing. It’s like layers, right. And the layers arrive at different paces, but they kind of build up, they kind of build up over time. And then, and then, in retrospect, 2017 was kind of the key point with the transformer. And that, and then as you guys know, there was this really weird four year period where the transformer existed. And then it was just like, let’s go.

But between 2017 and 2021, that was the era of which companies like Google had internal chat bots, but they weren’t letting anybody use them. And then OpenAI developed chat GPT or GPT two.

“this is way too dangerous to deploy”

And then they told everybody, this is way too dangerous to deploy. We can’t possibly let normal people, normal people use this thing. And then you guys, I’m sure remember AI Dungeon. So the only, there was like a year where the only way for a normal person to use GPT three was an AI Dungeon. And so we would do this, you’d go in there and you’d pretend to play Dungeons and Dragons and reality, you’re just trying to talk to, talk to GPT.

And so there was this long, the big, big companies, big companies are cautious and the big companies were cautious. By the way, it took OpenAI time to actually adjust, kind of redirect their research path. I think it was at Rosewood, the dinner that founded OpenAI was right there. But that dinner would have taken place in 2018. The formation of OpenAI as late as 2018. Sorry. No, I’m wrong. It should be 20. They just celebrated a 10 year anniversary. So it is 2025. So 2015, yeah, 2015.

But then, Alec Radford did GPT one in what? Probably 17, 18, 17, 18. So it is, and then they didn’t really, and then GPT three was what? 2020, 2020, 2020, because that became co-pilot immediately. Even OpenAI, which has been the leader of this thing in the last decade, even they had to adapt and lean into the new thing.

And so, yeah, I think it’s just this process of basically sort of wave after wave, layer after layer, building on itself. And then you kind of get these catalytic moments where the whole thing pops. And obviously that’s what’s happening now.

Is it useful to think about, will there be any winter? Cause there’s always these patterns. Is this endless summer? It’s something I constantly think about because do I get, do I just get endlessly hyped and just trust that I will only be early and never wrong. Well, are we, will there be a winter?

So there’s something about the following, there’s something about AI that has led to this repeated pattern. And you guys know this, but it’s summer, winter, summer, winter, summer, winter, and it goes back 80 years, 80 years.

• summer, winter, summer, winter, summer, winter, and it goes back 80 years, 80 years

2013
2017
2020

The original neural network paper was 1943, right. Which is, which is amazing. That was, it was far back that long.

And then there was, you guys, if you guys have ever talked about this on your show, but there was this, there was a big, there was an AGI conference at Dartmouth university in 1955. And they got an NSF grant for the all the experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get AGI on the other end. And they got there, by the way, they got the grant, they got the 10 weeks and then, making 55, no, no AGI.

And I said, I lived through the eighties version of this, where there was a big, a big boom and a crash. And so, so there is this thing and there, there is something about AI that causes the people in the field, I would say to become both excessively utopian and excessively apocalyptic. And, and it’s probably on both sides of the the boom bust cycle. You, you kind of see that play out.

Having said that, I think what’s actually happened is just in, and we now know in retrospect, an enormous amount of technical progress that built up over time. For example, we now know the neural network is the correct architecture. And I will tell you, there was a 60 year run where that was a, or even 70 years where that was controversial. And we now know that that’s the case. And so, we, we now, everything we’re building on today just sort of derives from the original idea in 1943.

And so, so in retrospect, we now know that these, these guys are right, they would get the timing wrong and they thought capabilities would arrive faster or there were, it could be turned into businesses sooner or whatever, but they were fundamentally, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing and, and, and the payoff from, from, from all their work is happening now.

And so, so the way I think about what’s happening is basically, I think, I think about basically the, the, the period we’re in right now is it’s, I call it 80 year overnight success, right? Which is, it’s an overnight success. Cause it’s bam, chat GPT hits and then, and then O one hits and then, open call hits. And these are open, these are, these are overnight, radical overnight transformative successes, but they’re drawing on an 80 year sort of wellspring backlog of ideas and thinking it’s not just that it’s all brand new. It’s that it’s an unlock of all of these decades of very serious, hardcore research and thinking; look, there were AI researchers who spent their entire lives. They got their PhD, they worked for, they’ve researched for 40 years and they retired. And a lot of cases they passed away and they never actually saw it at work.

So sad. It is. It is sad. It is sad. And I knew something was the last guy.

Well, there were the guys, Alan Newell. I mean, there’s tons of John McCarthy. John McCarthy was one of the inventors of the field. He’s one of the guys that organized the Dartmouth conference. And, he taught at Stanford for 40 years and passed, passed away, I don’t know, whatever, 10, 10 years ago or something. Never, never actually got to see it happen. But it is amazing in retrospect, these guys were incredibly smart and they worked really hard and they were correct.

So anyway, so then it’s like, okay, say, say history doesn’t repeat, but it rhymes. It’s like, okay, does that mean that there’s going to be another, basically boom, bust cycle. And I will tell you, looks like in a sense, yes, everything goes through cycles and, people get overly enthusiastic and overly depressed. And there’s, there’s a time, there’s a timelessness to that.

Having said that there’s just no question. So the foremost, the foremost dangerous words, it was different. Do you know the 12 most dangerous words of investing? No, the foremost, foremost, dangerous words of investing are “this time is different.” The 12 most dangerous words. And so I’ll tell you what’s different. Now it’s working. There’s just no, I mean, look, there’s just no question. And by the way, I’ll just give you guys my take. LLMs, from basically the chat GPT moment through to spring of 25, I think you could still, I think well-intentioned, well-informed skeptics could still say, oh, this is just pattern completion. And oh, these things don’t really understand what they’re doing. And the hallucination rates are way too high. And this is going to be great for creative writing and creating, Shakespearean sonnets and, as, as rap lyrics or whatever, it’s gonna be great at all that stuff, but we’re not going to be able to harness this to make this relevant in coding or in medicine or in law or in, kind of feels that, kind of really, really matter.

And I think basically it was the reasoning breakthrough who it was a one. And then our one that basically answered that question and basically said, oh no, we’re going to be able to actually turn this into something that’s going to work in the real world. And then, and then obviously the coding breakthrough over the, or basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.

We’re just like, all right, if, if, Linus Torvalds is saying that the AI coding is not better than he is, “that’s, that’s never happened before.” That’s the benchmark. “That’s never happened before.” And so now we know that it’s, it’s going to sweep through coding. And then, and then we, we know that if it’s going to work in coding, it’s going to work in everything else.

Right. It’s just that, cause that’s, that’s the hardest, in many ways, that’s the hardest example. And now everything else is going to be a derivative of that.

And then on top of that, we just got the agent breakthrough with OpenClaw, which is fantastic, which is amazing and incredibly powerful. And then we just got the auto research, the self-improvement, we’re now into the self-improvement breakthrough.

And so the, so the way I think about it is we’ve had four fundamental breakthroughs and functionality, LLMs, reasoning, agents, and then now RSI, and they’re all actually working.

• functionality
• LLMs
• reasoning
• agents
• RSI

And so I’m, I’m just, as you guys, I’m jumping out of my shoes, this is it, this is the culmination of 80 years worth of work. And this is the time it’s becoming real.

Yeah. I’m completely convinced. I think the anxiety that people feel is during the transistor era, you had Moore’s Law and it’s all right, we understand why these things are getting better. We understand the physics of it. With AI, it’s so jagged in the jumps where, you said, in three months, you have this huge jump, and people are, well, this can keep happening.

Right. But then it keeps happening. It’ll keep happening. And so how do you think about also timelines of what’s worth building? I think we always have this question with guests, which is should you spend time building harness for a model versus the next model just going to do it one shot in the latent space. And how does that inform how you think about the shape of the technology? You talk about how it’s a new computing platform. If you have a computing platform that every six months it drastically changes in what it looks like, it’s hard to build companies on top of it.

Yeah. So it’s a couple of things. So one is look, Moore’s Law was what we now call a scaling law. When Moore’s Law was a scaling law and for your younger viewers, Moore’s Law was every chip, chips either get twice as powerful or twice as cheap every 18 months. And that it’s gotten more complicated in the last few years, but that was the 50 year trajectory of the computer industry. And then by the way, that’s what took the mainframe computer from a $25 million current dollar thing into the phone in your pocket being a million times more powerful than that for 500 bucks.

And so that was a scaling law. And then key to any scaling law, including Moore’s Law and the AI scaling laws is they’re not really laws, right? They’re predictions, but when they work, they become self-fulfilling predictions because they set a benchmark and then the entire industry, right? All the smart people in the industry kind of work to make sure that that actually happens. And so they kind of motivate the breakthroughs that are required to keep that going. And in chips, that was a 50 year run, right? And it was amazing. And it’s still happening in some areas of chips.

I think the same thing is happening with the core scaling laws, the core scaling laws in AI, they’re not really laws, but they are basically their predictions and then they’re motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment dollars, required to basically keep the curves going and look, it’s, it’s going to be complicated and it’s going to be variable and they’re, they’re going to be walls that are going to look like they’re fast approaching and then they’re going to, engineers are going to get to work and they’re going to figure out a way to punch through the walls.

And obviously that’s, that’s been happening a lot, and then look, there’s going to be times when it looks like the walls have, the losses have petered out and then they’re going to, they’re going to pick up again.

Here’s what’s happening to the eyes. There’s now multiple scaling laws. There’s multiple areas of improvement. And I think I don’t know how many more there are already yet to be discovered, but there are probably some more that we don’t know about yet. They, for example, there’s probably some scaling law around world models and robotics that we don’t fully understand, kind of acquisition of data at scale in the real world that we don’t fully understand yet. So that one will probably kick in at some point here. There’s a bunch of really smart people working on that. And so, yeah, I think the expectation is that, the scaling laws generally are going to continue. The pace of improvement will continue to move really fast.

To your question on what to build. So I’m a complete believer of the scaling laws are going to continue. I’m a complete believer. The capabilities are going to keep getting amazing. Leaps and bounds, the part where I kind of part ways a little bit with what I would describe as the AI purists, which I would characterize as the people who are in many ways, the smartest people in the field, but also the people who spend their entire life in a lab and have very little experience in the outside world. The nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated. And it doesn’t, 8 billion people making collective decisions on planet earth is not a simple process of just seeing this happening now. It’s like a bunch of the AI CEOs have this thing, which is just this obvious set of things that society needs to do. And then they’re like, society’s not doing any of those things. Right. And it’s like, how can society not see X, Y, Z? And the answer is, well, society is number one, there’s no single society. It’s 8 billion people and they all have a voice and they all have a vote at the end of the day on how they react to change. And then, you know, human reality is really complicated and messy.

And so the specific answer to your question is like, as usual, it depends. It depends. Look, there’s no question people are going to like, there’s no question. They’re going to be companies. It’s already happening. There are companies that think that they’re building value on top of the models and then they’re just going to get blissed by the next model. There’s no question that’s happening. But I think there’s no question also that just the process of adaptation of any technology into the real, into the real messy world of humanity is just going to be messy and complicated. It’s not going to be simple and straightforward. It’s going to be messy and complicated and there are going to be a lot of companies and a lot of products, and in fact, entire industries that are going to get built to basically actually help all of this technology actually reach real people.

The amount of capital going into these companies. I mean, Dario talked about it on the door cash podcast and door cash was like, "why don't you just buy 10 X more GPUs?" And he’s like, "because I'm going to go bankrupt if the model doesn't exactly hit the performance level." How do you think about that? Also as a risk on, you guys are investors, and open AI and thinking machines and world apps, it seems like we’re leveraging the scaling loss at a pretty high rate. How comfortable, I guess, do you feel with the downside scenario? And say things peter out, you think you can kind of restructure these build outs and capital investment.

Yeah. So let’s just start by saying, so I lived through the.com crash. And I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was, it was apocalyptic. By the way, the, a lot of the.com crash was actually at the time it was actually a telecom crash. It was a bandwidth crash. The, the thing that actually crashed that wiped out all the money was the telecom companies. Global Crossing. Global, global. Yes. I’m from Singapore and they, they laid so much cable over, over our oceans.

Well, actually there was a scaling law in the.com era. And it was literally the, the U.S. Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter. And it actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is going to keep doubling every quarter. Doubling every quarter though, is, grains of chess and the chessboard. At some point the numbers become extremely large. Right.

And, and, and it really, and really what happened was the internet, the internet, by the way, continuously kept growing basically since inception. It is, it’s, it’s continuously grown. It’s never shrunk and it’s grown really fast compared to anything else, in, in, in human history, but it wasn’t doubling every quarter as of 1998, 1999. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that’s actually what caused the.com crash, which was they, they, they way over companies Global Crossing way overbuilt fiber, which is sort of the, by the way, fiber telecom equipment, so all the, all the networking gear, and then, and then by the way, the actual physical data center.

So that was the beginning of the, of the, of the data center build and then, and the data center overbuilt. And so you had that, but it was, it was literally, I think it was $2 trillion got wiped out. Right. It was a big, and by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt because tech companies generally don’t run on debt, but the telecom companies run on debt, physical infrastructure companies run on debt. And so the companies like Global Crossing, not just raised a lot of equity, they also raised a lot of debt. So they’re highly levered. And so then you just do the thing. It’s just, okay, you have a highly levered thing where you’re, you’re just over, you’re overbuilding capacity. Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right.

“it’s always the third owner of a hotel that makes money, right? It has to go bankrupt twice, right? You have to wash out all of the over-optimistic exuberance before it gets to actually a stable state. And then it makes money.”

So by the way, all of those data centers and all of those, all the fiber that they’re in use, it’s all in use today, but 25 years later, but it took, and actually the elapsed time was it took 15 years. It took 15 years from 2000 to 2015 to actually fill up all that capacity. The cautionary warning is the overbuild can happen. And, and, and, and, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital is wow, it’s just, I don’t know how to invest in these crazy software things, but for sure I can put, build data centers and for sure I can buy GPUs and I can deploy compute grids and, and all these things. And so, if you’re a pessimist, you can look at this and you can say, wow, this is really set up to be able to basically replicate what we went through, what we went through in 2000. Obviously that would be bad.

The counter argument, which is the one I agree with, which is the counter on the other side is a couple of things. One is the companies that are investing all the, the companies that are investing the money are the bluest chip of companies. And so back, back, back in the, in the doc, global crossing was an entrepreneur. it was a new venture, but the money that’s being deployed now at scale as Microsoft, Amazon, Google, Facebook, NVIDIA, and now, by the way, open AI and anthropic, which are now really serious size, as companies with very serious revenue, these are very large scale companies with lots, lots of cash, lots of debt capacity that they’ve, they’ve never used. And so this is institutional In a way that that really wasn’t at the time. And then the other is at least for now, every dollar that’s being put into anything that results in a running GPU is being turned into revenue right away. So, and you guys know this, everybody starved for capacity, everybody starved for compute capacity. And then, all the associated things, memory and interconnect and everything else data center space. And so every dollar right now that’s being put in the ground is turning into revenue. And, and, and in fact, I actually think there’s an interesting thing happening, which is because everybody starved for capacity, the models that we actually have that we can use today are inferior versions of what we would have, if not for the supply constraints.

If right. To pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful, the models would be much better because you would just allocate a lot more money to training and you’d just build better models and they would be better. And so we’re actually getting the sandbag version of the technology. No, everything we use is quantized because the labs have to keep the full versions, right? We’re not even getting the good stuff, but, but getting the good stuff is just, even if technical progress stops, once there’s a much bigger build of GPU manufacturing capacity and memory, all the, all the things that have to happen in the course of the next five or 10 years, once it happens, even the current technology is going to get, going to get much better. And then, as you know, there’s just a million ways to use this stuff. There’s just a million use cases for that. It, this isn’t just sending packets across a thing, whatever, and hoping people find something to do with it. This is just, we apply intelligence into every domain of human activity. And then it works incredibly well.

Here’s what I know. Here’s what I know. In the next three or four years, it’s somewhere between three or four years out, basically everything is selling out. And so the entire supply chain is, is, is sold out or selling out. And so there, there’s no, we’re just going to have chronic supply shortage for years to come. There’s going to be a response from the market that’s going to result in an enormous, it’s happening now an enormous flood of investment in a new fab capacity and everything else to be able to do that. At some point, the supply chain constraints will unlock, at least to some degree, that will be another accelerant to industry growth when that happens. Cause the products will get better and everything will get cheaper. And so, so I know that’s going to happen. I know that the deployments, the actual use cases are really compelling. And then, with reasoning and agents and so forth, I know they’re just going to get much, much better from here. And so I know the capabilities are really, real and serious.

I also know that the technical progress is not going to stop. It is, it is accelerating. The breakthroughs are tremendous. I mean, even just month over a month, the breakthroughs are really dramatic. And so, I think if you were a cynic and there are cynics, you can look at 2000, you can find echoes, but I can’t even imagine betting that this is going to somehow disappoint. And, at least for years to come, I think it would be essentially suicidal to make that bet.

“Who’s that Michael Burry?”
“Oh, that’s an interesting guy.”

We’ll pick on a guy. We’ll pick, let’s pick on one guy. Well, cause he did, he came out with, it was, it was, he doesn’t mind. It was the Nvidia short, right? He came out with the Nvidia short. And then you guys probably talked about this, which is the analysis now that the current models are getting better, faster at such a rate that if you are running an NVIDIA inference chip today, that’s three years old, you’re making more money on it today than you did three years ago, because the pace of improvement of the software is faster than the depreciation cycle of the chip. And then my understanding is Google is running, I don’t think, I don’t know exactly what, these are rumors that I’ve heard, or maybe it’s public, but, I think Google’s running very old TPUs, very profitable and very profitably. And so, so it actually turns out as far as I can tell that it’s actually the opposite of the Burry thesis is actually, he was actually 180 degrees wrong. It’s actually the, the, the old Nvidia chips are getting more valuable, which is something that’s literally never happened before. Like it’s never been the case that you have an older model chip that becomes more valuable, not less valuable. And again, that’s an expression of the, just a ferocious pace of software progress, ferocious pace of capability payoff that you’re getting on the other side of this. And so I just, the idea of betting against that, yeah, it’s an invitation to get your face ripped off.

One of my early hits was modeling the lifespan of the H100 and H200 and going, usually they advise four to seven years and it was maybe you sort of realistically cut it down to two to three, but actually it’s going up and not down. And that’s, I mean, that’s, I think that’s the dream. We are finding utilization and I think utilization solves all problems. You can find use cases for even the poor, even memory we’re having a shortage, right. And even the shittier versions of memory that we do have, we are finding use cases for it. So that’s great.

How important is open source AI and edge inference in a world in which you have three years of supply crunch? Do you think, if you fast forward five years, how do you think about inference in the data center versus at the edge?

Well, I think open source is very important for a bunch of reasons. I think edge inference is very important for a bunch of reasons. I think just practically speaking, if we’re going to have fundamental construct supply crunches for the next, if you just project forward demand over the next three years, relative to supply, one of the dismaying predictions you can do is what’s going to happen to the cost of inference in the core over the next three years. And it may rise dramatically. So what is, and then is the big model companies are subsidizing heavily right now. And so what’s the average person’s per day, per month token costs, three years from now to do all the things that they want to do. I have friends today who are paying a thousand dollars a day for OpenClaw tokens to run OpenClaw. So, okay, $30,000 a month. And by the way, those friends have a thousand more ideas of the things that they want their Claw to do. So you could imagine there’s latent demand of up to, I don’t know, five or $10,000 a day of tokens for a fully deployed personal agent. And obviously consumers can’t pay that. But it gives you a sense of the future scope of demand. So even if there’s a 10 X improvement in price performance, that’s still, it goes to a hundred dollars a day, which is still way beyond what people can pay. So there’s just going to be ferocious demand. By the way, the agent thing, the other interesting thing is I think the agent thing. Up until now, a lot of the constraints have been GPU constraints. I think the agent thing now also translates into CPU and memory constraints. CPU and memory. And so the entire chip ecosystem is just going to get with the network constraints. That will be the killer. That’s all bottlenecked and potentially for years. And so I think that Brad, and I think it’s actually possible. I mean, generally inference costs are going to keep coming down, but I think the rate of decline may level out here for a bit because of these supply constraints. And then at some point, maybe the labs stop subsidizing so much and that again will be an issue. And so there’s just going to be so much more demand for inference than can be satisfied kind of with the centralized model. And then, you know, the dramatic innovations that have happened in the Apple Silicon to be able to do inferences. It’s quite amazing. A level of effort being put, the open source guys are putting incredible effort into getting this recurring pattern where the big model will never run on a PC. And then six months later, it runs on a PC, right? It’s amazing. And there’s very smart people working on that. So there’s all that. And then there’s also other motivators, which is just, okay, how much trust are the big centralized model providers building in the market versus, at least for, in certain cases, With some people for certain use cases, people being “I’m not willing to just turn everything over.” So there’s all the trust issues. By the way, there’s also just straight up price optimization. There’s many uses of AI where you don’t need Einstein in the cloud. You just need a smart local model.

There’s also performance issues where you want your doorknob to have an AI model in it to be able to do access control. Obviously everything with a chip is going to have an AI model in it. And a lot of those are going to be local. And so, yeah, I think you’re going to have wearable devices. You don’t want to do a complete round trip. You want whatever your smart devices are to be super low latency.

The question, do we care who makes it? One of the biggest news this week was the collapse of AI to the Allen Institute, one of the actual American open source model labs. And I’m not that optimistic on American open source. You guys invested in Mistral and Mistral is doing extremely well outside of China. That’s about it. We’ll see.

Number one, I do think we care. I don’t think we care who makes it.

“the previous presidential administration wanted to kill it in the U.S.; they wanted to drown in the bathtub.”

And so they wanted to kill it. So at least we have a government now that actually wants it to happen. And you’re in the council and the new and the PCAST. This admin, for whatever other political issues people have, which are many, this administration has, I think, a very enlightened view and in particular an enlightened view on AI and in particular on open source AI. And so they’re very supportive.

My read is the Chinese companies have a very specific reason to do open source, which is they don’t fundamentally think they can sell commercial AI outside of China right now, or at least specifically not in the U.S. for a combination of reasons. And so they kind of view open source AI as a bit of a loss leader against basically domestic paid services and then kind of ancillary products; they’re very excited about it.

By the way, I think it’s great. I think it’s great that they’re doing it. I think DeepSeek was a gift to the world. I think the great thing about open source is the impact of open source has felt two ways:

• One is you get the software for free
• the other is you get to learn how it works

And so the paper and the code.

For example, I thought this was amazing. So OpenAI comes out with o1 and it’s an amazing technical breakthrough and it’s absolutely fantastic. But of course they don’t explain how it works in detail. And then of course they hide the reasoning traces. And then everybody’s like, okay, this is great. But who’s going to be able to replicate this? Are other people going to be able to do this? Is there a secret sauce in there?

And then R1 comes out and there’s the code and there’s the paper. And now the whole world knows how to do it. And then three months later, every other AI model is adding reasoning. And so you get this kind of double: even if the Chinese models themselves are not the models that get used, the education that’s taken place to the rest of the world, the information diffusion, is incredibly powerful. So that happens.

And then I don’t know, we’ll see. There are a bunch of American open source AI model companies. I mean, look, there’s going to be tremendous competition among the primary model companies. Depending on how you count, there’s like four or five big co model companies now that are kind of neck and neck in different ways. And then obviously both X and then Meta where I’m involved are both have huge attempts to kind of leapfrog underway. And then you’ve got a whole fleet of startups, new companies, including a whole bunch that we’re back in that are trying to come out with different approaches. And then you’ve got whatever it is. I don’t know how, how many, how many main line foundation model companies are there in China at this point? It’s probably six.

“It’s five tigers is what they call it.”

Qwen is in questionable because there’s change in leadership. Right. Yeah. But that does that include, that includes Moonshot. Yes. Okay. Yeah.

• DeepSeek
• 01.AI
• Qwen
• ByteDance

And then you’d say, ByteDance would be the next year, but they weren’t as prominent. They weren’t have a, but now, yeah. But they’re at least, see, see dance is very inspiring and presumably they have more stuff coming in Tencent probably has more stuff coming and so forth.

And so, so, so look, here, here would be a thing you can anticipate, which is there are not these markets. They’re not going to be between the U S and China right now, there’s a dozen primary foundation model companies that are at scale at some level of critical mass, it’s not going to be a dozen in three years. Right. It, just because these industries don’t bear a dozen, it’s going to be three, there’s going to be three or four big winners or maybe one or two big winners.

And so there’s going to be a whole bunch of those guys that are going to have to figure out alternate strategies. And I think open source is one of those strategies. And so I think you could see a whole, I think the questions like who’s going to do open source. I think that could change really fast. I think that that’s a very dynamic thing. I think it’s very hard to predict what happens. And I think it’s very important.

NVIDIA is doing a lot.

Well, I was gonna say, well, exactly. And then you got NVIDIA and then, and then, you know, just to get an industrial, there’s an old thing in business strategy, which is called a commoditize the complement. And that’s right. And so if your Jensen is just kind of obvious, of course you want to commoditize the software and he’s, and to his enormous credit, he’s putting enormous resources behind that. And so maybe, maybe it’s literally NVIDIA and I think that would be great.

Yeah. Yeah. Narrative violation to European projects. NVIDIA. I’m hosting my Europe conference soon. And I got both of them. They got us. They got us. Okay. Well, wait a minute. Where was Peter? So where was Steinberger when he did Austria? Yeah. Yeah. He was in Vienna. Oh, he was in Vienna. And then where is he now? He’s moving to SF. Okay. Okay. All right. Okay. There we go. And then, yeah, the pie guy, right. The pie guys are European. Yeah. They’re buddies in Austria. Mario is also there. Right. And are they, yeah, they haven’t announced yet any sort of change, changed or have they? No, they have a company there. Okay. Okay. Okay. Good.

Good. Anyways, I think Pi and OpenClaw, very important software things. And I just wanted you to just go off on what do you think? Yeah. So I think in the combination of the two of them, I think is one of the 10 most important software. OpenClaw got all the attention, but right. Talk about Pi. Pi is kind of the, yeah, Pi is kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software, basically from 1970 to, I don’t know, it still is very important, but like 19 from 1970 through to like basically the creation of Linux, which is basically this, this thing we used to call the Unix mindset. So, because there were all these different, you know, theories, there are all these different operating systems and mainframes and then, you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset.

And the Unix mindset was this thing where basically you don’t have these, like, in the old days, the operating system that made the computer industry really work in the 1960s was this thing called OS 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle of software. And by the way, it worked really well and they were very successful with it, but it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect of the system worked.

“No, let’s have a completely different architecture.” To work is we’re going to have, we’re going to have a prompt and a shell. And then we’re going to, all the functionality is going to be in the form of these discrete modules. And then you’re going to be able to chain the modules together.

And so the, it’s almost the operating system itself is going to be a programming language. And then that led to the sort of centrality of the shell. And then that led to a sort of basically changing the other Unix tools. And then that led to the emergence of these, these scripting languages Perl, where you could basically kind of very easily do this. And then the shells got more sophisticated. And then looked like that number one, that worked.

And that was the world I grew up in. I was a Unix guy, sort of from call it 1988 to kind of all the way through my work. And it worked really well. It’s in the background. Normal people don’t need to, didn’t need to necessarily know about it, but if you were doing system architecture application development, you knew all about it. And then it’s been in the background ever since. Look, your Mac still has a Unix shell kind of in there and your iPhone still has a Unix shell kind of buried in there somewhere. So they’re kind of in there. And then the Windows shell is kind of a sort of a weird derivative of that. But look, the internet, the internet runs on Unix, and then smartphones, actually both iOS and Android are Unix derivatives. And so kind of Unix did end up winning, but anyway, we just started taking that for granted.

So basically the way I think about what happened with Pi and then with OpenClaw is basically what those guys figured out is I always say the great breakthroughs are obvious in retrospect, right? Which is the best kind, the best kind. They weren’t obvious at the time or somebody else would have done them already. And so there is a real conceptual leap, but then you look at it sort of the backwards looking and you’re just

“Oh, of course.”

to me, those are always the best breakthrough. So actually language models themselves are like that. It’s just

“Oh, next token completion. Oh, of course.”

“What other objective mattered?”

Yeah. What other objective mattered? Yeah, exactly. But she’s even saying it wasn’t obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and very important.

And so the way I think about Pi and OpenClaw is it’s basically marrying the language model mindset to the unit, to the Unix basically shell prompt mindset. And so it’s basically this idea that what, what, so what is an agent, right? And as many smart people have been trying to figure out what an agent is for decades. And they’ve had many architectures to build agents and the whole thing. And it turns out what is an agent. So it turns out what we now know is an agent is the following: it’s a language model. And then above that, it’s a bash, it’s a bash shell. So it’s a Unix shell. And then the agent has access to the shell and hopefully in a sandbox, maybe in a sandbox. So it’s the model, it’s the shell. And then it’s a file system. And then the state is stored in files. And then there’s the markdown format for the files themselves. And then there’s basically what in Unix is called a cron job. There’s a loop and then there’s a heartbeat for this heartbeat and the thing basically wakes up. So it’s basically

• LLM
• shell
• file system
• markdown
• cron

LLM + shell + file system + markdown + cron

And it turns out that’s an agent. And every part of that other than the model is something that we already completely know and understand. And in fact, it turns out the latent power of the Unix shell is extraordinary because basically there’s just enormous latent power in the shell. There’s enormous numbers of Unix commands. There’s enormous number of command line interfaces into all kinds of things already in your entire, I mean, your entire, just to start with your computer runs on a shell. If you’re running a Mac or a phone, your computer’s running on a shell already. And so the full power of your computer is available at the command line level. And then it turns out it’s really easy to expose other functions as a command line interface. And so this whole idea where we need MCP and these fancy protocols, whatever, it’s no, we don’t, we just need a command command line thing. So that’s the architecture. And then it turns out, what is your agent? Your agent is a bunch of files stored in a file system.

And then there’s the thing that just completely blew my mind when I wrapped my head around it as a result of this, which is, okay, this means your agent is now actually independent of the model that it’s running on because you can actually swap out a different LLM underneath your agent. And your, your agent will change personality somewhat because the model is different, but all of the state stored in the files will be retained different instruction sets, but you just compiled it. Right. Exactly. And it’s all right. It was right.

Swapping out a ship and recompiling, but it’s still your agent with all of its memories and with all of its capabilities. And then, by the way, you can also swap out the shell. So you can move it to a different execution environment. That is also a bash shell. By the way, you can also switch out the file system. Right. And you can, and you can, and you can swap out the heartbeat for the CRON framework, the loop, the agent framework itself.

• swap out the shell
• move it to a different execution environment
• switch out the file system
• swap out the heartbeat for the CRON framework
• the loop
• the agent framework itself

And so your agent basically is at the end of the day, it’s just, it’s just its files. And then there’s, of course, yeah, it’s basically, it’s just the files. And then by the way, as a consequence of that, the agent, it’s, and then the agent itself, it turns out a couple important things.

So one is it, it’s, it can migrate itself. Right. And so you can instruct your agent, migrate yourself to a different runtime environment, migrate yourself to a different file system, migrate yourself to a different, we swap out the language model, your agent will do all that stuff for you. And then there’s the final thing, which is just amazing, which is the agent actually has full introspection and actually, it actually knows about its own files and it can rewrite its own files. Right.

“Oh, I have my OpenClaw, do whatever, connect to my eight sleep bed. And it gives me better advice than sleep.”

Which by the way, is basically no widely deployed software system in history where the thing that you’re using actually has full introspective knowledge of how it itself works and is able to modify itself like that, there’ve been toy systems that have had that, but there, there’s never been a widely deployed system that has that capability. And then that leads you to the capability that just completely blew my mind when I wrapped my head around it, which is you can tell the agent to add new functions and features to itself. And it can do that. Right. Extend yourself, extend yourself, give yourself a new capability. Right.

And so, and so literally it’s just like, you run into somebody at a party and they’re “Oh, I have my OpenClaw, do whatever, connect to my eight sleep bed. And it gives me better advice than sleep.” And you go home at night and you tell your Claw or if they’re at the party, by the way, you tell your Claw, “Oh, add this capability to yourself.” And your Claw will say, “Oh, okay, no problem.” And it’ll go out on the internet and it’ll figure out whatever it needs. And then it’ll go out to cloud code or whatever it’ll write, whatever it needs. And then the next thing, you know, it has this new capability.

And so you don’t even have to, you can have it upgrade itself without even having to do anything other than tell it that you want it to do that. And so anyway, so the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it’s just incredible. If I were, if I were 18, this is what I would be spending all of my time on. This is such an incredible conceptual breakthrough.

And again, people are going to look at it and they already get this response. People are going to look at it. They’re going to say, “Oh, well, where’s the breakthrough? Cause these, the, all of these components were already known before,” but this is the key. The key to the breakthrough was by using all these components that were known before you get all of the underlying capability of this buried in there. And so all, and so for example, computer use, all of a sudden just kind of falls trivial, trivial. Of course, it’s going to be able to use your computer. It has full access to the shell. Right. And then you just, you give it access to a browser and then you’ve got the computer in the browser and often away it goes. And then you’ve got all the abilities of the browser also.

And so, and so the capability unlock here is profound. My friends who are deepest into this are having their Claw do a thousand things in their lives. They have new ideas every day. They’re constantly throwing new challenges. It’s the thing. And by the way, it’s early and you know, These are prototypes and there’s, as you guys know, there’s security issues. And so there’s a bunch of stuff to be ironed out, but the unlock of capability is just incredible. And I have absolutely no doubt that everybody in the world is going to, is going to have at least an agent like this, if not an entire family of agents, and we’re going to be living in a world where I think it’s almost inevitable now that this is the way people are going to use computers.

I was going to say for someone who is deeply familiar with social networks, the next step is your Claw talking to my Claw, posting on Claw Facebook, posting their jobs on Claw LinkedIn and Claws posting their tweets on Claw XAI or whatever. I do think that that is how we, we get into some danger there in terms of alignment and whether or not we want these things to, to, to run.

You guys never rent a, rent a human.com. Yeah. I mean, it’s Fiverr, it’s test. Sure. Of course. mechanical Turk. But flipped. Right. The agent hiring the people, which of course is going to happen. It’s obviously going to happen.

I’m curious if you have any thoughts on the engineering side. So when you build the browser, the internet, just a bunch of mostly plain text files, plus some images. And today every website and app is so complex and somehow the browser kept evolving to fit that in. Are there any design choices that were made early in the browser and the internet and the protocols that you’re seeing agents similar today? It’s like, Hey, this thing is just not going to work for this type of new compute. And we should just rip it out right now.

There were a whole bunch, but I’ll give you a couple. So one is, and we didn’t, to be clear, this was not, this was totally different. We didn’t have the capabilities we have today, but we didn’t have the language models underneath this, but we did have this idea that human readability actually mattered a great deal. And specifically in those days, it was not so much English language, but there was a design decision to be made between:

• binary protocols
• text protocols

And basically every basically old school systems architect that had grown up between the 1960s and the 1990s basically said, “what do you know about the internet?” It’s starved for bandwidth. You just have these very narrow straws. Look people, when we did the work on Mosaic, people who had the internet at home had a 14-kilobit modem, right. And so you’re trying to hyper-optimize every bit of data that travels over the network. And so obviously if you’re going to design a protocol like

HTTP

you’re going to want it to be binary, highly compressed binary protocol for maximum efficiency. And you’re going to want to have it be a single connection that persists. The last thing you’re going to want to do is bring up and tear down new connections. And you definitely are not going to want a text protocol. And so of course we said, no, we actually want to go completely the other direction. It’s obviously we only want text protocols. By the way, same thing in HTML itself, we want HTML to be relatively verbose. We want the tags to actually be human readable. We want to use the most inefficient things possible.

We want to do the inefficient things. You’re the original token maxer. Basically it’s just, well, this was actually the conscious thing, which basically says assume, assume a future of infinite, infinite bandwidth built for that. And then basically what it was, is it was a bet that if the system was, if the latent capabilities of the system were powerful enough, and that was obvious enough to people that would create the demand for the bandwidth that would cause the supply of bandwidth to get built, that would actually make the whole thing work. And then specifically what we wanted was we wanted everything to be human readable because we, at the engineering level, wanted people to be able to read the protocol coming over the wire and be able to understand it with their bare eyes without having to disassemble it or whatever. Right. And have it converted out of binary. Right. And so all the HTTP and everything else where it was always text protocols, and the same thing with HTML. And in many ways, some people say that the key breakthrough in the browser was the view source option, which is every webpage you go to, you could view source. “Which means you could see how it worked, which means you could teach yourself how to build right new to build new web pages. There was that. So human readability and again, human readability in those days still met technical specs. Now it means English language, but there’s an incredible latent power in giving everybody who uses the system, the option to be able to drop down and actually understand. I see how it’s working and that worked really well for the web. And I think it’s working really well for AI. That was one.

What was the other, a big part of the idea of web servers was to actually surface the underlying latent capability of the operating system and to be able to surface the also the underlying latent capability of the database, because basically what was a web server, what, what, what, what is a web server fundamentally architecturally it’s, it’s, it’s, it’s the operating system.

So it’s the operating system’s ability to manage the file system and do everything else that you want to do and process everything. And then of course, a lot of early, a lot, a lot of websites are financed to databases. And so you wanted to unleash the underlying latent power of whether it was an Oracle database or some other Postgres or whatever, whatever it was. And so a lot of the function of the web server was to just bridge from that internet connection coming in to be able to unlock the underlying power of the OS and the database.

And again, people looked at it at the time and they were, well, is this really, does this really matter? Is this important because we’ve had databases forever and we’ve always had user interfaces for databases and this is just another user interface for a database. And it’s, okay, yeah, fair enough.

But on the other side of that is just, this is now a much better interface to databases and one that 8 billion people are going to use and is going to be far easier to use and far more flexible. And, and, and you’re not just going to have old databases. Now you have a system where people can actually understand why they want to build a million times more database apps than they have in the past. And then the number of databases in the world exploded.

And so again, this goes to this thing of building, building in layers. Some of the smartest people in the industry look at any new challenge and they’re, okay, I need to build a new kind of application. So the first thing I need to do is build a new programming language. Right. And then the next thing I need to do is build a new operating system. Right. And the next thing I need to do is I need to build a new chip. Right. And they kind of want to reinvent everything. And I’ve, I’ve always had, maybe it’s just, pragmatic mentality or something, or maybe an engineering over science mentality, but it’s more like, no, you have just like all of this latent power in the existing systems. And you don’t want to be held back by their constraints, but what you want to do is you want to kind of liberate that power and open it up. And so I think, I think, and I think the web did that for those reasons. And I think it’s the same thing now that’s happening. It’s a good perspective on the web.

Programming languages is another good thing. We have Brett Taylor on the podcast and we were talking about Rust and Rust is memory safe by default. And so why are we teaching the model to not write memory unsafe code? “Just use Rust and then you get it for free.” How much do you think there’s time to be spent, recreating some of these things instead of taking them for granted? I’ll be, Oh, okay. Python is kind of slow. Python type scripts. You know, it’s, as imperfect as they are, they are the Lingua Franca. I mean, I think this is going to change a lot because I don’t think the models care what language they program in. And I think they’re going to be good at programming on every language. And I think they’re going to be good at translating from any language to any other language.

So this gets into the coding side of things. I think we’re going through a really fundamental change. And I grew up, I grew up hand code, I grew up hand coding. Everything I did was actually, everything I did actually was written in C. I wasn’t back in the day. I wasn’t even using C plus plus. So I, or Java or any of this stuff. Right. And so, I, everything, everything I ever did, I was managing my own memory at the level of C. And then I, you know, I’m still from the generation that, I knew assembly language and, you know, I, I could drop down and do things, right on the ship. And so we, we’ve just, we’ve all, all of us, we’ve always lived in a world in which software is this precious thing that you have to think about very carefully. And it’s really hard to generate good software. And there’s only a small number of people who can do it. And you have to be very jealous in terms of thinking about how do you allocate what are your engineers working on and how many good engineers do you actually have and how much software can they write and how much software can human beings kind of maintain. And I think all those assumptions are being shot right out the window right now. I think they’re, I think those days are just over. And I think the new world is actually high quality software is just infinitely available. And if you need new software to do X, Y, Z, you’re just going to wave your hand and you’re going to get it. And then if it’s, if you don’t like the language is written and you just tell the thing, all right, I want the right now, I want the rest version. Or, security, security, we’re about to, by the way, go through computer security is about to go through the most dramatic change ever, which is number one, every single latent security bug is about to be exposed. Right.

So we’re going to have the, we’re, we’re set up here for the computer security apocalypse for a while, but on the other side of it, now we have coding agents that can go in and actually fix all the security bugs. And so how are you going to secure software in the future? You’re going to tell the bot to secure it and it’s going to go through and fix it all. And so this thing that was this incredibly scarce resource of high quality software is just going to become a completely fungible thing that you’re just going to have as much as you want. Right. And that has tons and tons of consequences. In some sense, the answer to the question that you posed, I think is just somewhat, I don’t know, simple or something or straightforward, which is just, if you want all your software and rest, you just tell the bot you want all your software and rest, the things that used to be the hard or even seem like an insurmountable mountain to get through all of a sudden, I think become very easy.

I think Brett had a theory that there would be a more optimal language for LLMs. And so the contention is there isn’t just don’t bother just whatever humans already use LMs are perfectly capable porting. I think we’re pretty close to being, I don’t know if this works today. I think we’re pretty close to being able to ask the AI, “what would its optimal language be and let it design it.” Okay. Here’s a question.

• Are you even going to have programming languages in the future?
• Or are the AI is just going to be emitting binaries?

Let’s assume for a moment that humans aren’t coding anymore. Let’s assume it’s all bots. What levels of intermediate abstraction do the bots even need? Or are they just coding binary directly? Did you see there’s actually an experience? If somebody just did this thing where they have a, they have a language model now that actually emits model weights for a new language model, right? And so will the bots predict the weights? Yeah. Well, the bots literally be emitting, not just coding binaries, but will they, will they actually be emitting weights for new, for new models directly, directly and conceptually there’s no reason why they can’t do both of those things.

Architecturally, both of those things seem completely possible.

Very inefficient. You’re basically very inefficient simulation of a simulation in a simulation inside of weights. Yeah. Yeah. Very inefficient, but look, LLMs are already incredibly inefficient. Ask a favorite thing. Ask Claude: add two plus two equals four. Right. It’s just whatever billions and billions of times more inefficient than using your pocket calculator. But yeah, the payoff is so great of the general capability. And so anyway, I kind of think in 10 years, I’m not sure. Yeah. I’m not sure there will even be a salient concept of a programming language in the way that we understand it today. And in fact, what we may be doing more and more as a form of interpretability, which is we’re trying to understand why the bots have decided to structure code in the way that they have.

I mean, if you play it through, you don’t need browsers then that’s the death of the browser. Well, so I would take it a step further, which is you may not need user interfaces.

So who is going to use software in the future?
Other bots. Other bots. Yeah. And so you still need to, I don’t know, pipe information in and out. Really? Well, what are you going to do then? Are you sure? You’re just going to log off and touch grass? Whatever you want. Exactly. Isn’t that better? I want software to do stuff for me. Isn’t that, but isn’t that better?

I mean, look, I, I don’t look like the arguments here, it was not that long ago that 99% of humanity was behind a plow.

Right. Right. And what are people going to do if they’re not plowing fields all day to, to, to grow food? Right. And it just turns out there’s much better ways for people to spend time than plowing fields. Yeah. Do is growing. Exactly. Talking to their friends and look, I’m not an absolutist and I’m not a utopian.

And I, and to be clear, I’ve, I have an 11-year-old and he’s learning how to code and I’m, I think it’s still a really good idea to learn how to code and so forth, but I just, if you project forward and you just have to think forward to a world in which it’s just, okay, I’m just going to tell the thing what I need and it’s going to do it.

And then, and then it’s going to do it in whatever way is most optimal for it to do it. Yeah. Unless I tell it to do it non-optimally, if I tell it to do it in Java or in Rust or whatever, it’ll do it. I’m sure. But if I’m just going to tell it to do, it’s going to do it in whatever way is the optimal way to do it.

And then I, and then if I need to understand how it works, I’m going to ask it to explain to me how it works. Right. And so it’s going to be doing its own interpreter. It’s going to be the engine of interpretability to explain itself.

And I just am not convinced that—I’m not convinced that in that world you have these historical, the goals of the abstractions will be whatever the boss need at what the human’s right. Yeah. Yeah.

Well, I’m curious, if that’s true, then shouldn’t the models providers be building some internal language representation that they can do extreme kind of RL and reward modeling around?

Because it’s today they’re kind of tied to TypeScript and Python because the users need to write in that language versus they can have their own thing internally.

And they don’t need to teach it to anybody. They just need to teach their model.

And I think that’s how you get maybe the version between the models, going back to the PI open cloud thing.

“Oh, I built all the software using the open AI model and I’ll switch to the anthropic model, but the anthropic model doesn’t understand the thing.”

So I, it feels like there still needs to be some obstruction, but maybe not, maybe that’s the lock-in that the model providers want to have. I don’t know. I’m not even sure that’s lock-in though. Cause why can’t the second model just learn what the first model has done? Exactly. Okay. So, okay. Give me an example.

So as you know, models can now reverse engineer software, but isn’t it the whole thing now where people are reverse engineering Nintendo game binaries? Yeah. So you have, I’ve seen a bunch of reports this where somebody has a favorite game from the 1980s and the source code is long dead, but they have a binary bird to do a chip or something, another reverse engineer to get a version of the rest of their Mac.

Right. And so if you reverse it, if this is what I kind of say, if you’re reversing x86 binaries, then why can’t you reverse engineer? Whatever they create.

Yeah. And because we’re all on a Unix based system, it has to be reversible because it needs to run on the target. Yeah. Yeah. Yeah. Yeah. Yeah. Basically.

And so I just, I just think it’s this thing where it’s just, and by the way, everything we’re describing is something that human beings in theory could have done before, but with enormous cost and labor for prohibitive reverse engineering. I learned how to reverse engineer. Human beings can reverse engineer binaries. It’s just for any complex binary, you need a thousand years to do it. But now with the model, you don’t.

And so all of a sudden you get, you get these things or another way to think about it is so much of human built systems are to compensate for the human limitations. Yeah. Right. And if you don’t have the human limitations anymore, then all of a sudden you have, and it’s not that you won’t have abstractions, but you’ll have a different kind of abstraction. Yep.

I have two topics to bring us to a close and you can pick whichever ones are just talking about protocols. Was it you or someone else? I forget my internet history. We said that the biggest mistake that we didn’t figure out in the early days was payments.

“Yes.”

“Was that you?”

“Yes.”

It was a 402, 402 payment required.

We have a chance now. I don’t think we’re going to figure it out. I don’t know. What’s your take? Oh, I think we will. Yeah. No, now I think it’s going to happen for sure. Yeah. Yeah. And there’s two reasons it’s going to happen for sure.

One is we actually have internet native money now in the form of stable coins, stable coins and crypto. And this is, I think this is the grand unification basically of AI and crypto is what’s about to happen now. I think AI is the crypto killer app, I think is where this is really going to come out. And then the other is, it’s just, I mean, it’s just, I think it’s now obvious. It’s obviously AI agents are going to need money and it’s already happening, right? If you’ve got a, if you’ve got a Claw and you want it to buy things for you, you have to give it money in some form. I would say the adoption is probably 0.1% if that, but yeah. Oh, today. Yeah, yeah, yeah. But think forward. It’s, where is it going? Forward thinking.

The ultimate principle of everything and everything that I think we do is the William Gibson quote, which is the

“the future is already here. It just isn’t distributed.”

It isn’t, it isn’t distributed yet.

My friends who are the most aggressive users of, of, of, of OpenClaw just have given their Claws, bank accounts, credit cards. And, and, and, and, and, and not only have they done it, it’s obvious that they needed to do it because it’s obvious that they needed to be able to spend money on their, it’s just completely obvious. And so, and again, so the number of people who have done that today to your point is like, I don’t know, probably 5,000 or something, but that’s how these things start. Actually, I mean, since you keep mentioning. And by the way, OpenClaw, by the way, if you don’t give it a bank account, it’s just going to break into your account. It’s going to break into your bank account anyway and take your money. So you, you might, you might as well do it. You might as well do it.

By the way, I really love, I got to tell you, I really love the phenomenon. I love the YOLO. I’m not doing it myself to be clear, but I love the people that are just like, what is it? Skip, skip, skip, dangerously, which by the way, it’s a Facebook thing. “Okay.” Because in Facebook, they have this culture to name the thing dangerous so that you are aware when you enable the flag that you are opting into a dangerous thing. Okay, good. And they brought it into OpenAI. And of course, that makes it enticing. Sam runs Codex with skip permissions on his laptop. Yes. A hundred percent.

And so I think the way to actually see the future is to find the people who are doing that. There’s a madness, you know. Log everything, you know, just watch it. Watch the logs. But let’s actually find out what the thing can do. And the way to find out what the thing can do is just, yeah, let it try everything. Let it unlock everything. By the way, that’s how you’re going to find all the good stuff it can do. By the way, that’s also how you’re going to find all the flaws. I think the people who turn that on for bots are like, they’re like martyrs to the progress of human civilization. I feel very bad for their descendants that their bank accounts are going to get looted by their bots in the first like 20 minutes. But I think the contribution that they’re making to the future of our species is amazing. It’s like gentleman science. Yes, it’s, yes, yes. Experiment on yourself.

• Ben Franklin out with trying to get lightning to strike his balloon and seeing if he gets electrocuted.
• Jonas Salk with the polio vaccine injecting it.

Yes. So, yes, I think we should have like a glory, we should have like flags and like we should have like monuments to the people that just let OpenClaw run on their lives. More anecdotes. I was like, what are the craziest or interesting things that people listening to this should go up, go home and do? I mean, this is, this is, this is the extreme thing is just the straight YOLO. Just, yeah, turn, turn your life. That’s a general capability. Yeah. Yeah.

Like a specific story that was like, wow. And everyone in the group chat just lit up. I mean, tons of, there’s already tons of health, there’s the health dashboard stuff is just, it’s just absolutely, absolutely amazing. The number of stories on, I’m trying to just don’t want to violate people’s, obviously personal. But, one of the things OpenClaw instances are really good at is hacking into all this stuff in your land. It’s really good. So, internet of things, AKA internet of shit, super insecure, but great. Discoverable. Discoverable. OpenClaw is happy to scan your network, identify all the things.

And then my friends most aggressive at this are having OpenClaw take over everything in their house.

Yeah.

• It takes over their security cameras.
• It takes over their access control systems.
• It takes over their webcams.

I have a friend whose Claw watches him sleep. Put a webcam in your bedroom; put the Claw in a loop.

I have it wake up frequently and have it watch it and just tell him, > “watch me sleep.”

And I’ve seen the transcripts and it’s literally like Joe’s asleep. This is good. This is good that Joe’s asleep because I have his health data and I know that he hasn’t been getting enough sleep.

And so it’s really good that he’s getting sleep. I really hope he gets his full, whatever, five hours of REM sleep.

Joe’s moving. Joe’s moving. Joe might be waking, waking up. This is a real price. Joe wakes up now. He’s going to ruin his sleep cycle. Oh, okay. It’s okay. Joe just rolled over. Okay. He’s gone back to bed. Okay, good. All right. Okay. I can relax. This is fine. He’s monitoring the situation and being a bot; it’s just very focused. It’s just like, this is his reason for existence is to watch Joe sleep.

And then I was talking to my friend who did this; on the one hand, it’s, “all right, this is weird and creepy,” and I need to maybe this has taken over my life. And then the other thing is, if I had a heart attack in the middle of the night, this thing literally would freak out and call 9-1-1. There’s no question this thing would figure out how to alert medical authorities and probably summon SWAT teams and do whatever would be required to save my life.

Right. And so that’s happening or what else?

It’s a company Unitree that makes the robot dogs. And then I actually have one at home, which is actually really fun with the Chinese companies. The Chinese companies are so aggressive at adopting a new technology, but they don’t always take the time to really package it and maybe think it all the way through.

At least the Unitree dog I have has an old non-LLM control system, which, by the way, is not very good in markets. In practice it’s not that good. It has trouble with stairs and so forth. So it’s not quite what it should be, but then the language model thing comes out in the voice, so they add LLM capability and then they add a voice mode to it.

But that LLM capability is not at all connected to the control system. So you’ve got this schizophrenic dog that is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics in a plummy English accent. It’s absolutely amazing.

Jagged intelligence. Talk about jagged. And now, obviously what’s going to happen in the future is they’re going to connect together, but right now it’s not that useful.

And so I have a friend who has one of these who had his Claw basically hack in and rewrite the code, write new firmware for the Unitree robot. And now it’s an actual pet dog for his kids.

You should do that before, after the motion.

Yeah. It’s good. You said it’s completely different. He said it’s a complete transformation.

And whenever there’s an issue in the thing, now the Claw just rewrites the code. You go, does the code. And so it kind of goes to your thing here.

So all of a sudden, this is why we want to think about AI coding. AI coding is not just writing new apps. It’s also going in and rewriting all the old stuff that should have worked that never worked.

I think the internet of shit is basically over. I think everything, there’s a potential here where all these devices in your house that have been basically marginal or basically dumb might all get really smart.

Now you have to decide if there are horror movies in which this is the premise. And so you have to decide if you want this, but this is the first time I can say with confidence I now know how you could actually have a smart home with 30 different kinds of things with chips and internet access where it actually all makes sense. It all works together and it’s all coherent in the whole thing.

And to have that unlock without a human being having to go do any of that work. I’m waiting for a story, Mark. I can’t let you open that fridge door. Exactly. Yes. Because you’re not supposed to eat right now. I have all of, yes, I have every thread of health information, and I know you think you’re doing dah, dah, dah, I don’t think you can do this, but this is a real, are you really sure? And you told me last night, you really don’t want me to let you do this. So, I’m sorry, but the fridge door is locked. Open the fridge door. Exactly. And by the way, I know you’re supposed to be studying for a test. So why don’t we, why don’t you go when you can pass the test? I will open the fridge door for you.
Final protocol.
And then we can wrap up a proof of human.
Yes.
Right.

There’s two massive, I would say sort of asymmetries in the world right now where we’ve known these asymmetries exist and we societally have been unwilling to grapple with them. And I think they’re both tipping right now and they’re the same thing as virtual world versions, physical world version.

So the virtual world version is the bot problem. We’re just like, the internet is just a wash and bots. Internet’s a wash and fake people. It has been forever. By the way, a lot of that has to do with lack of money.

This is my spicy take: these two are the same thing and corporations are people too.

Okay. So a bank account is proof of human. Until you give the bots bank accounts.

So, there’s that, but the bot problem is a big problem. Every social media user knows this: the bot problem has been a big problem forever. It’s a huge problem and it’s never really been confronted directly.

The physical world version of this is the drone problem. We’ve known for 20 years now that the asymmetric threat, both in military conflict but also in security on the home front, the big threat is the cheap attack drone, the cheap suicide drone with a bomb. And we’ve known that forever. It’s very disconcerting how every office complex in the world is unprotected from drone attacks. Every stadium, every school, every prison—okay, we’ve known that and we’ve never done anything about it.

One possibility is just leave them unprotected forever and live in a world of asymmetric terrorism forever. The other is take the problem seriously and figure out the set of techniques and technologies required to be able to deal with that. Whether those are:

• lasers
• jammers
• early warning systems
• personal force fields
• kinetic personal force, personal force fields

In both cases, these are economic asymmetries. It’s really cheap to feel the bot, but it’s very hard to tell something about it. It’s really cheap to feel the drone. It’s very expensive to defend against a drone. But you see what I’m saying: it’s the virtual version of the problem and it’s the physical version of the problem. The virtual version of the problem: what we need quite literally is proof of human.

The reason is because you’re not going to have proof of bot, especially now that the bots are too good; the bots can pass the Turing test. And if the bots can pass the Turing test, then you can’t screen for bot. You can’t have proof of not a bot, but what you can have is you can have proof of human. You can have cryptographically validated: this is definitely a person and this is cryptographically validated: this is definitely like something that a person said. This video is real.

Just to double click on, do you think Alex Blania with world, do you think he’s got it or is there an alternative? Oh, so I mean, there’s going to be, I think many people will try. We’re one of the key participants in the world, in the world project. And so we’re partisans, but yeah, I think, so we think world is exactly correct. And the reason is it has to be, it has to be proof of human. It has, because you can’t do proof of not bot. You have to do proof of human. To do proof of human, you need, you need biological validation. You needed to start with, this was actually a person, right? Because otherwise you have bots signing up as fake people, right? And so you, you have to have like something, you have to have a biometric and then you have to have cryptographic validation and then the ability to do, to do, to do the lookup. And then by the way, the other thing you need, which that you also need selective disclosure. So you need to be able to do proof of human without revealing all the underlying information.

By the way, another thing you’re gonna need, you’re gonna need proof of age, right? Because there’s all these laws in all these different countries now around, you need to be 13 or 16 or 18 or whatever to do different things. So you’re gonna, you’re gonna need, you know, sort of validate a proof of age to be able to legally operate. Right. And so that, that’s coming. And then you’re going to want like proof of credit score and, you know, proof of like, you know, a hundred other, that’s a tricky one.

It is a tricky one, but you’re going to, you’re going to, there’s no reason, like if somebody’s checking on your credit, somebody shouldn’t give you an example, somebody shouldn’t need to know your name in order to be able to find out whether you’re credit worthy. I see independently verifiable pieces of information, pieces of information. It’s like just likely disclosed. And this is the answer to the privacy problem writ large, which is, I only need to prove I need to prove at that moment. So like, you’re going to need that. And I think their, their, their architecture makes sense. So that needs to get solved.

I think language models have tipped the bots are now too good. And so they’re undetectable. And so as a consequence, we now need to go confront that problem directly. And then, like I said, and then the other problem is we need to go actually confront the drone problems. The Ukraine conflict has really unlocked a lot of thinking on that. Now the Iran situation is also unlocking that. And so I think there’s going to be just like this incredible explosion of both drone and counter drone. Our drones are better than their drones. It’s supposed to keep it that way. Yeah. And counter drones.

I think we can sneak in one more question. I’m trying to tie together a lot of things that you said over the year. So at the Milken Institute debate with Teal, which is amazing. You talked about the lag between a new technology and kind of like the GDP impact of it, the other idea you talked about is bourgeois capitalism and how, you know, it’s kind of managerial class was needed because of this complexity. And I think if you bring it into the fold, you have like much higher leverage people. So like if you have the Musk industries, and you give Elon a GI, you can run a lot more things at once. That’s right. And then you have the social contract. And I know you received a clip of some moment saying, “we’re rethinking the whole thing.” and you’re like, absolutely not. I was at an event with Sam last night. And he actually said in the last couple of weeks, he felt like now people are taking that seriously. So I’m just curious, like how you’re seeing the structure of organization changing, especially when you invest in early stage companies and, yeah, just like how the impact of work structure and all of that is playing out.

Yeah. So there’s a whole bunch of, there’s a whole bunch of times. I know. We could spend, by the way, we’d be happy to spend more time, but we could, we could spend more time on all that. So just for people who haven’t followed this, so this, this, this, this term managerial comes from this thinker in the 20th century, James Burnham, who just one of the great kind of 20th century political thinkers, societal thinkers. And he sort of said as, and he was writing in like the 1940s, 1950s. And he said kind of the whole history of capitalism until that point had been in two phases.

Number one had been what he called bourgeois capitalism, which was thinking about as like name on the door, like Ford motor company. Cause Henry Ford runs the company. And Henry, it’s like a dictate dictatorial model. And Henry Ford just like tells everybody what to do. And he said, the problem with bourgeois capitalism is it doesn’t scale. Cause Henry Ford can only tell so many people to do so many things. And then he runs out of time in the day.

And so he said the second phase of capitalism was what he called managerial capitalism, which was the creation of a professional class of managers that are trained not to be like Car experts or to be whatever experts in any particular field, but are trained to be experts in management.

And then that led to the importance of Harvard Business, management consulting firms and all these things.

And then you look at every big company today and most of the executives and most of the Fortune 500 companies are not domain experts in whatever the company does. And they’re certainly not the founders of those companies, but they’re professional managers. And in fact, in the course of their careers, they’ll probably manage many different kinds of businesses. They’ll rotate around and they might work in healthcare for a while and then work in financial services and then go work in something else, come work in tech.

And what Burnham said is he said that transition is absolutely required because the problem with bourgeois capitalism is it doesn’t scale. Henry Ford doesn’t scale. And so if you’re going to run capitalist enterprises that are going to have millions to billions of customers, you’re going to need to operate at a level of scale and complexity that’s going to require this professional management class. And he said, “whether you think that’s good or bad or whatever, it’s what’s going to be required.” And basically that’s what happened.

Right. And so he wrote that book originally in the 1940s. Over the course of the next 50 years, basically managerialism took over everything. And what I’m describing is basically how all big companies run and how all governments run and how large-scale nonprofits run and kind of everything runs.

Basically, what venture capital does is we basically are a rump sort of protest movement to that, to try to find the next Henry Ford, or just to say Elon Musk, or the next Elon Musk, or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg. And so we start these companies in the old model. We start them out in the Henry Ford model. And so we start them out with a founder or a founder with colleagues, but you know, there’s a founder CEO. And then we basically bet that the startup is going to be able to do things specifically innovate in ways that the big incumbents in that industry are not going to be able to do.

And so it’s a bet that by relighting this sort of name on the door, this new innovative thing with a king, monarchical political structure, that they’re going to be able to innovate in a way that the incumbent is not going to be able to because the incumbent is being run by managers. And by the way, and of course, venture being what it is, sometimes that works, sometimes it doesn’t, but we’re constantly doing that. But I’ve always viewed it in my entire life as, “we’re like raging against the dying of the light.” We’re constantly trying to fight off managerialism swamping everything and everything getting basically boring and gray and dumb and old. We’re trying to keep some level of energy and vitality in the system.

AI is the thing that would lead you to think, wow, maybe there’s a third model. Maybe it’s a combination of the two. Maybe the new Henry Ford or the new Elon Musk or the new Steve Jobs plus AI is the best of both. Because it’s the spark of genius of the name on the door model, the Henry Ford model, but then give that person AI superpowers to do all the managerial stuff and let the boss do all the managerial stuff. That may be the actual secret formula.

And we’ve never even known that we wanted this because we never even thought it was a possibility. But what is the thing that these bots are really good at? “They’re really good at doing paperwork. They’re really good at filling out forms. They’re really good at writing reports. They’re really good at reading. They’re really good at doing all the managerial work.” And so, yeah, I think the answer very well might be to get the best of both worlds by doing this.

• Elon Musk
• Steve Jobs
• Bill Gates
• Mark Zuckerberg And then the challenge is going to be twofold. The challenge is going to be for the innovators to really figure out how to leverage AI to actually do this. And then the other challenge is going to be for the incumbents that are managerial to figure out, okay, what does that mean? Cause now they’re going to be facing a different kind of insurgent competitor that has a different set of capabilities than they’re used to. And so this really, I think, is going to force a lot of big companies to kind of figure out innovation, either “figure out innovation or die trying.”

Do you feel like that structure accelerates the impact on the actual GDP and economy? If you look at SpaceX, it’s the growth is so fast. And instead of having these companies peter out and growth and impact, they can keep going if not accelerating.

That’s for sure. The hope, the challenge and look, the AI utopian view is of course that’s going to be the future of the economy. And it’s going to grow 10 X and a hundred X and a thousand X.

And we’re entering this regime of much higher economic growth forever and consumer cornucopia of everything. And it’s going to be great. And I hope that’s true. I hope that’s the current kind of utopian vision. I hope that’s true.

The problem is it goes back again. The real world is really messy. And I’ll give you an example of how the real world is really messy. It requires 900 hours of professional certification training to become a hairdresser in the state of California. So it’s 35% of the economy. You have to get some sort of professional certification to do the job, which is to say that the professions are all cartels, right?

And so you have to get licensed as a doctor. You have to get licensed as a lawyer. You have to get licensed as a, you have to get into a union. By the way, to work for the government, you need to have both civil service protections and you have public sector unions. You have two layers of insulation against ever getting fired for anything or anything ever changing. I’ll give you another example: the dock workers went on strike a couple of years ago. Because robotics, if you go look at a modern dock in Asia, it’s all robots. If you go to American docks, it’s still guys, dragon, strike, dragon stuff by hand.

The dock workers went on a strike. It turns out there are 25,000 dock workers working on docks in America. It turns out they have incredible political power because it’s a unified block of things. They won their strike. And so they got commitments from the dock owners to not implement more automation. We learned a couple of things in that. So number one, we learned that even a union, the smallest 25,000 people still has tremendous political stroke. We also learned that it actually turns out the dock workers union has 50,000 people in it because they have 25,000 people working at the docks. They have 25,000 people during full paycheck sitting at home from prior union agreements.

I’ll give you another great example. There are government agencies. There are federal government agencies where the employees have civil service protections and they’re in public sector unions. There are entire federal government agencies that struck new collective bargaining agreements during COVID. Not only do they have their jobs guaranteed in perpetuity, but they only have to report to work in an office one day per month. And so there are entire office buildings in Washington, D.C. that are empty 29 out of 30 days of the year that are still operating and we’re all still paying for it. And so what the employees do is they’re very smart in this way. And so they figure out, they come in on the last day of a month and the first day of the next month. And so they’re in the office two days per 60 days, which means these buildings are empty for 58 days at a time.

And you see where I’m heading with this, this is locked in, right? This is locked in in a way that has nothing to do with capitalists, it’s restrictions on trade. It’s restrictions on the ability to change the workforce. And so so much of our economy is the, I’m describing the entire healthcare system. I’m describing the entire legal profession. I’m describing the entire housing industry. I’m describing the entire education system, right? K through 12 schools in the United States, they’re a literal government monopoly. How are we going to apply AI on education? The answer is we’re not because it’s a literal government monopoly. It is never going to change the end and there is nothing to do. By the way, you can create an entirely new school system. That’s the one thing you can do is you can do what alpha school is doing. You can create an entirely new school system. Other than that, you’re not going to go in and change what’s happening in the American classroom. K through 12, there’s no chance. The teachers are 100% opposed to it. It’s a hundred percent not going to happen.

So you see what I’m saying is, there’s this massive slippage that’s going to take place. Both the AI utopians and the AI doomers are far too optimistic, right? You see what I’m saying? Because they believe that because the technology makes something possible, that 8 billion people all of a sudden are going to change how they behave. And it’s just, nope, so much of how the existing economy works. It’s just, it’s just wired in. And so we’re going to be lucky as a society. We’re going to be lucky if AI adoption happens quickly, right? Cause if it doesn’t more, we’re just going to have a stagnation.

I know you got to run. Yeah. I don’t know if you’re still welcome, but it was such a pleasure talking to you.

“We’re truly living in an age of science fiction coming to real life.”

Yes. Yes. Could not be more exciting. Yeah. Really. Thank you, Mark. You guys. Awesome. Thank you. That’s it. Good. Thank you.

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Vol.126 中共一大背后的李汉俊:辛亥之子与工运先驱

过去与未来一样崭新。我是许知远。欢迎收听历史学人播客。我们将探讨历史的偶然与必然,以及生活在历史中的个体的无数的可能性。

可能对黄陂南路或者新天地站不太陌生。出站走几分钟,大概就会看到一排石库门的建筑,上面挂着中共一大会址的牌子。每到节假日的时候,这里一般都会由人如织。熟悉中国历史的朋友,大概都会对这个地方不太陌生。

但是很少有人会想过一个问题:当年的中共为什么会在这个地方开会?这个地方到底是谁的呢?

要回答这样的一个问题,实际上就要涉及到在早期中共史,或者是在中共的创建史上,非常重要但在后来慢慢淡去的一个名字。他的名字就是李汉俊。李汉俊,湖北潜江人,早年东渡日本留学。他在1920年的时候与陈独秀、李达共同筹建中国共产党,但很早就退出。1927年,李汉俊在白色恐怖中牺牲,享年37岁。

围绕着这位青年俊秀和他的传奇人生,我们今天非常荣幸地邀请到中国社会科学院近代史研究所的副研究员、中共创建史研究中心特约研究员李丹阳老师来和我们聊一聊真实的李汉俊。

李老师除了研究者身份之外,还有一层身份,就是李汉俊的兄长李书成的长孙女。李老师,方便和大家打个招呼吗?

“我很高兴和大家谈谈李汉俊。”

各位听众好。我很高兴和大家谈谈李汉俊。

那方便和我们的听众分享一下您的家世渊源,还有您是怎么开始去研究李汉俊的吗?

我原来是在中国社会科学院近代史研究所研究近代中外关系的。我开始研究李汉俊有多种因素促成。1979年,沈雁冰在他发表的回忆录中以相当篇幅谈到李汉俊,这引起李汉俊家乡潜江的一位工人刘日明的注意,就写信给茅盾。茅盾与我的外公冯乃超很熟,知道他的夫人我的外婆李声韵。李声韵是李书城的长女,又是李汉俊的侄女。而茅盾先生的儿子韦韬恰恰与我父母在故乡就认识了。茅盾的儿子知道我,当时也想收集李汉俊的资料,所以把刘日明给茅盾的信转给了我。我们通信以后,刘日明一再催促我利用在北京和在研究单位的条件来研究李汉俊。我也觉得研究李汉俊是我作为学者和后代义不容辞的责任。

于是与丈夫刘建一开始利用业余时间收集李汉俊的资料,包括其著述译文、内外报刊和档案上的记载,还先后采访了大约80位认识或了解李汉俊的老人。40余年以来,我写了十多篇关于李汉俊的论文。我的博士论文《李汉俊与中国早期共产主义运动》。我参与编辑的李汉俊文集已经出版。随着史料的积累和研究的深入,李汉俊这个人物的历史面目趋近于清晰。我希望这次简要讲述能让大家初步了解一个真实的李汉俊。

您刚才也提到在工作的时候利用业余时间找了大量当事人,收集了大量口述材料。这些东西对于我们后面去研究中共创建史、了解那段历史其实特别重要。

说到李汉俊,很多人对他的认识基本上一开始就是在1920年代他和陈独秀他们开始创建中国共产党时的形象,但对他的早年经历比较陌生。您可不可以聊一聊早年的李汉俊,还有他的成长环境是怎么样的?

李汉俊之所以成为一个有反叛精神特质和特立独行人格的人,确实与他成长的环境、他的家庭、小时候受到的教育和影响及他少年特殊经历有关。他原来的名字叫李书思,是思想的”思”。汉俊实际上是他的号。1890年他生于湖北。父亲中年才中秀才,先后当门馆先生和小学教员,母亲操持家务兼种田。他引导学生关心社会、关注时局。受教于他的有加入兴中会参加庚子起义的傅慈祥烈士,有同盟会员牺牲于辛亥革命前夕的刘静庵烈士。李汉俊和李书城兄弟从小随父读书、随母干活。

在近代湖北开办新式学堂之先,清末张之洞任湖广总督期间大力兴办新式学堂,大量派遣学生出洋留学。1902年,当李书城被派赴日本留学,李汉俊到武昌上了高等小学堂。1903年初,他哥哥李书城回国,给李汉俊带来了新鲜的思想。李书城在日本留学期间曾见过孙中山,一起开过会,接受了民族民主革命思想。他与黄兴是同学,一起去日本留学。

李书城归国后参加了以兴中会会员吴禄贞为首的湖北革命知识分子在武昌花园山建立的秘密机关。他们议定了革命方法和途径:先在湖北知识界宣传反清革命思想,再介绍一些有志青年加入新军,然后让他们在军中秘密建立革命组织,进而由新军发动武装起义推翻清朝统治。李书城在这个机关中负责秘密联络军队,他曾经把父亲的学生刘静庵带到武昌从军。哥哥李书城的革命思想和行动给李汉俊以很大的影响。

刚才听您聊的,我感觉包括李汉俊在内,还有毛泽东,很多早期的共产党员都有一段民族民主革命的经历或记忆。有的人像毛泽东可能亲自参与了,有的人像李汉俊是因为家庭环境和武汉当时的革命氛围的影响。在早期中共党员的履历中,留日经历也是一个特别重要的履历,像李达也是,李汉俊也是。您能不能聊一聊李汉俊是怎么去日本留学的?

谈到李汉俊年纪小就去日本,还要提到他的哥哥。李书城回国前写了一篇文章,鼓励湖北学子跳出故闭的小圈子,留学外洋去见识浩瀚世界。回国后,他介绍的外面的世界和思想潮流使李汉俊不再安心于在学堂读书。1904年,李书城准备再赴日本学习军事,李汉俊非要跟哥哥一起去。吴禄贞听说后主动承担了他的旅费和学费。年仅14岁的李汉俊得以跟随李书城到日本留学。

他一去日本时就在革命派的核心圈子里。资助他的人中有吴禄贞。哥哥去日本肩负学习军事、以后推翻满清这样的使命,而且是冒用别人的名字去的。李汉俊到日本的第二年在他哥哥的一个朋友家见到了孙中山。此时同盟会刚成立,李书城已经以李唐的名字入盟。当时一次聚餐,孙中山说他自己是孙权,说刘成禺是刘汉,然后说李书城叫李唐。后来我外公真的以李唐的名字填写的同盟会入会名。那回到李汉俊,当时他更多是在读书、在学校里。

他见到孙中山后,孙中山看到那么小年纪就想往革命,就夸他:”好小孩,有志气,中国有希望了。”以后李汉俊上高等学校时到东京来,经常住到黄兴的家里。李书城与黄兴关系很好。在这些革命党人的启迪下,李汉俊接受了民族民主革命的思想。1912年春天在南京加入同盟会。但从现有资料看,他在留日的十余年里并未参加同盟会的革命活动,看来他比较专心向学。

那时李汉俊在东京的学习情况如何?

在中共创立者中,很多人有留学经历,但唯独李汉俊留学时间最长、在日本的学历最为完备。1904年5月,他先在为中日留学生开设的预备学堂经纬学堂学了7个月,还没读完补习课程就进入日本著名的教会学校晓星中学–是法国天主教传教士在日本办的学校,就连日本少年都很难考进去。学校课程很多,不少课本是法文,用法语授课,几乎全是日本学生,对清国学生严重歧视,但李汉俊脱颖而出。三年级时获得全班第二等优等奖,并以优异成绩毕业。

那时日本中学通常只有约4%的考生能考上高等学校,而他直接考上了作为大学预科的高等学校。1910年从晓星中学毕业后,他没有接着上高等学校,而是回国,可能是没有钱了。后来经历了辛亥革命,民国成立后他随哥哥到了南京。在吴禄贞的追悼会上,他和他哥哥参加了,黄兴等人也参加了。大约1912年秋他取得了民国政府的公费留学名额才又到日本继续上学,这次他上的名古屋的日本第八高等学校(即今日的名古屋大学)。在该校作为大学预科分科时,李汉俊选的是工学科,他当时想以后回国建设祖国。

他学了三年,不仅打下了坚实的功课基础,还学了一些自然科学和社会科学方面的课程,比如:

• 生物学
• 哲学心理学
• 法学经济学

还掌握了德语等外语工具。我有他历年的成绩单,每年成绩都是名列前茅,以优良成绩毕业,取得了东京帝国大学入学资格。东京帝国大学是日本乃至全亚洲最好的大学。他在东京大学读的是土木工学科,课业特别繁重,每年要学十到十二门课程,学生淘汰率很高。我查到与他同入学的47名土木科学生里,到毕业只剩35人。除了他以外其他学生都是日本人,而他经过刻苦学习成功获得了工学士学位。

他后面回国后转向社会科学研究,一方面因为他早就成为民主革命阵营的一员,另一方面与他在日本接受社会主义思潮和马克思主义有关。李汉俊说他自己大约在1916到1917年在大学期间接触马克思学说。有人说他信仰马克思主义是受到师友关系的影响,但这种传言没有根据。

李汉俊是1918年7月大学毕业,年底才回国。这段时间他在日本的经历值得探讨。1918年8月,日本发生席卷全国的米骚动和罢工浪潮,大批工会和左翼组织兴起。对日本社会运动一贯关注的李汉俊阅读了一些新出版的进步刊物,结识了在日本发起解放运动的进步社团的一些成员,比如:

• 新人会
• 黎明会
• 民人同盟会

这是他自己文章里写到的。其中有一位是新人会的发起人宫崎龙介,是宫崎滔天的儿子,是他的老朋友,二人在高中时期就认识。李汉俊还与后来发起日本共产党的堺利彦、高畠素之等人有联系。他回国后翻译过山川均、堺利彦、福田德三、佐野学等日本马克思主义者的著作,也可能在日本就读过这些人的书。总之在日本,李汉俊经历了新思潮的洗礼后回到祖国。

因此,李汉俊一方面因家庭的革命环境而成长为革命者,另一方面因留学时期遇到日本社会主义运动的影响,成为一个在1918年前后受日本社会主义运动影响成长起来的青年。

我们也讲到他在1918年底回到中国。那时中国的现状和环境是什么样?他在1919年底在给董必武等人的信中说自己”处在惊天裂地之间,满目伤心之中”。当时中国军阀混战不已,有的地方军阀提前征收税收十几年,兵匪动不动就抢老百姓。李书城曾写当时的民国是”群雄扰攘,国困民贫”。 北洋军阀把持的中央政府与列强签订了一系列丧权辱国的条约。总之人民无法照着生活下去。

当时李汉俊内心真实的想法是什么样子的?他有什么想要改变的办法吗?他曾经说过当时的中国是一个死牢。其实像他类似看法的,比方说李大钊说中国当时的社会是死社会,鲁迅形容为一个铁屋子。

当时一些在华的外国人也有这个深刻的观察和评论。比方英国驻华领事叫Hewlett说:

中国处于最黑暗的混乱时刻。

波兰裔的美国人Sokorsky写道:中国国内要变革不可避免的要采取革命手段,也将与外部发生战争,这会使中国成为亚洲的打火侠,叫Tinderbox。一位法国传教士叫Bornat,他说被民族灾难激怒的学生喊出中国正在被处死。士兵们感到十分不满,很容易被卷入任何革命运动;而农民变得非常绝望,准备追随任何承诺给他们更好命运的政党。

这些中外人士观察到的中国情况,并不像现在有人说的那样民国岁月静好。本来李汉俊回国是要建设的,学土木工程的,可当时的中国根本没有给他应用和发挥他的专业的环境和条件。

那那个时候,就是李汉俊回国之后,他去哪了呢?他1918年末回国后,主要是住在哥哥李书城在上海的家。他居住和活动的主要范围就在现在这个黄浦区。他在这个中国最大的工商业中心和中外资本家的乐园,看到了地狱般的情景。

这是他自己写的:他工人两班倒,每班12个小时。如果没夜班,那工时可达18个小时。辛苦一天的工资甚至不够买一根冰棍;衣不能暖,居不能避风雨。一旦失去劳动机会,就会饿死。而资本家因工人的劳动,得以住豪宅、穿利服、吃山珍海味。对这种社会的不公,他感到愤慨。

作为一位有良知和强烈社会责任感的人,一贯认为知识分子不应当只图一己的安逸舒适,而无视人民的痛苦。他这个想法跟当时的很多的新青年其实都蛮像的。对,所以这样他们才又聚集在一起。

我记得李汉俊回国之后,还在留学生的《救国日报》上发过一些文章。那您可不可以跟我们聊一聊他回国之后的一些具体的活动,就是他在上海那段时期的事情?

他20年初的时候,在留学生创办的《救国日报》上发表一篇文章说:到国外留过学、深见世界情境的留学生,应当肩负及指导普通大众改造中国,使之适应世界潮流的责任。他对改造中国有一些想法,他认为挽救中国与危亡要使人民幸福,局部的改良没有用,必须进行全部改造的社会革命。而且他认为解放和改造要从努力和奋斗中去求,拿出创造的精神来。就是说大破坏与大建设的功夫,这个先破坏再建设,孙中山也说过这个。

他说留学生要指导大众适应世界潮流。当时的世界潮流是什么?他那个时期就正好是第一次世界大战结束。那时让人看到了资本主义的无序发展和恶性竞争,带来资本主义国家贫富悬殊的社会问题,导致了人类大规模屠杀的战争。然后帝国主义的侵略和掠夺,又使殖民地、半殖民地国家的人民遭受苦难。俄国十月革命以后,世界社会主义运动达到高潮。

李汉俊参与翻译的一篇日本社会主义者山川菊荣的文章《世界思潮之方向》里面写到:俄国革命发生以来,世界实在向无产阶级的解放一方面正在突飞猛进,已经成了大事。李汉俊对国民党(当时也称一度改为中华革命党)领导的旧式革命很不满。他去见过孙中山几次。那个时候是在上海的孙中山。他见过几次,不光是跟宫崎龙介见过。他自己还见过。

英国的档案里头写到了,他曾经对孙中山说过,国民党不应当只运动军队和土匪,应当注意主义的宣传。1919年春夏之交,他在达费路附近与董必武、张国恩等几个湖北国民党人一起聚谈。他们都觉得孙中山依靠军阀搞革命的路子不对,都认识到要学习马克思路线的理论和俄国革命的方法,而革命之成功必有待于新兴势力之参与。但那个时候的他所谓的新兴势力,实际上是指的是俄国的这个布尔什维克。

1920年9月发表了一篇文章,实际上是山川菊荣那篇文章的附记等于是他的提出来,不能依靠已有政党,希望平民无产阶级靠自身结合力组织起来,从事社会主义革命。他这个观点挺像是毛泽东那个平民的大联合。田子渝教授就说,这个时候李汉俊可能已经有建立无产阶级政党的打算了。我认为他至少已经有依靠劳动者另起炉灶想法。实际上是在旧民主革命向新民主革命过渡的那个时期,是一位先驱性的特殊人物。

他原来是国民党员,后来又主张搞一个新的无产阶级平民的政党。在国民党里面他其实也算老资格了。他实际上后来到孙中山成立中华革命党的时候,他没参加,他的哥哥没参加,因为不喜欢孙中山要大家打手印宣誓效忠。他个人和黄兴在欧洲美国转一圈,就是考察。

李汉俊是不是有组建政党的那个意识,这个不是很清楚。至少他认为旧的政党不行了,国民党不行了。他说没有新的思想,又没有新的力量,国民党就是那些武人官僚军阀被这些人裹挟了。他说我们自己就是平民,就是无产者,就是什么,他说过这个话。但李汉俊那个时候的主要的活动,还是在于这个理论的研究和传播这一块。

您能跟我们稍微聊一聊当时他是怎么去做这种所谓的主义的传播的吗?有人就是说他是一个马克思主义的传播者。实际上他也真的有那个意识,传播马克思主义思想的种子。1921年他对来访的日本作家芥川龙之介就说过,他要搞社会革命便不得不依靠宣传鼓动,当务之急乃是表示要不避劳苦,倾全力把手里的种子撒向中国万里荒芜的大地。他这个撒的种子实际上就是马克思主义。

刚回国不久就为上海的《星期评论》、《觉悟》、《建设》(主要是国民党系的报刊)写了多篇介绍马克思主义的文章。1919年8月,他写的《怎么样进化》就运用马克思主义观点来解释历史变化。他特别指出,在近代由于资本家垄断了生产机关和交易市场,使工人变成同机器一样的器具,并使弱小国家人民陷于贫困,更造成经济危机和世界大战。人类要改变这种薄弱社会朝幸福安定的方向发展,就要把机器的所有权普及于一般运用机器的人。

他这个说法实际上就是现在所说的以人为本,关心劳动者的解放。他是与马克思的出发点是一致的。马克思的早期文章实际上就是反异化。关于异化的一些讨论和批判,就人不能变成器具,而且是为了人类的幸福,他一开始就有这样的想法。他接受的可以说是比较原始的马克思主义。他这个理论深度感觉跟当时的李大钊有点像,因为李大钊也是在日本留过学的,他是有点像。

有一个学者在哲学研究上有一篇文章,说是价值理性还是工具理性,就认为李大钊是价值理性,说陈独秀是工具理性。我就看过这么一篇文章。其实李汉俊就有好多想法是跟李大钊类似的。

当时李汉俊他在上海。我们知道上海是一个国际性的大都市,是五方杂陈之地。然后李汉俊他又是留日学生,有这个非常复杂的社会关系。那个时候他在上海有没有跟其他的这些各种国际人有过这种交流或者是这种活动?

其实早在1919年10月份,英国情报机关通过侦查就认为李汉俊是中国的布尔什维克,就这样写的。1920年2月在上海,他又有一个情报侦查到李汉俊与一些对先进的社会主义思想有所了解的中国人,还有朝鲜人李光洙(李光洙也是一个朝鲜的小说作家),还有那个俄国人李泽洛维奇,他们一起开会商讨组成一个革命团体,筹备出版《劳动者月刊》。就这个事呢,日本的情报也记载了,说李汉俊(那时候说他是李人杰)与俄国人阿格列耶夫,还有朝鲜人吕运亨筹办《劳动》杂志。

1920年3月1日,朝鲜人在上海举办纪念三一起义周年的纪念会,李汉俊是唯一一个代表中国人致辞。朝鲜人致辞中有当时的韩人社会党的委员长李东辉。韩人社会党实际上是在哈尔滨成立的,后来到共产国际注册的一个社会主义政党。1920年5月份他又变成了高丽共产党。这个李东辉是负责人,还有那个跟他一块筹备《劳动者月刊》的那个吕运亨也是独立运动人士,后来也加入这个高丽共产党,成为党中央的翻译部负责人。他是首位把共产党理念带入朝鲜的人。上面还提到了李哲瑞,就是他们一起开会讨论的。他是来自英国的俄国人,他呢与英美社会党后来都组成共产党了,有联系。

这阿格列耶夫呢是来自海参崴(Vladivostok),这两个俄国人当时都在上海,为苏俄,后来又为共产国际工作过。杨之华就回忆曾经谈到李汉俊早年与朝、日、俄的朋友,甚至与那些地方的共产党有联系。这样他还是有点根据。一位韩国的学者的论文里认为在后来成为中共领导的人物中,1920年春以前只有李汉俊与朝、日、俄社会主义者有密切联系。

我们知道中国的共产主义运动是世界共产运动的一部分。信仰马克思主义,了解世界大事,又掌握多国语言的李汉俊是在中国较早参与国际运动的人,在早期的东亚共产主义运动中有着重要的地位。这是当时李汉俊和国际共产主义运动就有交集的那一面。

他不光是跟苏俄,他跟英美的社会主义者、共产主义也有关系,还有东亚的像韩国高丽。实际上日本的共产党和韩国共产党成立都跟苏俄有关系,但李汉俊大概是比较特殊,就是在上海就与英美的社会主义者有间接的联系。就我说的那个李哲瑞,他曾经接受过英国后来共产党的负责人Sylvia Pankhurst的信。李哲瑞从英美接受过一些像社会党的刊物,后来又是共产党的刊物,就是New York Call那些。李哲瑞和李汉俊的关系大概比较密切,因为他们都懂英语。

后来《新青年》上的好多翻译的文章,原来是美国社会党刊物刊登的一些文章,李汉俊他间接的就是与这些国际共产主义运动有关系。咱刚才聊的是国际线,接下来咱聊一聊国内线,就是在1920年到1921年这期间内,其实全国各地主要是省会城市出现了各种各样的共产主义小组。

那么当时上海的这个早期党组织,它里面有很多的成员实际上是来自于当时的杭州浙一师,这些人其实是跟一个杂志有特别大的关系,就是这个《浙江新潮》。后面还有一个很重要的杂志是那个《星期评论》。那李汉俊跟星期评论这一拨人有什么样的联系呢?

谈到中国共产党运动和组织的起源,不可不提到上海的星期评论社。《星期评论》是国民党系的刊物。李汉俊加入过同盟会,所以他回国后不久便为这个刊物撰稿,后来又成为编辑了。这个刊物刊载了不少介绍马克思主义、社会主义、提倡劳工运动的文章。与北京的《每周评论》被誉为舆论界中最亮的两颗星。

20年初,星期评论社迁到李书城、李汉俊的家,就上海法租界白尔路三益里17号一栋三楼三底的房子,比较大。到春天,俞秀松、施存统他们两个原来是浙江的浙一师,后来又到北京参加那个工读互助团,然后他们到了上海住在李汉俊家。陈望道也在这儿待过,然后他翻译《共产党宣言》,然后还有丁宝林,很多人就到星期评论社这边过来。这些人很多人就住在他们家。这样子后期的星期评论社成为南方有先进思想的人士聚集的重要中心,我觉得都值得研究。

俞秀松、施存统从北京专门跑过来,后来陈独秀也跑过来。1920年4月的时候,俞秀松在一封信里说,他那个信件还是写的星期评论社,他就说这里的同志男女大小14人主张都激进。正好英国的6月份的一份情报也写着:布尔什维克代理人在上海的活动集中于星期评论社。据说该报社聚集着14个男人和两个女人,确信他们都在为事业而工作。而这一星期评论社的思想领导中心据杨之华说是李汉俊。

20年春天起,星期评论社就是开始接受了一些来自英美社会主义、共产主义政党的刊物,李汉俊有的把他们翻译成中文了。在共产国际二大(就是7月份召开的)中国的代表刘绍周在发言中甚至称《星期评论》为马克思主义政党的周刊。那这个评价还是蛮高的。

其实我觉得可能当时就有一个计划。1926年的俄国的一个顾问卡拉乔夫在中共简史里头写,陈独秀20年去上海是因那里有星期评论社。就美国学者德里克他写中共起源,据他的研究,他说围绕着陈独秀和星期评论社的一小群人是形成中国共产党的重心,其中只有陈独秀是newcomer,新来的人。所以他指出如果上海有什么中心的话那就是星期评论社。这个后来瞿秋白、李立三在党史报告里也写到说星期评论社是形成共产党的一个细胞,的确就是参与。 筹建中共的大部分人就来自星期评论社中,有激情、有理想的群体,而其核心人物就是李汉俊,还有像李达、陈独秀。

按照石川祯浩老师的说法,大概是从1920年的4月份俄国的维经斯基来华以后,这个时候的上海的党组织可以叫发起组。4月份应该还没有发起组,是说他开始有点筹备,就开始进入一个筹备阶段了。那么从1920年的4月一直到1921年的7月份,整个过程当中都可以说是一个筹备、创建的这样的一个阶段。对,确有这么个形成过程,当然是从1920年春开始酝酿。

俞秀松在苏联写的自传里头说,”20年春我们曾想成立共产党,在第一次会议上我们之间未达成一致意见。” 在那个党的酝酿时期,实际上当时的参与者信仰什么的都有:共产主义、民主社会主义、基尔特社会主义、无政府主义都有。那时候其实有两种思路:维京司机觉得多多益善,因为你中国信仰共产主义的人就少,因此他们在酝酿讨论建党的时候就有分歧。

5月底,李汉俊在批判张东荪一篇文章中说,起了争执,是不是主义前途的障碍呢?与其由混杂分子组成一个庞大不纯的团体,不若由纯粹分子组成一个随小而纯的团体。这段话透露出在早期讨论中,李汉俊主张应该由纯粹的马克思主义信仰者组成政党,成员宁缺勿滥。他这个思路跟陈独秀的这个思路很相近。其实陈独秀自己那个时候,马克思主义不是特别的,还在学习摸索阶段。

请问一下李老师,就是在中共发起的过程当中,李汉俊他到底起了一个什么样的作用呢?首先他是一位主要的发起人;其次,又是早期党组织的负责人。所以李达他回忆他自己20年9月到上海,就听说陈独秀和李汉俊正在准备发起组织中共。李达其实是9月份回来的,他不是最早发起的。

包惠僧就说,中共成立之初,李汉俊在党内地位仅次于陈独秀。而日本的一个档案写明说李汉俊是上海共产党的副首领。所以20年底,陈独秀到广东前是让李汉俊来代理党的书记。最近在俄国档案中发现了1921年春,有书记李汉俊以上海共产党革命局书记身份为外国语学校的学员开具的赴苏学习的介绍信,盖着图章,那个图章写的就是人杰两字。

为筹备一大,李汉俊呢直接与陈独秀、李大钊通信。21年6月,共产国际的代表马林到上海,向李汉俊要工作报告、工作计划和预算。这都说明李汉俊在一大召开前不久仍然是中共临时中央的负责人,而且实际上李汉俊在中共刚刚发起的时候,他还为党写了一个等于是党纲的草案。

我们知道,中国共产党的第一次代表大会是在1921年的7月底召开的。这个会议上其实并不是说大家所有的事情都一致举手表决通过的,而是有很多的讨论,也有很多的不同的意见在互相的交流。那么在当时一大召开的过程当中,李汉俊对于当时中国共产党建党他有什么样的看法或者意见呢?他是少数发表了一些不同意见的人。

我梳理了一下,他的主要意见就是针对党纲和工作计划中的某些条款。党纲中有一条是革命军队必须与无产阶级一起推翻资本家阶级的政权。那鉴于中国当时并非资产阶级掌权,无产阶级还比较幼稚,李汉俊就主张党的当前任务不是领导无产阶级夺取资产阶级政权,而应当先支持孙中山领导的革命运动,以实现民主政治。他不赞同刘仁静说的以无产阶级专政为斗争的直接目标,和张国焘讲的”不管各国情况怎样是要无产阶级专政”的说法,认为中国国情特殊,以后是否适用于无产阶级专政还应当研究。

其次,党纲中有中共要彻底断绝同黄色知识分子阶层及其他类似党派之一切联系的条款。讨论中还有人讲知识分子都是资产阶级思想的代表者,一般应拒绝其入党;还有人说知识分子动摇不可靠,在吸收他们入党时应该特别慎重,一般不容许他们入党。李汉俊的主张是对知识分子要放宽些,只要他信仰了解和宣传马克思主义即可入党。他主张应该重视对青年学生的教育,要以掌握了马克思主义的知识分子做骨干去组织和教育工人。

还有,对于党员不得担任政府官员或国会议员的条款,和有的代表发言中说的中共目前不应参加实际政治活动的主张,李汉俊认为必须把公开的和秘密的工作结合起来,因为公开宣传我们的理论是取得成就的绝对必要条件。他建议可以挑选党员做国会议员,以利用同其他被压迫党派在国会中的联合行动部分取得成就,比如改善工人状况。同时他又指出不应该对议会斗争抱有过高幻想。他提出的那个修正案是共产党员不得做政府的政务官(即事务官)。

在讨论劳动运动方案时,张国焘和刘仁静说要尽先把产业工人组织起来,职业工人无关重要。李汉俊的意见是容许职业工会,这与毛泽东等做实际工作的代表意见是一致的。但是最后的决议为本党的基本任务是成立产业工会,没有提职业工会。

还有关于与其他政党的关系,张国焘等说不要与任何政党联合,甚至有人认为南方政府与北洋政府是一丘之貉。李汉俊则提出在目前斗争中应当支持孙中山先生的革命运动,援助国民党。最后的决议为对现有其他政党应采取独立攻击排他的态度,不同其他的党派建立任何关系。

对一些代表他认为可能不懂策略吧,李汉俊感到很遗憾,但在他自己的意见和提案遭到否决的时候,坦率地表示服从多数的决定。在一大选举中他没有被选入中央领导机构,却被委任与董必武起草给共产国际的报告。这个报告反映了李汉俊的一些观点。

其实从您刚才的讨论,我们可以看到在当时早期的这些中共一大代表里面,李汉俊按左右划分其实他是有点偏右的,所以也有人就说他说是有改良主义吗?还说他右倾机会主义者,还有合法马克思主义者。但实际上过了一年以后,随着中共二大的召开,整个的路线其实也是走向了一个很务实的角度,做了一个修正。

您刚才还聊到一个问题,我觉得特别有意思,就是毛泽东因为这种学术方面的储备是远远不如李汉俊的,但是李汉俊和毛泽东他们在一些涉及到工人运动、涉及到政党合作的方面却有着非常高度相近的看法。那么这个跟李汉俊他的工人运动观还有他的实际的革命运动有什么样的关系呢?

我先说一下毛泽东主席,虽然你说他当时可能学历各方面可能不如李汉俊,但是他比较实事求是,他始终没有对李汉俊有恶评,几次都是说李汉俊是牺牲了的。然后呢,他在七大上他说一大代表当时对马克思主义了解有多少、世界上的事如何办也不甚了了。解放以后,他是签署了给李汉俊的烈士证。我听那承武将军说在延安的时候,董老还有林伯渠,他们就谈论过,当时就认为李汉俊应该算是烈士,就是没有像张国焘他们那样给他扣帽子。董必武后来也觉得当时一些决议是关门主义的政策,是二大的时候做了适合国情的一些调整,对党的任务和策略。

然后很有意思,在三大,李汉俊又未被选为中共中央的候补委员后,马林托李大钊带了一封信给李汉俊,他说在第一次会议上小组在上海对你的态度是很错误的,现在我们的同志都同意这种意见。这就是说明李汉俊当时的主张其实是后来获得了大家还有包括共产国际的一定的认可。

对,我还是想回到刚才的那个问题。我们知道共产党他的一个很重要的活动就是从事工人运动,那李汉俊对于工人运动,包括对于当时的工人他还是怎么看的呢?

李汉俊很早他回国不久,他就很重视注意调查工人的情况,十分看重工人自发的罢工,后来又积极参与对工人运动的指导。有人甚至说他是中国工人运动的先驱。他19年就认为知识分子应该与工人结合,脑力劳动者应该从精神上打破知识阶级的牢狱,谋脑力劳动者与体力劳动者的一致团结。后来又号召知识分子要以同情互助和牺牲的精神尽力贡献自己的能力于社会改造事业,这是他的一个始终不变的初心。

19年就对上海那些罢工的现象问题做了总结。当时好多人就反对那个工人罢工,然后他就充分肯定工人罢工的合理性和正当性,就认为过着非人生活的工人怎能不进行抗争呢?他为了开阔工人的眼界,还写了不少介绍当时欧美、日、俄等国的劳工组织和运动的文章,也介绍了从第一国际到第三国际的世界劳工运动史。

他为了具体的指导一些工运,在《劳动界》他是主编,先后写了《工人如何对付米贵》、《汉口人力车夫罢工的教训》等等文章,启发指导工人提高阶级觉悟,让他们团结起来争取自己的权益,然后最后引导工人就在生产关系和政治上夺取支配权。

他是身体力行地参加了20年4月份上海船务栈房工界联合会和上海机械工会的成立会。党刚成立不久,实际上他是主持这个工运工作的,曾经派李中组织机器工会,派李启汉组织纺织工会。他代理党的那个书记,以后又成立了职工运动委员会。21年春天,他又亲自指导了上海法租界电车工人的罢工。

所以日本的21年的那个情报就是说李汉俊成为上海各种工人运动的煽动者,在蓬勃发展的工人运动中被视为中心核心人物。他虽然没有像那个报告说的是他被选为纪念五一劳动节筹备会的会长,但实际上李汉俊也参加了李启汉出面办的那个会,而且也出了主意。这个筹备会后来租界的人就把这个地方抄了。实际上在建党之前,他一直实际上在负责上海的工运,非常厉害的一个人。

我们知道中共二大以后,中国的工人运动进入的一个所谓的第一个高潮。那个时期的李汉俊他在做一些什么?21年底他离开上海,他后来写的就是说自己决心卸脱一切在上海的党的责任地位,专心教授及劳动运动。21年底到武汉之前,曾经到北京与李大钊和邓中夏等商讨过工运工作。到武汉以后,他就又跟包惠僧他们联系上了京汉铁路工人,后来又到粤汉铁路、汉阳铁厂、汉口英美烟厂等处到那里去负责指导工运,还有工人俱乐部的成立,后来又是工会的组织,他都参与了。他跟林祥谦、向警予、杨德甫等工运领袖都成了朋友。

所以日本档案甚至说他是陈独秀派遣的,在武昌城内设立支部,让包惠僧接管事务性工作开展活动。22年就成立了湖北全省工团联合会,李汉俊任职委员,还有兼教育主任委员。23年京汉铁路总工会的成立大会,他又带着几个学生到郑州参加成立大会。在总工会成立遭到阻挠的时候,当晚工会党团召开紧急会议,然后李汉俊也参加了。他赞同总罢工,但认为张国焘提的条件过多,不仅难以实现,也树敌过多。他的意见没被接受,但仍然积极参与指导罢工的准备工作,动员学界声援。有人后来认为他是二七惨案的幕后指挥者。二七惨案后,他就被湖北军阀通缉,逃到上海去了。

他在北京为二七受难者的家属捐款,积极联络湖北的国会议员胡鄂公等人在国会提出弹劾吴佩孚镇压京汉铁路工人、解散京汉铁路总工会的提案,要求政府制定保护工人权益和工会的法律。23年的4月,内阁向国会提交了工人协会法草案,李汉俊马上就写了文章,对这个工人协会法提出批评。第一次工运的高潮,它的最直接体现就是京汉铁路大罢工和香港的海员大罢工,李汉俊在当时的京汉路上他发挥的作用可以说是非常至关重要的。

工运进入第一次高潮,党内有一些失败情绪。当时总书记陈独秀就认为中国工人落后、幼稚、缺乏觉悟,不能成为独立的革命势力;资产阶级力量比农民集中比工人雄厚,应当让国民党走革命中间道路。而李汉俊在二七惨案的一周年就是24年写了《纪念二七的意义》,他仍然肯定工人是革命的中坚,直到二七年他还宣称农工是世界解放的钥匙,这是他一贯的看法。

在京汉铁路事件之后不久,在中共四大召开前,李汉俊其实就脱党了。真实原因是什么?他为什么会脱党?有对张国焘、陈独秀有不满,李汉俊认为他们有的事做得不对,尤其张国焘特别排挤他。当时矛盾就说他脱党是因为他高傲气质和坚持个人的独立见解。的确李汉俊就是有什么就说出什么来了,还有就是他也不愿意放弃共产国际籍和屈从某些党的领袖的错误领导。

21年马林刚到上海的时候,李汉俊就表示中国共产主义运动应当由中共自己负责,共产国际只能在协助地位。我们可以接受其理论指导并采取一致行动,在经费方面只能在我们感到不足的时候才接受补助,不期望靠共产国际的津贴来发展工作。

在一大,他更是发表了一些与众不同的意见。刚才说了有一个苏联顾问认为一大因为李汉俊的反对才没有通过加入共产国际的决议。二大召开前后,他又给党中央写信提出一些意见和建议,其中一条是反对领薪水,主张党员不能只靠吃革命饭,而应当有自己的职业和收入。他这个建议实际上主要是不希望党完全依赖和受制于苏联共产国际。

二三年的时候,马林以共产国际代表身份指令中共党员以个人身份加入国民党,采取党内合作的形式。 李汉俊在参加 北京党组织的讨论时,他表示国民党是代表资产阶级的政党,而共产党是代表无产阶级的。根据马克思主义原则,共产党员不应该加入国民党。他预言这样的党内合作会把中共搞垮的。这是他脱离党的一个重要原因,就是政治观点不大一样。还有一个原因就是1923年他到北京以后,在外交部、教育部任职,他的薪水除了维持家用,还用来资助:

• 夏之栩
• 徐全直
• 陈碧兰到北京求学

这些湖北的女共产党人还为抚恤二七罢工的死难烈士家属捐款。他不接受北京那个党组织要他辞去政府职务,所以免于被开除。23年5月就写信声明退党。退党以后呢,没有马上被开除。一年后24年,大概在四大前被除名。

那李汉俊脱党以后呢,他在北京或者说在其他地方,他具体的生活是什么样子呢?他还是主要是教书,在湖北的武昌高等师范学校,后来又叫大学教书。他离开了党没有放弃信仰,还在那个大学里教授马克思主义,仍然也听从党安排做这些事。陈独秀给他写了一封信,让他到上海大学授课,他就去了,还曾接受李大钊的安排,去冯玉祥的部队里讲课。他跟李大钊关系一直比较好,李大钊对一些党员说要对李汉俊表示温暖。他虽然反对共产党员参加国民党的党内合作形式,但他在一大就提出过要援助国民党,所以他并不反对国共合作。

二六年春,他在怀念孙中山的文章里说:

共产党没有加入以前 国民党是一个没有气的皮球 没有煤炭的车头 共产党加入后 才成为一个有气的皮球 有煤炭的车头

很肯定共产党在国共合作中的重要作用。二六年下,他由董必武和张国恩介绍加入国民党,但是在同年秋,他又提出了恢复中共党籍的申请。湖北区委经过讨论,一致同意他的申请,但是最终被陈独秀否决了。他虽然没能如愿重回党内,但他常对人说:

“我不能做一个共产党人 做一个共产主义者 亦属心安理得”

大革命期间,他与共产党人携手合作,作为湖北省教育厅长,常与中共湖北省党委宣传部长交换意见。七一五以后,武汉方面分共清党,李汉俊有人说国民党这样清党,把一点革命力量都清去了,国民党也就要完蛋了,革命的希望还是共产党。可见他是一直心向共产党的,也以国民党的身份为共产党做了不少事。从这里可以看出来,其实李汉俊他从一开始他的建党思路都是保持党的独立性。他对这个问题他很看重。

另外就是说他的性格跟后来的李达、施存统他们比较接近,就是有点那种知识分子的性格。陈望道他们其实他们是这样的一批人。我们一说到这样的一些人,或者我们说到这个知识分子,我们经常会想到这些人其实是穿着这个西装革履的。那其实真实的李汉俊他的这个形象是什么样子?有的人大概觉得李汉俊是留日归国的应当是西服革履,其实是想当然。李汉俊这人十分朴素的,他曾经对家里人说穿着简朴一些方便与工人联系。

21年,芥川龙之介见到的李汉俊他描述是穿的是灰色大褂和中国布鞋。他的那个学生回忆老师说他衣着极为朴素,为中式蓝布打长衫。沈雁冰(茅盾)也说他是衣服朴素如乡下老。我的那个老外婆曾经告诉我一件事:有一次李汉俊和一位外国人在上海进一家饭店,门卫见李汉俊穿得太土不让他进门,还是那个外国人说了他是我的朋友才让李汉俊进去的。实际上现存的李汉俊穿西装照是他结婚的时候拍摄的,那个西装还是借他的一个兄长李书城,就是随着黄兴到美国流亡时穿过的旧的燕尾服。李汉俊也不讲究吃穿。

他嫂子回忆有一次米饭夹生,别人都没动筷子,他却匆匆吃下就没觉察出来。他写文章的收入常用来资助党的活动,如果不够甚至当掉亡妻的首饰。有人见他在上海任党组织书记时候就生活很苦。在武汉的时候,他每月把当教授的收入拿出一部分交给党组织,这是夏之栩告诉我的。夏之栩在那给他当等于是秘书跑东跑西,他就把每月给她钱然后拿到她母亲家,她母亲家是党的一个活动地点。他还经常资助一些青年党员,自己生活十分简朴却对党很慷慨。

李汉俊是在四大之前就是脱党,虽然后面还是和党组织有着密切的联系,但是在形式上其实已经脱离了党组织。既然这个样子的话,为什么在1927年的时候李汉俊会被蒋介石杀害呢?其实像蒋介石他们还有湖北的一些国民党的反动派挺恨李汉俊的,因为他虽然加入了国民党,又在改组清党以后他又留下来,但是他做的很多事情就比较为共产党做的。当时七一五以后,董必武、恽代英等人就劝他们留下来做点事,所以李汉俊就参加了改组后的省政府和省党部,安插了一些共产党人和共青团员,然后当时又组织这些革命力量和反动势力做斗争。

当时他动员很多中山大学的人到那一个广场打击那些反动派,支持工运什么的,也把一些进步人士包括李达呀到中山大学来任职。他是中山大学的校务委员嘛,所以当时那个国民党反动派对他必欲除之而后快。他知道自己这样做有被杀的可能。27年四一二政变以后,国民党在南京成立政府,一成立就秘密发布了一个通缉共产党首要令,李汉俊就名列其中。在五月份,李大钊他们在北京牺牲了,然后就在武汉举行了一次追悼南北烈士大会,他就在演说中就说我们不论何时何地均必须有牺牲的决心。后来南京方面派遣的西征军到了武汉,他很快就被武汉卫戍区的人把他以湖北共产党首领的罪名被抓了,没有审讯就杀掉了。

当时是不是也是害怕他的哥哥李书城过来救他,害怕劫狱救他。还有一个杀的原因就是怕他们响应广州起义还有好多因素吧。就有人说是胡宗铎、陶钧这两个武汉卫戍区政府司令把他们叫屠夫啊。其实这两个人的那个性格因素只是一方面,主要是南京有那么一个通缉令,就是李汉俊和詹大悲一块被杀的。刚一枪决就马上胡宗铎、陶钧就上报给国民党中央特别委员会,就说明实际上他们是在执行上级的命令。李汉俊很早其实就被国民党右派那边盯住了。

眼中间这样的角色还有一个就是说在那西征军到武汉之前,那个李书城和李汉俊他们把一些什么共产党员、国民群众给放掉了,放掉了200多人吧。他牺牲的时候只有37岁。李汉俊在这个年纪牺牲,其实算是英年早逝了。他去世的时候孩子太小了,那个李声簧。对这块我就想问一个问题,在您的生活当中有没有听到这些前辈们对李汉俊是怎么看待或者怎么评价的呢?

我多次访问过我的老外婆叫屈文淑，她做了好多口述，我给她整理成文发表。我的外婆李声韵一大的时候她那个时候10岁也在家，她说她不记得什么开会的事，就是讲的当时家里老有人来都可以算作开会吧，她都不知道哪天是开一大。她那个时候学钢琴，所以现在那个一大会址楼上有一架钢琴在那放着。我那个外婆吧保存了很多年，就是李汉俊在三益里他们全家的合影，那合影里头有李汉俊。79年的时候她拿出来给中央音乐学院当时研究者，然后呢她让我去拿回来，我就给了当时的还叫中国革命博物馆，这就是一大后来展出的那个照片。她也在八十年代初跟我和我爱人谈了一些关于她叔叔李汉俊的事。

我就念她说的,她说我在北京师范大学女附中上学时偏爱文科,叔叔知道以后对我说应当文理兼通并给我买了各类的书鼓励我阅读。而汉俊与李四光很熟有意介绍我与李四光认识,希望我多了解自然科学。我外婆还说三十年代初在为汉俊举行安葬仪式时,陈望道写了挽联”欲哭无泪”。邵力子呢打算抚养生簧,生簧就是李汉俊的儿子,我父亲没让。我的外公呢冯乃超虽然没见过李汉俊,但他从在日本第八高和东京帝大他也是在大学读书期间接受马克思主义的,与李汉俊有类似经历。后来呢他回国也间接了解一点李汉俊的情况,他对我们说二十七年我们几个人从日本回国到上海,彭康从对我说在中国第一个介绍社会主义的是冯自由,第一个介绍马克思主义的是李汉俊。冯自由他是外公的堂叔。二零年三月就曾经写过《社会主义与中国》提出中国应提前探索社会主义道路。

此外外公还几次讲有日本人称赞李汉俊的日语说得很流利。后来我知道这个日本人就是著名作家芥川龙之介,他访问过李汉俊,他就写李汉俊日语讲得极为流畅,甚至有些很复杂的道理他都让对方领会,所以他的日语可能比我还好。芥川还在信中称李汉俊堪称出类拔萃之才。我们外公呢三一年翻译过芥川龙之介,所以他应该读过芥川评价李汉俊的文字。顺便说一下外公一直很支持我们的研究。这些前辈还有很多人的当时的口述,比方说什么刘仁静、江亢虎、罗章龙很多我们当时访问了,这些口述回忆收入了我们编的《李汉俊传》这本书。

还有一本就是我们的论文集《中国共产主义运动探源》收入了我们研究李汉俊的一些论文。这两本书即将出版,期待老师的这个著作早日的上市。谢谢李老师。今天我们的节目就录到这里,感谢各位的收听。

109.春天的人间喜剧,小说要写给被生活冤枉的人×肖一之

Hello, everyone. Welcome to the new episode of the “痴人之爱”. I’m 阿卓. Today I’m going to talk to you about our friend, 肖一之。

话说萧师傅,之前包慧怡包老师是怎么形容你在播客界的名声的?

他说你是一个几乎不在自家耕种,整天跑去别人后院务农的人。

而且因为他的节目叫《此处有龙》,当时还有人空耳听成了你是一个不在自家耕种,总是跑到别人后院务农的人。

那么热爱在人家后花园里务农的萧师傅你最近过得怎么样?

听起来我不是一个什么正经的人类,念大学的时候,我们体育课有一个选修课,真的是舞龙舞狮,我没有选上。

在物理上没有实现舞龙,但是在播客界成为了舞龙的大触。

那就加油,今天要舞一个大龙,最近反正有很多事情要做,热热闹闹地在赶工。

我是不是可以打一下广告了,耶,我最近交了两个书稿呢?

• 我翻了弗吉尼亚·伍尔夫和利顿·斯特雷奇的书信集,这本已经交稿咯,应该是今年五六月份反正赶书展之前要出来。
• 还有一个二六年嘛,大家都知道的阿加莎公版,我因为一些没有推脱掉的原因,最后也参与其中,因为这个最新版的阿加莎会有好几家了,反正某一家的尼罗河上的惨案会是我的。

这是最近交掉的两本,当然了,现在还在一边写论文,一边在赶一本巨大的东西,还有一本巨大的丹尼尔·德隆达,先把flag插上,我的目标是年底交稿,好,插完了。

你前段时间又给自己立了一个flag,我们夏天还要再来录一期米德尔·马契。

这个不怕的,因为这个我要教的,所以我在夏天将有充分的材料可以慢慢录。

我们今天要聊的作品,佩内洛普·菲茨杰拉德的《早春》,你也是这副语气,一点都不怕,因为你手上有充分的材料。

但说起来也非常惭愧,我跟萧师傅是从去年开始就一直说今年我们要很应景地聊一个早春,但是拖到现在,时令意义上的早春其实早就已经过去了,冬天的痕迹在绝大多数的城市现在那是一点都没有了。

确实我跟肖师傅今年开年以来都很忙,春天真是一个残忍的季节,我觉得年轻的时候没有这样的感觉,就觉得春天到了,风吹过来的空气都是新的,稍微一下你就可以出去玩了。

特别轻快,但现在春天一到,万物复苏,什么事情都是新的,成年人的事情堆在眼前真的很多,各种各样的变化让人应接不暇,前段时间还跟朋友感慨,一年一年的时间过得飞快,人生真是充满了虚无和倦怠。

所以春天到来的时候,跟年轻的时候不一样了,这种更新和变化的感觉再也不能这么自然而然地进入到你的生命,一点都不轻快,一点都不轻盈。

我觉得在春天我们要踊跃地去进行一些春天的仪式,用现在网络上的说法,让自己的尸斑变得淡一点,因此我跟肖师傅好像都还是度过了一个比较热闹的春天,而且这些事情基本上都是跟搬家、跟猫、跟植物以及看书有关。

那肖师傅,你要不要说一说你们家的七猫事变?

我想先澄清的是,我们并没有那么在实力意义上错过春天,我们录这期节目的当口,春分才刚刚过去两天,OK,我们还是赶上了春分后面一点点。

刚刚阿卓说了我们最近有一个七猫事件,它其实先有一个忧伤的故事,我们搬家之后,我来上海养的第一只老猫没了。

它真的是我来上海那一年,17年来的时候在松江捡的猫,然后我现在又搬回松江了,所以我之前经常看着它算你几岁就说我来了上海多少年了,这种你人生差不多有十年的时间,这十年有的时候你可能需要想,但如果你记住你的猫,它就是这十年所有的见证,你看着它在你面前,是一个非常活生生的证明,但非常不幸的是这只猫,前年查出来了肾病,你平时觉得它喝水喝这么多,怎么都不应该得肾病,但它就是得了,猫的肾衰、慢性肾病很难治,它也没有什么根治的方法,你只能靠在家里给猫输液,所以在过去的一年里,我们都习得了熟练地给猫输液的技巧。

因为它查出来就比较重,兽医也是说的,它肯定会进入一个平台期,然后像所有的慢性病一样,稳定在一个平台,然后突然一下就崩塌。

在春节的时候,它大年二十九开始拒绝输液,然后不吃不喝,好像这个猫决定了,OK,我已经活够了,我不想受罪了。

它脾气也非常大,脾气大了一辈子,到最后你想给它灌水都灌不进去。

所以就没了,大概年初四初五的时候我把它带去火化,有这么一个忧伤的故事。

为什么说这是一个忧伤的前奏? 是因为把时间倒回去年的夏秋,我们搬到了现在这个地方,有了院子。

大家不知道还记不记得曾经有一个古早的游戏叫院子猫咪,你在手机上可以放各种东西就会来各种不同的猫,事实上证明你不放东西也会来猫,只要你有院子。

于是各种各样的猫陆陆续续在我们家的院子里出现,去年我们一直慢慢地自己一点点弄这个院子,到现在其实没有彻底弄完。

院子里头有时候就会堆着一些杂物,对猫来说简直太棒了,它们就躲在里头。

有两只特别亲人的猫,有一只在去年秋天就确认它是一只被人做完了绝育然后被遗弃的猫。

变冷的时候我们先把它养进来,也因为生病的老大不是很介意它的存在。

经过多次尝试发现它和老大的关系还很好,有一个生病的老猫,你在往家里放新猫的时候会很担心,结果它们俩还挺好。

把这只院子猫收进来之后,它似乎把自己的求生经验又传送给了朋友。

它的另外一位朋友又出现了,是一只小小的玳瑁。

为什么我要强调它是一只小小的玳瑁? 因为它来的时候大小是三四个月大的小猫的大小。

它在我们家院子里过了一冬天之后你发现它没有变大,就意识到后院猫咪抽到了一个SSR。

它应该是有一个基因变异长不大的小猫,维持在三四个月幼猫的体型上。

尽管如此,它其实也是一只成年的猫了,于是春天的时候有几天,万物生发,大家都兴致勃勃,猫们就会开始在各个地方大声嚎叫。

它好了两天回来,前段时间上海开始下雨,因为老猫也没了,觉得还是把你养进来吧,这下雨看着怪冷的你一个小小的猫在外面。

当然就要去医院体检,体检一看,果然怀孕了,怀了三个。

现在的故事是我们现在养着四只猫,再过一段时间就会四变七,我们要想办法把这几只小猫都送养出去。

目前已经完成了三分之一点五,还有一只疑似可能已经完成了,所以压力不是很大。

这个春天讲完就会觉得真的就是一些生生死死的故事,猫的寿命没有人那么长,人这一辈子如果要养猫,可能会经过很多猫来来去去,就是这样的一个欺猫的故事。

对于猫寿命比较短暂这件事,我比较乐观,猫到了养老送终的时候,人还是活门乱跳,我们可以替猫完成他们所有的生后事,但是如果人先没了,猫还活着,后面的事情就不知道了。

人的寿命比猫长是好事,你能够帮猫去完成他的生老病死,而不用担忧在你死后你的猫怎么办,这是非常有逻辑的乐观。

我来给大家提供一些实用的指南,如果大家在上海家里有宠物,不幸遇到宠物去世的情况,大家可以搜一下导航去上海市小动物无害化中心,他们可以收费帮你完成宠物的火化,如果你收养流浪动物然后不需要把骨灰领回来,30块钱一只,它就会和其他被送到他们那里的小动物一起火化;如果你要领回来的话,600块钱一只,他们会单独火化你们家的小动物,给你一个进炉的视频,然后还会给你一个骨灰盒搞得非常正式,因为它是政府的机构,你会比较安心,不会有那种收了你的钱实际上不知道拉到郊区什么地方乱七八糟地烧了的情况。

说到生生死死,我今年搬了新家之后有了一个新爱好,前任租客给我留下了一整屋子的绿植,我之前属于养仙人掌、养多肉可能都会养死掉的人,被塞了一屋子的龟背竹、秋海棠和铜钱草,真有种老鼠掉进米缸里的感觉,他给我留下的龟背竹其中有一株长得比人还高,像龟背竹的树一样,所以今年春天我正在积极地学习各种绿植的知识,并且积极地进行一些差异能的习得。

习得–水培就是把植物拔一根下来插进水里,土培就是把植物拔一根下来插进土里,看看我的家到了夏天会不会变得更加繁盛,我觉得也是一种在生生死死方面的经验,人终归要跟猫和植物在一起,才能获得一些春天的宁静。

我这个春天延续了上个冬天非常痛苦的任务,去年年底我鬼使神差加入了熊阿姨的读书会,从去年圣诞节到今年春节,群友们一起要读完托马斯·曼的《魔山》,圣诞节之前大家还兴冲冲地说,等读完《魔山》,大家还可以一起去吕贝克,托马斯·曼的故居打个卡。

结果开始读《魔山》以后,大家越来越怀疑世界,为什么要读《魔山》? 这书越读越读不懂,越读越云里雾里,连成句子看得人脑壳突突地疼。

冬天读这书越读越冷,越读越咬牙切齿,越读越心如死灰。

我撑到最后的信念感是,等我读完这本书,我一定要背着这两本板砖一样厚的《魔山》丢到吕贝克托马斯·曼他家的门口,用行为艺术表达我对托马斯·曼的愤恨。

最后因为这两本书实在太厚了,我想了想也没带上去吕贝克的火车,这两块板砖被非常喜欢《魔山》的某人又扛去了纽约。

这也是一种春天的际遇,围绕这次吕贝克的行程,我的二月和三月排满了各种各样的文化活动,包括但不限于电影节、柏林电影节、波士顿的文学节。

最近埃彭贝克的客乡,也就是《海姆加特》的戏剧版,最近也在柏林上映,反正就是各种热热闹闹的美术馆展览,这个春天感觉一场活动接着一场活动完全停不下来,心力交瘁。

我也在那个群里,一开始就没有加入这样的活动,熊阿姨今天最新的推送已经是《魔山》读不完了、不想读,读到一半不想读了,到此读书会已经变成了一个行为艺术。

强行扭回话题,《早春》这本书非常适合春天读,我觉得它治愈了我在《魔山》里受到的创伤。

如果从《魔山》直接过来,《早春》太治愈了。

这个感觉是你在凄冷的荒山峻岭上挨吹挨冻,挣扎着看不到头的路,好不容易下来了,现在走进一间温暖的农舍,里头有温暖的火炉,还有甜美的点心在等着你,差不多就是这样的落差。

而且还有一个壁炉,你推开门,春风料峭,枝头上长了嫩嫩的芽,还有花苞,真的非常好。

读的过程中我还忍不住重新去看了一下侯麦的《春天的故事》和《小津安二郎的早春》。

一下子春天的仪式就拉满了,春天你要做一些春天的事情,要读一些春天的故事,真的有一种顺着毛去撸猫的感觉。

如果我们要列一份春天的书单,虽然现在列可能有点晚,但佩内洛普·菲茨杰拉德的小说必须放在里面,之前说过春天是非常残忍的季节,但读菲茨杰拉德和《早春》会给人一种略带黑色幽默但依然春风和煦的轻喜剧感觉,整个小说给人一种微妙、明快的幽默感。

你会觉得这些在生活中挺普通有瑕疵的人,一旦出现在他的小说里,仿佛多了一点幽默的光韵。

我在想,这会不会和这位作家年近六十才开始真正进入写作事业有关系,因为什么让我想到上星期去波士顿参加萨尔曼·鲁西迪的活动。 说起来也蛮好笑的,鲁西迪今年竟然已经79岁了。我对他的印象一直都是,哎,这不是《午夜之子》那本书封面那张中年男人硕大的脸盘吗?结果这次看到他,第一个反应是,天哪,这个人怎么老成了这个样子。过了一会儿我才反应过来,哦,《午夜之子》这个经典封面是多少年前的事情了。

当时那个主持人问了79岁的鲁西迪一个问题:

“哎,你觉得对于作家来说,是不是年龄越大,生活经验越多,写作是不是就会越容易?”

鲁西迪的回答是恰恰相反,他觉得越年轻的时候写作是越容易的,因为年轻的时候你什么都不怕,不管写得好不好,但是你心里憋着一股劲儿,你一定要把这本书写出来,一定要把它发表。这样的勇气和决心随着年龄变大可能会越来越少,而且随着年龄的增长,作家写的东西越来越多,你要从有限的生活经验里再去挖掘新的类型和新的故事,其实非常难。

当时觉得鲁西迪的经验之谈蛮有意思,但我在回来的车上翻《早春》的时候,想到佩奶奶她的写作生涯其实跟鲁西迪说的那种状态完全不一样。某种意义上还有点相反,因为1916年出生的佩内洛普·菲茨杰拉德其实是在1976年她丈夫去世以后才正式以作家身份出道。她早期的小说基本上都跟她的人生经历比较重合,比如说书店的故事,就是取材于她早年在书店工作的经历,书里面的那家人住在泰晤士河旁边破破乱乱的船屋里,也是跟她以前的生活经历有关系。

到了后面,她的小说题材似乎越来越宽泛和跳脱,经常在日常生活的言谈和细节里写不同国家和不同时代的历史小说。我觉得此处必须要高亮一下历史小说,这是肖师傅的爱。比如说:

• 《无辜》,她写的是1950年代,意大利佛罗伦萨的莫罗贵族家庭的故事。
• 《早春》,我们今天要讲的,写的是十月革命之前的1913年的莫斯科的故事。
• 《天使之门》,写的是一战之前的剑桥。
• 她最后一本小说《兰花》写的是19世纪德国浪漫派的诗人诺马利斯的早年生活。

此处预告一下,我前两天刚把《蓝花》的flag发给了之前刚跟我录完凯罗斯的王凡柯,也就是饭团师傅,希望这位本雅明电台大触,他能在五六月份的时候跟我填坑。因为我觉得菲茨杰拉德写的历史小说真的很有氛围感,不仅是语言和结构上的精巧与缜密,还有非常细腻的人物对话和日常的空气。加上她会写到很多她对历史和思想背景的研究和了解,但没有一点掉书袋的感觉。作为读者,你自然而然被她营造的气氛牵着走。最后感叹一句,太适合春天来读了。

那么接下来请肖师傅讲讲,你非要过来舞龙跟大家安利佩奶奶的理由是什么?在春天重新再读一遍《早春》是一种什么样的体验?以及我们之前说过,这次录播客因为赶时间毫无准备,但你手头有很多年前这本书中文版出来时攒的笔记,就算没有准备,你也可以随时张口就说。那就让我们看看你到底准备了多少压箱底的史料吧。

这真的都是史料。其实还蛮巧的,你知道吗?你刚刚提到鲁西迪的时候,我正在这边狂笑。我觉得可能你没有意识到这件事,还是你意识到了,因为我们今天要聊的《早春》和鲁西迪的《撒旦诗篇》是同年的。在那年布克奖的时候,他们同样进入了那年的布克奖短名单决选,当然最后是鲁西迪拿到了奖。但是,佩奶奶在她的书信里留下了很多吐槽,因为大家都知道《撒旦诗篇》当年惹出了大事。发布布克奖的时候,鲁西迪还没有收到来自教派的追杀令,但那时已经有人在威胁他了。佩奶奶给朋友写信说,今年的布克奖颁奖的地方占满了警察,因为她管鲁西迪叫 “Salman R.”,因为这个 Salman R. 说有人要威胁他的性命,他处在生命危险当中,所以到处都是警察。老太太就这么吐槽。接着她又扭头说了句:

“Poor S. Rushdie, or Rich S. Rushdie”

她说这整件事情听起来像一个

“a publicity campaign that went dreadfully wrong”

这就是她写给朋友书信里的吐槽。

当年在最开始聊这本书的时候,这一段我完全没有用上,因为好像没有找到把鲁西迪塞进来的地方。但你看这么巧,五六年之后因为阿卓充满文艺活动的早春生活,我没有把它接上,这材料又用上了。这个世界上没有浪费的笔记,我跟你说。

你知道到现在鲁西迪的安保仍非常严密。我在参加活动之前收到了主办方的邮件说,鉴于严峻的安全问题,希望大家提前半小时过来安检,而且提出每一个进入现场的人随身携带的包不要超过A4纸大小,任何超过A4纸大小的包都不能进入活动现场。因为我是第一次去波士顿,但你怎么找到活动现场呢?你看到哪儿警车最多,那就是活动现场。进去时我经历了最严格的安保,查了包,查了身份证,查了保温杯,整个现场被黑人白人猛男保镖包围。主持人也问了鲁西迪一句,说你现在已经被视为言论自由的象征了。鲁西迪还说了一句:

“我不明白你说的这个象征是什么意思,我是已经死了吗,我还没有死。”

他差点,对吗?不是前两年又被袭击了吗?但这个安保措施听你描述就像去了一趟天安门,安保就是那样。

回来说在春天读《早春》,在上一次读完后我中间还翻过几次,但真的没读完,所以这次隔了多年再次把它读完,感慨特别多。这次真的是在春天读《早春》。我觉得有必要描述一下我现在住在什么地方,我现在住在辰山植物园旁边,所以我们多年后终于实现了可以去辰山看早樱,等着东京樱开了还可以再去东京樱,还可以去看夜场的梦想。因为我现在离辰山很近,所以日常生活里你会一直看到季节变化的明确迹象。我能看到小区后面的稻田如何从冬天的黄色变绿,春天的各种草开始长满它。到山脚下那片油菜花田现在开满了,一片金色的毯子,远远看比较好,靠近因为油菜花太多会比较头晕。有时上下班我会骑车路过辰山植物园门口,辰山植物园的停车场不用买门票,站在停车场里望就可以了。它在靠里地方种满了一排排的二乔玉兰。二乔玉兰是一种特别漂亮的玉兰,是白玉兰跟紫玉兰杂交出来的,花特别盛,在它盛放时整棵树会变成一簇花球,哪怕远远看也是非常开心的。这两天上海天气也还行,蓝天下的玉兰大家可能都看过很多美图,实在是美丽的春天迹象,如果玉兰都开了,就结结实实觉得春天来了。

其实还有更多细碎迹象,比如我们还没整理完的院子有很多杂草,其中一个是金花苜蓿或蓝苜蓿,我们再给它改一个名字。如果把它嫩叶掐下来,它就变成草头,我现在可以草头自由。我的草头都是自己在院子里掐的,我估计下个星期又可以再掐一轮。这些是非常明确的迹象。别说我还是个鸟佬,我们鸟佬对季节变化无比关注,春天意味着迁徙季来了,一些如果这次没看到可能要明年再努力的鸟都会要路过上海,所以你会通过各种方式关注最近有谁在过境。这个季节的节奏、韵律,我现在有了更多感知方式。

哪怕在变成鸟老住到这里前,大家肯定还是会关注春天。但像阿卓说的,年轻的时候体力更旺盛,更能经受世界的毒打。冬天日照短没问题,现在想想都有点后怕。我念博士的地方在新英格兰,冬天下午三四点就黑,早上也很晚才天亮,整个冬天日照可能只有六七个小时。现在想想天哪,我是怎样在那种地方活了六年而没有心理问题,年轻真好,耐力强,抗性好,这样的生活也能扛得住。即便是更皮实的年轻人,春天的到来也会让人特别欣喜。因为是新英格兰,你没有像现在在上海看到这么多花。新英格兰的春天第一个迹象通常是大家自己种的或自然生长的风铃花、雪地花、还有水仙,它们在林下开出来,其他植物例如水仙、风铃花、雪地花往往没怎么长叶子就先开花。在枯树林下它们一长出来,你就觉得可能残雪都还没化完,但你就知道春天要来了。地球的转动不可抵抗地转回来了,我们现在知道太阳离北回归线越来越近,北半球的人要逃脱黑暗,进入生机勃勃的季节。虽然季节还没完全到来,你现在见到的只是最初的符号,是冬日萧条的终结,是一个终结的时刻,有新的可能性要出现。

我觉得现在只是绕了一个非常远的路。回到《早春》,这一次再看非常明确,《早春》是一本关于可能性的书。可能性也是好的说法,可能性不好的说法是什么?就是动荡、不确定性。你要随时面对可能出现的问题,所有变化不以你的意志为转移,也可能没有任何可循规律。比如有一句英文谚语,如果想咒这个人有一个非常文雅的说法,叫:

“May you live in interesting times”

听起来不是很狠,但这是非常反讽的话,这所谓的 “interesting times” 是什么呢?我们中国人都知道那句叫做江山不幸,史实所指。这个 “interesting times” 从远处看很爽,但如果你生活在其中呢?当你困在不断变动的可能性里,这是怎样的生活体验?你要知道哪怕王安石这样的人,铁杆改革家,在霸相之后也会写什么愿为武林轻薄而身在贞观开源时,斗鸡走犬过一生,天地安危两不知,王安石又不想过在 interesting times。我们一般人谁想?没人想。

《早春》就是在这样的 interesting times 的一本小说。阿卓刚说《早春》的设定在1913年,《天使之门》其实这个时段也是佩内洛普·菲茨杰拉德非常在意、非常关注的。她自己差不多出生在这之后,对她来说这是她出生前的那个时间。曾经世界有另外一个可能性,但20世纪初所有的乐观、所有美好可能性在1914年戛然而止,因为第一次世界大战的发生,20世纪的梦想在那里破灭。

再说回《早春》更具体的,它还有1917年俄国革命的背景。小说里主人公里德一家人虽然出生在莫斯科,但其他家人都是英国人。我们可以想象革命爆发后,他作为一个英国资本家能留下来吗?他也留不下来。虽然书里没有写,我们肯定可以预料他们一家最后一定要逃离莫斯科。这个作为作家的莫斯科, 他是待不下来的,对吧?所以充满可能性,但他未必一切都是好的,他有可能就是这么一个动荡的故事。所以从这样的一个大背景下,我就可以切回阿卓之前在台里头给我那个问题了,我们是怎么样把历史和一个看起来这么充满幽默故事的家庭伦理轻喜剧放到一起呢?那就是这样了。

因为这个家庭伦理轻喜剧的参与者是生活在历史里当中的,非常具体的人,对吧?我们要记得一个人的命运固然要靠个人的奋斗,也要考虑历史的进程。当你被卷入历史的时候,你根本就没有办法来抵抗这一切。

那我们可以先稍微具体地把情节先说一下,要不然可能我们俩现在进入各种各样的细节,可能大家都还有点晕我们,因为这个故事的情节虽然叫清洗剧,还是多少有那么一点刺激的。

因为我们之前有讲过这个小说,它开篇第一句说这就是1913年的莫斯科,之后我们会在人们各种各样的言谈、各种各样的对话、各种各样打交道的过程中,你会在整个莫斯科的空气里面感受到这个时代的动荡,冬天的结尾和春天即将来临的灰暗和不安。但是就是在这样的一个非常非常抑郁的氛围里面,我们的故事有一个非常有意思的开头,那就是1913年的3月,印刷厂老板弗兰克·里德的妻子内利带着他的三个小孩–多利、本、安什努卡–坐火车离家出走,从莫斯科经过华沙然后去英国了。

这个开头这么一讲,我其实在读的时候有一种温州皮鞋厂老板带着他的小姨子跑路了的喜感。对,就是这个故事它开头,就是一种猝不及防的感觉。

所以在小说第一章,也就是内利出走之后,弗兰克他面对这个猝不及防的突发事件,内心地动山摇,瞳孔地震。但是他作为一家之主,你当着一家子的听差、保姆、厨娘、帮佣,还有马夫的面,你一定要维持一种基本的一家之主的素质,你要淡定地面对这种鸡飞狗跳的混乱场景。

• 听差
• 保姆
• 厨娘
• 帮佣
• 马夫

当然就很想说,早春它是不是应该有个戏剧版,因为这个场景它真的很像在剧场一样的。

结果到了第二章,就是在内利出走的第二天的一大早,弗兰克他接到了亚历山大火车站的站长的电话,说你们家的三个小孩,多莉、本,还有安妮什卡,他们在前一天半夜被留在了这个车站,而且还强调了一下,他们随身只有一个脏衣篓,现在需要你过去领取你的小孩。那个场景就真的特别的好笑,感觉弗兰克的痛苦面具都要裂开了,但是他还在努力地让自己表现得非常的镇定、非常的从容,而不是一个被妻子抛弃、还丢下了三个小孩的问题男人。

对,他不仅要在仆人面前维持已经不存在了的体面,第二天他还要去车站去接小孩子,他要去面对这种社死的场景。看小说头两章的时候,你其实都快忘记了,这其实是一个setting在莫斯科十月革命前夕的历史小说,就你一下子就被投入到了这种家庭伦理轻喜剧的场景里面。

明明家里出了这么大的事,然后弗兰克他想要维持一切都还比较正常的表面现象,但是他的表现又非常的僵硬,非常的狼狈。

我先来插一个刚刚阿卓说的这个地方,其实就是佩内洛普·菲茨杰拉德非常厉害的一件事情,就是他是在写一个历史小说,但是他写历史小说的写法–我们应该此处 cue 一下–如果没有听过的朋友,先去听一下前面的跳岛,这个历史小说到底要怎么写,可以先把跳岛三部曲听完了再回来。因为在这一点上,菲茨杰拉德跟曼特尔有一个共通的地方,就是直接把我们投进那个地方去。

刚刚阿卓说小说的开头非常的妙,妙到我其实觉得小说开头的第一句话是非常值得把它念出来给大家品一下的。小说开头的第一句话就是这么写的:

1913年,从莫斯科乘车,经华沙转车,前往查令十字,全程需要花费14英镑六先令三便士,以及两天半的时间,

你知道吧,这就是一个完全没有任何情节,然后你甚至都可以追溯它是从哪来的,因为菲茨杰拉德有留下她的记录,她参考资料是当时非常流行的贝德克尔旅行指南,就是那个时代的孤独星球,这个很有可能就是她直接从里头抄出来的一句话。但是就是这么一句话,马上从时间和地点还有历史感就把我们投到了那个地方,我们就一句话到了1913年。

然后妙又妙在什么地方呢,就是我们接下来又进入了阿卓刚才说的这个家庭轻喜剧,但这个家庭轻喜剧,里德的妻子出走这个时刻,为什么会有这么长的戏剧感呢?还有一个蛮重要的事情是,里德其实是最后一个知道的,家里所有的仆人都知道,因为夫人走的行李都是他们打包的。里德要等他下班回家,接到信使,你可以理解为当时的快递员,他们当时莫斯科有这么一个信使协会,专门跑腿送信的,这个快递员把信送来了,然后再跟你家的仆人喝茶聊天,然后你要回来拆信,其实家里所有的仆人都知道这点。拆完信之后,你还要装作、扮演出这么一个震惊的主人的形象,你还要跟仆人交代说,我妻子走了,还带走了三个小孩,他们其实早就已经知道了,就在等你回来宣布这个时刻。

所以你看这个小说,一开始就是把里德摆在了一个这么尴尬的位置上,他是一家的主人,然后尤其在小说里头这个设定,我们可以看到,其实非常巧妙地把当时的俄国的这种主仆关系写在里头,就是大家是一种非常明确的主仆关系,就很分明的,这个主人他就是家里的主人,这种拿决定的事情都要他来做,哪怕实际上这个决定的用人们早就把你做好了,这就是你表面上要同意一下,他要把流程给你走完,这就是好像非常充满流程性的话,但他就让我们看到了一件事情,就是里德这个人后来他就会说自己的生活状况,就是

“我好像一直就是站在戏台上的,我没有任何秘密可言,全莫斯科的人都知道我发生了什么,但是我就必须要在台上把这场戏演完,”

是这样的一个人物他要面对的故事。而且我觉得他里面的人物对话也特别出人意料,包括我们刚才说的第二幕的戏,明明是三个小孩被扔在火车站,应该是三个惊慌失措的小孩,结果你到了车站一看,发现这三个小孩都特别的淡定,特别是年龄比较大的两个小孩,因为他最小的那个小孩还是个婴儿嘛,就嗷嗷叫在那睡觉,但是他的大女儿多莉和本就特别好笑,本还在那边说,哎呀,都是因为安妮什卡,她太难带了,如果没有她的话,我觉得妈妈应该是可以把我和多莉一起带走的。多莉她大女儿还在那边说,哎呀,我们家三个小孩也不是她带的,都是保姆带的。对,但是她走肯定是有什么原因的,就是小大人嘛。

这个其实是菲茨杰拉德在各个小说里头都会出现的人物,当然如果你要说的话,这个人物的源头是可以一路拉回19世纪,狄更斯底下也有很多这样的小大人。其实在日常生活里头也会碰到这样的故事,就是不靠谱的父母一定会养出小大人一样的孩子。就是这样,多莉就说了,妈妈从来没有带过我们,我们都是保姆带大的,内利其实就没有亲自干过活,因为他们家在莫斯科,当时那个条件下家里有一大群仆人,倒不是说他们家特别有钱,而只说在当时的那个俄国社会里头,他就是这样的,就是这么一个非常小大人的人,站出来开始评价一切,你早就该来了,怎么把我们在这放了这么久。

但是我觉得多莉出场最妙的时刻是什么呢,是她回家之后你还记得吧,就是你想,你一个小孩,你妈突然觉得走我们回英国,就把你们哐哐都带走了,又把你们撂到火车站,不行我带不走你们,等你爸爸把你们接回去吧。这样把你接回了家之后,她做的是什么事情呢,她换好了校服,过来找她爸辅导家庭作业了,她说我远足中我还需要上课的,把自己的地图册、尺子还有地理课的练习册都拿出来了,跟她爸说我们现在在学英国,我们要标出工业区和养羊的地方。她爸就,你把这些都带上了火车,她说对的,我觉得可能会派上用场,对吧。一个冷静到让人觉得这个小孩怎么回事,就是一个纯纯的小大人。

多莉在小说里头还有好多这样的时刻,她就代表了菲茨杰拉德小说里头,一种非常典型的小大人的形象,他们同时也会非常敏感,非常有天生的这种敏锐,他们会一张嘴就说出大人们隐藏的话,或者一些大人们不敢说的话,或者讲出很多有预见性的话,这个是菲茨杰拉德特别爱写的一类人物。

刚才讲到了这其实是一个英国家庭嘛,我觉得这个其实就涉及到了《早春》另外一个非常有意思的特点,它不仅是一个英国人写的setting在俄国半世纪之前的小说,那么故事的主人公他也是一个生活在俄国的英国人,小说里面就会有很多的出场人物,他的生活圈子就会有很多很多在俄国出生和长大的英国人。比如说弗兰克他自己就是父母那一辈跑来俄国投资印刷厂生意的英国人,那他的妻子内利也是一个英国人,包括小说里面还有一个非常非常重要的人物,塞尔温·克兰,他也是一个在俄国长大的英国人,他既是里德一家的”好朋友”,真的是非常好的朋友呢,也是弗兰克印刷厂里面的会计。

与此同时他身上最重要的一个喜剧特色就是,他是一个狂热的托尔斯泰铁杆粉丝,他非常非常地热爱俄国的文化,他甚至还写了一本俄语的诗集,在印刷厂出版了名字叫做《白桦之思》。白桦也是俄罗斯文学里面经常出现的意象,白桦树,包括家庭教师金斯曼小姐,包括他们的社交圈子,就是什么使领馆呀、教会的朋友,这些生活在俄国的英国侨民,他们其实是有自己的一个生活的圈子,他们有自己的熟人的渠道,包括开印刷厂,它的整个厂子的运行模式,你要找订单、管理工厂里的雇员,其实在这个过程中就透着一股非常英国的资本主义的和工业文明的生产方式的感觉。

但是这种非常英国的资本主义的生活氛围里面,它又混着一些非常奇特的俄国的官僚气,英国人的那一套就是我们是日不落帝国、殖民地宗主国的那种姿态,在俄国也是行不通的。他们日常生活中跟俄国人打交道的时候,英国常识里的那种委婉含蓄和体面,还有那种阴阳怪气,在俄国这种充满官僚气的,以及非常粗犷的战斗民族的气质前面,你经常毫无办法。在俄国真的就是有一种乱拳打死老师傅的感觉,在这个里面你的这种一整套的英式文明,不如给找上门的俄国警察塞一个厚厚的装了钱的信封来得有效。我就觉得这个小说里面的这种混合式的场景出现还蛮搞笑的。

对,它其实是这样的,就是佩内洛普·菲茨杰拉德她自己肯定没有这个经验,她去过俄国旅行,但是她没有在俄国生活过那么长的时间,她肯定是有各种各样的资料的。讲资料之前,我其实想再举一个非常典型的刚刚阿卓说了这种混杂的故事,它里头有很多纯俄国味的故事,比如说那个二等商人库里亚金他们家的孩子和熊的故事,什么叫真正的熊孩子,就是字面意义上的熊孩子。你要给自己家的儿子 Misha 过生日,最后找你在远东的熟人搞了一头棕熊崽子,就这么顺着西伯利亚铁路就运到你们家来了,但是可能现在的人看起来,这也可以,但是她就是可以打个板条箱,就能把熊从西伯利亚送到莫斯科。送过来之后了,家里的熊孩子说让这个熊跳舞吧,这个熊也不会跳舞,熊孩子开始玩腻了,然后发现熊渴了,熊渴了就给熊喝伏特加吧,就开始给熊喝伏特加,那熊当然就只能喝醉了,喝醉了的熊跳到了桌上,把桌布这么一拉好,稀里哗啦,精美的餐盘、晚上的晚宴准备的东西全都砸碎了,还把自己身上搞着了火,就是这么一个熊孩子的故事、各种意义上的熊孩子故事。

但是你读到这的时候,你会有一种实在是忍不住要爆笑,因为这个哪怕是在现在,还是会让我们觉得,这个实在是一种太刻板印象上的俄国了,但是它又实在是太好笑了。这个故事就可以用来做一个非常好的切入。因为实际上这个故事是佩内洛普·菲茨杰拉德从历史学家哈维·皮彻要来的故事,就是有一个研究英国侨民在俄国历史的这么一位英国历史学家,他叫哈维·皮彻,他写的书是菲茨杰拉德写这本小说的时候的重要参考资料,菲茨杰拉德读到了他的这个熊的故事,她觉得这故事实在是太好了,她就专门给他写信,问我的小说里可不可以用这个故事。

此处 cue 一下那些擅长异曲同工的作家们。 行业规范是什么样子的, 对, 标准的行业规范。 你看哪怕在没有互联网的时代, 大家也会知道我要用这个故事, 我要先给原作者写个信, 征求您的授权, 然后再进行了一些改写, 因为这位历史学家记录的这个故事里头, 是一个英国的女教师, 家庭女教师在俄宝斯科生活故事里头, 她看到的, 所以在真实历史当中, 这头熊最后被送去了马戏团, 而在小说里, 佩内洛普 给了她一个更好的结局, 比被虐打之后送去马戏团还是要好很多。 但这一个例子, 我们能看到她在这儿会给我们一些非常明确的俄国时刻。 你管她叫一种象征, 或者刻板意义上的俄国风情也好, 或者像刚才阿卓提到的各种官僚的故事, 各种一个人需要表演的时刻, 然后各种会在这头出现的俄国食物, 包括像多莉的舅舅也就是内利的哥哥会来到莫斯科访问, 然后他的侄女带着他去逛商场, 商场里头卖什么, 然后买什么纪念品回家等等等等。 这一切都是可以找到佩内洛普给她做的考证。 她这个小说就像她所有的历史小说一样, 她做了大量的工作, 但是她都不经意地把它揉了进去。 这一点其实还很奇妙, 你来读这个小说的时候, 你会非常明确地读到这样的一个混杂的气息。

刚刚阿卓说的那些可能是在事件上, 或者是在整个故事线上比较明显, 而实际上在语言上它也非常明显。 只是都翻译成中文之后, 这个差别稍微有一点被抹平了, 它不像在英文里头这样明显。 因为它在小说里头会让这些人物不停地在英语和俄语之间切换, 比如说他跟俄国人说话, 他得说俄文, 他跟英国人自己说话, 他们又会说英文。 那么某些时候某些人不懂俄文, 说话的时候变成一个三方对话要处理, 那语言就会相当混杂。 有的时候, 是一些习惯,一些非常日常生活相关的词, 食物, 酒, 他们就会很自然地用俄文, 因为俄文用的是西里尔字母, 写成英文小说, 她就用英文的字母来拼西里尔字母的单词, 附上它的意思的解释。

所以我们来套一个非常学术的话, 这是一个跨语际的写作, 它是在两个语言之间操纵的。 我们现在读的这本中译本, 我觉得黄昱宁中译本还非常尽力。 我们尊敬的黄昱宁老师把所有的这种时刻, 她都在译注里提醒出来了, 会告诉大家, 这样的一个语言切换的效果, 是原文刻意造成的。 因为这种语言的切换,再没有比她能够更具体地呈现出, 这样一个人生活在两个国家, 或者两种文化之间, 这样的一个夹杂混的状态。

因为里德这一家人, 我们刚刚说了, 他们是属于英国侨民, 但是这帮英国侨民, 其实他跟英国的关系非常淡。 他是在莫斯科出生长大念书, 然后在完成中学教育之后, 才被送到英国去当学徒, 在英国的各大印刷公司工作, 学习手艺和技术。 英国对他来说, 是成人之后再去的地方, 而他自己的生活, 他自己的情感寄托, 都是在莫斯科的。

小说里头有一段场景我印象特别深刻, 这么多年没有读, 但我一直记得小说里头写里德和莫斯科的情愫是怎么样的。 因为20世纪初的莫斯科, 它还不是现在这样的一个大都市的状况。 尤其在当时的俄国, 你把莫斯科和彼得堡来比, 彼得堡是那个更先进的城市, 莫斯科其实有点土, 还有点乡村气息。 小说里头会交代那个时候的公共马车, 可能都还不是电的, 是马拉的, 所以早上还会听到有牧人放牛。

在这样的一个夜晚时刻, 里德和他的莫斯科是这样的关系: 在某些奇怪的不恰当的时刻, 在某些不起眼的地方, 弗兰克才会猛然喜欢上莫斯科。 亲爱的邋遢的莫斯科母亲,160座教堂的尽头让她迷茫。 她不偏不倚地庇护着工厂、剧院和金色的穹顶。 她受到了希腊人、波斯人、不明事理的村民,和走偏道电车轨道上的神学院学生的干扰。 她将那座神圣的城堡作为中心,却不断向外延伸, 带着一股霉味,一跃跨过林荫大道来到一片新天地, 那里有工人宿舍和铁路尽头, 修道院依旧坐着祷告。 最终她来到另一片新天地, 那里有猪圈、圆白菜地、土路、土厕, 在那里莫斯科似乎重负地变回了一个村庄。 当然最后的村庄是最重要的。

里德和莫斯科的感情是非常亲密的, 而且莫斯科对他来说不是一个庞大的现代都市, 它是一个人跟人之间关系更紧密的状态, 更像村庄的社交集体。 所以在那儿生活的人, 他的情感寄托当然是跟莫斯科更紧的。 所以虽然他是英国人, 虽然他在法律意义上都是一个外国人, 但是在他的感情和文化认同上, 他也有很大一部分认同作为莫斯科人。 不能叫他俄国人, 至少是作为莫斯科人的这一部分。 我们都熟悉的俄国文化的另一面对于精神和灵魂的追索, 这个也会对另一个英国人带来重要影响, 这个我们一会儿再讲。 反正里德这一家人就是站在英国文化和俄国文化的交叉地方。

刚才讲到《早春》作为一个历史小说的创作背景, 菲茨杰拉德本人主要是去俄罗斯旅行过, 这个小说里面很多场景的描绘, 很多文化现象的描绘,大部分都是来自于英国的研究学者对于英国侨民生活的描绘。 我其实非常好奇, 如果在俄国读者看来, 他们对于她所描述的这个世界是一种什么样的感受呢? 此处我们需要去抓一个俄罗斯人来让他读一下这个小说才知道, 我都不确定它有没有俄文译本,这是一个好问题。

但是我可以说, 她重构的这个历史的俄罗斯画卷, 她做的工作的源头, 除了我们刚刚提到的贝德克尔旅行指南, 她还看了大量当时英国的泰晤士报的俄罗斯增刊, 但是它是用英文出的。 她把自己放在一个英国侨民的状态去了解他们的历史。 我们刚刚提到的哈维·皮彻的研究, 然后还有另外一个在俄罗斯长大的英国裔作家, 叫尤金妮·弗雷泽, 她有一本书叫 The House by the Dvina, 即 Dvina河边的家, 是她的人生故事。 通过这些文献资料, 她可以拼接出一个非常能够令人信服的历史图景, 至少对我们来说, 她这头介绍的各种历史知识,跟你在俄罗斯小说里读到的东西是能够对上的。 比如说伏特加的专卖, 一个非常俄罗斯的著名制度, 让所谓的鞑靼人,也就是信伊斯兰教的人, 因为他们不能喝酒, 负责来经营这种政府专营的伏特加专卖店, 这样就不用担心卖酒的人偷酒喝了。 这个你会在各个小说里读到。

所以我想说, 除非我们能找到一位读过佩内洛普·菲茨杰拉德的俄国读者, 否则我们只能作为中国读者,根据自己在俄国小说和对俄国历史的了解来说, 你读这个小说你会觉得这个感觉是挺对味的。 你不会读着读着觉得它不像俄国, 它像英国了。 它的莫斯科味包括空气里渗透着的寒冷, 这样的一个冬至的严酷味都是很对的。

好, 我们接下来可以聊一聊内利的这个角色, 因为她在小说里面几乎没有正面出场, 但好像始终在场。 她的缺席恰好造成了小说中心的真空地带。 故事的开头是内利走了, 不知道什么原因; 故事的结局是内利回来了, 也不知道什么原因。 她的出走和回归的背后原因本来应该是小说发展的线索, 但是《早春》恰恰绕开了这条线索, 线索之外的所有东西都写了。 像弗兰克, 他一直觉得我的家庭是很自然地在运行着, 但是等内利走了之后, 他突然发现, 哦, 原来这个自然和正常都是他妻子存在带来的状态, 但他无法理解这种混乱。 这种混乱包括这个家里怎么一下子变得这么乱, 他老婆为什么出走, 内利的内心世界到底是什么样, 这些混沌的感性东西他完全没有办法去处理。 他也试图去跟内利出走以后混乱的家庭秩序相处。

我们看到对于家里的小孩子来说, 他们也不觉得妈妈走了就是妈妈不负责, 这是个坏妈妈。 他们会觉得, 一定是在大人的世界里有一种我们无法解释的东西。 这个小孩非常早熟和敏感。 包括家庭以外的社会圈子, 我们会看到围绕内利出走, 大家也会有各种各样的揣测和风言风语。 看到内利作为一个妻子和母亲, 你就这么不负责任地走掉了, 这个事情在外人的讨论里面到底还是涉及道德问题的丑闻。 不仅内利会受到大家潜在的侧目和谴责, 连带弗兰克也成为了问题男人。 这也特别有趣, 并不是说妻子走掉了男人也是有问题的, 毕竟一个巴掌拍不响, 你的妻子像温州皮鞋厂老板一样跑路了, 你肯定也是有责任的。 所以弗兰克在外面打交道时, 他会时不时收到大家又是同情又是嘲笑的调侃。 毕竟你是一个不知道怎么回事突然家里没了老婆的男人, 小说里时常有这样的时刻: 你明明在外面处理一些公共事务, 进行正常社交, 但是大家说着说着就会带着一种调侃同情的眼光看着你, 同情里面还混合着一点谴责, 这很有意思。

更有意思的是, 虽然有这样那样的描写, 但是我们仍未知道弗兰克的妻子内利出走的原因。 我们只能根据小说里的各种细节去猜测, 可能是因为婚姻生活, 也可能是因为母职的倦怠。 从大女儿多莉的视角出发, 她说, 对于这个妈妈来说, 照顾小孩是很难的, 因为这个事儿都是我们家里的保姆杜尼亚做的。 那我们又会觉得可能相比母职的倦怠, 可能还是因为跟弗兰克的婚姻太无聊了。 后面又提到内利出走的原因可能涉及一些情感纠纷, 也有一些受到了灵性和道德的托尔斯泰价值观的影响, 对, 就是那种到现在也很流行的精致白人受到托尔斯泰式思想影响的田园生活, 反对消费主义, 要反对家庭私有制, 大家找一个山清水秀的村里, 每天穿着袍子, 做瑜伽, 熏香, 灵修, 整天在那神神叨叨的乌托邦生活。 小说里的这个部分非常有意思, 有的时候没有解释本身也是一种充分的解释。

那么肖师傅你是怎么看内利这个形象呢? 首先她当然非常非常非常非常重要。 她重要到什么地步呢? 我要先接一下故事情节。 刚才我们讲了那么多内利, 这本小说的故事概括起来最简单的方法是, 里德的老婆内利跑了, 留下一堆小孩, 所有人像刚才阿卓说的, 见到他都会说,

“你该找个女人来帮你照顾一下家里”

大家试图塞一个自己的选项给他。 有一位找不到工作的金斯曼小姐, 待会我们会说金斯曼小姐, 这是一个非常棒的人物。 还有我们之前说过的这位托尔斯泰的信徒, 他的朋友, 他们家的会计塞尔温, 最后是塞尔温跑去说,

“我在商场里碰到了这么一个姑娘,我觉得她非常合适”

然后我们就得到了小说的另外一位女主人公, 但她的话也不多, 因为她完全不懂英语, 她是一位俄国少女。 故事到这儿大家都懂了, 这个人的老婆跑了, 他们家现在多了一位美丽忧伤、看起来非常神秘的俄国少女, 故事就会这么展开。

在我们先说丽莎之前, 我们先回到内利。 实际上在菲茨杰拉德最初给这个小说的规划当中, 她要写的东西有一个逐渐的流变。 我们知道小说里里德开的印刷厂, 但实际上菲茨杰拉德最开始决定写这个故事是因为她听了自己一个朋友家的故事。 他们那朋友家在俄国革命之前并不是开印刷厂, 他们是在俄国做温室生意的, 养花、养蔬菜, 供应给俄国贵族, 这样的生意。 她朋友讲了她家族故事之后, 菲茨杰拉德觉得她想写这样一个时代, 写这样一大家侨民的故事。 所以最开始的工作笔记, 这个小说的项目1.0版叫温室, 后来她写着写着就没有写温室的故事了, 写了现在这组人物。 当她最开始告诉自己编辑的时候, 她想的题目是什么, 内利和丽莎, 实际上两位女性, 现在这个题目是她的编辑改的, 她的编辑觉得这个可能不是特别好卖的题目。 它听起来可能是不是有一点老套,对吧。有点十九世纪小说的味儿了。编辑给她建议,我们叫The Coming of Spring,菲茨杰拉德觉得这个还不错,我们改一改吧,叫The Beginning of Spring。

所以这个早春是这样一路一路来的,但是不管怎么样,所以我们能看到在这里头,这两个女性人物都是非常非常的重要的,所以讲内利,当然要讲丽莎,当然也必须得讲,塞尔温放到一起,内利为什么要出走,我们的确没有直接,因为在这个小说里头,内利真的就是一个巨大的谜团,她在第一页出走,她在最后一页回来。

在真实的时间线里,就是在1913年这条时间线里头,我们是没有听过内利说一句话的。内利的故事,要在哪呢,我们对内利的了解,只能靠小说在回顾他们的历史,在讲里德的人生和内利的人生,讲这俩人怎么认识、怎么结婚的时候,我们才有一些内利的所作所为和她的言行,你才可以来判断内利是一个什么样的人。

所以内利这个故事,你如果仔细想一想,我们要讨论的事情,其实就是,对吧,去年还是前年那个电影,这就是出走的决心吗,她为什么获得了出走的决心。弗兰克是在英国的一个小城市,叫诺伯里,这个地方菲茨杰拉德也是非常着力设计的。内利来自一个英国外省小城市的家庭,她的哥哥是一个小地方的律师,然后她是住在哥哥嫂嫂家里的,就是他们俩相遇的时候,她是这样的一个情况,26岁,然后在考学校的资格证书准备去当学校老师,就是已经教了四年书了,但是还没有考证这样的一个状态。

他就问她就说,”那你是不是非要考这个,非要喜欢教书吗?” 内利就说:

“我不喜欢教书,但是我想工作,为什么呢,因为工作的时候我不用待在家里,这样我就不会碍我嫂子的事,就不用整天看着她。”

我们会意识到这是一个我们现在会很熟悉的故事,就是最基本的独立的故事。对吧,当你作为一个独立的工作女性在外面,你有自己的一份收入,你不光不是在家里碍你嫂子的事的问题,你在家里肯定也会更硬气,因为你的经济是独立的。不管怎么样,哪怕你跟家里人闹翻了,你是可以自己生活的。

然后当这两个人终于商定了要结婚的时候,弗兰克和内利会有聊更多的时刻。内利说了一句什么话呢,这个时候内利她带着弗兰克去看她的结婚礼服,就有什么紧身胸衣这样的东西,内利把紧身胸衣拿起来丢到地上,说:

“我是不会穿这种东西了,从现在起我要像那些投身于艺术,以工艺美术运动的女性一样挣脱束缚。”

这个地方其实也是非常特定、非常着力的安排,因为如果你熟悉英国文学传统的话,内利这样的一个人在英国文学传统当中,她是有名字的。19世纪晚期20世纪初独立工作的女性,在英国文学当中,她们会被统称为新女性,就叫new women,然后在20世纪初,她们会成为发动女性参政运动的人,会成为那些要求给女性投票的人,会成为那些在社会上要求女性和男性同工同酬的人。

菲茨杰拉德笔下的内利是一个,虽然她很普通,她没有介入很多特别大的这样的事情,但是她的的确确已经是一位现代女性了。她要求的是自己的独立,她是有主见的,甚至我们会读到在小说的40页,当她跟弗兰克订婚之后,是内利主动对弗兰克发起了性的要求,是她开始的。

所以在这个小说当中,只有这么一点,能够直接看到内利的言行的地方,你会知道对于内利这样的一个脾气的人来说,她不可能嫁给弗兰克之后就改了性子,变成一个老老实实做家庭主妇的人,不可能的。哪怕她是在家里,她也应该是要有一些超越日常的事情来寄托的,甚至包括嫁给弗兰克这件事情本身,对于内利来说,就是她逃离自己英国小城市生活的这么一个选择。选择嫁给弗兰克,她会明确的告诉弗兰克,”我不允许他们哪儿都不去,什么都不做,却比我有见识,我不会让他们把我比下去。”

他们就是指着她生活的小城市周围的那些人,那些没有受过教育,或者是安于传统生活安排的人。内利会觉得我见过,我听说过这个更好的世界,我要去远方,我有这样的一个意愿。这是一个非常非常明确的现代女性的形象,所以拖了一整本书,此处先把内利放下,拿起另外一个我们之前提到的人物,那就是塞尔温。

大作已经说了好朋友嘛,对好朋友,大家都知道的,这个故事里头突然又有一个男的,他一直围着弗兰克说各种奇奇怪怪的话。我们刚刚提到了塞尔温,他是一位诗人,没错,他是一位诗人,但是这位诗人的诗集《画术之诗》里头写的都是什么呢?我们会读到他特意念给弗兰克听的这么几行诗:

画术妹妹你觉得冷吗? 不雪哥哥我不觉得什么不冷。

这啥?这是什么?就肯定不是什么好诗,但是他为什么要读这个?你看到最后才知道,哦,这是一个理论上来说有非常高的道德感的,他毕竟是托尔斯泰的追随者,他试图想要告诉自己的朋友,哦,对不起,我犯了一个错,这个错就是我没有扛过,两性之间互相吸引的这件事情,我想要告诉你这么一个事情。

内力的出走其实是一次出奔,按照计划,本来是应该和塞尔温两个人一起在车站碰头,然后再走,但是呢,塞尔温在最后的这个时刻怂了,他觉得这个事情好像不是很好,或者他只是单纯的懦弱,他就是不敢,所以他去了,他没有跟他见面,他就躲在火车站旁边的餐厅里头,看着内力,把孩子放下来,看着内力,等了半天发现没有人来,然后又上了火车。

所以是塞尔温,他是这个三角的另外一角,所以他会给我们一个他看到的内力是什么呢?他看到的内力的故事是,内力现在有了精神上的追求,他想要跟我一起去某个更自由、更天然的地方,或许在天空下有一片长着松树和画树的森林,在那里一对男女可以全身心的结合在一起,那我们明白他们在这世界上到底该做些什么。

这个虽然是用塞尔温这个听起来非常不靠谱的话描述出来了,但是我觉得他可能也说明了一种问题,就是内力为什么会对塞尔温吸引。因为塞尔温在任何的世俗意义上都不可能是比李德更好的伴侣,唯一能解释的就是,李德是一个非常实用的人,然后有很多东西他是注意不到的,他自己也没有一个特别说像塞尔温这样的精神性的、或者信仰的危机时刻都没有。小说里头李德会明确的说我自己没有什么信仰,当然也不是说他就没有什么痛苦,或者他看不起别人的信仰,就是对他来说这个事情我不知道,我没有塞尔温当然是一个充满精神性的这样的一个人。

所以你看这是一个很典型的出奔的故事,就是一个老实但是比较木讷的这么一个老公和一个看起来走偏锋、不按常规形式神神叨叨,但是他似乎不停的跟你描述着日常生活之外应该有更伟大的地方,我们应该实现人类的可能性。我们只能猜到这儿,因为小说就只给我们这么多。

接下来的故事就是因为塞尔温没有去,内利一个人去了英国,他在英国去了哪呢?在哥哥家待了一晚上,然后就去了当时英国已经有托尔斯泰信徒聚居者开出来的聚居点,就是阿卓刚才描述的,听起来背景音乐会播放新造的人的这样的一个地方,在这样的一个聚居点去过这样的纯粹的精神生活,然后他也不行,他在这待不下去。

因为你可以想象如果他是一个这样极度追求自己独立的人,如果现在在莫斯科的生活让他不满,是觉得让他受到了压抑的话,在这样一个集体里头,他可能只会感觉到更多的压抑跟束缚,所以内力离开了这样的一个地方。在小说的结尾,小说的最后一句话,内力回来了,接下来要怎么办,我们不知道,小说就停在这个地方了。

所以内力的故事就是一个非常庞大的空白和谜团,但当我们面对这个庞大的空白和谜团的时候,我们看到的是什么呢?我们看到的其实非常重要的一点是,我刚刚说的新女性的故事,给斯加德想通过内力的缺席凸显出来的,就是不管怎么样,这是一个二十世纪女性能够做到的故事。你可以把内力的故事想成另外一个角度讲述的娜娜出走之后的故事,只是说这次的视角不在娜娜身上了,在被他留下来的男人和其他家里头的人身上。

所以内力这个故事有非常多迷人,会让我们迫切想知道的地方。就是假如有人写同人那就更好了。顺着内力其实还可以接着再往下去讲一下丽莎,对吧,因为这两个人物觉得不太好叫镜像,或者说对弗兰克而言,他们两个人都意味着非常不太一样的觉醒时刻。

在说丽莎之前,我还要再补一下塞尔温的部分。我是把它称为这本书里面最不省油的喜剧担当。你必须要到小说的后半截,他突然跳出来自爆狼人,你才能够发现前面小说里面埋下来的那些伏笔。原本已经是一个非常滑稽的人了,托尔斯泰主义表演艺术家,但是后来当你知道竟然是你扮演了这样的一个角色之后,你就会发现他之前所有的表现就会变得非常的意味深长,而且你对于这个人的性格深处的懦弱和猥琐会有一些更深的感受。

他突然出来跟弗兰克自爆,原来内利当时走的时候是这样子的一个事情的时候,我立刻想到的是小说的刚开始,在弗兰克知道内利出走了的时候,他其实是给塞尔温打了一个电话。这个时候塞尔温他明明都知道发生了什么样的事情,他依然跟弗兰克说,”你似乎有些不对劲,弗兰克跟我说说,你遇上什么事了,是高兴事还是伤心事?” 弗兰克跟他讲说,”塞尔温,这事跟内利有关,我猜她回英格兰了,还带走了孩子们。”

然后塞尔温说,”三个都带走了吗?她会不会是想去见一见自己的母亲?” 这个时候的塞尔温他什么都知道,但是他依然表现出一副这么无辜的样子去进行表演。打完电话之后他还跑到了弗兰克家里面,他说,塞尔温身体前倾,一双浅褐色的大眼睛异常专注地看着弗兰克,眼中洋溢着温和的好奇与善意,他说,”弗兰克,到了夏天,我们一起去徒步旅行吧,我虽然已经很了解你,但若能在天气晴朗的时候一起漫步在平原和森林里,我肯定可以更好地了解你,弗兰克你很勇敢,可是我觉得你没什么想象力。”

你在知道他自爆狼人的这个部分之后,你再来读第一章里面,他跟弗兰克的每一句对话都觉得,意味深长,讽刺意味拉满。这个小说,它的开头看起来如此的平淡,但是它的后劲,真的是会在小说的后面一阵一阵地往前泛。

我们说回到丽莎的这个部分,丽莎这个角色真的特别有意思。而且我们刚才讲到丽莎,她其实是塞尔温介绍来的,塞尔温介绍丽莎进入到里德家的整个过程,她也充满了一种心虚的弥补的感觉。虽然她的原话大概是说丽莎她的处境很不好,因为她那个时候正在一个百货商店里面工作,就当时她看到她哭,她觉得丽莎是一个非常需要帮助的女孩,那么你为她提供一份工作,那就是我们能够为这个世界上需要帮助的人做得最好的事情。

这是她明面上的理由。我们知道所有的真相之后,她推荐丽莎进入到里德家背后的心思真的是一言难尽。丽莎她其实一个非常年轻的俄国的女孩,她第一次进入到里德家的时候,里德她被丽莎那一头金色的头发给晃瞎了眼。但是随着小说的进展,你会发现丽莎她虽然年轻,她虽然看起来好像是一个没有经验的角色,但渐渐的她很快就进入到了这个家庭,成为了这个家庭各方面的一个替补选手。

她一开始替补了这个家里面家庭教师的职责,她替补了这个家里保姆的职责,接着她替补了孩子们妈妈的角色,她甚至在某种意义上,她也替补了弗兰克内心妻子的角色。对,包括小说后面写到的弗兰克对于丽莎的那种情感的纠葛,虽然我觉得这个部分真的还蛮恶劣的,放到现在她就是雇主老板对于年轻女下属的职场性骚扰,真的非常的恶劣。

我们也会在这里看到弗兰克这个人物角色的某种阴暗面,但是确实在故事的结构上,丽莎的人物设置非常的精巧。她一点一点进入到这个跟她毫无关系的家庭的过程,一点一点的进入到这个家里的厨房和客厅,进入到孩子们的作息和教育,接着跟孩子们之间产生一些情感联系,接着又在弗兰克的内心的情感上填补她的空虚和缺憾。

但是在这个过程中,你其实会意识到弗兰克她对丽莎的迷恋和吸引,混合着依赖和感激的那种暧昧的情感,就是一种生活上非常失能的男人,对于一个有着照护技能的女性的那种功能性的依赖和需求,你觉得弗兰克他其实在一种家庭功能的缺失里面,对于一个能够对这个功能补位的人产生的那种依赖的情绪。对,我觉得在这个过程中,内利她出走之后造成的真空,每一个处在变动中的人物形象都是非常有意思的。你会觉得在这个故事里,每个人他都好像是这个家庭里的某个拼图和组件,因为缺了一块,其他的板块和组件,他需要在这个家庭里面去调整他的位置和功能状态,让整个系统继续稳定运作。

在这个故事里,我在人和人之间,其实不太能够看到情感上的联系,我看到的感觉就是一个模型,像是一个组件,内利的出走造成的功能的缺席,大家在那儿移动,然后让这个系统继续稳定运行。

好,阿卓刚才讲的非常好玩,我们其实可以看到,保姆这件事情是非常明确的存在的,开始有一个外界的压力,是说大家会跟弗兰克说:

“你老婆没了,你需要找一个女人来照顾家。”

不光是塞尔温会跟他说,家里的仆人会这么跟他说,牧师太太,英国侨民们的新教教堂的牧师太太,也会不停地跟他说。牧师太太还试图给他介绍金斯曼小姐这样的一个角色。

金斯曼小姐那个故事,我们刚才说了,现在可以先从她这儿开始讲起。这个故事也很好玩,就是她是一个不讨人喜欢的角色,然后弗兰克不想顾她。这么一个角色,之前整个社区都给她捐过钱了,给她赞助了路费,送她回英国,这么一个人不想走。她现在知道了这么一个机会,但她其实不认识弗兰克,她一路尾随弗兰克,想要找弗兰克来介绍一下。弗兰克一直躲躲躲,从各种背街小巷到处走,最后走到了一个大教堂边上,有一个、这个故事非常的莫斯科,大家喜欢站在那儿看冰的地方,下面有一个浮桥,你可以一直走到冰面上。

他以为自己摆脱了金斯曼小姐,他走上去了,然后在那儿看冰,结果被金斯曼小姐刚好在浮桥上堵住了,退不能退,你现在必须面对这一切。他还以为她要说什么的时候,结果金斯曼小姐说:

“你能不能帮我介绍一下这个里德,告诉我他们家在哪里。”

就是这么一个很滑稽的时刻,但是在这样的滑稽时刻,你也能够看出来,弗兰克、里德,就是佩内洛普·菲茨杰拉德笔下经常写的那些被世界把各种事情强加在他们身上的角色,他是一个相对比较软弱的角色。这一类人物在她的笔下会一直不停地出现。简单地说,这一类人物不光是她自己的一个道德偏好,也是跟她自己的人生经验有非常密切的关系。

在某种意义上,她自己的丈夫就是这样的一个角色,就佩内洛普·菲茨杰拉德自己的丈夫,就有这样的一种被世界凌辱的感觉。好,有这样的一个角色,因为大家都要给他往家里塞人,所以我们才出现了丽莎。丽莎在小说里头的确会像阿卓说的,如果我们现在再回去看这个故事,是否有一点不太恰当的老板骚扰的故事,我会想说的是,菲茨杰拉德给了我们更多关于丽莎的故事,因为丽莎其实是一个很神秘的故事。

丽莎来的时候说的,像是她只会讲俄文,什么都不懂。刚刚阿卓说了她的辫子,但实际上小说那地方具体的话是怎么说的?她说丽莎的头发,她的头发那头浓密的金发在电灯下闪闪发光,一边是淡金色,另一边是浅灰色,从中间向两边分开,梳成两根亚麻色的辫子,就像农民的发型。更准确地说,她像出现在芭蕾舞团里的农民,觉得自己无法忍受这一切。

这个无法忍受是一个非常微妙的情绪,她无法忍受的是什么,她无法忍受的是自己的冲动。佩内洛普·菲茨杰拉德给了丽莎一个非常明确的设定,她在里德身上激起的是欲望,是一种基于身体的欲望。这一点你如果回去看,之前第一次他和内利亲密情节的时候,你可以看到在那个故事里头,里德那个时候很慌,他有一种,我就这么继续下去了,我必须要继续下去,我现在已经被推到这个位置了。在丽莎这个地方没有,这样的冲动会裹挟着里德一直不停地在做一些我们会看起来很离谱的事情。

而且同样地,因为我们说过了,全莫斯科都知道他在做什么,其他人也会问他。牧师太太会非常试探地说:

“你们家找了一个新的保姆,她要在你们家过夜吗?”

意思非常明显。包括他大舅哥查理来了之后,很明显查理也被丽莎迷得五迷三道。丽莎这个角色有这么一个魔力,好像能够激起男性的欲望。我们直白地说了,因为还有一个待会会说到的非常重要的角色,那位大学生革命小伙沃洛佳也是这样的–在商场看到了丽莎,在图书馆阅览室看到了丽莎,就觉得自己不行了,上头了,写信给丽莎,还要做出更多离谱的事情,稍后再说。

不管怎样,丽莎在小说里成了一个神秘角色。里德真的爱上了丽莎,因为他做了一个非常离谱的事情。这个跟我们刚才说的英俄交错的部分也有关系,是非常巧妙的事情。查理来的时候,弗兰克和查理在说话,说到了丽莎到了大厅里头,然后丽莎跟他用俄文说:

“不好意思,不好意思我打扰你们了,你应该是要跟你内兄要说话。”

弗兰克在这个地方,用英文回的她:

“我不想跟他说话,我爱你,” “大哥查理,什么没听清楚啊,没听清楚。”

但我们其实不知道,丽莎到底懂不懂英文,能不能听懂这些。小说的所有迹象都让我们没有办法确定这个。因为此后没多久,弗兰克非常明确地向丽莎表白,他说不要走留下来陪着我吧,我的孤单寂寞,我的所有痛苦在你的出现之后,它们都被激活了,我现在因为你的存在,意识到自己生活在一个痛苦的状态当中,你的活力能够让人意识到这些,你留下来吧。

小说也有一个非常明确的时刻,他们的关键夜晚是这样被描述的:弗兰克爬上漆黑的楼梯,走到房子后面,敲响了丽莎的门。他没想过门会锁,门也确实没锁,可他还是等在门口,直到听见她光着脚走在地板上过来开门。这蛮重要的。我们要说明的是,虽然现在可能你看起来这一切带着职场性骚扰的嫌疑,但佩内洛普·菲茨杰拉德选择的是让丽莎走过来,里德站在门口等着丽莎带他进去,所以主动权还是被放在女性手里。

至于后来这个故事更加神奇了,跟当时俄国的背景有关系。人们有一个叫国内护照的东西,可以理解成俄国身份证。如果你是一家人的仆人,这是要上交给自己老板的,就等于去打工,你的身份证压在老板那里,你不能随便跑。但是里德把它还给了丽莎,像是作为一种证明,”我信任你”,或者是出于他对你的感情的证明,你拿回去吧。结果丽莎跑了,带着他的孩子们去乡下别墅。

在小说靠近结尾的时候,突然有一天,里德在家里接到了亚历山大火车站站长的电话:

“喂,你们家孩子又来了,请来领一下。”

丽莎又走了,但对孩子们来说很伤心,孩子们非常喜欢丽莎。这次的多莉就跟上一次不一样,这次多莉见到里德、见到弗兰克的时候是抱着爸爸,很激动的样子,再也没有上一次的冷静了,因为很明显大家都被丽莎吸引了。

小说当中最神秘的一幕同时发生在他们住到林间的乡间别墅。晚间,多莉没睡着,听到丽莎起来,她跟着起来一起去了林子里。她们走到一个林间的空地,站在一块月光照耀下的空地上。多莉看见每一棵树旁都站着一个紧靠着树干的男人或者女人,她们分开站着身体紧紧贴着自己的那棵树,转过脸来看着丽莎,发白的树皮衬托出他们脸上的白斑。多莉现在看到,他们的人数比她先前看到的多得多,一直深入到森林中树木最为茂密的地方。

此时丽莎又说:

“我来了,可我不能留下,你们所有的人都是为了我才到这么远的地方来的,这我知道,可我还是不能留下,你们也看到了,不得不带着这个孩子。如果他把这件事说出来,没人会相信他。如果他记住这一切,等到时机成熟,他便会明白自己看到的到底是神。”

首先这个场景哇哦,你想一想,这是一种非常神奇的画面:月光下的白桦林,白桦林上每个树旁边都站着一个人,大家抱着树把手伸出来。看你自己要接哪部片子了,这可以接神秘片,也可以接恐怖片,也可以接历史正剧。实际上这个地方的关键是,丽莎不是看起来的那个样子。小说给了一个非常明确的设定:当丽莎走的时候,弗兰克自己也会问自己,我到底放走了一个什么样的人。弗兰克原本非常肯定丽莎可以做他的情人,结果天知道她的身份到底是什么。丽莎逃跑了,她可能是一个非常危险的人,甚至按照当时俄国的景况来猜,你会猜丽莎其实是一个地下组织的领头人,有这样的感觉,这一群人会为了她去那个林间,月下相会。

如果回到菲茨杰拉德的笔记,她非常明确地说,在这个场景里她想要让多莉看到这些,做好了准备,建造自己生命的人。她对这个场景显得非常认真,但不能太过头,不能把事情写得太实,所以有一种梦境般的质地。但你回到1913年的俄国,大小革命组织、各种思潮都存在,那些看起来在商场里卖手套的简单女性,这个神秘女子有各种能量,可以让身边所有男人都为她激动的丽莎,好像不是一般人,小说有这个设定。

我记得小说里,弗兰克跟塞尔温有一次对话,弗兰克曾经问过塞尔温:

“对于你来说,你觉得我是不是一个不友善的人?”

塞尔温跟他说:

“回到你最开始问我的那个问题,我觉得你不友善,或是有可能表现得不友善吗? 弗兰克,这个问题跟想象有关。我的意思是,得设想一下他人的痛苦。你要知道你不是个想象力丰富的人,弗兰克。如果说你有什么缺点,那就是你没有办法理解超出理智或理性范围的那些东西有多重要,但那些东西本身就自成一个世界。我们流着泪哭喊到溪流在哪里,可抬头看看吧,有一条蓝色的溪流正缓缓流过我们头顶。”

我会觉得这是塞尔温歪打正着的一次描述。最后丽莎看到了白桦树的场景,然后每个人她都跟树站在一起,这种充满神秘主义的现象,在某种意义上这难道不是塞尔温所向往的托尔斯泰精神的一种极致表现吗?在自然精神达到这种神秘主义的融合,这反而是塞尔温整天挂在嘴边的托尔斯泰主义的一个极致体现。

但是塞尔温本人是一个表演艺术家,他其实并不知道自己每天挂在嘴上说的那些宏大理念、那些人类的理想到底是什么样的东西。最后是丽莎在一种神秘主义的场景,展现出塞尔温和弗兰克这两个同样非常软弱的男人所无法理解的感性的、神秘的东西。我觉得这个设计还是很有意思。

对,反正就是这两位女性,我觉得当你把丽莎和内利放到一起,你会意识到最初想用她们两个人的名字做标题这件事确实可以讲得通,因为这两位女性才是在这里触碰到了更大的历史前进脉络的人物。对弗兰克来说,他老老实实做生意,是一个很笨拙的人。刚才塞尔温对他的总结很对,他没有想象力,他的存在是要解决今天的问题,要解决今天的工作,日复一日地做这些非常实际的事情,但对于更大的潮流如何拨弄着他,他自己没有感知。

塞尔温是表演艺术家,我觉得这是一个很好的总结。我还记得多莉带着她舅舅去他家说我们来找你喝个茶吧,结果他说我这没有一般的茶,我这只有我自己调的九种草药茶。小说也特别逗,还给了我们一个完整的单子,我不知道好不好喝,就是我给大家念一下, 如果有人要去调配的话,这个喝出了问题要找菲茨杰拉德负责。

塞尔温喝的饮料是:

• 芹菜花
• 莱特里草
• 玛格丽特花
• 野荨麻花
• 野香芹
• 圣约翰草
• 三叶草
• 指甲花
• 大麻

他把它们采集回来晒干,然后泡水喝。这个单子一给出来,你就觉得这个人什么状况,这也太神神叨叨了。就是这样的一个人,他肯定不是说作为这种超越生活的超现实存在的最好的代表。

而在这个书里最后我知道了,这样的一个力量其实落在了丽莎的身上,都落在了女性角色的身上,而且她甚至有一个女性传递的脉络,是丽莎故意带着多莉去看的,而且告诉她你以后长大了,你会明白自己看到的是什么的。等于说这两个重要的女性其实都是谜团。现在你回来看,内利也好,丽莎也好,都留下了两个巨大的谜团,其他的男性人物就是被卷进了她们的生命议程当中。

都说到这了,该讲一下另外一个被卷进来的男性人物,革命小伙,沃洛佳。我们刚刚说了,他在看到丽莎,在商场里看到,在阅览室里看到,他就被丽莎吸引住了。他就发现,丽莎现在去了一个商人家里当住家的保姆,这怎么可以,他就夜里闯入了印刷厂,持枪,还开枪,威胁了里德。

但他第一次出来的时候,大家其实都以为,这是一个20世纪一几年俄国革命的故事,这是一个俄国革命小伙来了。我们现在看到了,里德的印刷厂是怎么要被卷进了十月革命的前奏了。没有,后来发现人小伙跟里德坦白交代的说了,对小伙来说,竟然会有像里德这样的人,对着丽莎呼吸,触碰她,接近她,跟她说话,不对,他受不了这点,所以他要去夜入印刷厂,他想要枪击里德。

所以说白了,我不是来搞革命的,我是来搞情杀的。你看丽莎对男性的诱惑,她就是这样的强大。你读起来之后觉得很好笑,但是又觉得是有这样的一种可能性,她就是在这个书里头,作为了一种更超越日常的力量,落脚的一个人物。

沃洛佳这个形象,跟塞尔温两个人,也是一个很有意思的对照组。虽然他这个小说写的时间这么早,但是这两个人物形象,是绝对非常具有当代性的,非常经典的白人形象。就是这两个人,都是可以进入白莲花度假村剧组的。那个塞尔温,包括你刚才讲到草药,混着大麻,他不就是欧洲现在非常流行的那种什么天体俱乐部的那种成员吗,一到夏天,阳光明媚的时候,一群裸体白人,躺在什么沙滩,躺在草坪上,然后大家一起在那儿喝草药,吸大麻,做瑜伽,也不知道他们在干什么,追求非常灵性的生活,在那儿神神叨叨的。

然后那个沃洛佳,就是那种满嘴都是政治口号、政治理想,结果你冲上街头,还没走出三步就被石头绊倒的那种愣头青白人青年吗。这两个男性的形象,放到现在都非常具有代表意义。原来这种白人病是半个世纪之前就已经有了的,这种白人病难道不是有,白人那年就会有的吗,它就是这样的,就像你说的,它有一个更强的表演性,但它其实没有切近地和生活踩在一起。实际上你如果再去看同时当时的人的技术,你其实也会看到很多这种类似,它的确是有历史的真实的原型存在的。

比如说塞尔温的那个原型肯定就是托尔斯泰最著名的英国粉丝 Aylmer Maude,这个大哥就是在莫斯科生活,加入了托尔斯泰协会回到英国,在英国成立这种托尔斯泰主义者的聚集会,然后翻译出版托尔斯泰,成为托尔斯泰亲定的最好英文译者。对塞尔温的描述形象很明显,菲茨杰拉德是有这样的一个历史原型在身上的,但是这位历史原型肯定还是比塞尔温要好很多的。

但就是这样的一些历史问题,或者像刚刚说是更像现代人要面对的问题,那没错,这个时代已经开始了。菲茨杰拉德在80年代写这个小说的时候,回望这个时代这个世纪的开头,可以把这些人物的表演都容纳到我们最开始说的这个世纪的可能性当中。对于这些人来说,可能我们看来她这个表演性更强,但是对她自己来说,她可能未必会是一个纯粹的表演。

这个地方又会涉及到菲茨杰拉德小说的一个非常核心,或者她自己表述过的一个东西,菲茨杰拉德对自己的人物其实都是充满怜悯的。她应该引用过福楼拜的一句话,或者是巴尔扎克的吧,法国这两位作家之一,说的就是,英文里头不是有一句话,就是这种不起眼的小题大作风暴,叫 storm in a teacup,但菲茨杰拉德就会说了,哪怕是这种茶杯里头的风暴,对于卷进里头的人,卷进里头的小虫子来说,它也是一个可怕得不得了的东西。

我可能会想强调一下,哪怕我们会读出这些人物各自有各种各样的不堪,但是你其实读小说的时候,小说的笔法和小说的笔触,最多就是给他们一些淡淡的讽刺,但是对他们都是充满同情的。比如说像我们刚刚已经疯狂吐槽的塞尔温,当他向弗兰克坦白自己的行径的时候,他感觉到弗兰克要生气的时候,塞尔温会马上要说:

“我反对暴力,我反对暴力”

一种非常弱弱的,让你看了实在是会忍俊不禁的时刻,但是你会知道小说家对这个人物最狠,她也就到这样了。感觉佩内洛普·菲茨杰拉德真的是非常有爱的一个作家,她其实对她笔下的人物都是非常温柔的。

对,因为我们如果从整体上来说,佩内洛普·菲茨杰拉德的小说艺术是什么呢?就是她的人物,她其实不光写小说,她也写传记,但是她写的都是局外人,她写的都是一些格格不入的失败者。菲茨杰拉德自己的传记作者,英国当代那个特别出名的传记作家,她概括给她的人物就总结为了两类,她说在菲茨杰拉德的世界里头,她把世界分成了两边,一边是清除者,一边是被清除者。她的英文用的是 exterminator, 跟 exterminate,exterminator 在英文里头你会用到它的时候,都是什么除虫害,这种非常彻底的强有力的,可以把它抹除的这样的一个力量。而对菲茨杰拉德来说,她喜欢那些看似生来就被打败了,甚至深陷泥淖的人,她觉得自己是站在 exterminated 那边,写的也是这些被清除的人的故事。小说是要写给被生活冤枉的人,她的喜剧,她的确是觉得自己写的是喜剧,但是她的这些喜剧是生来就被打倒的人的勇气,强者的脆弱,因为误解和错失良机产生的悲剧,我尽力把这一切都视为喜剧,要不然我们要怎样承受这一生呢。

当她讲这个话的时候,你其实会有更多的感慨,是因为你会知道,这是她自己的故事。具体她自己的故事是怎么样的呢,欢迎大家收听很久没有更新的跳岛的第33期,已经是上一个世纪的2020年。其实我们当时录过一期菲茨杰拉德的节目,然后在里头会讲到更多她们家的生平故事,你如果知道更多关于她的生平故事,你会明白她为什么会站在这样的人旁边,因为她就生活在这样的人当中。在她人生已经步入老年,终于被这个世界认可成为一位作家之前,她自己也是这样的一个人。

菲茨杰拉德的温柔来自于一种见惯了世界的波澜壮阔的起伏之后的这样的一种同情,我觉得这个其实是一个非常宝贵的同情态度。

好,那我们最后绕回来再来讲一下早春的结局吧。因为我们刚才讲到内利的归来,它是一个永远的谜题,我们永远不知道弗兰克的妻子内利,到底是因为什么样的原因出走,到底是因为什么样的原因回来。但是在小说的最后我们看到等待了很久的春天,它终于来临了。

因为其实整个小说它虽然叫早春,但是整个小说里面它前期一直铺垫的都是一个冬天结尾的气氛,真的是直到小说的最后,春天来了,内利回来了,然后这个故事就结束了。

在这个故事里面,我们看到的春天它到底意味着什么呢?首先我要说的是,这个结尾可能是我最喜欢的小说结尾之一了,就是这么一个洗洗涮涮,然后你看到一个非常具体的这样的一个春天到来的仪式。因为在莫斯科这种非常冷的地方,你冬天是要封窗的,是要在你平时的窗户外面再加一个窗,中间还要用油泥把所有的缝都封起来,这样才可以保温的。

所以在整个冬天外面的世界都变得朦朦胧胧的,所有的声音都是不太能够传进来的,就在这么一个时刻,外面已经够暖了,我们可以把它拆掉了。你把这个东西拆掉,全家人在院子里洗洗涮涮,然后被关了一冬天的鸡、狗都被放出来在院子里头。这样一个确认季节流转生命要勃发的季节,所以内利回来了,你看一些新的可能性是不是已经要诞生了。弗兰克和内利各自都面对了自己的人生中的一个危机时刻,那以后他们是不是可能能够更坦率地交流,或者能过上更好的生活,还是各自决定我们现在需要分开各自追寻新的人生,不管怎么样,都是一些可能性现在在下面翻滚了。

所以可能性季节的变化,像我一开始说过的,这个肯定是季节变化的一个最明确的象征符号。但是如果我们在具体说到春天象征的变化是什么,春天象征的是在冬日的肃杀之后,新的生命的出现,新的一轮轮回的开始,它象征的是复活。此处的复活着重就是在指托尔斯泰的《复活》,因为其实托尔斯泰在这本书里的存在感实在是太强了,最具体的是托尔斯泰的这本《复活》出现在了这个小说里。

你得知道另外一件事情,菲茨杰拉德她其实是一个非常擅长在自己的小说里头隐藏前代作者,她不是那个异曲同工的故事,但凡她隐藏了前代作者的故事,或者是她对前代作者有仿写,她一定会非常明确地让你认出来。在《天使之门》她的另外一本小说也是设定在一战前的世界里的故事,她有一整章都是在仿写一个非常经典的英国鬼故事作家 M. R. James 的鬼故事,然后那一章的前后给了你足够的铺垫让你明确,你下面会读到一个鬼故事就是 M. R. James 的。

我们回到托尔斯泰的《复活》,当她有意在小说里头安排提到复活出现了,复活你会想到什么?春天就是复活的故事。因为在《复活》当中《复活》的一开篇,托尔斯泰给了可能文学史上最著名的关于春天的力量的描写,尽管好几十万人聚居在一小块地方,竭力把土地糟蹋得面目全非,尽管他们肆意把石头砸进地里,不让花草树木生长,尽管他们除尽刚出土的小草,把煤炭和石油烧得烟雾腾腾,尽管他们滥伐树木驱逐鸟兽,在城市里春天毕竟还是春天,这是《复活》的开头。

所以当你意识到这个时候,你再回来看《早春》的时候,你会觉得,如果我们说早春除了刚才佩内洛普·菲茨杰拉德给我们她用的种种参考资料之外,她还有更多的参考资料就是托尔斯泰,就是俄国文学,《复活》是她最喜欢的托尔斯泰作品。她在后来89年接受俄国的报纸访问的时候,他们让她选自己最喜欢的作品,她就选了《复活》。

那当然还有别的很多,这个是春天一个非常明确的季节变更,生命的更替,新生命的勃发,一个复活的故事。但是除了季节之外,我们除了一年四季之外,我还会觉得这个春天还可以是一个更大尺度的春天,那也就是1912、1913年,它是20世纪的春天。在这个小说里头我们刚刚提到了,它被故意写在一个更大的变动来临之前,但是这些变动的波澜全部都已经出现了,整个20世纪的这些希望,大家当时觉得一切可能都会变得更好,因为那时候没人知道一战会来,没人知道转年就要打一战了,所以你只是朦胧地觉得有一个变动要出现,但是你完全不知道自己被卷入了其中。

在我们可以看到的后来她自己在讲座当中的时候,菲茨杰拉德自己说的,《天使之门》和《早春》都是刻意放在这样的一个时刻的,20世纪充满希望我们会想要看到新生命、新世界、新女性、新的人和人之间的关系、新的艺术家和手工艺人之间的关系,可能出现的这么一个可能性时刻,然后这样的一个可能性被一战打断了。

所以如果我们要来说早春的早春,它到底指向一个什么样的春天,我会继续绕回我一开始的话,它是可能性的春天,它是个可能性。它可能是一年季节轮替带来的新生命的可能性,是人和人的关系的可能性,但是它也可能是20世纪整个历史的可能性。但是这个历史的可能性在真实的历史当中被终结了。像我们刚才说过的小说是在历史停笔的地方开始写的,佩内洛普·菲茨杰拉德就选择回看这段让她着迷的历史。 去想象描述生活在那些动荡开始,但是希望还没有终结的时代的人的生活。

所以在这个意义上,你刚才说的第一点的意义上,早春它其实也可以是托尔斯泰的同人文。

当然可以是了,包括塞尔温和内利出走那一幕,你想想看,一位女士和她的情人约了在火车站相见,她的情人没有出现,> “请问这是哪个故事?”

安娜·卡列尼娜。幸亏没有出现,所以女主角避免卧轨自杀的结局。

对啊,就这个故事里头是她出现了可能要卧轨自杀的,对的。

好，那我们今天差不多就录到这里。春天的故事虽然比早春略微晚一些，但是好在春天还没有结束，相信大家依然可以在新的一年一起享受春光，大家一起出门去看看外面的春天吧，那么我们就期待下一次夏天米德尔马契再相见。

我们今天要凑一个一年四季是吧,那就我们努力啊,我们先努力先把夏天录了,我们下一次将在夏天为大家带来一本关于三月中旬的书,这是在三月中下旬发的一个预告。

好,那我们下次再见,拜拜。拜拜。拜拜。

平淡的死亡与泥泞的战争:托尔斯泰在《十二月的塞瓦斯托波尔》

The End

听众朋友大家好,欢迎收听机核网。这个Gadio Pro相关的这个节目。我是老白。我对面是朱老师。今天是又一场这个红白合战。那记之前我们讲过一期这个庄园领主相关的这个故事,绥拉菲莫维奇。那都过了得有一年多了。一年多了。一年多了。

今天在这个新春佳节之际,万象更新之时,我跟朱老师又坐在一起,聊一聊托翁相关的部分,而且和这个季节也十分应景,因为这个选题是朱老师最近读完了书,掩卷长叹,对。那么慨然沉思之后,跟我说”我有个选题,你来跟我录一趟白”。我说”好的”。星夜奔赴济南,到了朱老师家里,打开了一本书,名字叫

12月的塞瓦斯托波尔

这个12月塞瓦斯托波尔其实是托翁的关于塞瓦斯托波尔创作的第一篇。熟悉托翁的朋友都知道,大家一般看托翁都是长篇。12月塞瓦斯托波尔如果你翻看的全集,是相对来说我们说短的,短片集,绝对是绝对意义的短片。是的。

然后他和后面的这两篇关于塞瓦斯托波尔的文章,共同构成一个我们说塞瓦斯托波尔这么一个小的系列。是的,托翁那块是一个小系列。然后这个创作有一个背景。我们之前聊庄园领主,其实说到过就是托翁去考的那个喀山大学。说是考,实际上咱们上次聊的过,就是他怎么入学的以及他到底毕业与否是个谜,大概率应该是没有毕业。没毕业。学的是阿拉伯语、土耳其语、波斯语什么的,所谓的叫什么东方语。东方语严格来说并不指向单一语言,就是在欧洲人眼中的那个东方,可能近东远东,这个东方能用上的语言他都给你设置上。

其实是一种挺殖民的,是一种很东方视角的,他是很东方主义、东方视角的那个东方,甚至这里面都大概率没有汉语。有时候没错,所以他就这么一个东西。托翁肯定他富家少爷的品性,也很叛逆,所以他学不明白。学不明白可还行。闲下来的功夫,托翁主要是读了些没用的书,尤其是什么卢梭,这那的。启蒙思想很快就嵌入了他大脑,所以就搞人生而平等这一套,说咱们老俄国不行了,就是贵族阶级最腐朽。别人学生可能就说你不照着镜子,你骂谁呢。托翁说”我告诉你,我跟你们不一样,我不一样,我打算散尽家财,至少送十分之一吧。”他说为什么,他说因为这些穷人跟我们一样,他都是人,都是上帝的造物。对吧。他就混合了宗教热情和这种启蒙道德的劲。

那么混合了之后这种热情驱使托翁干嘛,我他妈直接反正学不明白我不学了。所以这四五年入学,四七年他就退学了。那很明显就是没读完,然后回到故乡就搞农奴制改革。这事咱们到这,是庄园领主的类似的背景。我们也讲了庄园领主的一开始他给他小说里假托主角给他姨妈写封信,姨妈说你根本不行,你整不明白这那了。现实中的托翁确实搞了点有的没的,搞了个子弟学校,整点教育,整点慈善,然后全部失败了。失败之后农奴们其实不太领情。

这个时候托尔斯泰其实面临一个问题,就是俄国的农村有一个结构性的问题。上期也都聊了过,你去施点这种恩惠,小惠未遍民弗从也,大家不会领你情,就说拥戴你了,这不可能。他只是眼前的东西。所以托翁到这个时候他已经感觉有点不太对,但是他又不愿意去完全否定沙皇制度、去否定土地所有制,怎么办。他这个时候一度就自暴自弃了,所以托翁开始干赌博、酗酒、借债,彻底放荡了,然后欠了一批赌债。为了躲债权人,类似于小贷上门开始抹红油漆了。老哥经历。对,然后他就这么着了,就是不过了,我直接当兵就得了。正好他有一个当兵的哥哥,叫尼古拉,是个军官,然后这么低着嘟嘟的他就混到了沙皇的高加索的军队里面。在最近三年也有记载,对对,就是讨小贷然后当兵,差不多吧,给沙皇去当这个军官去了。

他五一年应该是加入了高加索的军队,然后迎头撞上了克里米亚战争。这军也不白当,兵也不白当,让你上场。而且咱们得说,托翁当兵的时候因为有贵族身份加成和亲缘关系,他跟灰色牲口的逻辑可完全不一样。其实当军官是俄国贵族一个很好的出路。是的,因为这个你反正在比较安全的地方吧,你惜点命,注意点,就差不多能活,而且这也是他们老托翁也是这个组上也出过好几个能打的,我们上次都聊过。是的。

所以托翁在这个时期反倒是视死如归,表现的还挺英勇,然后他在日记里写自己要克制赌博欲望、性欲和虚荣这些欲望。我们说完全战胜都是扯淡的,只能说他比他之前农奴制改革失败之后的放纵收敛了一点。这里边其实可以提一下其他的文学大家,比如一些老先生:

• 季建林先生
• 胡适之先生

这些人年轻的时候日记那也都没法看。我觉得这就是人嘛,男性嘛,就是在那个阶段各方面的大脑分泌一些东西使他去干这些事,浸泡在这些玩意里。

很有意思,这一时期托翁就开始写作了。他把写好的一些小说,比如说《童年》、《少年》、《青年》,还有我们刚才说的这些塞瓦斯托波尔这些故事,就陆续往杂志上投,走上文学道路。就这么着。55年的时候他离开军队,这个时候他就已经有点声望了,开始结识那些作家,什么屠格涅夫、涅克拉索夫这些人。但后来他跟屠格涅夫关系又破裂,那后话了。屠格涅夫有很多人关系破裂,这也是个大坑,屠格涅夫也是个很大坑,也是个人物。

回到托翁这个问题上,托翁很有意思,跟当时很多俄罗斯搞文学的人不太一样。因为托翁虽然一开始是贵族子弟出身,但是后来加入文学圈子时是参加过前线战斗的,属于作战英雄,属于模范,有一种模范光环的双方身份。然后因为经历过这些死亡,见到大量破坏和死亡,托翁再回到文学圈子进入文学圈子,他就感觉文人们整体上有点太放浪、太自命不凡了。他们的堕落和他们之间的破裂有多重意涵,不光是创作理念上的具体矛盾,也涉及道德主义和启蒙后人的整体道德解放的对抗。

在托翁眼中,他觉得你们这些人一方面批评沙俄、批评老俄国怎么不行,另一方面你们自己过得也很放荡,想干什么干什么,想玩什么玩什么,也没亏待自己。于是托翁走向另一方向,倾向禁欲。其实也不是,我们应该这么说,托翁幻想存在一种可能,如果我们都能克己复礼,我们都能禁欲,重新发明周礼,我们回归到咱们东正教老传统老俄国纯粹走这个道德主义,就能解决很多问题。当然他自己也做不到,他跟别人一样做不到,比如说他就放荡。对吧。当然他也不停的自责,相对来讲不那么放大。但是他总觉得为什么咱俄国问题没办好,是因为我老坚持不住,我下一次我就坚持住了。

所以从这读托翁的很多东西一定要从这来理解。有些人会认为托翁是不是一个伪道士,是不是一个说克己复礼压制人员。他没做到,所以他说起来有点不好意思。你看托翁小说里的角色总是一开始很软弱、一开始很不满、很躁动或者有很多不满,没有生而钢铁的那种人。然后经历了一些事,逐渐确立了”原来我要回归到一个俄罗斯的道德传统理去,强大了起来”,从肉体到精神逐步强大。他这么一个从内到外的转变。

这次读的这篇也非常典型,体现了托翁的风格。上次在农场他结尾也是说我们老俄罗斯人有一种坚韧、有一种美好、歌颂人民。不托邦的农村也许在那一天我们都达到那个思想境界就实现了。这一次我们说在克里米亚战争中因为托翁参与了,他见到一些东西,所以他也是被战争又给感动了。托翁是一个很容易感动自己的人。大家看这篇文章就明白了。

在过去的相关文学节目里我们多次提到,托翁在写作的时候如果一个情节没有把自己感动痛哭流涕,他可能就不往下写了,他觉得这有问题。这也是托翁的优点,他对于内心的描写很细腻。是的。

然后克里米亚战争怎么样?托翁回来的很好,因为他再不回来可能就被打死了。到五六年就是战败了。我们之前在不同节目里分别说过几次,其实它是非常残酷的,而且对于沙皇俄国的崩溃有直接影响,包括贵族对沙皇的权威、国内统治、经济的恶化,还有效仿老乌州的殖民政策等等。它可以说影响到了沙皇执政的各方各面,也为后来埋下了伏笔。反正背景就给大家讲这么多,感兴趣的朋友可以深入去了解这个战争。因为我们这次讲其实谈这个战争不多,它就是个由头,我们直接就进入小说原文了。

一上来很有意思,这是托翁特点的环境描写,都很大气。朝霞刚开始渲染,萨鹏山上的天空,深蓝色的海面已经抹去黑夜的朦胧,正等着最初的阳光发来灿烂喜悦的光辉。寒气和迷雾从海湾飘来,因为没有雪,一切都显得黑沉沉的,但凛冽的沉寒刺面,霜在脚下沙沙作响。特别好。这是非常托翁特点的俄罗斯风景。

从远处传来不停的海水的隆隆声,渐被塞瓦斯托波尔的隆隆炮声所淹没,只有这澎湃的波涛声打破早晨的寂静。从战舰上传出悠微的钟声。文字写得非常优美,仿佛诗歌一般,但其实是一个战争的全景画卷。然后在塞瓦斯托波尔的北部,白天的活动取代了夜晚的宁静。有一小队换哨的士兵走过,步枪碰枪作响,还有医生赶着到医院里去。还有个士兵从掩体里爬出来,用冰水洗那晒黑的脸庞,然后转身面对闪耀着红霞的东方,迅速画着十字祷告上帝。

一辆骆驼拉的又高又笨的马扎拉车,一种四轮大车,乌克兰南部的一种四轮大车,装着差不多堆到顶的血迹斑斑的尸体,喳喳响着朝着墓地走去,要把这些战争中死掉的人埋葬了。一上来这段描写特别牛逼,就给这种俄罗斯的那种苦逼劲,尤其那种老兵从沉默的人生泥坑里爬出来拿冰水洗脸然后祷告,灰色牲口这个劲全给你上来了。你看这一段描写有画面有声音,完全就布景就行了。这就是19世纪中期开始的时候,战场。

开始的时候你可以注意到它是从光开始描写,出现了光,然后出现了战场全景,然后战场里出现了人,然后出现了事件和行动轨迹。放在今天来看,那也是非常壮阔和动人心魄。而且最不可思议的,我就在想,他谈到乌克兰南部的一种运尸体的大车,放在2026年再来看这个东西,有种奇妙的时空穿越,但好像有些东西又没变。你不能说太细。现在乌克兰那边可能还是有车在运着斯拉夫人的尸体,不可思议。所以托翁的描写我感觉就是笨拙地堆弃着死亡的符号,你从里边看到民族的性格在文字里折射,这么多年了还是俄罗斯人还是俄罗斯人。

走进码头,可以闻到煤炭、粪肥、潮湿和牛肉发出的一股怪味。成千上万不同种类的物品:木柴、肉、土筐子、面粉、铁块都堆在码头附近。成团的士兵背着包和枪,有些没有背包和枪,都挤在那抽烟、骂街,把笨重的东西搬到一条停泊在浮桥旁边的冒烟的轮船上。私人的渡船也载满了各种各样的人:水手、士兵、商人、妇女,有的正在靠拢码头,有的正在从码头上开走。所以你看这会儿连气味都有了。全文营造的氛围非常强烈,而且这里边不但有嗅觉还有听觉甚至有触觉,它会把所有通感的部分都加注于文字,这个是非常厉害的写法。所以我感觉。 就是托翁给我们展示的一个

我觉得很像IPS场景导入的这么一个画面。特别好。如果你谁要做游戏,你就直接用这个。描写该有的符号全有。

然后我们说,接下来很有意思。托翁这篇小说没有用第三人称。它不是像我们之前看的地主早晨说这地主是谁他干了。这次托翁用了很罕见的一个就是第二人称。您接下来我在描述的时候我就用你比较多了我就用你了口语化一点。这个还得说一下,俄语里边您和你是两回事。对,就像说是北方很多时候就比如说中国北方很多时候就是像习惯性用您表示尊敬,但是如果说是在南方的话就觉得这个事就很奇特你是不是在嘲讽我或者说怎么怎么样。其实在俄国的文本表达里边两个人互制竞程甚至说父性这样一个事是两个人非常非常陌生才在干的事。用你是表达我们之间的关系变得亲密了。通常在于就是打买一轮仗或者说是喝完一顿酒大家开始用你。

所以这儿呢,水兵看到他用了”您”实际上应该是看到他有这个就是身份的标识。对,身份上是不一样。他是军官,然后看他的气质,然后看他的动向去推测他要去哪里,然后才问说您是要到伯爵码头去。

因为现代汉语是一个高度平民化的语言了。没错。我们这境语已经消灭的差不多了。所以我们要看这个原文呢,就会发现他还是身份等级差异非常大的。这篇整体表现都不强烈,其实他到后面两篇更强烈。

然后他一上来就水兵就给你这个看着托翁。其实是托翁。实际上是借这个第二人称描写了他自己的所见所闻,集中的展现了一下。

“那先生您到伯爵码头去吧,请上来吧。” 是吧。两三个退伍的水兵就开始向你兜揽生意。然后这时候你可能就会选一个这个比较近的小船,是吧。跨过躺在离船很近的烂泥里的一匹枣红色的马的半腐烂的尸体,然后你就朝着船舵走去。

我觉得这个确实是这个细节。你如果没有亲眼所见你真是很难添加出这种细节。这个就是我们说的艺术表达里边那些无用的细节。对。它对于整个的叙事的主线没有帮助,但它很真实。实际上这匹马为什么有可能会死在这个马头边上的这个烂泥里呢?很有可能这匹马已经受伤了,对,运不走了,但是又没法把它拉开,直接就当场就打死了。其实是弃置在这里就弃置了。对。

那么接着你就离岸了。这样来你就看到大海在朝阳下闪耀。在您面前呢,是一个穿驼毛大衣的老水兵和一个浅色头发的小伙子默不作声的使劲滑着脚。你眺望遍布在海湾远近的船只有条纹的庞大船身像小黑点似的在一片闪光的蔚蓝里起伏着的舢板。有大船有小船,眺望着在对岸出现的被朝阳的玫瑰色的华彩映照着的美丽明媚的都市建筑,眺望着防御栅山,就是那个水道上的就是海路水路上的栅栏,和在水面到处凄切的矗立着的这个黑桅杆尖的沉船,周围泛着泡沫的白线,眺望着在远处晶莹的水天之际隐约出现的敌军舰队,眺望着被桨激起的打着泡沫的浪花和在浪花中跳跃的咸水鸭。

您听着有节奏的打桨声从水面传到你的耳边的人语声。你感觉到越来越猛烈的塞瓦斯托波尔的威严的炮击的声音。一想到你此刻终于来到了塞瓦斯托波尔,是吧,一种勇气和自豪感就不得不深入到你的内心,你的血感觉都流动的更快,身上战栗,所以你就想啊这是一个–托翁在这之前实际上没有经历过什么这种大的这种战场,第一次来,而且在他来之前,这个战啊就是刚开始打了又一阵了,其实打了又一阵了。

他这个描写实际上是一个一般生活在和平环境中的人猛的看到这个战场他有个 shock,有一个震撼。臭气熏天,肮脏混乱,到处都是死亡和破坏的符号,而且你甚至能看到敌人的军舰也不远。这个时代的这个他围端就在天气线上,火炮射程也没有那么远,远处就能看到。我们能感觉到托翁描写的这些东西,为了营造一种代入感,这个代入感的思路很妙。就是为什么我说托翁是这个18世纪19世纪很多这个俄罗斯描写生活的一种这个指南呢,因为他有大量细节嘛。那这个细节其实在我觉得FPS游戏场景设计啊真的就是非常直观的,就是能够有一种引导。

其实刚才听过的朋友们不妨把这个第一段因为我们是播客嘛只占用你的听觉,你可以闭上眼睛重新听下这一段然后想象他是一个FPS开场,你就是主角,然后经历了这样一些场景然后即将进入战场。包括我想起有个电影叫兵临城下。兵临城下一上来那个新兵就是一个懵懂无知的从火车下来马上就发枪让你过那个河渡嘛。对对对,然后就很多跟你一块人还没搞清楚发生什么事命都丢了渡河过程中就被打死了。虽然说法国人这个行吧,但是他对于当时的战场情况的描述还是实在想象不来什么样,看这片子就行了。

然后这时候老水兵就跟你说话了,说”先生,直接从康斯坦丁旁边穿过去吧。” 他说的还土字不清楚。康斯坦丁。一边呢一边呢回过头来看你长舵长得怎么样别小船走偏了。人家跟你说呢,又舵。然后这边这个战舰上的炮还是全的呢。这小伙子在滑过康斯坦丁军舰的时候看这个战舰就说呀,而这个老水兵就说”那当然,这是新船,这个科尔尼洛夫在上面还待过呢。” 他这科尔尼洛夫是当时俄国海军一个中将,后来就死在这场战斗里了。所以能感觉他这场仗打的后面非常激烈。是的,俄军伤亡很惨。

然后小伙子又沉默了之后就看着远处这个南湾高空出现一片这个正在扩散的白色烟云,随之而来的是榴弹爆炸的这种剧烈的响声。他说那边爆炸了。老水兵就说到”这是敌人在新炮台上开炮。” 他漠不关心地在手上吐了口唾沫,然后朝着这个小伙子喊说”嘿,迷斯卡加把劲,赶过那艘舶船。” 你感觉到这种老兵是见惯了。

这样来,我们小船就在这个海湾的这个宽阔海面上快速的划过,超过了这个有一艘堆满了这个麻袋和几个士兵这么划着的一个笨重的一个舶船。然后我们就靠岸了。整个这个是一个FPS过场动画,就到了进入这个城市。靠岸之后场景嘎直接切换来到了滨海大街。因为塞瓦斯托波尔这个滨海城市,没错。

这个街上是成群结队的穿灰色军装的士兵,还有穿黑色军装的水兵,水兵穿黑色,还有花花绿绿的穿的这个花绿绿的这些妇女就细细扬扬的走在街上。这里边通过视觉颜色就把不同人的身份给驱额开了。还有村妇在卖面包,专家汉在这个茶锤后面汉卖这个热风蜜水了。

就这样。那么也就是在这儿,在这个码头的头几个石头台阶上堆着大量的那种炮弹:生锈的炮弹、榴弹、线弹、还有各种的铁炮、各种口径。再往前走那是一大片空地。

• 空地上堆着这个方形的木头
• 这个还有炮架子
• 还有各种这个躺倒在地上睡着的这种疲惫的士兵
• 马匹、车辆、绿色大炮、弹药箱、步兵架着步枪、还有水兵、军官、妇女、儿童、伤人

就是密麻麻的人都在这个码头附近,来来往往。还有各种大车装着甘草、麻袋、还有各种大桶,咱桶里也装也不知道是啥。这时候你还能看到哥萨克和这个军官快速地骑马跑过去,或者是有拉着这个将军坐的马车。所有人反正都很忙。

在街的右边的筑集到这个街垒实际上是防御性的这种寨,寨的这个炮眼安着几吨小炮,水兵坐在里面抽烟斗。边上是一座这个华丽的大厦,山墙上写着罗马数字,墙根下几个士兵还有几副血迹斑斑的担甲。到处你都可以看到军营的这种痕迹,令人并不愉快和混乱。你最初的印象一定是极不愉快。

托翁就说了:军营生活、都市生活、美丽的城市和肮脏的野营的奇怪的混合物,不但不美,而且令人可憎的杂烂无章呈现在你面前,拼凑的。因为城市并不是为了战争而准备,它是一种临时的拼凑,所有人都在凑合着。你肯定会觉得大家应该会惊慌万恐、惊慌万壮、东奔西跑不知所措,但实际上托翁说你如果仔细瞧下那些来得往往的人的脸上就发现不是这么回事。

他说譬如比如说他就描述其中一个辎重兵，他说这个辎重兵一面牵着三匹枣红色的马去饮水，另一面就是他哼着个小曲。显然他并不是迷路的。对，他对周围这些乱糟糟的人群仿佛不存在，而且你就感觉到这个人无论执行什么勤务，是饮马也好还是拖大炮也好，都从容不迫自信，而且有种冷漠感。你先别急，急也没用。这好像这些都发生在并不发生在一个发生战争的城市而是一个和平的地方。

然后你还能看到一个军官的脸上没有表情,他戴着一个一尘不染的白色手套就从你身边路过了。再有的就是你看炮寨上抽烟的水兵,还有那些守在议会大楼的台阶上等着的担架兵,在他们的脸上你都感觉不到那种恐慌。包括那些他描写到有什么呢,这人群中有一个穿粉红色衣裙的姑娘他穿过街道从这块石头跳到那块石头上面以免弄脏自己的衣服的裙板,在他脸上你也看不到这种惊慌。

所以这个画面是很有意思的。他说你如果是第一次来塞瓦斯托波尔,你会发现了这一点之后你会感到失望。就好像我到沈阳,我想象的那个刻板我都没有看到大家那种苦哈哈劲,我感觉大家过得挺愉快,那不就是过得挺愉快吗?还能咋样?沈阳挺好的。我就得看你,你不能把沈阳所有工人阶级想象到那么苦,就得跟那个漫长季节不行不行不行。

我另说,就慌乱的老父亲打街上去,没有啊。就是都是而且大家既不慌乱也不慌张嘛,也没有另一种说是慷慨激昂说为国捐取我就怎么保住咱老沙俄帝国。没有,那是绝迹前朝。就是大家都是很冷漠的,就是平静地在干着自己手头的事。

然后你会进而你会为自己过度兴奋而责备自己。是吧,坐在沈阳出租车上我说这个人怎么我没有见识没出息我为什么会期待这种东西呢。对吧。所以这样来你甚至会怀疑这报纸上我之前看那些塞瓦斯托波尔这些守护者他们怎么怎么英勇的啊这些行为真的是是真的吗,是带上来这里吗?是这些这么冷漠的这些人能做出来的事吗?你会感到怀疑。

其实这是两种不同的环境或者说是截然不同的生活给人带来的一个认知的反差。所以我觉得托翁这段心理的这种反思和描写特别棒。就是我觉得确实是经历过这个的人才能写出这种东西来。对,我们在战场或者说是接近战地的地方看到的那种景象它是真的,但是呢也是假的,它只是一层表象而非战争的本质。很多的报道会更多地关注到最激烈的部分。是的,但实际上激烈的哪怕是最激烈的战场前线的城市也不能24小时始终激烈。

是啊。所以这样来托翁就拖着我们往前走,就是这个等于推动摄像机往前走,就来到两个他就说到两个比较激烈的地方。这个时候其实托翁的这个笔触他就很像电影之眼,对,就基加贝尔托夫畅导的那样一种,就是摄像机就是一只眼睛带着你到从而有过的地方。而且这篇小说它是以第二人称写的。没错,所以它这种你阅读的这种第一人声的趣味就很强了。

然后他就说来到了哪,就是塞瓦斯托波尔的议会大楼台阶上。我刚才说了有这个守担架的士兵,所以这款实际上赶上临时的战地医院。托翁就说接下来的这个场景是什么样的:可怕而凄惨,伟大而有趣,惊心动魄却能使灵魂升华的这么一个景象。

然后就走上台阶进入这个议会大厅,打开门你就看到躺着什么呢?四五十个截肢和重伤员,有的躺在病床上大多数却躺在地板上,就是很仓促的一个战地医院。是的。那么这个场景再加上这个室内的气味估计很多人就懵了。实际上我们很多人你现在去医院毕生也没见过去骨科去急诊半夜来急诊血不刺啦。你想现在这一个人他乘以四十乘以五十塞在一个大厅里是很恐怖的一个场景。而且这一对于生命里边他们都饱受摧残,有的可能马上就死了。对。

所以托翁就说很多人是不敢敢进去,然后他说这种让人感到怯懦的感情托翁认为不好。这个不好,你就应该进去。你也不要觉得说好像我是来瞧瞧这些受难者不好意思我是有一种我作为一个健全人我来奇观了。对。而且托翁说也别不好意思跟他们去交谈。实际上他说不幸的人愿意看到人们同情的面膨,而且这些不幸的人愿意讲自己的痛苦,他们愿意听到那些关爱他们同情他们的话。

所以谈到他就说在病床之间走走着走着他停在一个面色不那么痛苦的一个病人跟前,就是犹豫不决畏畏缩缩的问一个骨瘦临寻的老兵您在哪受伤了。那这个老兵呢坐在病床上就用和善的眼光看过来好像在邀请你跟他再靠近一点说话。其实他是想说话的,其实他也愿意跟人交谈。所以托翁呢 就是觉得自己 是很不好意思的 你好像是故意触碰人家伤口,是吧。实际上人士兵很爽快地说,就是腿上、腿上受伤的。

那在这个时候,你从被子褶皱上能看得出来,这个老兵他的一条腿从膝盖上面–膝盖应该是,从膝盖的上面往下都没了。然后这个老兵说:

“现在谢谢上帝,我快出院了。”

我们说这个主人公就问,说你受伤多久了,是吧,是不是早就受伤,你能够出院了。这个老兵说已经六个星期了。先生,怎么样,你现在还疼吗?老兵说不疼了,只是在下雨阴天的时候我小腿肚子有点疼,这出现患疾了,患痛是吧,平时倒是没什么。你是怎么受伤的?是在第五龙堡先生第一次炮击的时候,我把炮瞄准了,刚要去另一个炮眼的时候他就打中了我的腿。我好像一只脚踩空,掉到坑里似的。再一瞧我的腿没了。那你在那一刹那不觉得疼吗?老兵说,那都没什么,就好像一个热乎乎的东西使劲捅了一下我的腿。那后来呢?后来也没什么,只是在他们把我的皮肤绷紧的时候,好像火辣辣的有点疼。主要的是,先生,你不能想太多,你不想他就没什么,这痛苦多半是因为人在想。

这是老兵的原话。托翁写老兵、写的伤兵也很有意思,不是一种刻板的伤兵的形象,没有身影,没有自怨自艾,而且是乐意跟人交谈,乐意聊到自己怎么受伤,还反过来劝别人:你也不用感到难过。这种反常是很有意思,这种反常恰恰说明作者是绝对亲临现场。你愣编、你坐在书桌里,书里编是编不出来,所以他格外有分量。

伤者在经历过巨大的痛苦,就是肉体的痛苦、精神痛苦之后,他在情绪和表达上都会有一个反弹。他想竭力像每一个跟他交谈的人表明:没那么严重,第一没那么严重,第二我还是能够回归社会,我还是有功能。他不愿意被大家视作一个异类、完全的弱者。

这时,过来一个裹着黑头巾、身穿灰色条子连衣裙的女人,他也加入了对话。他说到四个星期以来,这伤兵遭老罪了。说这个伤兵受伤的时候,他都已经被人抬到担架上了,他非要担架队稍微停一下,他要看一眼几方的这个炮台、发射排炮,”我刚才我对着这炮眼,我看着炮开出去。”这女人还谈到,亲王们来了,跟这个伤员还谈了话,赏给他25卢布。伤员也表示说,如果我还能这个,要是活下来,我就不能去作战了,那我想回到棱堡去教,去教士兵怎么去战斗,护到这些炮。

女人一口气说完之后,一会儿叫水兵。水兵却好像把脸不愿意看这个女人,不愿意听她说话。然后在扯枕头上的一个棉线团,女人的眼睛里流露出一种特别高兴的光芒。这两个小动作描写非常生动。水兵说,先生,他就是我老伴。水兵说这句话时的表情,托翁写得像是在传达另一句话:你得原谅他,大家都知道这女人就爱说废话。反正就是这么一个情况。

这里边的描述是两个人的不同立场:女人要照顾他、持家;他在医院里要吃药喝,在行动不便时需要人搭把手。女人迫切希望有一个外来的力量能够对他们的生活状况稍作改变。他特别提到亲王们来看望并打赏,眼睛里闪出高兴光芒,他可能认为又一位达官贵人来探望他。但是老兵其实有点不好意思。对。

其实托翁的小说对这些人情世故的描写特别强。你去看托翁的很多描写,话里话外藏了很多信息。你小时候像我小时候看很多东西看不出来,只有工作之后,自己养家了、开始挣钱了,经历的人多了、遇到的人多了,你才能明白有时候话里话外,托翁很多信息是藏着的,没必要说出来。反正因为他是有关人的东西,反正就是这样。

托翁听完这番话觉得太崇高、太坚强,觉得这里面是一种沉默的、不自觉的伟大。这老兵身上蕴含着一种巨大的力量,但自己又没什么好说的。你新来的、你刚来的、你也没打仗,所以他只能说:愿上帝保佑你早日康复。说完往前走,又看到另一个病人。

这个病人躺在地板上,好像在忍受难以忍受的痛苦,好像在等待死亡。他头发是淡黄色,脸浮肿而苍白,仰面躺着,把左手甩到身后,显露出剧烈痛苦的样子。干裂的嘴张着,呼吸困难,发出嘶哑的声音,呆滞的蓝眼睛向上翻着。裹着绷带的右手残肢从滑落下去的被子底下伸出来,腐肉的恶臭使人感到惊骇。

从这个受难者的四肢散发出来的消耗体力的内热,好像也在侵袭着你。你走进那种伤员能感觉到一种腐臭和热。这是真实的,这是一个典型的暂时感染,发烧,正在发高烧。这时你看到面前还有个女人,你跟她说:怎么样,这个伤员呼过去了吗?女人转过来亲切地看着你,仿佛看到自己亲人一样。她小声说没有,她还能听见,病情很严重,我刚才给她喝了点茶。尽管不是自己的亲人,人总要有点怜悯心。她差不多都喝不下去了。

你问她觉得她怎么样,她的状态。这个伤员听到你的声音把眼珠转过来,可是有一种视而不见、眼睛没有对焦的意思,好像没有听懂你在说什么。但是她还是说话,都是心里烧的。实际上处于高热的状态下,精神换下了。继续往前走,看到一个年老的士兵在换衬衣,她的脸和身体都变成咖啡色,瘦得像具骷髅,她的整只胳膊都没有了。她很有精神地坐着,已经痊愈了,但从她没有生气的暗淡眼神里,从她瘦得可怕的身体和满脸的皱纹上,你能看到这个人已经在忧虑中度过了一生中最好的时光。接下来等着她的是漫长的走向死亡的绝望生活。

你又看到一个女伤员,病床上是一张女人的苍白娇嫩、充满痛苦的脸,面颊烧得通红。这是一个水病的老婆,她是在给丈夫送饭那天被炮弹打伤了腿,所以也是截肢。你要是精神足够强,你就继续往里走,能找到一个正在包扎伤口、做手术的房间。军医们从手到胳膊肘都沾满鲜血,一个个面色苍白而阴沉,站在病床周围,给一个已经湿了麻药的躺在床上的伤员做手术。伤员睁着眼睛无意义地说一些胡话,但很多时候却是朴实而动人的话。军医们拿着锐利的手术刀去切割他白色健壮的肉体。伤员在突然恢复知觉的时候会发出撕破人心的叫喊和咒骂。医生把截去的胳膊扔到墙角,这个场面太恐怖了,但实际上非常真实。战地救治的目的是什么,就是让这个人活下来,先截肢,看能不能活下,看感不感染,看自己的抵抗力。

这就像之前唐老老师讲在野外缺习手药的时候怎么止血,抓一把土摁伤口上,然后他的脊手师骂他说这会感染的,唐老老师说感染是活人的事。反正就是这么一个情况。房间里另一个躺在担架上的伤员可能刚过来,瞧见自己的伙伴正在动手术,他虽然还没动手术,也惊软着呻吟起来。与其说是肉体上的疼,更多的是来自等待。他看别人做手术,就像小孩排队扎针,针扎你也没多疼,但是你看别人被扎针共感,你就特紧张。这就是战争。

托翁说:战争不是队形井然、美丽雄壮的队伍,也没有军阅悠扬、战鼓动、军旗飘扬、骑着高头大马的将军。战争的真相就是流血痛苦和死亡。奇妙的是,他在议会大陆里的经历一上来给你个感觉好像还行,这个战地医院撕开有关战争的幻想。你再往前走,就看得狠了。他最后给你端出手术室的样子。如果用图景来描绘,有一部俄国人拍的片子叫炼狱,实际上讲第一次车臣战争时候的事,里边关于痛苦、流血和死亡的部分很符合托翁的描写,推荐大家看看。你对比托翁的描写会发现很多战争电影其实是粉饰拍的,不是那样,出门美化,非高度美化。人死的时候见出一点点血不是那样的,肠子脑子掉一地,很混乱,都是散着的。电影画面有时候反倒是很干净,甚至有美感,因为要追求审美。

总之,托翁离开之后说感觉离开这地方人都轻松了,因为你不用看那些痛苦的画面了。清醒自己还健康地活着,意识到自己非常渺小,既然鼓足勇气就敢朝着棱堡走去,向真正的战争前线走去。这是很奇妙的一种想法。托翁说为什么会这么想,说和这么多人的死亡、这么多人的痛苦相比,我这么一个微不足道的死亡和痛苦算得了什么。斯拉夫人,这嘴硬、这劲上来了,其实是那种朴素平等的概念:大家都是人,那么受伤的伤兵他本身遭受的痛苦,我也是人,这种痛苦也有可能发生在我身上,那我与他有什么两样。见得多了反而释怀了,是的,麻了。

托翁写到一看到明朗的天空、灿烂的阳光、美丽的城市、洞开的教堂、熙熙攘攘的军人,你又会感觉到心情恢复。这是如此草率,那种你又恢复到一种轻率的、斤斤计较的、只顾眼前常态的生活,原来是这样的,你很快就淡忘了刚才所看到的那些事。非常奇妙,那种非常斯拉夫人的劲。走到街上看到军官的葬礼,听到棱堡的炮声,托翁说出殡、军官出殡的场面美丽雄壮,炮声就好像给配乐一样、雄壮的音乐。人们并不会把战地医院的惨状和葬礼炮声联想到一起,这是很真实的。虽然从理性上你应该把这三者联系到一起,但实际上并不是,你在感知上不是这样。

托翁对自己内心的观察是非常强的,这是他最强的一个点。到现在来讲,近现代葬礼本身是一个礼仪,提醒人们不要忘记死去的人,但死去的人真正变成什么其实并没有人在意。然后他继续描写走到城市里比较热闹的街道,周围都是小铺子,还有饭馆的招牌,一些商人戴着帽子或者包着头巾的女人,然后穿着非常讲究的军官,这些人都给你一种坚定而自信的感觉。

我们进入了一个餐馆,换场景了。餐馆里听到各种讨论,人们叽叽喳喳地说话,听到一个很逗的对话:前一句在说你这肉丸子又贵又难吃,下一句话锋一转说你知道那谁谁吧,死了,昨天被打死了。这个对话太真实了。托翁谈到一个很具体的例子:一个年轻军官说他妈的我们那边糟透了。那位军官可能都不是一个桌的人,很好的起头,扭过头的问他您哪的,你说那糟透哪。年轻军官说第四棱堡。听到这个名字,你就感觉周围的人交谈的声音都变小了,好像都在侧耳听你要聊什么。饭馆的人摔了个杯子,很多人忽然关注同一个人,玩人那个劲。

这个年轻军官是什么做派?托翁说是满不在乎的神态,指手画脚的姿态,高声谈笑,感觉非常轻浮。但是此时此刻托翁又觉得这很正常:一来人家确实是拼命,生活在前线;二来他年轻,经历了危险之后,他维持一种好勇斗狠的情绪也很正常。在和平安全的环境里,他想表达其实我回来我很牛逼,这是自然的情绪表达。你以为这个军官要给大家讲他在第四龙堡怎么糟透了、怎么被炮打了,结果不是那么回事。军官说他遭殃是因为泥太多了,周围都泥泞成泥潭,都走不到炮台边去,烂泥都没过小腿肚子,你把大伙叫出来就为这个事。

另一个人说他最好的一个炮掌今天特别倒霉,脑子直接中一个弹片就死了。然后另一个军官就问谁谁死了,是这个米秋欣吗? 对面说不是。

那谁谁死了。

那米秋欣怎么着了。

你以为这俩人就得顺着这个话题说,是吧,是那谁家那小谁。下句又拐到饭菜上了,就说这个米秋欣死的那人说,我点这小牛肉,你们上不上吧。混账东西,就嫌这个店家上台台慢。然后呢,下一句又拐回到这个炮掌是谁的身上了,这问题话还能接。死的那个不叫米秋欣,叫阿布拉西莫夫。那小的挺牛逼,他参加过六次出击,就是从壕沟线出来冲对方的阵线。能冲六次不死,就死了这个炮台了,让人这个命运无常。这就是命运。

所以就这样,托翁就说,这一路上你只要呆在这,你能听到很多这种关于前线的故事,你就被吊足了胃口了,就非常想去第四棱堡看看去,我得瞅瞅。就托翁那种年轻那种躁动那个劲,其实是因为大量的生死和日常混合在一起,使得人们在这种麻木之中有一种奇妙的亢奋。而且他就说,围绕这个第四棱堡,大家形成一种奇妙的攀比心,甚至趣味:我得去看看。这第四棱堡成梗了,你知道吧。就人们只要一说到我到过第四棱堡,就带着一种很特别的高兴和骄傲,而且每当有人说我要到第四棱堡去,他就一种装逼那个劲,就是要么就是轻微的激动,又不能太激动。早上我们家录音,或者是装出这种过分的冷:我就是去第四棱堡,没法,小事小事。吃了饭,说的跟去他家阳台似的,对吧。

就是每当有人想拿另一个人开玩笑的时候,就说

“你个王八蛋,就该给你送到第四棱堡去”

因为生存率比较低。然后你看到有人抬单价,你就问从哪来的,大概率也是说第四棱堡。所以就这样,围绕这个第四棱堡,就形成两种截然不同的看法。

完全没去过的深信:就是说这第四棱堡就跟地狱一样,你去了就死,进去完,和我们的想象一样。然后还有另外一批人就认为,他们是去过第四棱堡,甚至在那战斗过的,他说到第四棱堡,他就总跟你谈一些细枝末节的事。你以为他讲的永远是生死、生离死别、就是战斗激烈的,他老跟你说一些什么,比如说那块土地,是泥特别多还是比较干,这个眼壁布、那个小坑,是很冷还是还挺暖和,都不在一平道上。说这种生活的这种小细节,所以就是让陀峰觉得太奇妙了,太奇妙了,说什么咱得去抄着去。

而且这是一个特别典型的没有经历过战场的人的感知。如果就是,你,我还是举个例子,就是现在还是在俄国再打的战争,如果你就是经常看这个就是俄国或者是乌克兰前线的这些士兵,他们自己的一些频道或者视频记录的全都是类似这样的东西。因为是什么呢,死亡只是一瞬间的事,而活在战壕里非常难。你距离死还得有一会儿,万一是半会儿不死呢?不死呢,你得对付当下。你比如说冷热,对吧,就涉及到你是不是得准备点柴火呀,对,生点火呀,得取个暖。

就比如说像这个,我们也讲说俄国冬天吧,就是也是靠近就奥特萨或者是塞瓦斯托波尔这个部分,虽然说是平原上,但是平原上战壕里边士兵考虑最多的东西是什么呢?到哪能整点东西来垫在身子底下,避免身体直接和冰冷的大地接触。还有就是,我想起来二战的时候,很多士兵在回忆录中谈到多准备袜子。是的,因为你在呆在战壕里的潮湿啊,战壕族嘛,你就会得战壕族,所以你多准备点袜子,那就很有用,就保持你的身体干爽,保持你的热量,不要向外散发过多。在没有枪炮的时候,或者是没有袭击的时候,尽量手动,然后当袭击发生的时候,根据你的生物本能选择是战斗还是逃跑。

所以很有意思,到这块我们说整篇小说呢,都还没有接近前线,其实说了全都是后方的码头啊,都是重电、城市里的街道啊、后方的占地医院的状态,是吧?我们在这块稍微休息一小会。好嘞,欢迎回来。刚才呢,我们其实一直是在战地的后方,不断的跟随着托翁比处上上下下转悠,既见识了死亡,然后也见识了活着的人们的麻木、平淡于庸长。那么真实的战场到底是什么样呢?所以托翁他觉得吧,就是不去趟能行吗?这逼装不上能行吗?确实,对吧。你不让他装上这逼,比他杀了他还难受。这个狗得抓住,老斯拉夫人那个劲来了,来了来了。啥也别说了,出餐馆,走吧,第四棱堡我得瞅瞅。

一路上就看到什么呢?这个各种程度这个受损的建筑。因为这时候离前线近了,那总有那些炮打高了,对吧,就砸到后去了。后面地上就开始出现什么炮弹坑、水坑,对吧。还有很多行人,这时候还有很多行人,有士兵啊、军官啊、格萨克呀、女人啊。再往前走呢,发现开始房屋变少了,只能剩下一些什么奇奇怪怪的一堆碎砖,一些木板翻出来的粘土地被炸翻了,对对对,原木,你都不知道是什么了,就看不出这个东西的形状了。就房子,也许之前是个形状,也许之前是个房子,吃了一个炮弹被还原了。再往前走呢,房子就彻底没了,你已经看不出人的建筑的痕迹了。只能看到什么呢?黑须须的泥顶的啊,挖满壕沟的空壁。这种描述其实就很接近这个一战的时候,对,因为炮已经开始主宰这个战场了。是的。

再往前走,就是第四棱堡了。到了。在这儿没有女人的痕迹了,只有士兵们急急忙忙的走着。这个路上到处都是血液、雪堤。很有可能会碰上这个四个士兵台的担架,为抬这个受伤的军人。担架上准有一张蜡黄色的脸和一件血污的军大衣。你问这个人哪受了伤,如果他是轻伤,担架员一般会这个不屑一顾的、气冲冲的回答说:腿上或者手上。但如果你要是在担架上连脑袋都看不见,就可能蒙上布了,或者蒙上大衣,那就能说什么呢?就说这个这人已经死了,或者是重伤。那这时候呢,抬担架的人呢,索性都不会理你,板着脸一声不吭,继续往前走。

你继续往山上走,就会听到炮弹或者榴弹划过空中的呼啸声,这时候你才明白啊,你在城里听到那都不算什么。你在城里听到炮声啊相比这个就是太柔弱了,啥也不是。这时候你开始犯嘀咕了,这个逼还装不装,对吧?还非得去这个棱堡吗?值得吗?装这个鼻。对,因为这个是很具体的对生命的威胁。我跟你说,他在空中都造出这种声响,对吧。他砸到地上,你不死也残。

然后呢,这个就在你犯嘀咕的时候呢,你就看到一个士兵啊,挥动着胳膊,在打出了滑,就在这个泥泞的山上就滑下来了,在烂泥江里啊,迈着这个快步,笑着打你身边就跑回去了。你看到这个人,就莫名的又鼓起勇气了,挺起胸膛,然后昂起头,决定继续爬那座又滑又粘的小山。可你刚爬了没两步,子弹开始从你两侧收收的就是掠过,这时候你就开始考虑,要不咱还是走嵌壕吧。但是嵌壕都是臭泥巴,都能摸过。前面说我摸过小腿肚子臭泥巴,这是想起那餐厅里那事的。那怎么办呢?算了,还走小山吧。不是你能那个就是选定条路吗?反来不去的。他怎么呢?他不好意思。为什么呢?因为走嵌壕不嫌你怂吗?哥们来都装逼了,对吧,这逼首先是装到底吧。大家都走这小山,对吧,我就不走这嵌壕了,我就走这开过的。

再往前走二百来步,进入一片坎坷不平的泥泞的空地,周围到处都是土框子、土堤、炮弹库、平板车和土屋子,上面安着几门铁柱的大炮和一堆一堆的马得很齐的炮弹。乍看之下,这些东西似乎没有任何联系、没有秩序的,就这么乱堆在一起。但士兵们就坐在这会儿,有一群水兵坐在炮台上。在空地中央有一门被击毁的大炮,半截埋在烂泥里。有一个步兵扛着步枪,费力的从烂泥里把他脚拔出来,一步一步的走向炮台。

我这解释一句,为什么水兵老出现在这个城市里?因为这就涉及到当时这么的策略了。因为俄军在守卫塞巴斯多波尔的过程中,发现英法他们这边水军有点猛,打海战可能不好守这地方,索性有些战舰就自尘爆炸,自尘去阻塞水道,把炮拉到路上炮台,让路炮来用, 就陆基要塞。所以这就为什么有大量的水兵出现在这块。其实我们之前有一期跟卡尔斯有关的部分,就是塞瓦斯托波尔战斗的海部一班兵,海军步兵的传统。那是二战了。俄国军队自古以来就有海军步兵的传统,因为实在不擅长在海上打仗,你就发现千百年来毛泽是一样的,一样的。这个故事无数次重演,这就是同一个地方。是的。

所以陀翁就说到处都是碎瓦片,没有爆炸的榴弹炮弹,还有营地留有的痕迹。我觉得他在想,二战老兵如果读到这一段,这不就是我吗?对,没错,本人吧,就本人。一切都在烂泥江里,然后远处的炮声叮咚的炸,还有四面八方传来声音各异的子弹:

• 有的像蜜蜂的嗡嗡声,嗡嗡就这么过去了;
• 有的就凑凑凑的,跟琴弦或者发出鹰鹰的声;
• 但最狠的还是炮声:隆隆的。

他搁这大钟小时候落雨盘上,所以托翁这时候你看,他就不说什么雄壮的音乐,只说炮声让人心惊肉跳。你也不知道这一炮是奔你来的,这时候你就会想,原来这就是第四棱堡。确实有点让人害怕,但是忽然又觉得我挺牛逼,你看,哥们来了。对吧,哥们也是来过了。

但是呢,托翁到这时候才告诉你,还不是第四棱堡。这时候你刚走到,费这么大件还不是,叫亚佐诺夫多面堡。这地方是比后方危险,但是呢,比第四棱堡相比来说呢,它是一个相当安全,而且不可怕的地方。就是,我操,这就是地狱吗?这就是传说的地狱吗?哥们不是。门刚打开,托翁已经犯嘀咕咕了:这就是第四棱堡。后面呢,他才知道,原来这还不是第四棱堡。那要走到第四棱堡,你还得继续往前走。这时候啊,已经完全没有什么说开过地、走小山、快乐的什么水兵、什么这个迈着快步走,没有了。这块就得弯着腰,沿着嵌好走,你也甭管那泥浆了。不想死,你就老老实实就弯着腰得了。

这时候景象就更不一样了。就这块啊,只会看到什么呢?担架、水兵、还拿着铁锹的士兵。会看到很多地雷的导火索,还有泥坑中的掩蔽部,只能容下两个弯着腰的人就穿在那小坑里躲了炮击的。这时候呢,托翁忽然又描写了一些生活化的场景,只不过这一次呢,是黑海大队的戈达克步兵们。说这些人呢干嘛呢?就大家都弯着腰啊,躲在掩蔽部里,或者是这个往前走或者挖坑。这些这个戈达克大哥们呢,就是换靴子、吃东西、抽烟斗,就是生活起居那点事。就感觉就是他们跟战场就是格格不入,非常淡定,干嘛干嘛呗,过呗。对吧,哥萨克就这样。

在这之外呢,还是臭气难闻的泥泞,还有军营留下各种痕迹,还有各式各样的废铁。要继续再往前走,走过这群格格格大哥所在的地方,再往前走三百多步,走过这一切,终于你来到一座炮台。在这块布满坑洼的空地上,周围堆满了那种装满了土的那种土框子,架在板车上那种大炮和土壁垒。到这才是第四棱堡真正的一线。

我解释一句,为什么有很多土框,是这样,因为那时候那种炮弹它这个动能,你想让它充分的释放掉,保护自己,最有效的办法就是堆土。是的,因为土很松散,炮弹一下砸到地面,它就散开了,或者弹片也能少一点。相对来说。

那么这时候你看的怎么呢?水兵们干嘛呢?四五个围在胸墙下面打纸牌,打牌呢。然后你还会看到一个海军军官,这海军军官能看出你来,因为你一看就新来了,因为你的眼神里有很多好奇。对,因为大家都已经麻了。是的,天天就在这要么就挨对方的炮,要么就打对面,打对面,麻了。军官却并不感到反感,他会欣然的给你介绍:

这是胸墙
这是炮
这是炮弹
这是炮刷子

怎么的,给你解释。另一个军官非常淡定,他就坐在炮管上,拿着个黄纸就卷烟抽,然后再不就是从一个炮眼走到另一个炮眼,就是镇定而毫不慌张做事的跟你说话。就与此同时,他就装着个逼同时子弹就嗡嗡的从头上就飞过去了。但你就好像被他感染了一样,就是你也冷静下来了,你还跟他聊起来,说你认不认识谁家那小谁,他开始听他讲一些拉家常。

这军官就讲了什么?他说五号,五号那天炮击可立了。但是他说,托翁还专门强调这还不是他专门要夸耀。所以托翁主动去问,因为托翁应该是这还听到五号那天炸特狠,问那天发生了什么。人家才回答你,他谈这个事就说到那天是什么,应该是1854年的10月5号。当然我们这个里面,涉及到俄国人用的立法和欧洲剩下地方用的不一样,当时俄国人在用儒猎立切换中格里高历历,应该是10月17号。这无所谓,反正就是这一天。英法调集了百门以上的重炮,各自调集百门以上重炮,也就合计将近300门的重炮去炸这个萨瓦斯多布尔。

所以这个军官就回忆起这一天,他说炮台上就剩一门炮能打了,所有炮手就活了八个,但是到第二天早上,经过一宿的抢修,他让所有的炮都开炮,很厉害。所以他就说到在五号炮击那天,有一颗榴弹直接击中水兵的掩壁部,死了11个人。然后他一边说,从那炮眼就直远处敌人的炮台,你看欠豪都在哪。然后托翁就从炮眼去看,身头去看,然后嗖嗖的子弹就飞。他说这种时候人一般是很紧张的。是的,所以你不敢多停留,就是大概你什么都看不到,其实就看一眼。但如果他说就算你看见了,你会很惊讶的发现,其实敌人离的非常近。 所以我们说整个故事到这块。其实托翁作为他作为亲自跑的战场的人,他亲眼所见的整个战争故事。大家可以去看,给自己去搜一搜整个塞瓦斯托波尔的这一场守卫战。

俄军打的还是很有章法。包括我们刚才前面谈到沉船去堵港口入口,包括舰炮拉上来,当这个暗防炮。当然了,最关键的一个点,就是刚才军官谈到的,他们土木作业。英法炮击虽然猛,但我连夜能修炮台,所以这也是还是能抵抗。

回顾上战争吧,核心的一个话题。到这你就觉得是不是差不多了,哥们往回走吧,体验也体验完了。你看完了,可能是出于虚荣心,也可能就为了高兴一下、爽一下。

前面那海军军官说来且了,是吧,来客人了。他开两炮就喊人,说”炮着”“炮着过来”,就来了,大伙乐乐,差不多。14个水兵把手头收拾收拾,烟斗塞在衣服袋里,嘴里面包嚼完了,迅速而快乐的走了过来。他们钉了铁掌的皮靴,咔哒咔哒响,一个一个上了炮位,走到大炮跟前,开始往里装炮弹。

托翁说这一幕特别大动的,说这些人的动作和表情,如此沉着,如此坚定,从而不迫。他觉得这就是我们俄罗斯人,咱老俄罗斯战斗民族,这个劲,朴实顽强。从他们脸上、皱纹中,你就能感觉到,除了战争的危险、仇恨痛苦之外,这里面还有一种尊严、品德情操。然后就开炮了。

他这么想的话,还是新来的,极为可怕的巨像震动耳膜,然后传导到全身,给你震一哆嗦。紧接着就听到炮弹远去的呼啸声,然后一股火焰,就是激发药的浓烟,就把炮位整个给淹没了。所有都看不见了炮位,包括水兵们都笼罩在里面。

紧接着,这不是炮弹不打飞了吗,水兵们就开始讨论打中还是没打中。大家是很兴奋的。然后他说这就是一种藏在灵魂深处的像敌人报仇血恨的那种情感,跟那么样箭差不多。开炮,对吧。虽然是把血仇给炸死了,但是他在电视剧里是很过瘾的一幕。包括我觉得,我印象中我的团长我的团也有这么一幕,其实都有。

其中有一个兵特别急切的跟团长说能不能让我开一炮,我必须得开一炮。对,克鲁伯。对。然后像托翁描述的这种,就是战地的攻击和反击的情况。

有一部俄国的战争电影,就是第九连。第九连里边有一个场景,就是他们换防到了一个新的防御据点,晚上士兵们在据点里边正在吃饭,连吃饭带问讯这个新来的换防兵说外边发生了什么,说说说几仰了,就两边人准备动手。这时候外边的那个班长就挑帘进来了,说有人要参加音乐会。这个音乐会实际上就是当时驻扎的阿富汗的苏军和对面阿富汗楼乡之间每天晚上的例行公事,回想打几枪高兴一下。

然后因为他们为什么说那个晚上来这个音乐会呢,因为彼此都知道对方动向。俄国人这边有意识仪,他们能看见那个阿富汗人从哪来,这事就变得调诡起来,就跟刚才托翁描述的事一模一样。对垒的对手彼此熟悉对方是什么样,然后生死相关的这个部分就跟拉家常一样平常。

带他们出的这个班长拿着枪装上老的那个夜视仪,就看对面有人爬的动作非常熟悉,说他妈的,然后他就把枪放下了,就问对面说阿克米,阿克米是你吗。那边喊对是我,是我。然后说你哥他妈老农民怎么还不死。这里俄国人那边问,你这个老农民怎么还不死。那边用俄语回答”我还在这趴,你不用管我”。阿富汗人也用俄语回答。在夜色之中你还看到举手挥了挥拳,像老乡是问好。

然后就说你不用担心我,就是你爸你妈你全家都死之前我是不会死的。行。俄国人哎呀你他妈还是太年轻了,来上RPG。然后一炮轰过去,对面人就都走了。然后这一晚上的夜席就结束了。这就是前线的真实状态,大家打累了会歇一些,但其实你要非要坚持打也能打。

就像托翁来了,来了就说这时候水兵就说咱这刚才这炮打得不错呀,咱们给他们对面炮严打中了。你看这死俩人,你瞅,抬走了,你看,有战果了,有战果,挺高兴。可以,刚才这炮可以,但是会引发敌人的报复。对呀,敌人说刚才不歇的挺好吗,皮痒了是吧,来吧,报复。对面那边有一炮,你看见火光一闪,玉璃白烟,光传播的速度快。

这时候就有哨兵站在胸墙上大喊敌人开火了。这个炮弹揍子就飞过来了,扎在土里,机器周围的泥土石块像漩涡一般向上飞溅。敌人的这次还击给炮台的指挥官惹火,说继续还击,把第二门、第三门炮也都装上炮弹,形成了死亡的循环。

本来大家在歇着,忽然就这么互相干起来,还是因为来起敌人继续还击。哨兵就大呼敌人开火了。就这样,同样的响声和爆炸声,同样的泥石飞溅的声音。然后我们还听到人喊什么呢,喊臼炮。臼炮是一种弧线相当高的一种,臼炮这个东西你理解为它是臼炮,就当臼炮理解就行了。它是打取射的,炮弹本身非常重,口径非常大,纯纯的动能碾压。

托翁说你会听到一种均匀、相当悦耳的呼啸声,一种难以和任何恐怖的事物发生联想的呼啸声。因为它是一个臼炮,等于射线,它是一个带有一种马蹄的那种声音忽然远忽然近,渐次增强从天而降。然后这个呼啸声一种极快的速度向你逼近,然后你看到一个铁球落到地上,发出一声震耳欲聋的爆炸声。弹片带着这个尖利的呼啸声向四周飞溅,石块嗖嗖地飞入天空,泥土溅满你身。震耳欲聋说这是一种非常奇特的体验。

他说你听到这些响声,所有这些响声你既感到快乐又感到恐怖。尤其在这一刹那你知道炮弹朝你飞来,你心里就会想完了,这炮弹大概得给我打死。但是自尊心又作祟,支持着你让你保持镇定,所以周围能看不出来,但其实你内心是非常痛苦的。但是等炮弹从头上飞过去没有打死你,你又变得活跃起来了,一种喜悦的无法形容的愉快心情在这一瞬间占据了你。就这样你在危险中,在这个生与死的游戏中发现一种独特的魅力,你甚至会希望只要不打死我,这炮弹它落得离我越近越刺激越爽,变成了一种游戏。对,变成了赌徒游戏了。

这时哨兵又喊臼炮,对面的迫击炮又打过来了。榴弹的呼啸声、落地声、爆炸声,所有这些声响混成一片。但是托翁没有在这种类似听交响乐的混合了赌博游戏的享受中停留很久,因为这时候传出一个人的身影中断了这种奇妙的体验。是一个伤员倒在血泊和泥泞中,显出了一个奇怪的、简直不像人的模样。为什么呢,因为它胸口的有一部分被炸飞了。

在最初的几分钟,它那溅满污泥的脸上只有精黄和一个人处在这样的境地里所常有的一种装出来的、似乎稍嫌过早的痛苦表情。因为人体有那种止痛的机制,所以在这一瞬间肾上腺可就是蹦,它甚至感觉不到疼痛。是的,但是它已经明白自己发生了什么,它被击中了,对吧。但是所以它会表现出疼痛的表情来,但也没有很夸张,也没有特别夸张。

这个时候担架就过来了,它自己把没有受伤的半边身子躺下去。这种时候你会发现它的这种表情被一种本来是痛苦的表情被这种昂扬的难以表达的崇高的表情所替代。它的眼睛更亮了,牙齿紧紧地咬着,头使劲昂得很高。当它被抬起来的时候,它叫担架停下来,它吃力的声音发抖地对战友们说:

弟兄们再见

它还想说点什么,显然是还想再说点什么感人的话,但是没想出来,重复了一遍弟兄们再见。这时候一个水兵走进来把军帽戴在了伤员支起来的头上,然后水兵挥一挥手沉着冷静地回到自己大炮跟前。海军军官仿佛是为了回答你脸上惊恐的表情一样,他对你说每天都这样,七八个人,其实就是每天被收割的生命。然后他一面打着哈欠,开始用黄纸卷他的烟。战场上的无常,配合朱老师刚才讲的东西了。

可以再推荐一部电影,虽然是二战期间的,就是勒热夫战役。勒热夫战役的开场就是一群红军步兵在准备冲锋,他们在雪地里潜伏,然后每个人其实都在说一些无关紧要的东西。有人在嚼着偶然发现的酸肠果,然后有一个小孩是刚上步兵、刚上战场的步兵,领子上的星掉了,他想办法要把那个红星别上,重新别上。

这时候他的指导员过来后脑勺来一把,说这么冷趴在战壕里边不戴手套,你守不完了吗。但是他们没有想过之后几秒钟就会发起的冲锋,里边大部分人的生命是都会被收割的。那就像朱老师讲的,托翁说这个交互炮击的战场上的状态,手里边那个领子上的星星掉下来的小孩手里捏着红星冲锋,但是摔倒在战场上,身边冲过去的战友被MG-42被机枪集群收割,然后又是步兵炮铺天盖地的轰炸,只有很少的人能活下来。剩下的人冲到了敌人的战壕开始肉搏战。

这个孩子在电影里陷入PTSD那样的状态,他不知道自己在哪里,也不知道自己在干什么,完全打懵了,手抱着红星抱着枪,露出牙齿傻笑。到最后,就像朱老师讲的,这是一种奇妙的体验,那是一种特别典型的快乐与恐怖交织在一起的感觉。演员的脸上表达这种情感,他笑得最开心的时候一颗炮弹在他前爆炸,黑土血污冲在他脸上把他冲倒。最后在青草战壕进入肉搏状态时,他稍微回神想要站起来,一颗榴弹打中他的脑袋,死了。行,对,这就是战场。

反正托翁说防御这地就是这么回事,战争就是这么回事。你已经见到了塞瓦斯托波尔是被什么样的人守卫着。然后你在走过去的路上,托翁说面对路上不断掠过的炮弹和子弹已经感觉毫不在意了,怀着一种平静而昂扬的精神走着。这是一种从阵地上带走的一种愉快的信念–活下来了。他就感觉塞瓦斯托波尔不可能被占领。不但塞瓦斯托波尔不可能被占领,他觉得任何地方想要动摇俄罗斯人民的力量都是不可能的。

他说这种不可能的不是说因为我们修了很多的壕沟、胸墙、遮弹墙,我们布置了很多地雷、布置了很多大炮。他觉得这些东西不重要,关键是人们的眼睛里在举止上能感觉到一种他认为是塞瓦斯托波尔保卫者的这种精神。他们要干的时候就干得那么干脆、那么轻松、那么卖力气,让你觉得他们还能再多干一百倍,他们什么都能干。你了解到使他们行动起来的感情并不是任何的浅薄的、虚荣的、健忘的感情,而是一种很豪迈的感情。

这种感情使他们在枪林弹雨下,在人人都会遭受的九死一生的机遇中,在不断的劳动、熬夜和泥泞的条件下泰然生活。为了十字勋装,为了加官进爵,或者在威胁之下人们是不可能接受这样的条件,一定有一种崇高的令人鼓舞的原因。这个原因就是俄国人心中的一种羞涩的、难以行诸于色的藏在灵魂深处的感情,就是对祖国的爱。

哪怕是在塞瓦斯托波尔被围困的初期,在那些日子里,当时还没有攻势、没有军队、没有用来坚守的物质条件,但是也丝毫没有人去怀疑他会不会投降。所以在这些日子里,当时有一个克尔尼诺夫,他说托翁描写与古希腊媲美的英雄。他视察的时候他说弟兄们我们宁可死也不会放弃这个塞瓦多巴尔。于是我们向那些不善于说空话的俄国人答道宁死不去污了。他说只有现在关于那个时期的故事对你来说才不再是一个美丽的历史传说而变成了千真万确的事实。

但是这是克里米亚战争,最后这个城没有守住。没有守住,包括这个中将也死囚了。这个就要说到这是俄国海军步兵的优良传统,就是这个指挥突出是一个又菜又猛。像克尔尼诺夫本身也是战死在前线,直接被炮弹打中送走了。海军步兵付出了非常大的伤亡,很奇妙,很奇妙。

这一幕恰如此时此刻恰如别尔哥罗德百年之后,就是前段时间就在去年俄军黑海舰队的海军副总司令也是前线在当步兵作战的时候被弹片打死。又恰如前不久。就感觉这毛子进入一种循环,是的,一种循环。这是很又菜又猛,又菜又猛,很奇妙,真的很菜。

最后托翁写这个,他最后收尾收得非常高,他说这个已经是黄昏了,临去的夕阳从遮满天空的灰色的云层中透射出来。 差时间发出万道血红色的霞光,照亮紫色的云彩,照亮沧海,和在浩渺平稳的海面上起伏的巨舰和小船,照亮城市中的白色建筑,和街道上稀稀往往的行人。

林荫道上的军乐队奏出一支古老的华尔兹的曲调,飘过水面,同棱堡上传来的炮声奇妙的应和着。是吧,所以他最后落在一个这么一个点上。是吧,说这个咱俄罗斯人民确实是英雄的啊,这整个这个塞瓦斯托波尔发生的事情是一个史诗。我们俄国是万古长存,我们这个有种壮志凌云的精神,又热情又冷漠,又悲伤又高傲,很邪门。是的,这篇文章看的人觉得很多种感情交织在一起。没错,他有很多反英雄的东西,对吧,描写了很多很血性的,比如战地医院,是吧,包括伯爵码头,那个场景,那个混乱,其实是比较消极的,对吧。对,因为你日常生活被打乱了。是的。对吧,然后这个死亡,大量的死亡,还有第四棱堡,这种有点荒诞了。是吧,其实可开炮可不开炮。对吧,这个为什么非得干呢?我今儿高兴,对吧,我今儿高兴,来吧,开炮吧。对吧,哥几个真的是自门意思,我今儿高兴,而且你真的会死人。对吧,就是牺牲了,无意义的牺牲,可以说,很荒诞的。

然后呢,我们最后就是,我想谈一点什么呢,就是托翁。其实在这个过程中,很有意思,他这篇文章他要转载,他要连载在当时的报刊上,所以你有两个方式去理解托翁的笔触,为什么最后落在一个我们说相对积极、相对强的爱国主义的这么一个点上。首先托翁自己到这个时期,他还没有变成到后期的那个样。对,他对世界的认识还在进行之中,他还是比较积极的。我们说这个时候比较偏向于改良派,他觉得帝俄有点问题,沙皇有点问题,但是不至于说我们就完全背弃他,没有到那个阶段。他实际上,你可以说他人生末年都不一定到了那个阶段。对,因为他主张的还是道德主义。对,他毕竟不是列宁。是的。

然后这个,但是呢,你又能感觉到他有很多的反思。所以我怎么说呢,我看了一个说法,是认为托翁这么写是为了确保这文章能发出来。你又写太狠了,说咱那老个物人死,战友太信心了,毫无意义,我们前线死得非常惨,能让他删黄,能让你发这个东西吗?对吧,所以你可以这么理解。另一方面,你也可以理解为,托翁的这个时期他的思想还没有发展到后面那个阶段。这个人嘛,不同阶段他想法不一样,所以你看到他写了很多东西,写了很多,比如说有抽烟的,有这个少女跳跃石头块,有这个资政兵哼着小歌小曲干自己手头活给马喂水,就这种淡然的这种坚韧,确实是我觉得是他亲眼所见,而且呢,也是托翁的比例,仿佛长远的地方,就是他没有去写激昂的那些东西。我想想他如果要写一个很激昂的战场,但是他不想写这些东西,他其实也是在刻意的去反对传统的这种战争史诗的叙事里面。对,因为他所有人都过度亢奋的,是的,就是大家比方说像这一篇短片故事里边,如果说透光采用其他的叙事结构,比如说这个叙事从地磁龙堡开始,地磁龙堡开炮开始,然后就是满含激情的,然后占地奇观的向人们描述,然后在实际上是以中心点向两端展开,那他的阅读体验或者说认识就又是另一回事了。

所以,我觉得很有意思,托翁这篇文章是一种我感觉是他前后两个托翁的一个转折点这么一个存在。很奇妙。你能感觉到托翁还有很不成熟的东西,就是他对战争这些事想的也不是特别明白。我们能感觉到他还有一种老俄罗斯贵族咱们就咱老俄国人怎么怎么着要替一面,咱们那种要替一面那个劲,就是你包括我们上次聊地主的早晨,他收尾的点不是一个很消极的东西。是,你说这个地主失败了吗?他已经预见到了纯粹的失败,他已经从一开始实际上他姨妈已经跟他说了。对吧,这事成不了,他自己也预见到了,但是他最后非要落在一个点上,他一边弹钢琴一边的想象,说完美的理想的俄罗斯乡村该什么样。他这篇文章也一样,他已经看到了很多很消极的东西,就是这种无意义的死亡破坏,但是他非要落到什么。你看结尾那个水兵,高贵的”别了弟兄们”这种俄罗斯人那种深沉的,其实生日见素,就只够他说了一句话。但是后面你实际上你读完这篇整篇文章你扎门扎门,你感觉最震撼你的其实是什么,是消极的东西,是勉强能称作壮美,或者奇观的,近乎奇观的一种独特的奇观。

我觉得对于现代人来说,反倒因为看了很多战争片,所以对我们来说这种奇观对我们的震撼反倒没有那么强了。对,所以我们今天回看这篇文章很有意思。因为托翁,我给大家选了这篇文章,这是他的系列里的第一篇,创作时间应该在54年底,发表时间到你看他结尾落的款是落到55年,1855年4月份应该是落在发表在当代人这杂志上面。到这个就是从他开始写到他这个发,我们说整个都是塞瓦斯托波尔整个这场战役的这个前期,其实所以他描写的这个时期他描写的这个城市里状态其实还行。中后期的许荣莫判那个部分就实际上大家如果把它三篇关于塞瓦斯托波尔的文章都看下来,你会发现到第二篇就不是这样。是的,到第二篇就完全不是这样。到第二篇叫做五月的塞瓦斯托波尔这篇文章打半年了,就是我们今天读的这一篇发的时候到55年的春天发表到55年的6月,这个时候你感觉到托翁就变了,就是他明确的开始否定所有的浪漫化了,没有浪漫化的叙事。军官们非常虚荣,而且非常懦弱怯懦不想去前线,在想我为什么非去前线,那谁谁他妈的非,为什么要请病假,我今天去肯定死,我为什么要死,我死有什么意义。然后与此同时在前线后方的这些高级别军官还扯些有的没的那种蛋,前方吃紧后方紧吃开宴会怎么玩女人怎么专大老贵族体面这一块。

所以到时候托翁到那一篇就说什么,战争唯一的英雄是死亡。那托翁就是那个劲的,有点急了,急了。到第三篇八月的塞瓦斯托波尔也就是到55年的秋天了,这时候已经快失手了。那一篇你们去看,就是托翁已经完全连那种细腻的演都不演了,那种描写都没了,就是非常的粗力,非常的残酷的一篇了。

所以我带大家读的很有意思,就是正好是它这个系列的第一篇,我觉得你在这块能感觉到最多托翁还带着一种幻想,他幻想俄罗斯民族有一种高贵的精神可以带他们去怎么摆脱目前的所有困境。最终我们还不是死,还不是个打,对吧,能击败我们老俄伙人吗?对吧,还是那种劲。到后面就不是这样,后面就没有那种说对凯旋的幻想,对什么这种悲壮的欧哥没了。其实就是在整个战场之上,他除了就是身体遭受这个战争的拷打之外,他的精神也在不断的被战争摧毁。对,所以我们说整个三篇塞瓦斯托波尔三篇我们读了一篇,另外两篇我们不一定读了。大家因为也比较长大家可以自己找来看一看,就是很有意思。

也很有意思托翁写的时候我们说他还不是文学的大师了。是的,这个时候其实他我们前面介绍过这个时代背景是上军队里边去混一混的主要是因为他欠了债欠债,而且大学也没读下来,实际上摸得出路无事可做给自己谋一个出路这么一个状态。到后面他不写了他离开军队,所以他没有经历沦陷塞瓦斯托波尔沦陷的过程。实际上也是沙皇你就觉得就是这个小托别他妈写了再写的话容易朕还他妈不清楚吗这他妈万一你再写再也再写。对吧,他沦陷了。对吧,你该不是要当反贼吧。对吧,所以就给他拽下来了,拽下来了。

所以托翁就是这整件事,塞瓦斯托波尔这些变化战场的变化我们也能见证托翁的成长,他开始更加深入的怀疑俄罗斯这个民族是不是在思想上在政治制度上出了大问题了。这一点呢可以说也贯穿他一生。他到晚年越来越觉得问题还是很大,但是他始终不能彻底否定自己。他到最后呢我们说他是一种更舍得了,就是这个财产我不要了。对吧,他已经无限的接近一个共产党了,但是呢没有能走到那一步。是的,但那不是他的问题了。对吧,那个问题要交给列宁统治去。是的。

而且就是托翁本身对于人的思考是在他的时代里边已经竭尽所能了。我们可以说是竭尽所能,已经是启蒙主义的色彩了。是的,而且就是他人的处境,他与其他的就是杀了贵族啊,和包括说军官也好啊或者文人也好,他自身兼具着多重的身份,这也使得他在每一个角色上都有足够的反应权。对,你打个比方说你是贵族然后你有文化,但是呢你没打个仗那你有文化你打个仗但你不是贵族或者说是你是贵族你打个仗但你说不出来前线人的死亡生命的尊严然后被收割战争的无意义死亡的虚无这些东西是什么?这是托翁最奇妙的在这他是很多件事的亲历者。是的,你看奴奴是改革他改的沙皇前面塞瓦斯托波尔战争他到前线。是的,对吧,他是很多件事的亲历者,所以呢他的亲历带给他的书写就格外的强,是个强力的一个支撑,就是他有大量的细节这些东西你今天你再回头看你会发现同时期的很多作家其实达不到这个深度,这也是为什么我们会把它不断的翻出来看就推荐给别人去看。

我得说这个事啊其实是一个俯察沧桑巨变功的这样一个问题,就是你必须得亲眼或者说亲身体验过巨大的变化冲击给人带来影响你才能写出时代图景。所以就抛开这个托翁本人成长抛开俄国历史这个话题,我就是单纯从文学的角度来看就是文章非常好,就是一个范本一样的写作。这居然还是托翁早期的作品这还不是他成熟期的早期就能写到这个程度你确实你得赏你得服气,确实是老天爷赏范,牛逼。没错,他这种洞察力太强了。

再有呢,就是我读这篇文章我就感觉,就为什么我们成不了托翁呢,很大一个原因跟你没什么关系。你在说什么呢,很大一个原因就是我们没有精密这个我们就你知道吧就各家的痛点是吧我们就也得去喝着黄泉水然后那个扛着枪就跑那个壕沟里去,你别指望这个,没有这个别乱想别乱想啥也别说了。一会儿录完节目我就去美国劫法场我就马自罗/莫斯科没有大哥委内生意咱也不做了我去跟古巴人民我守卫古巴。因为就是前两天我刚跟那个就是罗新/比特跟人聊太平天相关的事,然后我就说说我们这个时代其实挺有趣的,因为我们有幸生活在一个能够普通人读历史的时代。就你如果说再往前推你别说往前推100年了你往前推50年。对吧,何必说是就是70年代末40来岁的人对于历史对于战争就真的有兴趣的人然后他想去了解其中的东西他很难的,非常难,就各种渠道的描述你说的这个就是人的这个一个历史感就是人会不会清醒的意识到自己是历史中的一个部分人经常会忘记这件事人在做事的时候走着会觉得自己是比较特别的。对吧,历史感呢是一个是一个阅读的产物是一个后期学习的产物它不是人的一种天然的一种它不是天然的感知,它不是天然的感知。你大量的阅读之后你会恍惚就觉得我操这事是不是以前发生过。对吧我是不是在这个链条里的一个部分。

而且就是所以这也是我们读这篇文章的一个趣味。还有一个就是因为现在的每一届的发达包括说是那个电影游戏这些东西它能够给人们还原具体的场景。如果说你给就是七八十年代的人去讲这个一战的欠行拧拧这些东西它可能不太容易直观的感受,但是它对于玩家或者说是电影观众来讲它有先天的这样一个优势去感知这个途径里边所带来的信息。还有我就特别我觉得特别好的脑就是这篇小说就启发我未来你说如果VR设备再进化嗅觉未知行走在塞瓦斯托波尔这种东西你要做出来我跟你说就可能达到娱乐的一个前所未见的高度。这个就是对于成平日久的普通人来讲可能就是你真的到达那样一种情况就技术还原能够到达那样一种情况的话你走长不如吐了哥们。你看我们葛山东是吧,没事就模仿攻打县城就已经成为一个文理项目。是,我在想未来VR模拟一个塞瓦斯托波尔,托翁说我来给你介绍一下塞瓦斯托波尔目前的状态,推开进一个饭馆,周围AI生成的那些人,你不就想要西部世界吗,哥们,还是带劲。生活还是很无聊,好事都过久了,人就这样,人真就这样,就好事都过久了。

我拿一个另外一个作品来给咱们这一期的,就是12月的塞瓦斯托波尔收尾,是这个事是发生在一战之后,捷克作家亚哈谢克。 写的这个好兵设计历遣记

他讲的其实是奥匈帝国在这个第一次世界大战发生的时候,人们从惊慌无措到逐渐习惯战争的这样一个过程,包括说被征召老兵,然后斗志昂扬的新兵,军官团们的表现,然后前线的战斗,后方的运输。

• 被征召老兵
• 斗志昂扬的新兵
• 军官团们的表现
• 前线的战斗
• 后方的运输

他里边其实给我留下最深触动的一个部分是什么呢?就他们接近前线了,但是还没有到前线,因为当时的主要的就是交火的地方,就是大功被刺,然后就是今天的这个塞瓦斯托波尔变成了主要的可能发生剧烈摩擦的地方。

那么奥匈帝国和德国是同盟国、协约国,在这对着干。

那么他们接近前线的时候,主角帅客跟自己的团队走散了,实际上他就掉队了。掉队的时候,亚哈谢克写了他在啤酒馆里歇脚。在啤酒馆角落里边有一个匈牙利的伤兵,他只会说三个字:

“劈 啪 干” “劈 啪 干” “劈 啪 干”

然后把自己面前的啤酒一饮而尽。这个人在那里坐了很久很久,他是一个前线退下来的伤兵。然后主角帅客跟他喝了很长时间的酒,他就离开了这个啤酒馆之后再也没有回去过。这就是战争朋友问。

我想起来,我准备这个节目的时候正好是上周,周四、周五,黄金暴涨,暴涨暴跌。那两天不是特刺激吗?你这么在节目上受刺激很刺激。我身边很多人,本来不关注金融的人突然就开始找我来了,就问我,你说能不能买。我说,我说发财不差这一两天。

我其实周四已经清仓了,我清的高点了。我犹豫,我说要不要。今年,然后一犹豫的时候,正好我就想想这这稿子,我再看看吧,我再改改。我看着呢,我一边看稿子,我就沉浸其中,就没来及交易。你再一看,我还过点了,有点懊悔,刚才是不是应该再买点。然后呢,就发生了这个,这边我们转层过来,大家就踩踏下去,暴跌了一下。后来我就感觉,托翁好,得读。行吧。

那我们这一期,这个有关12月的塞尔森博尔的节目就先到这里。如果说大家喜欢的话,之后我们想着多聊、多录、多看。那也推荐朋友们看一看托翁的还有两个作品,对,还有两个。其实也不局限于这些,也不一定这个故事。

当然了,从目前的角度来讲的话,不太推荐看战争与和平,比较长、比较长,退休之后再输。没事,他们可以先把坑挖。

行,那我们就先到这里。我们下次再见。拜拜。拜拜。

优优优独播剧场–YoYo Television Series Exclusive

Why it Sucks to Work in AI in China + Open Source with Kevin Xu

Kevin Xu of Interconnected Blog and Capital, as well as Jordan Schneider from China Talk here.

We’re going to workshop my take that it is way less fun to work in the Chinese AI ecosystem than the Western AI ecosystem.

And then we’re going to dive into Kevin’s opus, exploring the past 20 years of open source technology in China.

So I guess I’m here to kick it off.

The general gist of the take is that if you are at:

• OpenAI
• Anthropic
• DeepMind
• XAI
• Meta

the amount of compute you have access to to do your super cool research is an order of magnitude or two higher.

The level of business pressure that you are under is high, but the upside is just also an order of magnitude or two more positive for you as an individual contributor, as a founder, as someone playing in the broader ecosystem.

If you’re in the West, in contrast, the Chinese new entrance to the field, the 01.AIs and Minimaxes and even DeepSeeks of the world have not had anything remotely close to the type of success that OpenAI, Anthropic, or even the sort of tier two AI startups that have been founded over the past few years have been able to deliver.

So the pressure is enormous.

It’s kind of indefinite because you haven’t, even though you are making the best models in your country, it’s not that’s transforming into revenue in the tens of millions of dollars.

Kevin, we should get into the Alibaba firing resignation of Lin Junyang too then, since you mentioned the “cushy job at Alibaba”, which really only lasts about a couple of quarters.

I think that has been dealt to China tech in general, probably as soon as export control and tech sanctions and then China’s own crackdown became the norm that everybody in China has to operate in.

And AI doesn’t change that AI probably accentuates a lot of those challenges as far as lower ceiling, but also less resources with just about as much expectation for the output and the traction and the metrics.

And I think the latest personnel change over at Alibaba’s Qwen team, this just happened a few days ago, is the latest manifestation.

I think China tech or China AI, maybe, especially in the open source world, has maybe enjoyed quite a year of, I wouldn’t say euphoria, but at least a lot of really welcomed and perhaps long overdue traction, attention, not just from within China, but all over the world.

And whether that actually continues this year is a huge question mark.

And what has happened over on the Quen team may be the first sign that the economics and the expectations of all these decently well-funded labs in China, but nowhere near as well-resourced as the open AIs of the world and the open AIs of the world are still crying out for even more resources.

So you do have this dynamic of higher cost, but higher reward on the Silicon Valley side.

And the China side is just a very different set of hands that you still have to play.

I would say one thing that I mentioned near the end of my so-called opus of the history of China’s open source ecosystem, it’s 6,000 words long.

It’s probably way too long.

I don’t expect anyone to read the whole damn thing.

But near the end, I wanted to articulate one thing, which is that open source as a strategy to expand is likely one of the best, if not the only avenue in which a lot of Chinese entrepreneurs, tech entrepreneurs have at their disposal these days to really go beyond their own border.

And everybody wants to expand overseas, despite the sanctions, the geopolitical tension, the different barriers to go into different markets.

The desire and the passion and the energy has not stopped.

In fact, it probably only gotten more intense, partly because the domestic economic situation, frankly, hasn’t really improved dramatically.

So there’s only so much you can get out of the domestic pie.

They have to go overseas.

And open source is one of these ways where their own technology, at least the open source, the AI labs, can be seen and evaluated and being kicked and tested without the geopolitical stuff.

And all the toxicity of the China label as a brand for, a quarter, a quarter or two before you start to work on commercial relationships.

And so there is a very practical reason to open sourcing everything, despite the economics being initially not that great, because it’s one of the few vectors where you can break out of your market, your whole market into different markets.

Yeah, let’s do a little compare and contrast between China tech crackdown and what we just saw with Anthropic and the Department of Defense, because I think that kind of underlines another difference in the sort of ambition that you can have as a founder in this world.

I mean, Dario wants to shape the future trajectory of humankind.

I don’t think that’s really underplaying the vision that he’s put out in his first essays now that have turned into little novellas with the latest one that came in the new funding round. And kind of the idea that first, I think a Chinese founder could be that kind of loud and ambitious and discordant with the sort of general narrative about what the government thinks AI should be used for.

And then the, look, we talk about America has military civil fusion as well on certain dimensions, but the idea of a Chinese lab saying no in a dramatic and public way like this would just not happen.

People have learned that lesson: this is not the way to sort of have what would have been the equivalent of Dario writing that memo and it coming out in the public? It would just be someone crashes out on WeChat Moments and it gets screenshotted and sent to things.

And then three days later, all their social media disappears and they’re probably gone somewhere and maybe not coming back, ever, or at least for a good while, which is kind of what famously happens with Zhang Yiming and the crackdown on some of the ByteDance products, which weren’t even trying to reshape the face of the society’s relationship.

Just low brow humor was what Zhang Yiming’s initial wire that got tripped.

They just had fights and nudity and that’s all it, that’s all it took. Not saying no to doing autonomous weapons.

So, that’s less fun. You can’t make as much money. You can’t play machine God. And I think it’s an interesting thing.

It’s an interesting point, Kevin, about how doing open source is maybe one of the few ways you can express yourself ideologically in the Chinese technology ecosystem that by doing, but by working on these projects and releasing your technology to the world, that comes with a sort of ideological valence, which as you pointed out, starting in 2021 is something that the government sort of saw.

What you end up having to do is actually a very classically liberal democratic governance process, where you get a bunch of people who have a lot of stake in the project’s future. So, you decide on a set of priorities, you decide on a set of priorities, you vote on a lot of things, you usually have a technical committee, you can just look up any of your favorite open source foundations, whether it’s a Linux foundation, whether it’s a cloud native computing foundation, which is a sub, a subsidiary of the Linux foundation, the Apache software foundation.

• Linux foundation
• cloud native computing foundation, which is a sub, a subsidiary of the Linux foundation
• the Apache software foundation

All these open source bodies have these democratic processes and transparency and openness built into it that a lot of the AI generation of founders in China have likely lived with and grew up in for a long time as they study and level up their own technical chops.

Because everybody learned from open source in the universities, no one buys a proprietary piece of Oracle software to learn about databases, right?

That’s just ridiculous.

And now they’re expressing it in different ways. And it’s funny, you mentioned that as another vector, right? To express their own identity, not just as a way to go abroad from a business perspective.

The first generation of founders, this is like the Zhang Yiming generation of founders. There are several people who also work in open source world and databases as well. They’re very liberal. They’ve all had traces on Weibo of expressions and opinions expressed about the state of their country that has nothing to do with the technology that they’re building. And now all of that has disappeared, right?

And I think the newer generation of AI founders, one thing I paid a lot of close attention to is not so much the fact that they open source anything at all, but the way they are doing it is increasingly more sophisticated and more exposed to different ways that Western communities are being built.

One example that I cited is Moonshot’s founder, Yang Zhilin, who in their shop makes a Kimi series of models. And it’s really interesting to see him coming out of his engineer shell and engage with the Reddit community during one of Kimi’s launches directly to talk with the community. It’s very Sam Altman-like as far as how exposed and accessible he is, right? To whoever wants to talk about Kimi or give him crap about Kimi. You have to have a pretty strong, thick skin, but also a good sense of humor to do Reddit really well. That is the kind of thing that the previous generation of founders, the Zhang Yiming generation, hasn’t quite cracked. But Yang Zhilin’s generation, I think, is cracking right now. He also did this, direct-to-camera release of Kimi k2.5, I think, which is very different from a lot of how Chinese models or Chinese products in general are being released. Manus did similar things for their product as well. All these things built on top of what I wrote in my piece, which is two decades plus of open source history that evolved from taking and consuming and just using free software for their own needs. And there’s still a lot of that going on and not enough giving back, unfortunately. But over time, the maturity has really evolved as far as contributing, but also engaging with a wider community to actually take in feedback, to prioritize feedback, letting people vote on certain elements of the roadmap, which is super, super common in the open source world. And the Chinese founders who engage in that world are frankly not that different.

So, in my view, those are the most, quote, Western part, the most, quote, Western portion of the Chinese tech community.

They’re also ones that are, of course, super aligned with the government. And we can talk about those as well. That’s kind of a different cloth. Well, I mean, Yang Zhilin’s an interesting one. He’s yet to agree to come on China Talk yet. TBD on that, guys. But he did a PhD in the U.S., right? And I don’t think that, I don’t know if there is any other, well, I guess the Pinduoduo CEO has an American degree from Duke or something. But aside from that, I don’t think any one of these top people have English as strong as, well, I mean, Jack Ma’s kind of an exception. But the fact that he lived here or worked at Meta and Google for a while is sort of illustrative of that. And, but, and yeah, his doing it, doing a YouTube video in English was definitely a step.

Kevin, why don’t you talk a little bit about the need for, or, or why these, kind of, everyone below Alibaba feels like they need to have kind of global adoption of their models. I think even Alibaba needs it. And I think that’s one of the reasons why the personnel change. But to more directly answer your question, the Chinese technique, the Chinese IT ecosystem as a business environment has been piss poor forever and continues to be piss poor. And what I mean by that is people just don’t spend enough money buying software solutions when they can hire five people to hack something on their own.

The business environments between the software makers, whether it’s a SaaS solution or AI model and sell it to would be buyers, whether it’s an SOE or a tech company or local government or a bank or whatever. That exchange in value has never grown to the level where you just could actually make a decent profit margin off of that. It’s incredibly, incredibly hard to make money off of code. So for any Chinese technology company where the core of your product is software, you have to go abroad at some point. You look for markets where there is a stronger penchant to pay for technology versus paying for people to hack your technology or to just jigger your technology into something you want. You never want to pay enough or you’re just going to pay some service contract, but you never want to pay a good subscription, all this sort of stuff.

So with that context in mind, the latest generation of AI labs from China, at the end of the day, they’re selling a model that’s packaged either in an API or packaged in some sort of a vertical chatbot or agent solution, TBD, what the form factor would be yet. And the only way they can really make good money is to go to a market:

• primarily the U.S.
• but also Western Europe
• maybe a little bit of Japan
• pockets of other parts of the world

where buying software is just not as scrutinized, where you can really scale that model, right? So they really don’t have a good option. It’s a very tough hand dealt when you’re a tech founder in China trying to make technology products to be sold and make a big company out of it.

And open source is this vector where you can at least get people to try yourself without, again, the toxicity I said, and kind of see it on the merit. And the playbook of packaging open source things to commercial software that you will subscribe to or pay for, that playbook has been played quite a few times in the U.S. market, especially in the cloud, the hyperscalers, packaging open source databases to be consumed on AWS. And most of the Chinese vendors and companies have really internalized how that playbook is run as well, which is why, if you want to turn it to present day, why Kimi and Minimax and now Tencent Cloud and every other cloud under the sun in China is packaging Llama almost immediately as a hosted solution to be consumed by the masses or whatnot.

“And that’s just a very simple open source to commercial playbook.”

And it’s more surprising to me that less U.S. players are doing that. We can speculate on why. But that playbook is very well run and well internalized on the Chinese ecosystem.

Yeah, China never figured out SaaS or was never interested in SaaS. And the sort of companies that ended up selling software ended up kind of being almost all forward deployed engineers without back ends of a thing to leverage.

It was just, OK, you’re just going to hire some software engineers and we’re not going to pay them a lot because in China, you don’t get paid a lot as a software engineer versus an entire economy being used to buying software, which then OpenAI and Anthropic can sort of leverage into and turn into — I mean, it’s three quarters of their business now is enterprise as opposed to consumer.

So not really having that as the thing that’s going to power your business means that you have to go to these B tier and C tier business models as opposed to just making the thing and selling enormous amounts of credits for it.

And then Anthropic now having Anthropic and OpenAI having forward deployed engineers is a nice thing, a nice extension as opposed to the core business, which is a lot more straightforward: keep building better models and keep selling tokens and try to get them cheaper and faster.

And it’s interesting. I think there was a moment in the Chinese tech ecosystem where buying software could become more scalable. It was trending that way all the way up until the tech crackdown—not so much on the Jack Ma side of things, but cracking down on education tech companies, cracking down gaming for a little while there.

Before that, there was a period where engineers were actually getting expensive in China enough where paying for the solution as opposed to paying for the body to make the solution might have been worth it economically. And then the crackdown just released a bunch of engineers into the market who are unemployed overnight and they need jobs.

So all the companies who weren’t going to pay because of the labor supply, let’s just say, just got a big boost of supply. So, oh, never mind. We’ve got all these engineers we can hire for cheap. Forget about paying a subscription for your stupid little CRM or something like that.

And so it’s funny if you want to replay that history, if the crackdown hasn’t happened, what would have happened to the software industry in China is another interesting branch that we will never get to really live, but we can speculate.

“That’s fascinating.”

Kevin, can you talk a little bit about the consumer fight domestically in China?

I mean, I’m a lot less close to the consumer side, to be honest with you. My investment is in the infrastructure side, the B2B side, the enterprise side. I would say most of the revenue is coming from consumer apps or APIs that power other consumer apps.

I think the consumer landscape in China is always a bit more—you can call it vibrant, you can call it fast moving—you can call it there might even be a higher willingness to pay on the consumer front. We were joking earlier in our live stream about gifting on live streaming platforms and whatnot. That’s a very well-baked business platform that is very to consumer in China for a very long time.

So I think the fight is there, but again, the market is just walled off. If you make the best Chinese speaking or the best chatbot, whatever consumer app for the Chinese market, the way you can really go abroad with that is hard, not impossible. TikTok has shown some ability to scale beyond that. If you find some commonality or common denominator to scale, right?

But as far as who’s up, who’s down, obviously Doubao from ByteDance seems to be the one. It’s a ChatGPT equivalent as far as the consumer level of both awareness, but also usage. Alibaba is actually quite behind, even though there might be nominally number two with whatever, the Qwen branded chat app that they’re releasing just very recently.

Another reason why maybe they’re much more commercially focused is because they’re losing that market share. While the cloud infrastructure B2B side is still going to be a very tough business for them to grow both domestically and abroad.

One of the interesting things about how this is going to evolve is we’ve recently got news that chat GPT is spending less effort on the shopping side of things. They had a big launch six months ago. I used it once or twice. It really wasn’t great. So maybe it deserved to die.

ByteDance and Alibaba sell a lot of stuff, sell outrageous amounts of things.

• OpenAI
• Anthropic
• ByteDance
• Alibaba
• Doubao
• Jack Ma
• Kevin “And if there are any two companies in the world that are going to figure out how to kind of get that sort of chat bot shopping experience, right?”

I have faith in the kind of Taobao and Douyin teams to make that happen.

And I think the WeChat, Tencent team too, for sure.

“They’re doing that as well, right?”

So it’s just it’ll be interesting because the kind of paths of highest revenue of just selling API access to enterprise isn’t really open to China.

That the sort of engineering hours that would have pursued those sorts of tier two things, as you said, the kind of open source bundling model or this shopping stuff.

We’re just going to get we’re going to get stuff.

Even if the Chinese labs aren’t making the frontier pushing models, we will still have interesting applications that the Chinese ecosystem is going to pursue sooner and with more energy than in the West.

And I think it is not a technical limitation on the models that is not letting ChatGPT or Claude help me pick out my new nail clipper or whatever.

“It’s just that it’s not enough.”

It’s not a high enough ROI decision to spend kind of management engineering hours on.

And it may be for that or other applications in the Chinese ecosystem.

Yeah, my speculation right now is that no matter how which direction the model game between the two sides evolve in the next year or two years or three years, the business environment or the trajectory of tech business in general will more or less stay the same in the sense that Alibaba or ByteDance or WeChat, Tencent will probably figure out agentic commerce, let’s just say, before Google and OpenAI does.

For sure, it will live within their own wall garden, but they will figure out that experience before while the U.S. side or the Western world will still race ahead when it comes to enterprise adoption because of all the dynamic that we just talked about, which is exactly the dynamic that we had 10 years ago or 15 years ago when it comes to which side is better at what.

And one last thing I would say about open source, which is that it isn’t just a B2B packaging play that is fairly natural.

The thing with open source is that it touches the developer more directly right away.

There is a vector in which it could touch the other kinds of consumer application or just landed application, practical application a little bit quicker than closed source kind of beautifully packaged but inaccessible models.

I kind of name check unitary in my long piece as a robotics firm that also open source their own VLA model.

So the hardware OEM side of things in China are also in the open source game to get their software packaged out there, to get more people interested in their platform if their vector into the market is actually to make the hardware really, really good, whether it’s quality or affordability or whatnot.

So open source could play in the consumer side, but it typically doesn’t play as well in the consumer tech side, but it could play well in the hardware side.

And that could be another thing that you see more offshoots in the Chinese ecosystem from the hardware vendors of the world.

What else do you want to talk about, Kevin?

Do you want to do it?

Do we talk about are you done with your conjecture about China?

Not less fun.

I thought it would be more personal than just the industry.

Oh, I mean, I think it’s a personal, I don’t know, personal.

It’s just, did you have a more personal take?

No, it was just, I mean, it’s tough, right? It’s a hard hand.

Well, I mean, like you just have, it’s, it’s, you have less money, more competition. It’s more intense. Upside is lower. Kind of political walls are way tighter closing in. You can’t be, you can’t aspire to become like a master of the universe in the way that you can in the West.

I mean, what, what, what, what, let’s, let’s talk to the other sides. What’s the upside?

You get to distill models. Everybody can do anything about it. Everybody gets to distill models.

I, I don’t know. What else is an upset? I guess you’re.

Yeah.

Right. There’s no upside. If you, if you have the ovarian lottery choice to be born 10 years ago and you’re a tech entrepreneur in your genes, which world you want to be born in. I think it’s 10 out of 10. You want to be born in the United States. There’s no question about it.

That that is a fork in a row. And it’s really unfortunate, right? Given the quality of, I don’t know, innovation or just grit, I think, to have to work within that much constraint and to still put out what you can put out to the world.

I think it’s admirable, but admorbidity only gets you so far when it comes to market cap.

And, you know, we’ll see how that goes.

And yeah, I don’t know if you want to talk about the whole Alibaba fiasco. I think it’s TBD where that’s going. But again, it’s just another episode, I think, of the whole difficult. Yeah, let’s do it. Well, let’s just, I mean, just to, just to kind of level set: Tencent market cap, $600 billion. Alibaba market cap is somewhere around that is 325 billion dollars. So those firms have been at it a long time.

If they were, I mean, the closest, the closest comps are in the multi trillion dollar range in the U.S. and little OpenAI and Anthropic have already, or on the, or on pace to surpass. Both of them having existed for three and let’s say 10 years, but we don’t really count the opening after 10 years.

So it’s not fair at some level that you can have just as sharp engineers doing, working just as hard, probably harder to be honest. And not, not having the same kind of ability to technical outcomes or overall compensation, global impact.

Yeah. Yeah. Should we address viewer questions? I feel that’s part of the job of a live streamer. Thoughts on XAI implosion from Daniel.

I stay fairly close to early stage investors on both sides of the aisle or both sides of the, I don’t know, ocean. And frankly, XAI’s financials and trajectory as a business is pretty terrible. And has been terrible since the get go, regardless of how you feel about the models and what it can do.

So really folding XAI within SpaceX and you kind of lump Twitter X in there too, as a conglomerate concern is one, the best way for the initial investors to have a good story or a good ending out of the XAI investment, which has always been pretty aggressive from the get go, given how much they’ve built.

And this is not to comment on the Colossus data centers, which I do think is quite a feat of physical accomplishment that only the Elons of the world can really push through to make it happen. Happens to be three hours from where I live at that. So very cool to see that happen near me, but as a business, right? As a concern, it will be very hard to survive on its own without more or less hiding in a giant conglomerate and kind of do what it does and see what they can do.

Right. I mean, we can ask the same thing for Gemini as well. We don’t know the P and L for Gemini as a, the vision. So in a sense, the independent model makers always have a little bit of a tougher time, maybe justifying the worth, but if they’re really doing well, I do think they probably deserve the valuation that they’re getting, which, again, and you can’t decide whether that is the right number or not.

Yeah. I don’t think the Gemini comparison is fair. The sort of the corporate thing that Gemini can plug into to boost growth is pretty enormous on its own. Compared to X, it would be pretty embarrassing if you were Google and you didn’t figure out how to use your giant models to make less, more money. And as, yeah, as you said, okay, we’re plugging it into X. I mean.

And what? And, well, I guess Tesla for the robots question mark, right? That’s a much more speculative, speculative thing than make the ads come up better on Google searches and YouTube. So. Yeah. Yeah.

Anyways, how is Minimax worth more than Baidu? Baidu’s got terrible vibes. I think they’ve just got terrible vibes for decades. Baidu, so yeah, this is maybe less about if Minimax is overvalued versus Baidu’s undervalued, right? You can kind of do both sides. I don’t know how you feel about it, Jordan, but Baidu’s probably undervalued from a pure financial perspective, but it just has kind of whiffed on almost everything up to this point.

Like literally everything, even including, I would say self-driving, which you’re fairly early into, from a platform perspective or robo taxi perspective, the model they were, I guess you can say early into, but it just isn’t the kind of shop that can deliver, unfortunately, based on the promises.

And Minimax, I do think it’s probably slightly overvalued in my opinion. I think there just aren’t that many ways to bet on a pure AI lab and both Minimax and Zhipu are the only ones out there in the public market right now, listed in Hong Kong.

So there’s probably a little bit of froth there as well, because if you look at the end of the day, Minimax gross margin has improved from 12% to 25% from its very first earnings report.

It is a doubling of improvement, but the base is quite low and the current state is still lower.

And that’s the thing on all these labs. It’s not like the open AI gross margin at this moment, at least it’s that much better, right? It’s in the forties, 30, forties range, depending on the business. So these are all very hard business to run, actually.

For some number, they’re both around 40, 45, 45 billion USD, which is, again, a failure for an A16Z. Maybe not a failure. We’d probably be happy with that in a 10 year fund, but still. What else is there to discuss?

Oh yeah, Alibaba implosion. We did a little bit of it earlier. “Why don’t you, why don’t you close the circle on that one, Kevin?”

I think for those of you who haven’t caught the news, there was a pretty public resignation of one of the tech leaders of the Qwen model team, Lin Junyang.

And he also has quite the big reputation within Alibaba for being the youngest P10 engineer, P10 being a very high senior engineering level that you don’t reach in your early thirties, right? I think it’s roughly where he is right now. It has open AI. Sam got fired by how it played out a little bit on Twitter. Of course, while China is actually asleep, which is super fascinating. Then a few other people also resigned. There was an all-hands meeting that happened with the CEO of Alibaba, who was also the CEO of Alibaba Cloud, Eddie Wu, to address the whole situation.

I wrote a little bit about it on the interconnected newsletter, but I think how this translates into financial results is TBD. I do think this is both somewhat idiosyncratic to Alibaba’s corporate culture, which is they’re very kind of anti sort of God figure, at least in the ranking files.

And I think Lin probably got to that level of celebrity, especially in the Western world as the only person or the most accessible and visible person that anybody from the West who interacts with the Qwen team. You basically talk to him and that he’s very good at being accessible, very helpful, generally a very good open source evangelist, not to mention really technical person as well.

So nothing against him whatsoever. But my way of thinking about the situation is the old JFK line, which is that is no longer about what Alibaba can do for Qwen is about what Qwen can do. For Alibaba. And I think that that is the strategic shift that we’re going to see as far as where can Qwen plug in to lift up the rest of the entire Alibaba ecosystem when it comes to shopping, consumer app, obviously the cloud. And I think Qwen probably vastly exceeded its expectation as well from the way it started.

I cannot believe the whole team is only about 100 people before the whole personnel change happened. It’s sort of a you’re a victim of your own success in a lot of ways. And then the top just comes in and sort of grabs it.

Here’s an interesting comp: Mira leaves open AI, brings 10 of her best friends, gets to raise $2 billion at a $12 billion valuation, has basically done. Has published nothing a year later, gets one megawatt of compute, signs a deal with Jensen, and is now raising at a $50 billion valuation.

If you take the Qwen team and bring them outside and put the hat out to the Chinese domestic ecosystem.

So you don’t want to do a Manus and I guess they kind of already are in Singapore. Maybe it’s a slightly different thing, but Kevin, why don’t you play out the two paths?

• If you’re that team in China, you want to make it in China, raise money domestically.
• versus you’re in Singapore and you’ll take Bill Gurley’s money, whatever.

Or else let’s do the global thing, but as a not Chinese company.

You mean if the crew that left Qwen wants to do their version of the thinking machine as a startup or as a Neolab, that’s the new term. It’s a Neolab. If they want to, what are the pathways for Neolab success? If you have the street cred and track record of the Qwen team, but you want to make it on your own today?

“I literally have no answer for what could be the successful outcome of a Neolab.”

It’s so crazy. We see the new Yang LeCun Neolab raising a billion dollars right off the bat as well. But that’s a $3 billion valuation. I feel like you could probably pull off domestically in China. You might get enough people to take a flyer on you for that, but not at the thinking labs. No, not at all. I think the 01.AI / MiniMax exit story or the going public story is probably the best comp for any investor looking at persuading the Qwen, where is it?

I want to think of a Fairchild, the trader is, I don’t know how many left the traders for five to start their own Neolab in China. That’s probably your best get alcohol from a financial perspective, right? But I think having that reputation that he has garnered could give you a really big jumpstart. And this is probably the last check that anybody, at least within China, but maybe even globally will write into a Neolab.

I don’t think there’s a good Neolab story at the Yann LeCun level, at the Mira Murati level that could really garner both that much money, that much attention with relatively little to have shown for up to this point. And I think the Qwen team actually was going to have a Singapore team as well. That was one of the things that was maybe on the table that got shelved because of this personnel change.

So my best guess is that they’re going to decant to Singapore and do Manus 2.0, but even cleaner, and see how that goes.

Well, it’s a really interesting question: if all this team wants to do is open source AI, and they are in Singapore, so not at all China adjacent, there’s no kind of export control risk, which Manus kind of had to deal with for a hot minute. Is that a viable business? Is that something investors would be excited about if you have a team which is very deeply ideologically committed to just making open source models?

Right. I think a lot of open source founders, quite frankly, start out that way. This isn’t, again, an AI thing. This is an open source thing. I’ve seen at least three generations of pure open source founders who got incredible traction with their open source databases, observability tool, whatever you want to bake, make open source in the cloud stack. Right now we’re in the AI world. And then you raise money and the moment you raise money, you slowly, but surely come to realize that you have to build a business.

So it really comes down to how malleable — let’s just play this out in full characters — that Lin Jun Yang is going to be the CEO. He’s going to be the figurehead, right? He’s going to be the Dario of this new lab; how good he is going to be as a CEO. He will be a CEO. He will not be a tech lead of a division within a giant corporation that has his own set of problems. Will he do that? Right. And if he can, then yeah, they’ll figure out a way to commercialize it somehow.

I think I have faith in figuring that out. Again, the playbook isn’t a mystery, but the timing and where you start to jigger and address those things is going to be very, very hard. And no one has really quite played that perfectly. I don’t think even a Red Hat — at the end of the day, it got sold to IBM, fantastic. Congratulations, but that’s about it.

But we should have a little bookie market. So the odds that he shows up at Meta revives Llama. He could get hired away. Exactly. Just go up to the Man of Singapore office that has Meta badges and just call it a day. He gets all the compute he wants, hangs out with Alex Wong. That’ll be fun. I think that’s an outcome.

We can probably rule out Anthropic. I think we can, I don’t know. This guy doesn’t seem like he. Well, Anthropic never wanted to open source anything in the first place. Even I try to straddle both worlds and try to be nice to everybody. But Anthropic was just “no.”

Well, what’s funny, I do want to talk about this a little bit. We wrote a piece for China Talk, exploring the ups and downs of Anthropic’s rep within China. And we had this fan, we had this fascinating swing with on the one hand, everyone was hating on them because they released that report saying that all the Chinese firms had been doing distillation of their models.

“oh, how dare you, you guys stole all this other IP. Don’t you dare give us any crap.”

And then a week later they have this giant blow up with the department of war and they’re like, “oh, wow.” Dario standing up for humanity, stopping the Terminator from taking over the world.

Which, incidentally, is an analogy that a PLA official shouted out, saying:

“we don’t want Terminator to happen.”

I don’t know. It’s kind of a fun wrinkle. I think he was persona non grata number one, for all of his stances around export controls and whatnot.

But felt like he gained a few a few popularity points with the whole Pentagon kerfuffle domestically.

• Domestic within China, domestic within the United States.
• Within China, within China.
• Certainly in the United States, I think he’s garnering quite a bit of domestic goodwill.

You can just see it from the Claude download chart. It’s finally at number one after being at, what, number 17 for ever.

Well, everybody I know, not to say that’s indicative for a big sample size, who is part of the Chinese ecosystem. They love using Claude. Most of them are engineers, who I know. They love Claude code. They love the product that’s coming out of Anthropic. I think that is a good way to understand the practicality of people who work in the tech world or the business world in China. Is that, from an ideological perspective, you probably don’t like what the guy who made the product said about your country or what you do, or whatnot. But a good product is a good product and I’ll still use it, and it’s still very, very popular.

And it’s, I don’t even know how that usage falls in an Anthropic financial statement. Do they even have a revenue line that says PRC on it? Because they’re definitely getting usage. I just don’t know how that actually works from an account, a receivable, or accounting perspective.

Well, it’s all Asia Pacific. It’s all coming through Japan and Korea, right?

What’s funny is they’ve really tried not to, they had this whole blog post saying

“we’re going to try to really shut down all the, all the accounts.”

And both, both OpenAI and Anthropic have since published reports saying, we caught some Chinese state hackers slipping, revealing that they were trying to find exploits or whatnot using our models instead of domestic or open source ones. But I don’t know, it’s hard to regulate this sort of thing.

VPNs can be really good. The thing about VPNs is at a certain point, if it’s really that important, you can just control someone’s computer who’s in Japan. How are they going to know? I guess, are they going to ban everyone who’s using simplified Chinese to use their models? I don’t necessarily think so. And if you’re not going to do that, then to a certain extent, especially with individual users, where there’s a will, when there’s a will, there’s a way.

Maybe with the kind of, you can at some point get a hand on the distillation stuff. It seemed the way they were writing it up, it wasn’t super sophisticated and it was kind of obvious. And the question is then how sophisticated can you get on the distillation side to a bit of a bit of a kind of mouse game between the Chinese and Western model makers.

I think it all has to do with traffic pattern, right? A normal person or even an engineer using cloud from China to do some normal engineering or side project will just look different on the observability level than a distillation campaign or a run or what have you. And that’s what any cybersecurity person needs to do for something that has as much traction to figure out the difference. And not just blanket ban or blanket allow whatever happens on your platform.

So yeah. Cool. All right. We’re good. Maybe we should call it there. This was fun.

“Everyone subscribe to sale.”

If you work at a large organization, reach out. you get a big bulk subscription to all of the greatest AI content on Substack. I read sale.com read sale.com, but this was fun. Thanks everyone for joining today. Thanks everybody for joining.

默茨访华之后：德国人也要开始“卷”了吗？

听众朋友们大家好，欢迎收听这一期的不合时宜，我是主播王庆。那今天，我们想来聊一个近期在新闻上也比较受关注的话题，也就是这个德国的总理朔尔茨最近来访问了中国，然后也由此引发了一些不然有意思的讨论。

那今天，非常荣幸地请到了我们节目的三位老朋友，去年大约是同一个时候来到我们节目聊过德国话题以及中德关系的三位记者专家观察家：

• 第一位是我们节目的老朋友孙谦，孙谦是为中文媒体担任驻柏林的记者，然后在德国从事国际新闻报道已经有十余年。先请孙谦给大家打个招呼。

到《不合时宜》，然后跟王庆一起，而且这次是跟两个老朋友一起了，重新探讨关于中国政治的话题。

• 第二位是理性批判，也是播客《理性批判》的主持人，我相信关注播客的朋友们也都会有所耳闻。他在德国获得了政治学博士，现在从事宏观政策咨询和战略咨询相关的工作。

感谢王庆，特别高兴能够再次来到节目做客，我觉得这个话题特别重要，特别让我高兴的是能够跟我们三位老朋友一块儿重新讨论关于德国的话题。

• 另外一位朋友是一位外国朋友，Lio。Lio现在是一位自由记者，平时在中德之间往返，他中文非常流利。大家应该去年听过德国大学的一起节目的时候对此印象深刻。

他大部分时间是给一间叫做China Table的德国媒体工作，China Table在德国的媒体当中是专事跟中国有关报道的一间媒体，近年来逐渐获得了越来越多的关注度和影响力。非常欢迎Lio。

大家好，很高兴有机会再次讨论，也有点荣幸，因为你现在已经说了我是老朋友，第二次参加《不合时宜》的团队，很高兴。

对，非常感谢三位，感觉非常开心，有机会跟老朋友们一起来返场聊一个我们一年之前聊过的话题的延续。我记得大概去年也是差不多这个时候，三月份的时候，朔尔茨刚刚当选德国的总理，然后当时也有很多猜测，这对德国政坛、对中德关系可能会意味着什么。如今一年过去，我们也是感慨世界局势千变万化。就在我们录制这期节目的今天，我们大家可能或多或少都有在关注伊朗的话题，感觉今年才过去了两个月，新闻头条已经换了一波又一波。

伊朗的话题我们今天应该不会展开，但我想它也为我们今天聊这个话题设置了一个背景色：在如此变化剧烈的国际局势当中，我们应该怎么样去理解中德关系，以及怎么样去理解德国。我先说一下朔尔茨访华的这件事。在过去这一周，朔尔茨率领了一个很大规模的代表团，对中国进行了一次访问。社交媒体上有很多有趣的片段，其中一个就是朔尔茨去杭州的宇树科技，去观看机器人的集体舞蹈，去参观故宫，以及他回到德国之后也发表了一个让人非常印象深刻的演讲。

这次访华的背景，一方面是中德的贸易正在进入一个新的阶段；另一方面，特朗普执政一年之后，美欧关系出现了一些非常剧烈的变化，从乌克兰到格陵兰，美欧关系变化之下，欧洲也包括德国在寻找一些全球范围内的新的锚点。中国作为德国最大的贸易伙伴，也成为了非常受关注的焦点。

中德之间当然仍然有非常大的不同，但我觉得随着这次访问以及接下来可能开展的一系列合作来看，也许是一个新的篇章的开始。首先把一个基本问题抛给各位：你们各自对于朔尔茨这次访华的观感是怎样的？你们印象最深的点有哪些？

从我的观察来看，这一次的来访在一开始或来访之前，双方之间的感觉是有一些紧张，有一点互相试探、想要破冰的感觉。但在行程真正触发之后，特别是朔尔茨在出行之前有了那个谈话之后，整个基调和轻松度开始慢慢展现出来。等到了北京之后，在企业家的互动环节后，第一天的晚上情况就开始逐渐轻松；到了第二天，整个行程在杭州以及企业家的参访，气氛活跃了很多。大概是一个从比较紧的状态，一直到大家互相接触之后产生一定的信任的状态。

我觉得很重要的一点是，对德国方面来说之前他们心里没有底，但我知道德国总理府为这次拜访做了很多事前功课。而且无论是在慕尼黑安全会议，还是来访前的各种预热活动上，都是有很多预热性的行为。换到中方视角，中方也是做好了很好的功课。比如说，中方在朔尔茨出行之前的谈话之后准备了一个非常惊喜的大单，”最多是120个空客飞机的这样一个大单”，作为一个惊喜。所以中方也很懂，对朔尔茨现在在德国要进行改革，你必须带有一定成果回德国，中方也是做好了非常好的功课。

总结来说，这一次来访对双方都是非常满意的。对中方来说，朔尔茨没有再提“系统性竞争对手”这一点，就让中方已很满意了。

我个人和理性批判老师的观感或判断还是蛮一致的。总而言之，朔尔茨来华比我想象的更顺利。他来华之前，第一，双方都有点紧张；第二，德国国内有很多声音想要说服朔尔茨对中国采取更对抗性的心态或表现。主要从经济角度来看，德国国内现在讨论的有一个很焦虑的问题是贸易顺差：德国对中国的出口越来越少，而德国经济高度依赖出口。

所以很多人说朔尔茨来华主要是要向中方提出很多诉求，关于平等的贸易关系。我觉得朔尔茨最后选择的办法是先把跟中国领导的关系搞好，毕竟是他第一次来华，而且来得比较晚，他现在担任总理已经一年多才来。所以他这次没办法带来很多诉求，还是要先建立一种良好关系。我确实也觉得他做的功课比较扎实。来华之前的那个星期，他就邀请了几位中国专家到德国总理府，聆听他们的看法。

但我觉得虽然他在访华时表现得对中国关系比较友好，但长期要搞好与中国的政治和经济关系仍是一个很大挑战。很多媒体在他来华之后说，虽然他可能第一印象不错，但之后还是会有很多留下来的工作。

如果谈到这次朔尔茨访华，我个人最关注的一点是这次访华本身传达出的信号：将来的中德关系如何发展。新的总理首次访华，他所传达的对华关系政策是会继续延续之前的政策，还是会出现转变？总体来看，中方媒体对这次访华抱着比较正面的态度，做出了很多正面评价，看起来中德关系进入了一个新的春天。

德国媒体总体关注度也非常高。不管是从媒体报道还是朔尔茨本人的态度来看，他都是从以价值观为导向的对华政策，变成更务实的对华政策。尤其在过去几年，欧盟对华有比较清晰的定义：系统性竞争对手、竞争者以及合作伙伴。这次朔尔茨访华时，他很清晰地选择了关键词——竞争。如果分析这三个词，他选择了中间一个比较中立的位置，他没有把姿态放得非常低，说“我们就只是合作者”，但他也完全没有提“系统性竞争对手”这个词。

所以我觉得务实是这次访问的关键词。一方面代表这次访问本身的态度，他带来了庞大的经济团队来寻求合作；另一方面也代表总体上德国现在以及将来欧洲对华的态度。

另一个方面是，朔尔茨上一次访华是25年前，他当时还是基民盟的议会党团主席，这25年间他没有来过中国。这次他做了密集的准备，一方面是中国和德国非常需要彼此，在当前复杂的地缘政治环境下；另一方面也表明他对中国其实很不了解。他虽然是非常重视并有很强的商业背景，但如果对比他对美国的了解（因为他马上就要访问美国），他对美国的了解与对中国的了解对比非常明显：他对美国非常了解，而且是坚定的跨大西洋关系支持者。这次来华其中一个重要目的就是获得对中国最直观的感受，了解中国发展到今天是什么样子，他们在哪些方面中德可以合作。另一个关键词是学习，虽然他可能没有直接用这个词，但他们的态度是这样的：从曾经德国有点像老师的状态，转换成合作者，甚至在一些方面要向中国学习。像专家们会说，德国企业到中国像“进健身房一样”，要迅速地学习，如何把一个产品的开发期从三四年缩短到一年甚至半年。

所以可以看到现在德国媒体和高层访华时的态度在某种程度上是一个转变，我觉得这是比较关键的印象。

沿着刚才孙谦分享的德国对中国态度的阶段性变化，特别是合作甚至学习这一点，我想到两个在社交媒体上传播较广的片段。一是朔尔茨去了杭州的语术科技，观看了机器人的集体舞蹈；那个舞蹈是今年春晚时流传较广的一个节目。朔尔茨到现场观看并表示赞叹。那个场景不仅在中文媒体上被广泛传播，我也在推特等国际社交媒体平台上看到了非常激烈的讨论。

如果从朔尔茨本人对中国的态度而言，他过去25年间都没有来过中国，对中国处于相对不太了解的状态，这可以解释他回到德国后发表演讲，大意是觉得中国的工作强度和工作心态有很多德国需要学习的地方，觉得德国现在的工作与生活平衡模式可能需要反思。这样解释我能理解他长期未到中国，见到眼前的一切带来的冲击。

我也想问各位对朔尔茨这次访华代表团的观感。朔尔茨带来了大规模的商界代表团，主要包括汽车行业和一些工业巨头。想问你们对这次代表团的分析：代表团的构成体现了怎样的优先级？你们觉得政界和商界关注的话题在对华政策上是否有差异？

我应该从两个层面回答：第一个层面，我们必须看到朔尔茨到目前为止一年多时间在欧洲起的主要作用，他不是一个经济总理，他是一个外交总理。他做的很多事情都是在外交层面。这次到中国他带的商务代表团庞大，规模差不多和默克尔当年相当。我们在机场接待的也是我国海关总署，这说明双方已挺有默契，把这次来访定义在经济合作框架下。我觉得这是他很聪明的地方，也是中方很聪明的地方。不能说中德之间没有政治问题，当然有很多政治话题可继续讨论，但对现在的德国来说非常需要的是经济话题。两者是如此大的贸易伙伴，分别又是第二大和第三大经济体，这部分非常重要，需开诚布公交流。

第二个层面，从企业层面来说，朔尔茨在2003年离开政坛后，也在各大投资银行和大公司担任董事会成员，他对经济界的所求所需心知肚明。同时我们也必须说德国的行业协会、机械工业协会、汽车工业协会以及德国驻中国的商会… 在他临行之前也都是做了很多的功课的，包括他们甚至也有一些的公开的希望，就是说总理能够帮助他们达成什么方面，这一批的商界的人士，他们活跃在中国的不同的德国公司里面，但是他们在上一届政府的时候，他们的个人感觉是，他们无法把自己的这些观点能够传导到总理府里面去，虽然朔尔茨总理也没有完全的100%的全部吸收进他们的主意，但是至少我们可以看到，他讨论的主题上面就能反映出来，他或者他的团队已经阅读过他们这些行业协会或者工业协会的这些研究吧。

另外一点就是这个企业代表团，其实大家仔细看的话，中方也给朔尔茨总理准备了一个和中国企业家的对谈，我觉得无论是中方而言，而是德方而言，都是想要借助于这样一次高层次的见面，在公司和公司的合作上能够有所突破，比如说我自己也知道，就是说有一家德国公司，他们就是通过了这一次的高平台的这样的见面，和中国企业建立不少的联系，比如说马上就会有一些他们之间的一些合作，慢慢地在新闻当中大家就会看得到了，所以我也会觉得从这个层面上来说，这样的交流还是比较务实的。

我觉得首先朔尔茨总理准备把那么大的一个企业的代表团来华，反映出他在国内面对的最大的一个困惑，因为德国经济现在其实经营的没有那么好，我们基本上没有经济增长，然后很多人还是对经济的未来有一些焦虑，所以我觉得大部分德国的民众，其实他们对中德关系，没有他们自己想要看到的一个方向，他们主要是想要看到德国经济要做得更好，然后我觉得朔尔茨如果他要连任，他要把他在德国国内的支持度拉得更高一点，那他还是要向德国一般的民众表现出，他能把经济搞得更好一点。

我觉得这可能是主要的一个处罚点吧，然后我会觉得他带一个那么大的企业代表团，也会带来一点误解，因为从中国的角度来看，你可以觉得哪些企业都想要跟中国做好生意，政府也要推动中德之间的好生意，但其实很多公司他们参加那个代表团，主要是因为他们面对很大的问题，可能最大的问题是那个稀土的出口管制，所以其实德国有很多企业，他们对那个稀土的供应还是非常焦虑的，然后也有一些其他的平等竞争的一些问题，所以我觉得他们想要来陪伴，朔尔茨访华也主要是为了提出这些问题，但是我还会觉得像那个理性批判老师说的那个企业的例子，我觉得他还是会带来一些成果吧。

现在就是中德关系的一个新春天什么的，我觉得中国媒体会多强调这一点，但是从德国的角度来看，虽然朔尔茨来华是比较顺利的，但是主要是在经济竞争这个层面还是会有很大的挑战。

“Leo,我想稍微可能问一个follow up的问题”

以你的了解来看，在德国媒体会怎么样去描述说商界和政界如此大的代表团，觉得中国这件事情，比如说是更会觉得这是一种基于现实主义的一种妥协，还是觉得它是一种成熟的体现，以及你所了解到的政府和商界还有民间对于访华相关的这个事情，有一些态度上的差别吗?

我会觉得有一些媒体他们提出的也是，某次带来那么大的一个企业代表团会给中国带来一种误解，因为可能中国那一方真的会觉得，中德关系现在进入了一个新的春天，然后会很快的变得更好，然后从德方带来那么大的一个代表团并不是这个意思，这是很多媒体提出的一点。

我觉得从商界的角度来看，其实那些不同的企业他们也有不同的利益，因为有一些企业主要是大企业，可能BASF或者大中这种大汽车的公司，他们在中国国内有很多投资，中国国内的市场对他们来说也很重要，所以其实他们想要的就是，还是把政治跟经济上的关系变得更良好，更和平稳定。

但是还有很多其他的企业主要是这种小型企业，还有中型企业，他们在德国国内有他们的工厂，然后其实他们最大的问题是，他们非常依赖出口，他们没有办法完全去中国投资，所以他们需要保障自己的出口市场，保障他们自己的未来，然后我觉得这两个不同企业的团体之间，有很大的一个利益的矛盾，因为一个其实是想要在中国有更多投资，有更多的合作，然后那些小型跟中型企业，他们至少有一些还是想要一种经济的保护主义，在欧洲的层面，因为他们说我们先要保护我们欧洲的市场。

我想先接着这个，就是你问Leo这个问题，我先补充一下，然后再回到你之前那个问题，就这次MOS带着经济代表团来，其中一个很大的诉求，就是因为德国在很长一段时间里面，中国是它做大的一个出口市场，但是人民币在过去的几年，其实它是一直持续的在一个比较低值的这么一个状态，其实对于中国的产品出口到其他国家的市场，是非常便捷的，但是这也导致了当德国商品出口到中国的时候，本身它就面临了一些产品的竞争力的挑战，对吧，比如说在汽车行业，电动车这个市场的冲击，它本身就受到一些挑战情况之下，然后人民币如果还持续的保持在一个比较低值的状态之下，这对于德国这样一个以出口为主导的经济体来说是非常困难的，所以这次MOS他言辞不是非常的激烈，但这个是其中一个很核心的诉求。

我在那个访问结束之后采访了伍德克，他是当年欧盟商会就是驻中国的一个主席，然后他也是当时朔尔茨访华之前请到总理府进行晚餐，然后吃饭的这其中五个人之一，然后他次取出的观点就是，这就是其实中德贸易中一个硬伤，然后这只是其中一个硬伤，还有就是中德必然在一些领域是有竞争关系的，就导致中国是不愿意后退的，比如说中国不能说，我为了让德国对中国的贸易逆差减少，我就减少向欧洲和德国的出口，因为这个不符合中国的利益，那么德国当然也是会争取自己的利益，就是说在竞争关系状态之下，虽然中国和德国都非常希望，也非常需要和彼此合作，但存在这种竞争关系的时候，是不是能够妥协呢?

就比如说像人民币贬值这件事情，以及包括中国存在的一些情况，比如说产能过剩，他就是有这个需求，一定要把商品出口出去，那德国本身他面对他自己的挑战的时候，他能不能有这个能力把商品，比如说中国市场现在就是需要电动车，那他还通过什么样的方式来进行，然后像一些德国的大企业，他正在转型嘛，他就是在中国为中国市场生产，比如说像奔驰啊，比如说像大众啊这样的企业，他已经考虑说我在中国建厂，通过现在中国已经有的比如说大数据的这种集成规模，来训练我的产品生产能力，然后在这种情况下，在中国为中国市场生产，但是呢，中国生产虽然他可能对这个企业是有帮助的，但是他不能够为德国本身创造就业岗位，所以这些都是一些矛盾，即使双方都有共同的议员，但这个客观挑战确实是存在的。

然后另一方面就是你刚才提到这个问题，我们如果看这次访问的代表团的话，大部分还是那些在中国有很强的这个历练的这些代表团，但是呢，即使就是我们耳熟能详的这些公司，比如说梅塞德斯，比如说大众，拜尔西门子，我们也可以看到，他们在寻求的这个方向也在变化，就是从曾经更传统的一些工业制造，更多像人工智能啊，先进的机械制造啊，新能源汽车生物技术这些领域更多的发展，然后包括这次还有一个模块团队，是清洁能源和绿色环保的这个团队，这个方面也有变化。

我还想补充一点就是说，德国国内怎么看这次访问呢，之前刘和李欣频老师都有提到，比如说有一个感觉是，这次是一个经济总理的身份来，然后呢，朔尔茨呢在德国面临的非常大的压力就是搞好经济，他刚刚上台的时候，包括受到右翼冲击的时候，他要强调价值观是非常重要的，但是当某种程度上讲，当朔尔茨所在的这个执政联盟，已经了向崛起的右翼做出了一些妥协，比如说移民议题很重要的时候，反而他也不能够完全的靠移民和难民这个议题来，获取新的这个支持率的时候，然后这个AfD（选择党）失去了独特的优势的时候，因为中间党派已经也向右靠了，然后这个时候大家都意识到，发展才是硬道理，只有搞好经济，你才能坐稳执政党这个位子，所以我觉得他的务实也不是突然转变的，那当他首要任务是抓经济，首要任务是抓生产的时候，然后他又在要访华的这个关头，我觉得很天然的事情就是，经济变成了一个重中之重的这么一个议题，然后国内可能毕竟他代表的是一个保守党派，他是执政党，然后代表了很大一部分人的利益，比如说商界的利益，肯定是有很大一部分人是满意的，但是在政治光谱上偏左的这些党派，他一定会反对，他说你朔尔茨对中国不够强硬，这个声音一定是有的，比如说中国的人群问题有没有得到改善， 有没有问这个问题，包括乌克兰的议题，虽然比重很小，但是欧洲和德国希望中国在乌克兰议题上对俄罗斯施压，这永远是欧洲和德国人最关注的一个政治议题，就这些东西他一定还是有人会去追问的，所以一定也是有些人他是不满意朔尔茨这次访华的这个态度的。

六也提到了这个春天这个说法，其实是在中国比较盛行的一个说法，我个人也觉得呢，就只是一个破冰的刚刚开始，还很难说到是春天，因为中德之间的这些刚刚像宋老师提到的这些比较深层次的问题，还是一直会存在的，而且不是现在存在，会是存在很长的时间，因为经济结构决定的，中国呢肯定是希望能够打造一个全产业链的工业强国，这样的一个未来的地位的，那德国也是一个工业强国，那跟过去的三十年非常不一样的地方就是，现在中德之间的经济结构上面是有点错配的，在很多的领域当中呢，大家是要互相竞争的，这样的一个关系，而且呢这个会持续很久的时间。

但是关键的问题呢，就是至少德国和现在的这一任的美国政府非常不一样的地方，就是说大家还是希望能够在遵守共同的规则之下互相竞争，我们要把竞争变成一个良性的竞争，恶性的这种竞争方式，无论是对于具体的国家来说是有伤害性的，那对于整个世界的贸易来说也是有巨大的伤害性的，这是第一点。

第二点呢就是，孙老师也提到了就是说，德国国内的对于这个莫茲的批判，说实话呢，无论是行前还是行后呢，批判都还是存在的，比如说呢，右翼的最大的批判呢，就是他们觉得莫茲呢，你去中国，你就是会完全的按照人家的agenda来走，也就是说你会被别人融入他们的体系当中去，当然了，从这一次访问的最后的结果来看，还是基本上是互惠互虑的，但是呢，那批批评的人呢，还是会这么坚持的，那对于左翼的这些批判呢，刚刚孙老师已经介绍了很多了，他还是肯定是固有的传统的这些类似的问题啊，当然这些问题也很重要了啊，但是呢，就是这种批判是会一直存在的。

所以呢，我觉得呢，未来的这些问题不是一时半会儿能够解决的，需要公司层面的互相竞争，互相合作，能不能有一些共同的发展的项目，通过这个时间来进行释缓的，那当然现在美国政府，给欧洲的盟友们带来的这个不确定性感，实在是太强了，下周他可能又要去访问美国了，但是这个访问美国呢，我觉得特朗普留给他失望的空间应该还是蛮多的，所以呢，在这种压力之下呢，两大工业国那肯定是要能够先要稳住，能够保持一个良性的竞争，然后再看看有什么合作的地方，我觉得这个是非常重要的。

那我觉得可以把话题再稍微往几个关键产业，就再延伸一下，我们刚才也已经多次提到，汽车产业相关的一些问题，因为汽车产业其实也一直是中德贸量吧，做主要的一个来源，也一直是中德外交之间的一个焦点，那在更早些的时候我们也提到，在过去啊，德国是长期被视为是一个这种工业强国，当然现在也是，但只不过在这个位置上稍微变得有点微妙，特别是在进入了这个新能源转型之后，老牌的汽车强国，德国也面临汽车行业的一些转型的这种压力，那现在也有一些这个研究数据表明啊，就是德国对中国的这个汽车出口，在过去这一年，2025年，其实是有一个明显的下滑，那同时呢，中国这个市场也在开展一些价格战，然后关税这样的一些外在的因素，也都在某种程度上去挤压到了德国车企的生存的一个空间。

我也想问一问各位，就是对于中德在汽车相关的贸易及外交关系的现状的一个这种评价，就你们觉得现在的这个关键词可能会是什么，在过去这种，比如说德国向中国出口汽车，中国作为德国的市场，这样的一个情况。 在未来是不是会成为律史? 那德国自己呢? 一方面需要去兼顾这个车企的竞争力的问题，需要去兼顾就业的问题，但同时德国其实也面临比如说气候转型、能源转型的相关的问题。那电动汽车它带来的这些新的挑战，现在在德国是一种什么样的存在?

也许我可以抛砖引玉，稍微先开始说一下。我觉得过去的，特别是从疫情开始吧，当然中国新能源汽车不断的崛起。在过去的四五年时间当中呢，的确呢，德国企业BBA，包括加上Volkswagen在中国的这个销量呢，的确是在逐渐的往下走，市场份额呢，也在逐渐的往下走。

当然中国的这个汽车行业的发展呢，也是早年呢，有很多人进行规划、进行发展。比如说中国并不是特别擅长变速箱技术，并不是特别擅长做这个压缩比特别高的燃油发动机，所以呢，中国过渡到一个比较倾向于混合动力、或者电力汽车的这个方向上，这也是中国尝试发挥自己的自主的优势吧、或者技术的优势。

我记得德国的一台电视曾经在上海做过一个街头的访谈，然后印象非常深刻的就是记者有问这个中国的年轻的消费者，”你是不是还会买一个Volkswagen的大众汽车的车”，然后那个女孩子立刻就反馈说，”那好像是我爸爸开的车”。然后这个段子呢，就回到了德国，变成了一个传播的非常广的一个段子吧，德国人也自嘲这个Volkswagen是Papas Auto，这个爸爸的车。

但是呢，我们也还是能够看得到，中国的这些传统的燃油汽车，他们其实是还是有比较多的优势的，比如说它的底盘技术，比如说它的对于安全的这些严格的要求。第二点倾向呢，就是说我能看得到2026年到未来的四五年当中，会出现很多的传统的核资燃油车企业，plus中国的智能方案，这种合作的模式在中国市场的广泛的出现。比如说我们在奥迪的新出的A6L，这些车型当中已经看到了这种合作的方式。

所以在过去的两三年当中，当然这些传统的德国车企在中国市场，是有一定的失势的情况，但是呢，从2026年的年初开始，这些德国的企业也在努力的能够适应本土的使用的场景，再加上自己本身所拥有的内燃机方面的变速箱方面的各种的优势，能够做出更适合中国消费者的这些产品，会越来越多的出现在这个市场上。

对，我可能要先承认一下，我自己没有假招，我不会开车，所以我不知道我这个方面话语权多么大，但是我还是会从经济的一个角度试试去分析一下。

德国就是大家会期待说一个成年人是会开车，还是不会开车有一个这种社会平均值的期待吗? 我觉得现在已经没有。主要是如果你是在城市长大的，因为德国公共交通还不错，但是我觉得还是比较少见的一个情况，因为很多人会在可能高中毕业之后就做自己的那个假照。对，我那时候有点懒惰。

我确实觉得在德国这种对汽车产业的那个衰落的一种焦虑，它还是蛮大的。对我们来说，这可能真的跟国家的一个身份认同感是连接起来的。可能对我来说不太是这样的，但对很多其他人来说是的。已经可能会有一些评论家，有一些蛮可怕的一些事项，比如说斯图加特会变成下一个低特律。

对我来说，我真的能判断德国汽车产业的衰落对德国经济的影响会多么严重，还是很难的。但是有一些人说它会非常的严重，可能五年或者十年之后，几百万人可能没有工作了，然后对德国经济结构还是会有很大的问题。这个问题在长期中的关系也还是会扮演一个很大的一个角色。

按照我的了解在中国市场那些最大的一些电动体制的公司，比如说BRD，他们基本上已经不太依赖那个补贴作为竞争的一个手段。虽然他们还会受一些补贴，但是他们自己竞争上就是非常非常强的。但可能在地方上，在很多比较小型的一些跟汽车产业有关的一些企业，他们受到的补贴还是很多的，这还是会比较影响重的一些贸易关系。

我觉得最终这么解决这个问题也蛮困难的，因为从德国的角度来看，当然你可以说要减少这些补贴，但现在中国的经济状况也不是那么乐观的，可能还是会有蛮多公司，他们也依赖这种补贴生存下来。所以如果你真的要从一个人的角度去解决这些贸易问题，我觉得这两个国家之间还是有一点矛盾。

然后我还说最后一点，就是可能一年或者两年以来，德国跟全欧盟在那个汽车产业跟中国的竞争，有一些被抛在后面的感觉。本来的目标其实是到2035年，已经要把所有的那些软油汽车的生产停掉。它已经放松了一些对未来新能源汽车的产业方向的目标。

所以其实现在欧洲的方向是可能五年之后，或者十年之后还是会有很多的软油汽车。所以我觉得现在可能欧洲的希望是可以在欧盟市场国内使定一个比较安全的供应，然后一个产业关系。但这个可能未来也会因为汽车行业、欧盟的经济保护措施、这种关税之类的会很严重的，因为我们还是要保护好自己的那个汽车产业。

我觉得两位老师确实说的已经很全面了，然后我想补充一些观察。首先我觉得谈到这个问题，我们可以问两个问题:

• 其中一个问题是中国电动车是不是真的全面领先了已经，然后为什么会领先。
• 第二个问题就是德国还有哪些护城河，在哪些领域还可以守住?

中国电动车现在领先很大程度上在于很强的供应链和成本的优势，然后以及中国有产量和市场的规模的优势，所以它就带来了很快的迭代速度，产生的创新能力是非常的强的。但是呢它背后第一是有很强的市场补贴。六老师提了接下来欧洲会不会打价格战，欧洲会不会也反过来进行补贴，比如说欧洲在将来会有一个更成熟的整个泛欧洲的资本市场的话，那欧洲是不是有能力某种程度上进行反制呢?

还有就是有一个专家这么形容，”中国现在生产的是带着轮子的智能手机”，就是说中国现在电动汽车的优势是软件、是这种生态体验，比如说智能坐舱，背后还有按摩的，然后整个屏幕像一个电影院一样，这种感受。比如说中国你说什么是豪车，就这个是中国的，尤其是新生代，软件的这个系统是新生代非常重视的一个体验。

但是这个东西是不是以后会改变呢? 德国的这个汽车如果在软件上提升了之后，然后德国的这种豪华车的这个品牌资产，它依然是在的，比如说它的这个尽力心，它整个这个封闭的能力，比如说50%你走到90%是容易的，中国车企是可以做到的，但是你想从90%再提升到95%的这个5%的这个豪华程度，其实德国的车还是有可以守的这个地方的。

然后所以我觉得在电动车和包括燃油车，以及将来的德国车在迅速发展自己的电动车的能力之上的时候，德国和中国各有自己的一部分优势，竞争的同时其实也是有合作的可能性的，但具体怎么合作，短期呢要看欧盟的关税、需求，长期会看德国和中国自己是怎么发展的。

然后很有意思就是，就在这么多年来，如果说德国车想要卖到中国，我们都知道它是必须是合资企业。那合资企业呢，德国车其实它一直是存在一种技术转移的。就比如说我们的车要在中国进行生产，那就意味着这个产业链大部分是在中国的，然后这个也是现在德国车企面临的一个很大的问题。

即使你掌握了一些核心技术，但是当所有的零部件，包括把这个车组装起来的能力，包括比如说座椅，然后方向盘，这些很小的东西，它整个这个整车的产业链都是在中国的。整个这个整车的产业链整合能力最强的也是中国。有时候大家只觉得这个最高科技的创新是发生在德国，然后你在某种程度下能够保护自己的一些核心技术，但是你在生产的过程当中，在这个产业链的过程当中，它是有很多微小的创新的，然后这些微小的创新全部发生在中国。

所以说现在一些德国的车企它说，我们要转变一些政策，我们要原来是在德国生产，然后出口到世界各地，现在更多的是我要在中国生产，然后让中国使用，甚至把中国生产出来的车卖到其他地方。这个企业在短期内，其实你从利润上看你是不会降低的，你还是能够赚和之前一样的钱。

但是呢，首先你把产业链全部集中到中国的时候，这个工业在长期发展，你有没有这个竞争力，这是一个问题。因为存在很多这种技术转移和产业链内部的微小的创新，然后这些全部都发生在中国。你培训出来的合国的工程师这些都在中国，人力资源也都在中国，然后这是德国车企可能他们最担心的一点，就是将来要面对的失掉这个产业链的竞争力。

然后另一方面就是车企它其实还是个企业嘛，它虽然会说我要为德国创造就业的需求，但是实际上他们第一想要追求的还是自己的企业的利润。所以德国政府也有时候也会有点难，就是你们还是要回到德国来啊，我们还是要努力吸引这个外资到德国啊等等等等。

但是你想要外资流到德国，然后你想要吸引其他国家的企业到德国设厂，创造更多的劳动岗位的话，你首先要有个适宜投资的这么一个环境。那现在德国在某种程度上在很多方面对于投资者来说不是很有吸引力的，包括他的官僚体制的问题、基础设施的问题等等等等。

所以这是德国的功课，如果他这个功课不做好，他很难吸引到海外的投资。但是这个问题德国本身也是非常清楚的，也在很努力的希望能够解决，然后莫茨本身对这个是最了解的，所以他在抱怨嘛，不仅在抱怨说为什么德国人这么没有生产力，居然还要四天工作制，而且他没来中国之前，他就抱怨过说德国人太能请病假了，他对这一点他其实是很清楚的，所以他也在非常努力的推动这件事情。

在这儿我稍微补充一下，我个人觉得这些中国电动汽车起来之后，至少起到了一个良性的作用，就是倒逼那些传统的中德合资的这些企业。我可以给一个切身的这个例子，早年的时候我刚在中国买第一辆车的时候，我去过4S店，进去了之后，销售人员说，”今天没试驾，我们这儿都是看了车就买的”。那中国的新能源汽车起来了之后，这两年你再去这些店，哪怕是那些所谓的顶车汽车品牌的那些店，销售都是非常非常积极的。

所以这种倒逼的这种，我觉得还是非常有意义的。第二点就是我觉得中国汽车行业千万不要因为现在的这些电动汽车发展了起来之后，有任何的自我的满足或者自我的骄傲的成分在这个当中。因为我们的内燃机技术上面差距还是非常巨大的。

而中国呢，是一个非常地大物博的国家，南北之间这个气温又差这么多，一旦到了东北，一旦到了新疆，都是需要靠传统的内燃机来支持我们这个国家的引擎不断向前发展的。这里面还有一个非常重要的点，就是如果大家仔细去看中国汽车向海外销售，特别是向第三世界国家销售的话，销售的主力均不是电动汽车，因为第三世界国家他们需要的也还是燃油机为基础，或者柴油机为基础的汽车，而不是电动车。

所以中国汽车行业需要发展的地方还挺多的，这个地方呢，也当然中国和德国可以继续合作，这里有很多的合作的空间。刚刚孙老师还提到一个德国的官僚主义那个话题，我觉得光这个话题我们至少可以谈一集的节目。像我这种在德国工作，德国又一路念书，真的是太知道这个德国的官僚主义。

像总理提到的那个，我个人都不觉得所谓的个人员工、四天五天的这个工作效率的问题是一个主要的问题。因为我有一段时间是在德国早起要去上班的，到了那火车站，我能看得到那火车站里面全是德国人，就是要跨城去上班的那些人。德国人是很勤奋的，德国人的工作效率其实也还是可以的。但是我觉得真正限制德国的发展的是官僚主义，真正的问题是在这儿。

要把这些官僚主义能够全头去除，是一个非常困难的问题，只有在德国长期生活过的人才有这种切身的这种体验。我们今天提到也是好几次的这个关于德国焦虑的一个问题，有关于汽车的焦虑、关于身份的焦虑、关于这个竞争力的焦虑。虽然我自己没有长期在德国生活过，但是这些年也去了德国不少次，然后我也从一个访客的角度观察到，德国生活现在一些这种感觉摩擦很多的地方。比如一个例子就是德铁，大家在过去就会觉得德国是工业世界的一个霸主，是优质基建的一个榜样，但是毕竟这些年一旦说到这个铁路通勤，德铁就往往被视为是一个极度不靠谱。 如果它比如说晚点一个小时，你就应该感恩戴德了这样的一些笑话，然后甚至在跟它的欧洲同行比起来，都是有一点在下滑的状态，所以我觉得这个可能也是一个切口吧， 说的是那莫茨讲到的那种，关于德国的生产力竞争力的一个焦虑，在社会的一些微观层面、细节层面是有所体现的。

另一个细节我也想分享一下的是，今年一月份其实当时我跟李欣频，我们一起见面喝了次酒，然后当时我们就提到一个现象，就是过去的时候中国的留学生在德国，每次从德国回国探亲的时候，会从德国买一些德国本地，或者欧洲本地的东西作为这个伴手礼，或者那个时候也有一些这个留学生在长期从事代购这样的行业，也就是你从国内去欧洲的时候箱子是空的，但是你从欧洲回到国内的时候，你的那个箱子是满的，但这几年我们也有一些朋友就是会发现，这个趋势好像倒过来了。

你从欧洲回到国内的时候，其实并不需要带很多东西，因为在国内都可以买到，反而是从国内再回到欧洲的时候，这个箱子往往是买的，比如说一些日常生活用品、家具用品，就是一些小的东西，那种可以让你的生活变得很方便的东西，有时候甚至可能是一个电锅、一个按摩枕、一个这种家具的小物件，反而你在从国内回到欧洲的时候，需要用一整个箱子去把这些物件塞满。

这两个小细节也是感觉说，在过去的这些年里，其实真的不光是在地缘政治的层面，也包括普通人的生活的层面，大家现在好像开始感受到一种反向的焦虑。我也想把这个问题再抛给几位，在你们看来，中国现在的这种状况在哪一些层面上可能触动了德国已经存在的一些焦虑，这些焦虑当中，有哪一些是投射的，其实事实并不是这样子，然后有哪一些焦虑其实是应该有一些现实的锚点，应该采取行动的。

我觉得要不我先说吧，因为刚才我记得是你最开始提到的焦虑这个词。

好的，而且可能我还是在德国活得最长的一个人，比如说德铁的问题，我觉得这个问题我现在快三十了，它其实一直有存在，我上初中高中的时候，已经有这种关于德铁的笑话。当然我们都在想，可能这个还是有一种恶化，但我觉得这种官僚体系，而且关于这种莫名其妙的、效率不高的生活中的一些现象，这个我一直都很熟悉，我觉得差别就是世界有好几个国家，然后中国就是最年轻的例子，他们就赶上了。

我觉得这种经济上的焦虑它是相对的，以前我们在德国国内会有一些抱怨，但是我们还是不会怕，我们自己的一些产业真的会被抛在后面，我们的职业岗位真的会受那么大的威胁。这种现在的经济上的焦虑是有根据的，我也不是很清楚要怎么解决，因为我也会觉得这是一个很自然的过程，西方的国家他们不能一直在那个世界经济最高的一个层面，我觉得只是本来是一个有点不逻辑的假设，为什么德国比较小的一个国家，为什么他的经济在国际出口上要那么庞大的?

所以在一个方面我会觉得可能西方他就要跟自己和解一下，我们在世界上的一个相对的衰落是不可避免的，而且它是一个自然的东西。我个人也不会觉得有问题，因为我可以向世界其他的国家学习什么，或者有一种更开放的一个态度，也会让我的世界变得更大、更有意思。

我觉得问题可能就是如果这真的会导致我们的生活品质甩落，然后很多工作之位失去。这个我还真的不是很清楚，最后的结果对我们来说会多么糟糕，然后我觉得还是在政治上就像中的这种两个大的国家之间要找到一种平衡，不要让我们想对的一个衰落变成一种非常绝对的衰落，因为我觉得这个对大家来说都不是很有利的。

第二点就是我觉得一个焦虑的来源是战争跟那个地缘政治的动乱跟不确定性。乌克兰离我们还比较近，我们也会直接受影响，会接受难民，然后很多德国人可能跟乌克兰或者其他东欧国家有一些个人的关系，这个还是影响会很大的。

我觉得可能我们现在已经慢慢习惯了我们住在一个动乱的世界里面，然后我们也没有办法忽视，这就是事实，可能这也是一种需要慢慢去接受的一个事实，然后慢慢要在这种动乱中找到一个新的平衡，我会觉得也有点难，但是也没有办法吧，这大概可能是我的一些想法。

对，我觉得Leo刚才提到一个表述还挺有意思的，你说这个西方的衰落或至少相对衰落在我们这个时代可能是不可避免的，所以西方人可能也包括德国人需要找到一种与此和解的方法。那我也想把这个问题抛给我们的另外两位嘉宾，就你们觉得从中国的角度来说，中国人需要和解的可能是什么? 以及作为一个在中德之间游走的人，你们会怎么样看待在过去这些年可能好像出现的一种中德之间的动态的平衡，包括德国的焦虑，中国当然也有自己的焦虑，然后那个焦虑可能是什么，特别是在相对于德国的位置上?

我分成两个层面吧：

• 第一个层面是安全的焦虑，
• 第二个层面是经济上的焦虑。

安全上的焦虑，那肯定是我们从慕尼黑安全会议上面就已经能够看得出来了，对吧? 去年的慕尼黑安全会议，那个美国的副总统那攻击力度是非常强的，对吧? 今年美国的国务卿稍微缓和那么一点点，但是对于德国的政治家或者政治精英来说，他们已经是非常明确了，这个时代的列车开走就是开走了，当下的美国是肯定没有办法给德国以及其他的欧洲盟友再提供过往的这种跨大西洋关系为基础上的这种安全的保护了，欧洲人必须要靠自己。

其实Leo也提到，刚刚说这个世界上没有一个国家可以长期一直保持他的霸权的地位嘛，我们生活的这个世界其实就是美国的霸权在慢慢的衰落过程当中，这个其实是所谓的朔尔茨提到过的这个新的常态的平衡。在这样的情况下，其他的欧洲盟友又没有多少可以进行负担然后再把欧洲再军事化的能力，这个负担其实就是落在了德国身上了，这种焦虑其实是对于德国的政治精英来说是非常非常大的。

因为在欧洲的本土上的这种攻防，它不会是所谓的这种核战争的这种形式呈现的，它都是像萨拉米一样的这种形式，萨拉米切片的形式，它其实是要有一定的常规部队作为基础的，如果没有常规部队作为基础的话，它是没有办法遏制另外一方采取萨拉米战术的，所以这一点是对于德国来说是非常大的安全上的焦虑。

经济上的焦虑呢，首先第一点，如果我明天见朔尔茨总理的话，我就会跟他说，就是你先把德铁花大价钱好好地搞好，因为为什么德铁这个东西是关于一个民族的精神的问题。接触的可能都是柏林之前的这些德国人，他们是非常尊重时间的准确性，我们说好14点，我14点就一定会出现，但是你现在这个时代，我感觉德国年轻人就已经开始有点接受了“对不起，我迟到了”“这个德铁的关系，我今天来不了了”——原来是一个高效率的民族，是一个追求守时的民族，有一点点因为这个东西变得有点松松垮垮下来了，这对于民族的精神是很伤害的。

第二点，是要把能源价格打下来，你才能把这些德国的企业、制造业的企业能够留在德国。工人的效率是很难很难提升的，因为这是一个大家要讲究休息，德国人周末是要娱乐要看足球的民族，对吧，工人效率和东亚国家相比来说较低的这个情况不是现在出现的，这是在早在皮耶希的时代，在那个时代的大众集团他们就已经有这个问题了，他们想要学丰田，能够达到日本工人的这种高的工作效率，他们也向丰田学习或者怎么样，但是实际上也只能打个八折来实现，所以这是一个基于自由准则而建立起来的一个民族规范、民族文化，也是没有办法再把这个工人效率拉爆，关键还是能源价格一定要打下来。

因为我有个年轻人也打交道，AI的时代像完全杀不住的一匹猎马，就这样直面地冲到了我们年轻人的面前，无论是对于德国年轻人来说，对于中国年轻人来说，你很难找得到第一份工作，这个是当下无论是德国年轻人还是中国年轻人都面临着一个非常非常巨大的挑战。中国现在很多人的焦虑也跟德国人一样在寻找机会，但是机会并没有这么多，中国社会需要年轻人来创业，德国太需要年轻人来创业了，现在德国的年轻人也跟中国年轻人一样，想考工、找一个稳定的工作，也不像以前那样这么自信、愿意挑战自己。

所以我觉得，无论是中国还是德国，大家都有各自的焦虑，但是我们要至少保持有效的沟通，这种竞争至少保持在良心的轨道上，为这些各国的国民至少能够创造一个自我实现的土壤。

如果我们谈这两个国家的焦虑，不管是国家的人还是国家作为一个政体，我觉得中国的焦虑和德国焦虑还是挺不一样的。作为中国人，对于这种各式的焦虑应该是很熟悉的，我们熟悉的是什么，自己的工作、自己的孩子、自己的家庭，是这种个人式的焦虑。

我觉得在德国谈到的这种焦虑感，还是一种国家层次的焦虑。当然，两位老师刚才提到关于德铁这个，现在如今我觉得对于德国铁路的吐槽，已经代替了大家对于天气的吐槽。就以前抱怨天气，现在抱怨德铁，但它为什么很重要，就除了李欣频老师之前提到的这个民族精神之外，第二点就是德国现在的物价疯狂的上涨，你作为一个民众，你的体感就是你的钱袋子变薄了，然后这两件事情是非常日常的事情，它是交织在你日复一日的生活当中的事情。

当你觉得说，我买什么东西都变贵了，我会觉得不敢花钱的时候，我也不敢用电，因为能源又非常贵。像俄乌战争刚刚爆发的时候，我会说，我回到家的时候，我房子很冷，然后再加上公交又不准时了，今天又有人罢工，然后我明天又没法去上班，就是它非常密集地交织在你的日常生活当中，它给人一种感觉，就感觉这个国家好像摇摇欲坠的，尤其是跟之前形成了鲜明的对比。

但是事实上，比如说刚才请你说到一些我们要购买什么样的商品的问题，包括我们周围的同温层，我觉得是存在一些生存者偏差的，就是我们会觉得说在这些年来，我们的购买力强了很多，就比如说回国吃喝玩乐都很爽，因为价格很便宜，然后物品又非常多样，物品多样这件事情是客观存在的。但是购买力是不是真的变强了，如果说觉得中国人的购买力已经超过德国了，或者是德国自己觉得说我的购买力已经低于中国了，我觉得这可能就是一种虚幻的幻象，因为德国人的平均购买力绝对是明显高于中国的。

当然在具体的消费领域，正是因为中国在日常的生活成本包括电动车、包括能源方面更具优势，但是德国有时候是在更隐性的方面，比如说社会保障、比如说高端的消费能力、失业救济、养老金、教育成本，这些方面它在一些可能更体制化、更大，但其实某种程度上讲，对于一个中国家庭来讲，负担更大的这些制度都还是在的。

所以我个人的感觉是，我们中国人担心的最大那些事情，比如说住房、养老、上学，我们的焦虑都投入在这些领域，它耗费了你的很多精力。德国人的焦虑在说我们国家怎么发展，但他们其实自己过得还可以，这个焦虑不是一种生存性的焦虑，而是说我今天日子过得没有那么舒心，所以我觉得这个焦虑的传递是挺不一样的。

当然，德国人本身我觉得人均也更关心政治的，当他有参政议政的能力的时候，他就会焦虑了我自己的生活之外，再加上他可能也更清闲一些，他没有为谋生而四处奔走非常疲惫的时候，他就会更关心我们这个国家和社会走向哪里，包括他的媒体也一直在告诉他，这个我们现在国家面临了什么样的困难，我们面临了什么样的困境，我们真的不行了。

我觉得即使是在替代国家进行焦虑的时候，这个国家的焦虑和一个个人的焦虑是很像的，都是没有安全感的时候会焦虑，不管是经济上的安全感还是军事上的安全感，他面对这个动荡的世界却无力掌握走向的时候会焦虑，德国是挺典型的这种状态。 他虽然在经济界是很强的， 那他由于军事实力的缺乏， 包括他经济领域的强， 他也是有依赖性的， 对吧， 他当年依赖俄罗斯的这个低廉的能源， 然后以及巨额的像其他国家的出口， 他不仅依赖美国， 然后他也依赖中国在经济上， 很强的依赖性， 然后在安全上更是非常的依赖美国。 这就导致了他在非常动乱的时候， 其实自己能够迅速调整自己的能力很弱， 然后当他面对这种自己无法掌控很多东西的时候， 他一定使这个焦虑感就会非常的强。

我就说两位受得特别好， 然后我能感受到， 虽然我们的焦虑在一个政治制度前面， 还是会有一些不一样， 关于国家的一些焦虑， 但如果你从个人的角度来看， 自己的生活还是会有很多共同点。 我觉得这还是会让我感觉到， 中国跟德国这两个国家之间， 有更多的一种社会上的一些交流跟对话， 可能也会有点想让我们感觉到， 我们在最终也不是那么不一样的， 我们还是面临很多一些相似的问题， 所以我会觉得很有意思。 如果我们从国家制度处罚， 我们会觉得我们那么不一样， 我们就像两个世界一样， 但是从人的角度就完全不一样。

我们刚刚有聊到过这个话题， 说一直有一个概念， 就是分类嘛，

所谓的民主国家， 非民主国家， 对吧， 20世纪60年代有这种说法， 有一些专家写的书， 这个世界上为什么有战争， 他说是因为国家制度的不同。

所谓的这些人认为就是说这个所谓的好国家， 就是民主国家， 所谓的坏国家， 就是非民主的国家或者怎么样。 但是在这个时代， 大家会发现， 具体到个人来说， 大家其实是共享很多的同样的问题的。 而且呢， 我也会觉得呢， 所谓的民主， 非民主在这个权力政治回归的时代当中呢， 这种分类呢， 也变得越来越不重要了。 我们能够看得见的是， 未来20年或者30年的时间， 它是又是一个非常明显的权力政治回归的这样一个节奏。

什么叫权力政治回归呢， 就是过去的靠这种分类来建立盟友关系的这种方式呢， 从特朗普2.0开始， 基本上也已经分崩离析了， 对吧。 大家看到的更多的是， 美国呢， 也是尽可能的获得他们能获得的这些资源， 来维持自己的霸权地位， 但是美国的盟友们呢， 其实也是非常非常不适应这种剧烈的变化的。 那所以在这种大的变革的格局之下呢， 更多的归类的讨论其实也意义不大了。 更多的， 还是要有更多的国际合作， 更多的就像你说的这个民间的交流， 社会的合作， 年轻人的交流， 这些东西呢， 能够互相增进彼此之间的理解， 也能在下一代当中， 不会再有这么大的ideology上的分歧吧。

我觉得， 大家都希望这个社会是和谐的， 然后也希望在不同中寻找相同， 然后仿佛看到了， 跨越国界， 我们还有一些人性上的相似， 然后让我们更好对抗这个动荡的世界。 但是某种程度上讲， 像例如刚才说的， 虽然都是年轻人， 然后德国人和中国人也有很多共同的焦虑， 但是我反而觉得， 至少是我的经验， 以及我身边的人， 在欧洲长大的孩子和在东亚长大的孩子， 是有一种本质的这种对安全感的不同的。 就即使有， 比如说提到面对AI的时候， 然后面对社会的老龄化， 然后比较高的失业率的时候， 是有一些共同支出的， 但是我们在闲聊的时候也会说， 感觉说， 德国的年轻人非常有底气， 想gap year就gap year， 想做一份什么样的工作， 就去做一份什么样的工作， 然后中国人呢， 永远有一种底层的不完全感， 然后要变成了一种自屈立， 然后再卷自己也再卷别人。 这个我觉得即使是有一些相同之处， 但我一直的感觉就是， 会有一个非常深层次的不同， 这一点我想回应一下。

另一点呢， 我们从现在往后看， 是不是世界会变成更多极化的时间， 是不是各国的这个对外的政策都会变得更务实， 我觉得这个东西我也要打一个问号。 现在我们确实能看到， 欧盟的内部， 尤其是技术官僚们， 然后包括德国在内， 他们是希望走一个更务实的路线的， 因为现在经济发展是重中之重。 他们也会说， 比如说我们跟中国有分歧， 跟土耳其有分歧， 跟我们政治体制不完全相同的这个国家， 有分歧的时候， 我们把分歧搁置， 或者我们把分歧固定在这个分歧的领域， 在其他领域， 我们还是要深度合作。 但是如果我们看整个慕安会的这个氛围的话，说要建立欧洲防务自主，它在防御什么？它其实防御的还是中国跟俄罗斯。即使这次朔尔茨访华，它带来的是一种要合作的信号， 要抱团的信号， 我们要抛弃偏见， 抛弃我们的分歧， 整体合作。 但是在关键的安防领域， 还是一种持续着这样一种某种意义上的冷战思维， 还是持续着这种不同的阵营之间要互相防御的这么一个状态。

而且恰恰是因为， 由于俄乌战争产生的这种强烈的不安全感， 然后这种不安全感是针对谁的呢？ 这种不安全感针对的恰恰是政治体制不同的这些国家。 然后包括现在伊朗的议题正在迅速的扩散嘛， 这个也是两大阵营之间的某种程度上的爆发的剧烈的冲突嘛。 至于由于特朗普上台之后， 显得好像这种阵营之间的边界被模糊化了， 但是还要打一个问号， 虽然特朗普是民选出来的， 但是特朗普对美国已经造成了很深层次的伤害， 但是特朗普总有一天是要下去的。 大家对现在的美国虽然已经很失望很失望， 但是对美国体制的还是有一种底层的信任， 它是有一个迭代机制的， 它是有一个制衡机制的。 我就想补充这两点。

我觉得孙老师说的这个角度也是非常好的， 因为我也觉得很多的这些ideology的三层次的这种分歧， 它不是靠一代人两代人能够解决的。 实际上显而易见这个防守要防守谁， 大家都是心知肚明的， 对吧。 但是我觉得我们这代人至少能够做的， 是努力尝试在阶级的拓展自己的国力， 拓展自己的产业的发展的同时， 能够尽量的按照这个游戏规则来玩。 这个能够为下一代的中国人， 中国的年轻人， 中国的国际形象奠定一个非常重要的基础。 哪怕按照规则来玩， 玩起来比较累， 哪怕按照规则来玩， 玩起来速度没有这么快， 但是我们至少是按照规则来的。 而且呢有什么问题呢， 努力尝试通过仲裁的方式来解决， 而不是用这种punishment的这种方式来解决， 要努力地尝试把这些分歧能够留在理性的框架上。 我觉得通过这样的方式呢， 可能会通过一代到两代人呢， 慢慢地弥合这种不信任。

对， 我其实刚才听完之后也有很多这个想要回应或者echo的地方， 但可能也很难在比较短的时间内去把它展开来讲， 所以我就想这里不然就留一个悬念。 我们看看也许明年如果同样的时间， 还有别的机会的话， 可以请大家再来做一次反场的节目。 我自己的一个感受， 这个点我还挺想表达一下的， 我其实还是大体上统一， 李清平凡老师对于说， 现在世界的这种旧格局是在崩溃， 然后一种某种意义上的强权政治和从业法则在回归， 就这样一个大趋势。 我觉得中美之间其实是在进入一种有意或者无意的是在把对方卷到这样的一种竞争当中的状态里。 包括现在美国其实我感触也挺深的， 美国对于中国的那种状态是中国是要崛起来跟美国去争霸主的这么一个心态， 所以美国虽然可能在全世界发言内有很多敌人， 但是中国是最主要的那一个。 我觉得这个趋势线其实在美国有一个跨党派的共识。

那在这样一个眼见着中美之间， 其实是会进入一个所谓的这个City的陷阱的过程当中， 我觉得其他的国家是有一个选择在的。 至于说选什么是不是一定要选边站， 如果不选边站的话， 有一个什么样的可能， 我觉得这种可能性是需要单个的国家去探索的。 回到说为什么我觉得中德关系非常的特殊， 也是因为我觉得德国治愈中国， 它既有这种旧的意识形态的分歧， 但它也有一些在新的格局当中继续去合作的可能。 那这个可能不管中德双方是不是愿意， 你的这个贸易量在哪儿， 你各自承担的这种角色和你在各自的区位的这种定位在哪里， 互相想要去谈判的时候都有一些砝码， 都有一些可以去leverage的东西。 那这个其实我觉得并不是所有世界上的国家现在都具备这样的资产或者资源， 或者这样的意愿和能力。

所以我会觉得我自己在看待中德关系的时候的一个私心， 确实是觉得中德之间是有可能可以去探索出一条， 在旧世界旧秩序崩坏的情况之下， 不光是中德也为比如说中欧之间的其他的国家， 以及甚至世界上很多其他的这种， 用加拿大总理他在达沃斯的这个讲话上的一个概念叫做中等国家， 我觉得是可以去探索出一些新的模式和新的这种路径的。 然后德国真的是为数不多可能有这样的位置可以去探索这样路径的国家之一， 所以我个人会还挺期待去看到中德之间更多的互动， 不管说是民间还是说政界商界层面的互动， 因为我相信更多的可能性也只有在更多的互动中才会出现。 那当然这个过程中也会要求双方都具有这种开放的心态， 具有良好的意愿， 然后也会需要更多的对话。

所以再次感谢。 我们也可以约定一下吧， 也许到明年的这个时间， 可以再来组织这样的一场聚会， 这样的一场讨论。 好的， 谢谢大家， 明年肯定要见面。 对， 而且我感受到大家浓浓的那个生存欲， 对这个世界抱有一种美好的期待， 在我们这么混乱的情况之下， 我从你们三个身上都看到了一种， 我还是希望这个世界朝更好的方向去发展， 如果说这个世界能够向更好的方向发展， 我们应该做手。 所以我从你们身上都感受到了这种能量。

优优独播剧场——YoYo Television Series Exclusive
优优独播剧场——YoYo Television Series Exclusive

The Book Club: Wuthering Heights

Hello, everybody. It’s Dominic here from The Rest is History, and I’m here to tell you about a thrilling new show that we have been working on here at Goalhanger. It is called The Book Club, and it is presented by me…

And me, Tabitha Syrett. And it will be coming out every Tuesday, and each week we delve into some of the greatest, the most fascinating, the most intriguing books of all time. And it’ll alternate every week between something a bit older, more classical, so for instance, Wuthering Heights, and then something maybe newer and a bit more contemporary, so The Secret History by Donna Tartt, or Never Let Me Go by Kazuo Ishiguro. And we will be digging into the kind of secret hidden stories behind the story. We’ll be uncovering the truth about the author themselves. We’ll be delving into the context, the history behind the book, and also kind of unravelling the book itself a bit for you.

Yeah, so to explain the background to this, Tabi and I have been working together on The Rest is History for three glorious years, Tabi. Feels like 30, but it’s actually only been three. And we’d often be nattering about books when we’re off doing our Rest is History tours and whatnot. And we decided that we would do a little bonus series for members of The Rest is History Club, didn’t we? So we did The Hobbit, and In Cold Blood, and The Handmaid’s Tale, and so on and so forth. And we were, I think the technical term is blown away by the reaction to that from the club members.

We were absolutely thrilled. Yeah, we were over the moon. And so we have spent the last couple of months putting together an exciting list of episodes for you. So we start with, of course, Wuthering Heights, the truth behind this iconic, famously kind of overwrought story, and obviously reflecting a little bit on the new movie. Then we do Never Let Me Go by Kazuo Ishiguro. And then what comes next, Dominic?

So we’ve got:

• The Great Gatsby
• Hamnet
• 1984

So in two days’ time, we’re recording an episode of your favourite book, I think, of all time. No? Sarah J. Maas’s A Court of Thorns and Roses.

Oh, come on. Don’t pretend that wasn’t your idea.

So this is my first foray into the world of romance. So basically, each week, we’re doing a different book. And it’s a little bit like The Rest is History. We will be talking about the author and their lives and the social context, when the book was written and so on. But also, it’s a little bit more personal, I suppose, because we’re also talking about our own reactions to the books. We’re having arguments about which characters we liked and which we didn’t. You have a unique identity, don’t you? Because you’re simultaneously, ludicrously well-read. Painfully well-read. But also, dreadful judgment. And I think that’s a really unusual combination.

And it creates a fascinating tension within the show, because you are very poorly read and have bad takes. So the combination of those two things together creates just dynamite. It’s amazing.

So to remind you, it’s out every Tuesday. So it’s in the gap between Rest is History episodes. It’s a different book each week. We will advertise the books in advance as much as possible, won’t we, Tabby, so people can read or not read as they see fit. Because basically, the beauty of this show is if you’ve read the book, brilliant, you’ll enjoy the conversation. If you haven’t read the books and have no intention of reading them, that’s great, because we did the reading for you. So you’ll appear incredibly well-read when you go to dinner parties. There’s no doubt you do, being Rest is History people.

So it’s every Tuesday. It’s the book club. And we’ve actually got a clip for you, haven’t we? We do. As a massive treat. We have a clip for you from our first episode of the book club, Wuthering Heights. So we hope you enjoy it.

Wuthering Heights by Emily Bronte, published in 1847. It's set in 1801, but it's looking back even before that.

Wuthering Heights by Emily Bronte, published in 1847. It’s set in 1801, but it’s looking back even before that. And Tabby, it’s one of the absolute canonical classics. And it’s actually regarded, isn’t it, as one of the great romantic novels.

It absolutely is. For instance, in the very famous 1939 Laurence Olivier film about it, they advertised it as the greatest love story of our time or of any time. And as you say, it’s an absolute classic. And it’s particularly famous because of its depictions of kind of wild moors, simmering tensions, unbridled emotions. And of course, this very, very famous love story at the heart of it all between Cathy, Catherine Earnshaw, and the kind of iconic, laconic, romantic hero, Heathcliff.

And you, I mean, you’ve been going on about Wuthering Heights for ages. I remember when, even before we were doing the show.

Yeah.

You saying it was one of your absolute favourite books. And am I not right in thinking that you used to read it?

Oh, no.

You used to read it every single year? “Yeah. But let me justify that.”

For 20 years. I want to explain myself. I basically read it first when I was about 12. And I thought it was just, devastatingly romantic, heartbreaking. I loved the love story. I loved Catherine Earnshaw, the heroine of the book, because I thought she was kind of fiery and beautiful and elegant. And then, as you say, I kind of put it on a pedestal and I would read it every year until I was about 17. A sort of sad teenage miser.

I stopped after that for a long time. And then, so this was my first time rereading it since I was 17. And so I was very intrigued to see what I thought about it this time. And my view on it has definitely changed. But before we get into all that, and before we get into what you think of it, I think we should give a brief outline of what Wuthering Heights is about. It tells the story of these two families, the Earnshaws and the Lintons, and they live very near each other across the Yorkshire moors. And it’s kind of about the tangled relationships between them. And these relationships are particularly tangled in large parts, thanks to the machinations of the iconic Heathcliff. And the plot is driven by his kind of crusade of vengeance. And Heathcliff is a foundling taken in by the Earnshaws as a young boy and is treated very badly by his stepbrother, but falls in love with, at a very young age, his stepsister, Catherine. There’s something of the Greek tragedy to it because it’s kind of these cycles of revenge going round and round and round.

So it falls into two halves, really. The first half is about, it’s Cathy and Heathcliff, basically. So Catherine or Cathy, she’s torn between slightly drippy husband, Edgar, and then harsh, much wilder, more violent Heathcliff, who is kind of her soulmate. And then the second half of the book is about her daughter, who confusingly,

“would it have killed Emily Bronte to give her a different name?”

Yes, it clearly would, because she’s also called Cathy. I think we could basically, just for the purposes of this, we can just call her young Cathy, old Cathy. Exactly. So young Cathy is living with Edgar, her father, in this civilised house called Thrushcross Grange. And then she ends up coming across Wuthering Heights, and she’s dragged back into Heathcliff’s world and the sort of mad stuff that is going on there. For those people who haven’t read it, and I’m guessing there’s quite a few people listening to this who haven’t read it, we won’t give it all away right now. But basically, there are going to be, I mean, it’s a book show, there’s about to be spoilers.

“Yeah, absolutely.”

But just on the way it starts, it gives you a clue to how the book works, basically. Because we began with that reading, we’re being told this story by a guy called Mr Lockwood. Mr Lockwood has rented Thrushcross Grange, so years after the events that are being described. And he’s a Londoner, isn’t he? So he’s out of his depth on the moors of Yorkshire. And because of that, because of the fact that he is a Londoner and quite sort of civilised or whatever, he adds a bit of comedy to it, because he kind of goes into this house, he goes to meet his landlord. And his landlord is Heathcliff, who lives at Wuthering Heights across the moors from Thrushcross Grange.

And it’s like he basically walks into an asylum and it’s like shadows everywhere. He keeps tripping over dead rabbits. He’s slobbered on by vast wolfhounds. And he’s utterly bemused by everything and everyone he encounters there. And there’s this kind of grotesque cast of characters that he meets at the heights. Anyway, so he ends up getting snowed in for the night, much to his distress. And he is put to bed in this room. And he finds a ledge in the room covered with writing. And it’s a name repeated in all kinds of characters, large and small. And it says Catherine Earnshaw here. And then it varies to Catherine Heathcliff. And then again to Catherine Linton. And so he then starts to read this Catherine’s diary. And this triggers the mad nightmare from the opening reading, which I think I injected with so much dramatic flair.

“Nice that you’re complimenting yourself yet again.” “Again, yeah.”

So basically, it’s a very, very violent scene. The child’s face slipping through the window. And actually, what he tries to do, Mr Lockwood, is he tries to basically slit the child’s wrist on some broken glass, which is, I mean, you would think, God, that’s a bit much in a Victorian novel. But of course, that sets the tone because there’s a lot more of this kind of violence and abuse to Come in the book. It’s a very, very nightmarish scene because it is a nightmare.

It is. And it’s very odd because Heathcliff comes into the room and he’s so angry. There’s a mad desperation to him. And it’s he has caught Lockwood dreaming the dream that he ought to be dreaming about this girl, Catherine. And we at this stage don’t know why. We don’t understand the relationship between them.

Yeah. So Lockwood has had this flipping weird experience staying in this hotel. I was about to say this hotel. You’d never stay at it. It makes Fawlty Towers look that or it’s.

Yeah, I’d actually rather stay at a hostel than that.

Anyway, he’s been staying at Heathcliff’s house. It’s full of all these mad people. There’s a lot of stuff with broken glass and ghosts. And when he goes back to his own house, he finds out that the housekeeper there, who’s called Nellie.

Nellie Dean.

Yeah, she used to work for the Earnshaws at Wuthering Heights. And she knows the whole story behind this because Lockwood is as lost as we are. And so she says basically,

“well, I will tell you the story.”

Yeah, it’s not exactly the kind of recuperative, pleasant tale that I think he was hoping for amidst his illness.

Exactly. So to cut a very long story and complicated story short, there’s these two characters, Heathcliff and Catherine Earnshaw, who later becomes Catherine Linton, who is now dead. And Heathcliff was the foundling and Cathy was his kind of adopted sister.

Yeah.

And they had this incredibly tortured and intense relationship. She married this bloke, Edgar Linton, who lived across the Moors. Heathcliff was driven sort of mad with rage. He ends up eloping with Edgar’s sister, Isabella.

Yeah.

Who’s massively fallen in love with him, but soon realizes she’s made a terrible mistake.

Yeah. And that’s kind of interesting because Isabella falls in love with him in the way that I think a lot of audiences now fall in love with Heathcliff without having read the book.

Yeah.

It’s the idea of him. It’s the Byronic heroic idea of him.

Exactly. She thinks he’s incredibly glamorous.

Yeah. And romantic. Little does she know.

She has a child irritatingly called Linton.

It makes you hate Emily Bronte.

Yeah. You could have thought of a different name. Maybe you just thought you’d use the surname again. Brilliant. Meanwhile, Cathy’s had a daughter with Edgar Linton, who she calls Cathy.

Cathy.

Yeah.

So, I mean, this is a huge part of the story is the doubling of names.

But it’s deliberate, I think.

Yeah, of course, it’s completely deliberate.

It’s the compulsive repetition. It shows you that you’re trapped in these dark webs of hatred and obligation and it goes on and on and on.

It’s what you were saying about the Greek tragedy thing. It’s basically the idea of the bitterness and the hatred and the jealousy being passed down through the generations.

Yeah. It makes you think, though, given the violence of it and how complicated it all is, it makes you wonder, what is going on in the mind from which it sprung, the strange mystery of Emily Bronte.

That’s a very nice segue, Tabby, into talking about Emily Bronte, right?

But first of all, I mean, should we talk about another fevered, imaginative writer? What’s your take on Wuthering Heights?

So I read Jane Eyre a few years ago for the first time, loved it, and then I thought, I’ll read another Bronte. And I read Wuthering Heights and I thought, oh, disappointing.

It’s not as good as Jane Eyre. Because I didn’t really get into the kind of fever dream side of things. I frankly did get a little bit lost. And I thought to myself, this is going to be very self-incriminating. I thought to myself, this is a bit of a book for teenage girls.

Oh, he went there.

It’s the book that a sad and lonely teenage girl would read every year, Tabby. No, but actually, I’ve changed my mind.

Oh, wow.

I will reveal all at the end of the episode, but I’ve completely changed my mind about it.

That’s exciting.

Before we do that, let’s get into Emily Bronte herself, because critics used to call her the Sphinx of the Moors, because she was such an enigma, such a riddle. We know so little of her inner life. And so that, of course, plays into the Bronte industry and the Bronte legend, because it’s meant that obsessed readers can project onto Emily Bronte whatever they like. However, there is obviously clearly a historical person here behind the kind of layers of wild myth and legend. Yeah.

So Tabby, you’ve done a bit of digging, haven’t you, into Emily Bronte’s life?

In large part, because of that thing, the kind of the myth, I kind of wanted to see how much reality there was to it. So Emily, she’s the fifth of six Bronte children. And she’s born in Thornton in Bradford in July 1818. And then when she’s, I think, almost two, they moved To a place called Haworth in the Pennines. And her father, Patrick, is the local curate.

And then she’s struck by the first of these three kind of early tragedies that will darken her early life, because her mother Maria dies of cancer when Emily’s about three. And then she and her older sisters, including Charlotte Bronte of Jane Eyre fame, they’re sent to this school for clergymen’s daughters, when she’s not yet six, and they’re treated really, really badly. And this school is actually the inspiration for Lowood in Jane Eyre, the kind of terrible school where it all starts.

And then while there, two of Emily’s older sisters die. So before the age of seven, she loses two sisters and a mother. And then, so there’s only four children left. And it’s Charlotte, it’s Emily, it’s Bramwell, their only brother, and then Anne, who’s also an author.

• And it’s Charlotte
• it’s Emily
• it’s Bramwell, their only brother
• and then Anne, who’s also an author

Anne’s always a bit of an afterthought, isn’t she? She is always. I always think that’s a bit harsh, though, because I really like the tenant of Wildfell Hall. Right. But she, she’s definitely not as famous or as impressive as the others, probably. Anyway, so they’re educated at home at the Parsonage by their father and their aunt, Elizabeth. And definitely a bit of a weird household, I would say.

People think of them isolated. In like this haunted house. Cut off from the world. But that’s actually rubbish. They were very literary. They subscribed to sort of heavyweight literary journals. The house is full of classics. And it’s not surprising because their father, Reverend Bronte, was a very impressive, very, very clever man. He came from County Down in Ireland, and he was actually the son of a farmhand. And then because he was so intelligent, he managed to get himself to Cambridge to read theology. Although that said, he is a bit bonkers. Didn’t he catwalk around with a gun all the time? Like all loving fathers, he walked around with a loaded gun. And I actually think you could see the side effects of some of this madness in some of the odd characters of Wuthering Heights.

He doesn’t let them eat meat? He doesn’t let them eat? No. Not for moral reasons, because he thinks it’ll make them entitled and spoiled. Exactly. Exactly. So he’s a fun guy. He’s a good time. And he’s also very much kind of lets himself be an outsider in their community. He keeps his Irish accent, so he stands out. And he’s known to be quite cold, quite distant. And he’s starting to have these mad, kind of fiery rages. Right. So he’s a delightful fellow. It sounds great. But I think it’s no surprise then that his kids are quite odd too.

So for instance, a local woman told Elizabeth Gaskell when she was looking into the Brontes, that the Bronte children had no friends in the village. And I actually quite admire this, because I think similarly, on one occasion, they were invited to a party and they showed no knowledge of the games played by their peers. And I actually quite respect that, because my heart always sinks when I go to people’s houses and they say, oh, should we play cards? Should we play Scrabble? So I’m on their side with that.

You don’t like cards or Scrabble? No. See, I don’t actually like Scrabble. It’s so forced. It’s like the forced fun of it. Can we not just… Would you never play any form of game? Yeah, well, I’m press-ganged into it, because you don’t want to be… Well, all those people who were thinking about inviting you to their house to play Cluedo are now changing their minds.

So because of this, because of their reluctance to sign up to annoying, boring, forced games, the siblings do become very, very close, unnaturally close, perhaps. They create these fantasy worlds, don’t they? This is very you, actually. Is it Glass Town and Angria? Yes, Glass Town and Angria, yeah. And the really funny thing about it is, it’s not quite, kind of Lord of the Rings, because they have little soldiers in them, like they have the Duke of Wellington in them. And actually, it’s very telling about how exposed these children actually were to the wider world, how much they didn’t live under a rock in Yorkshire, because they’re kind of set all over the world.

It’s in the Pacific, isn’t it? The Pacific, exactly, exactly.

So Emily, you know, they’re obviously going to have to get a livelihood or whatever. Yeah, well, they have to earn some money, yeah. Right, so she becomes a teacher in Halifax, and that’s a shambles, isn’t it? This is where me and Emily part ways, I think.

“I genuinely prefer the dog to you, I hate you.”

She says to the kids at one point, and you will discover in due course why that’s such a frightening sentiment, and why these children were actually… If there’s any dogs listening to this podcast, stop listening. Anyway, so that doesn’t work out. So when she’s 24, she goes to Charlotte to a girl’s boarding School in Brussels, and she teaches music there. But yet again, she absolutely hates it. She’s very snobbish. She’s quite up herself. She says that, oh, I’ll only teach the children at the end of the day when I’m done with my own studies. And that’s the only time that they have a break or free time. So she cuts into their free time and was unsurprisingly very unpopular.

That’s poor. She was also very unfashionable. She refused to wear the Belgian fashions. Would not wear the latest Belgian fashions? No. But what could a Belgian fashion possibly be? Well, you wouldn’t understand such things because you’re not a fashionista like me.

I do, actually. I just don’t feel like sharing it right now. Okay, fine. Anyway, so because of this, and I find this so amusing, there was a student called Letitia Wheelwright, and she said of Emily Bronte,

“I simply disliked her from the first. Her tallish, ungainly, ill-dressed figure, always answering our jokes with, I wish to be as God made me.”

She actually sounds like a terrible person. Yeah. The great critic, Catherine Hughes, I was reading an article that she wrote about this book, and she described Emily Bronte as the patron saint of difficult women. You’re not going to be sat next to her at a dinner party because she wouldn’t go. She won’t go out. But if by some terrible mischance… You’re having a Bible reading or something. Her physical portrait, you can tell from that, she’s very unfashionable. And she’s also said to be tall and had big bones.

Big bones. Generally, when people say you’re big bones, they mean that… It’s not a good thing. Yeah, in an unflattering way, right? And actually, there’s a movie that they did called Emily with Emma McKay from Sex Education, and they very, very much play into this. She comes across as almost autistic, I think.

Right. And people said that about her, didn’t they? Yeah, well, people have diagnosed her since with autism or with anorexia and all these kinds of things, which people love to do with characters in the past. I always think that’s slightly dodgy. I’ll tell you one thing that’s very dodgy, though, actually. So you mentioned the business about dogs.

Yeah. As we’ll discuss, Wuthering Heights is a violent book. There is a lot of abuse of various kinds. It’s shockingly violent, actually. As well as mental. Yeah. And you might say, well, where does this come from in this sort of quite withdrawn, lonely, very bookish girl? And an answer is, she herself is quite violent. So she had this dog called Keeper, and she loves Keeper, doesn’t she? But one day, he climbs on her bed with muddy paws, and she reacts by punching him in the face. But not just once, but again.

And if you love dogs more than people, you’re in big trouble if you’re one of her students. I think we have to be slightly sympathetic towards Emily. You know, she’s grown up with, as far as we can tell, a very angry father, and she’s suffered a lot of loss at a young age, and she’s very isolated. So I do have a little bit of pity towards her in that respect. But the other thing that’s worth remembering is, is that, you know, because people, they almost canonise Emily Bronte as kind of like a feminist icon, and we’ll touch on that a bit more. You know, she has quite kind of high Tory politics, doesn’t she? She would have set the dog on suffragettes or whatever, which is not in keeping with the idea of her as this kind of free-spirited sphinx of the Moors.

So that was a clip from our very first episode of The Book Club from Wuthering Heights, and we hope you enjoyed it. To hear more, search The Book Club wherever you get your podcasts. Goodbye. Bye-bye.

Mathematical Superintelligence: Harmonic’s Vlad & Tudor on IMO Gold & Theories of Everything

Hello, and welcome back to the Cognitive Revolution. The presenting sponsor of today’s episode is Granola. Regular listeners have heard me describe the blind spot finder recipe that I’m using on Granola to look back at my recent calls and help me identify angles and issues I might be neglecting.

I love that concept, but it’s also worth highlighting how Granola can help raise your team’s level of execution by supporting follow-through on a day-to-day basis. This morning, for example, I had two very practical calls in which I committed to a number of things. In the past, to be honest, there’s a good chance I’d have forgotten at least a couple of the things I said I’d do. But with Granola, I can easily run a to-do finder recipe and get a comprehensive list of everything I owe my teammates.

This is the sort of bread and butter use case that has driven Granola’s growth and inspired investment from execution-obsessed CEOs, including past guests Guillermo Rauch of Vercel and Amjad Massad of Replit.

See the link in our show notes to try my blind spot finder recipe and explore all of the ways that Granola can make your raw meeting notes awesome.

Now, today, my guests are Vlad Tenev and Tudor Achim, co-founders of Harmonic, an AI research lab dedicated to building mathematical superintelligence, and also the creators of Aristotle, an AI system that achieved gold medal-level performance at the 2025 International Mathematical Olympiad.

While OpenAI and Google DeepMind achieved similar performance by scaling reasoning in chain of thought, Harmonic stands out for their commitment to formally verifiable methods. This is because it generates candidate proofs in Lean, a programming language that serves as a proof-checking assistant by using a trusted kernel to confirm that every single step of reasoning follows from a few explicit premises and accepted logical rules.

Aristotle’s work can be automatically validated, and its performance is in principle limited only by the scale of compute available for reinforcement learning.

In an effort to better ground my own intuitions for mathematical superintelligence, we begin with a metaphysical discussion about:

• The nature of math
• What it is that mathematicians do
• The assumptions that underpin a Lean verification
• How Lean is already revolutionizing the math world by eliminating the need for traditional peer review

From there, we turn to the Aristotle architecture that delivered IMO Gold performance. It consists of:

- A large transformer model that uses a Monte Carlo tree search strategy, reminiscent of systems like AlphaGo, to discover valid paths from point A to point B in mathematical reasoning space.
- A lemma guessing module that helps manage context and keep things on track by generating candidate waypoints between a given starting point and a potentially distant end goal.
- A specialized geometry module modeled on DeepMind's alpha geometry.

We also discuss the Aristotle API’s informal mode, which attempts to auto-formalize whatever the user asks it to prove.

We discuss what its responses to my admittedly silly requests imply about the boundary between statements that could in principle be mathematically proved, and those which are sufficiently factual or philosophical in nature so as to fall outside the scope of the system.

Examples include propositions like:

“all is love”

and

“Epstein didn’t kill himself”

In the final section, we discuss:

• The role of entropy and the importance of taste to Harmonic’s future plans
• How the community is using Aristotle, sometimes on a standalone basis and sometimes in conjunction with other frontier models, to solve previously unsolved problems
• How we might use systems like Aristotle and Lean to harden mission-critical infrastructure and improve complex systems across society
• How Harmonic’s emphasis on verifiable outputs could create a superintelligence we can trust, even in the absence of mechanistic understanding
• What mathematical superintelligence might look like in 2030

On this last point, I have to say, with so many grandiose AI promises flying around these days — from a country of geniuses in a data center, to a century of progress in five years, to curing all diseases in our natural lifetimes — it is rare that I am genuinely taken aback by a company’s vision for the future.

And yet, as you’ll hear, Tudor did manage to leave me at least momentarily speechless when he described a future of theoretical abundance in which all physical phenomena we observe have multiple competing coherent explanations, which can only be separated by increasingly exotic experiments.

If you’re like me, you’ll find this episode a useful opportunity to:

• Improve your intuition for the nature of math
• Get an instructive preview of what’s to come as reinforcement learning continues to scale across the industry
• Receive an inspiring challenge to keep thinking bigger and bolder about the nature and impact of superintelligence With that, I hope you enjoy my conversation with Vlad Tenev and Tudor Achim, co-founders of Harmonic.

Vlad Tenev and Tudor Achim, co-founders of Harmonic, makers of Aristotle, and winners with an asterisk of the IMO gold in 2025.

Welcome to the Cognitive Revolution. Thanks for having us. Greetings and salutations. Thank you.

So this is going to be, I think, a fascinating conversation. It’s probably going to be more metaphysical than most of our episodes, but also there’s a lot of practicality because what you guys are doing certainly has aspirations to go beyond the pursuit of mathematical superintelligence.

Maybe just for starters, how do you guys understand what math is? That was something I was really wrestling with in preparing for this. And then, you know, that’s obviously very metaphysical. To make that a little bit more practical, what would you say are the core cognitive skills that people that are good at math really develop and excel at? And how do those skills do when we look at the performance of like the frontier large language models that all of our listeners are familiar with today?

“Yeah. Well, look, first, thanks for having us. It’s really great to be here.”

You know, when you ask, what is math? What is it useful for? What are the core cognitive skills? it gets like one of the core theses of our company, which is that mathematics is reasoning.

So a lot of people think of mathematics as this really esoteric thing. You know, you’re thinking maybe group theory, stuff you’ve seen in movies like Good Will Hunting, but mathematics at its core is the process by which humans understand the world by breaking their understanding down into small sequences of logical steps that other people can understand and verify for themselves.

So when you’re solving a physics problem or doing your taxes or thinking about what happened at the beginning of the universe, ultimately you have to have an explanation that is

• self-consistent,
• that follows from other facts, and
• that your colleagues or other humans can check.

And so when we talk about what it takes to be good at math, the question is what it does take to be good at reasoning. And so that’s, again, that ability to break this down into steps.

It turns out math is really useful for understanding the universe and building lots of engineering things, but ultimately it’s just about reasoning.

I watched your podcast that you did with Sequoia maybe 16 months ago or so now. And I recall Vlad’s story of like, basically,

“I thought that if I got good at math and I’d probably be good at other things and it sort of worked for me.”

So that’s like one way to, in a very practical sense, unpack the idea that math is reasoning. It certainly seems to help people generalize to at least related domains and be really effective, for example, in entrepreneurship.

But I’m not entirely clear still on like, are you making a more almost platonic claim there? It seems like there’s the very simple notion that like, okay, I should teach my kid a lot of math because then they’ll be smart generally. And again, that works for humans.

But is there something that you see as like a more fundamental law of the universe, sort of correspondence between what we are doing in math and what we are doing in these other domains? Because it doesn’t seem like we have the same sort of like verifiability in almost anything else.

We do have it a little bit in computer science, but even in physics, right? We’ve got like still very fundamental questions about

• is the paradigm even right?
• what would it mean for it to be proven right?

“I don’t think that stuff is at all agreed upon.”

So maybe you guys throw up your hands at this mystery too, or maybe you feel like you have kind of an intuition for what the answer is.

“Yeah, I can give you my perspective.”

I got into math through physics. So when I first came to Stanford as an undergrad, I had read Brian Greene’s The Elegant Universe, which was sort of like the first popular string theory book.

And when I was a kid, one of the earliest memories, one of the first full English books that I read was A Brief History of Time by Stephen Hawking. So I’ve always been interested in kind of the big questions, right?

• What happened before the big bang?
• How did the laws of physics come about?
• Is there just like one law, one particle, one force that eventually as the universe cooled and expanded splintered into all the different forces we have today—like gravity, electromagnetism, strong and weak force?

Cause you know, back in the day, that was not obvious. You know, we thought electricity was separate from magnetism and it was just like a really big… I probably think one of the greatest achievements of science is figuring out that these two are actually two sides of the same coin really. And then, and then the big question is like, well, what’s going on with gravity? Is it, is it the same? Right.

And, in the middle of this, we found out that the weak force and the electromagnetic force were also splintered off of one electro-weak force. So it kind of feels like there was just one thing at the beginning and we have to understand what that thing is.

And what I found when I became a physics major at Stanford, and I started asking all of these questions, eventually they’d send me over to the math department. And they’re like,

“Well, in order to understand string theory, you have to understand all of these other things. Right. And if you want to understand general relativity, you’ve got to get into differential geometry.”

And so that’s how I became a pure math major and ended up doing a PhD. The impetus was actually trying to understand the real world through physics.

If you think about what’s the usefulness of physics, I mean, all of the big inventions that humanity has that really push us forward are kind of like physics inventions, really. I mean, when you think about:

• Flight
• Rocketry
• Computers
• Transistors
• GPS (obviously one of the main examples of why relativity is useful)

They’re physics things.

So the real reason to do math is math is interesting and beautiful. There’s an art aspect of it, but it helps you. It helps you understand physics. Physics helps you understand engineering. And then you can create things that have huge value.

You were asking, how does math work in other fields where things are not as precise? I think math shows up just maybe a little more subtly than people think.

So there is this physicist, Eugene Wigner, who wrote a famous essay called the unreasonable effectiveness of mathematics, which was commenting on a really interesting phenomenon.

So Vlad mentioned differential geometry and special relativity. It turns out that when Einstein was creating that theory, he relied on these thought experiments from the 19th century around how to think about certain manifolds and their properties.

And that was actually the key tool that we use to explain what special relativity is, and then develop it for general relativity.

That’s a perfectly representative case because those thought experiments in the 19th century were almost preposterous. It made no sense to think about them because

“How could you possibly apply these concepts to the real three dimensional world?”

And then it turns out that it’s very useful for understanding the four dimensional world when you include time and curvature.

There are myriad examples like this.

If you consider number theory for a long time, that was really seen as an incredibly esoteric branch of math with no practical implications. But people pushed on that theory for a long time. And then it turns out that that’s the key tool you need to create a secure digital economy.

So now essentially all of human civilization has a digital economy, which is based on this branch of math.

So I think it’s almost the wrong question to ask,

“Well, I don’t know, there’s a lot of math out there. How is it useful?”

The point is you just do the math and then eventually some of it, not all of it, will be more useful than you possibly could have imagined.

So the investment in math is: it’s not just to build a really smart system. It’s to create a lot of new math that we can then figure out ways to apply later.

One interesting thing that the conversation reminded me of when you first asked,

“What is math? What does it look like?”

I think one of the reasons we got excited about applying AI to this domain is there are lots of different things that mathematicians do.

• Some of them are very creative, almost like artists. They may not be prolific, but they come up with something new once every five to ten years, and that can be an amazing accomplishment in the field. For example, Gregory Perelman.
• Others are just machines—they can read more papers and comprehend more papers per unit time than other people. What they’re doing is basically synthesizing all the knowledge, figuring out all the tricks, and applying those tricks quickly to new domains. They’re kind of like reusing these things.

And I think we’re very excited about the prospect of AI accelerating the former. We think that’ll happen.

But the latter is something that AI is already really, really good at today and was good to some degree when we got the idea for Harmonic. You know, you look at GPT-4, which had just come out when we started and it excelled at just pulling information, doing these types of needle-in-a-haystack tasks of, can you just really quickly go through all the literature and pull things that might be relevant.

And I would say even if you can be an amazing mathematician, you’re in that category.

I think a lot of the work could be accelerated if you just knew all the math that was being done and could pick out the relevant things to an unsolved problem that you have at hand.

So I think the problem itself lends itself really well to what AI is already good at.

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Okay, that’s quite helpful. I think, coming into this, I had focused my own mind on sort of two modes of math, I guess.

One being the kind of Einstein-like — obviously that’s a high-level example of a kind of eureka moment of having some insight that,

“hey, this highly abstract and, you know, seemingly perhaps like very esoteric formalism can actually unlock like major understanding.”

That’s kind of amazing. Very amazing.

And then there’s also this sort of grind-it-out, like I’ve got this thing that I want to prove, and I’m going to kind of, perhaps stumble my way even through the space of possible logical moves until I finally chart a path there. And then you’re adding another, a third layer, which is like problem selection in the first place, which I guess is pretty related to the Einstein thing, but certainly distinct in some ways.

Let’s take a minute before we get into the Aristotle system and how it works and how you’ve trained it and all that stuff to just talk about Lean.

Lean is basically a programming language that does this kind of very bit-by-bit logical maneuvering, right? Where you have certain assumptions coming in, you’re going to take these various steps, and the goal is to get to a certain outcome.

Tell us, because I’m just learning about this, in the context of preparing for this and a couple other podcasts, and I think most people don’t know anything about it.

So maybe give us a little bit of a more intuitive understanding of what Lean is. And I’d be keen to understand it on a little bit of a practical level to like:

• How many symbols are there?
• How many axioms are there that we’re starting with?
• How many rules are there that we can apply?
• How big is the space that we are manipulating our way through?

So Lean, in my view, is the best programming language ever created.

In Lean, you can write any program you would write in Python or C or C++, but you can also express essentially any logical concept.

So if we’re okay getting into a bit of the details, it is a dependently typed programming language, which means that at compile time, you can express very complicated properties of the program that you can check before ever running it.

So on the one hand, you have on one end of the spectrum, you have something like JavaScript, where you can check basically nothing. And then on the other end, you have Lean.

But the really cool thing is you asked about axioms. So when Aristotle produces any output, it’s produced as annotated Lean code.

So there’s the programming language Lean, we write theorems, we write programs, we prove things. And there’s a lot of comments explaining to the person reading it what it’s doing.

But when we talk about proving things, you end up relying on three axioms, in addition to just the basic concept of the calculus of constructions, which is what the programming language is based on.

• Two of them are extremely technical:
  • One is propositional extensionality
  • One of them is something about quotient soundness
• But the third one is the axiom of choice

And just as an example to show what an axiom means: the axiom of choice —

“It’s not saying anything that would be controversial, it’s saying if you have a non-empty set, it’s possible to choose an element from it.”

And so from these three extremely basic axioms, it turns out you can build:

• All of mathematics
• All of computer science
• All of mathematical modeling, physics, economics, stats, biology

It’s all based on this core set of axioms.

And so the goal of a system that outputs Lean is to find interesting statements and programs then prove things that just depend on these axioms. And that’s really where the difficulty lies.

As you alluded to, sometimes you have to make big logical leaps, sometimes you have to grind through a lot of math, but both of those are essential. So you can’t really skip one of those steps.

But the Lean itself is just incredible. You can express so many ideas in it, you can prove so many things, and you can use it as a programming language too.

So it’s really up there for me in programming languages.

I started playing with Lean when Tudor and I started making a plan for this business, and we had a pretty early decision about whether we wanted to go formal and informal.

One thing that struck me about it is, as a former mathematician, I barely used the computer when I was doing math.

I was in my PhD in the late 2000s, and the only time you’d really be using a computer when doing math is when you wanted to type up your homework or your research paper or something.

But all the thinking about it would happen on a chalkboard or a whiteboard. All the collaboration about it would happen in person at these conferences or on a chalkboard in one’s office.

For a while, it was just like maybe mathematics would always be this pure thing that would just be kind of untouched by technology.

But what Lean has done is it transformed the mathematics from kind of like chalkboard and couch to now it’s in VS Code.

You know, you can do it in Cursive. You’re putting your math on GitHub, where now you can run these large collaboration projects.

So even when you subtract out AI, I think the Lean by itself without AI changed how people do mathematics, because now you’re seeing extremely prolific, famous mathematicians running these large projects where they’re collaborating with dozens of people around the world trying to do things like formalize research or formalize the proof of Fermat’s Last Theorem. And more and more and more of the folks are adopting Lean as like an accelerant.

So I think it’s changing how mathematics is being done and actually accelerates collaboration and accelerates progress and sort of like removes this notion of peer review.

If you’re a mathematician, if you’re a mathematician and you want to prove something, a big part is getting someone to read it and actually spend the time to tell you if it’s correct.

And so, you have the proof of Fermat’s last theorem, which took many, many years to be proved.

What happened was sort of this collection of people got together and when they all agreed that the proof was complete, it was sort of like ordained that the thing was proven.

And I think another thing formal does is it makes it so that that’s unnecessary.

Like if the proof checks and there’s no caveat that there’s no bug in the Lean kernel or how you’ve set up the statement, you obviate the need for manual human verification.

And the implications of that are pretty interesting too, right?

You have all of these potential citizen mathematicians who now with AI can solve unsolved problems and they don’t need to get anyone at a, you know, PhD program, a lean institution interested in their problem in order to tell that it’s correct.

They just have to have the Lean certificate and the proof is correct.

So, yeah, I think that’s a powerful thing.

If you think about journals, journals and math exist for this: it’s like the prestige of the review board tells you whether you should read something or trust it.

So I do that. The notion of trust is really changed fundamentally with tools like Lean.

Yeah. And I think that the open source software community has really solved this problem a long time ago.

So if you go on GitHub, one can simply open a pull request on some repository.

• If it passes the tests and
• The author of the repository agrees to your style,
• That gets merged.

So now you’ve contributed.

That element of trust is not so present, you can just run the tests.

Also, when you talk about impact and prestige, you can look at the number of stars you have.

So if a repository is very popular, it gets forked a lot, it gets a lot of stars.

So you’ve disintermediated essentially any gatekeeper here, it’s totally open source, there’s no morning trust required, and there’s a measure of impact.

And so I think math is going to start going the same way.

Previously, mathematicians relied on their social networks to figure out:

• Who tends to do the right thing
• Who tends to not make mistakes

But with Lean, you can have a big math project, anybody can come and contribute a proof.

And if Lean accepts it, then it’s right.

If a lot of other mathematicians start to depend on that result, we’re going to notice:

• A lot of forks
• A lot of dependency graphs
• A lot of stars on it

And so then you start to measure the prestige that way.

So it would be very interesting if Lean is the one tool that allows you to go from kind of the cathedral style of development where very closed networks, et cetera, to more bazaar style development where it’s kind of wild west.

But Lean is like the computational certificate that everything is correct.

I wish I understood a little bit better, had a more intuitive sense for what exactly is going on with Lean still.

This is going to be hard, I think.

But in doing my kind of research, one thing that stands out is the kernel is really small.

So, in terms of what you need to trust, it’s a pretty small amount of core code that has been thoroughly vetted many times by many people.

So there’s kind of that level of understanding.

I think I would still love to have a little bit better sense because when you mentioned the three axioms, for example, it’s a little weird for people outside the field to be like,

“Oh, there’s two that are kind of bizarre and technical. And then there’s this one that’s like if you have a non-empty set, you can choose an element from it.”

And I’m like, that seems like common sense, but why was that ever controversial?

Is there a way to describe the sort of space of legal moves in math or in Lean in sort of— I don’t usually like analogies.

I often try to set this up as an analogy-free zone, but because I’m—I think I and a lot of others are going to struggle with the very literal understanding, maybe this is a time for an exception to my no analogies rule.

Is there sort of like a— I don’t know, like a chess analogy or something where you could say, like, here’s the pieces and here are the legal moves that you can make to kind of give people a little bit of a better sense of what it actually means to move through these spaces?

I think the chess example is perfect. So a theorem in Lean is something like, given this starting configuration of a chessboard, it is possible to get to this configuration. And a proof of this theorem would be listing the sequence of moves. And what the kernel is doing in Lean is saying for every single move that you claim is valid, it’s checking, “hey, does this rule exist in my rulebook?”

So the theorem says you can get from A to B. The sequence of moves is, okay, here’s the sequence. And the kernel is just saying, “yes, this step is right, this step is right, this step is right.” And now I’ve confirmed that I’ve ended up in a target state. So Lean is doing that, but of course, the individual steps are different, they’re mathematical steps, and they depend on one or more of these three axioms.

The three axioms, although they’re technical, they’re very short. So if you write them down as mathematical statements, they’re under, I think, each of them is under a tweet in length. Like the axiom of choice definition in Lean is maybe 10 characters, and the other ones are maybe 100. So they’re not very complicated, they’re just a little bit annoying to write in math.

And then people say, okay, well, if we assume these axioms are true, and they’re also common sense, just like a bit more complicated. And we’ve checked every single step against those axioms, then we say the whole proof is correct.

Could you give like a few examples maybe of like the pieces and the moves? Obviously, we can’t come anywhere close to being exhaustive, but what are the primitives in terms of the…

I’ll give a mathematical but simpler example of a primitive. So let’s consider first-order logic.

So the deduction rules you have are:

• If A then B.

So let’s say you have a proof that says: if I have A, and I know if A then B, and if B then C, the theorem says C is true.

And the proof of that says:

A is true,
I have if A then B, which means B is true,
And then I have the step B is true,
I know that if B then C,
And then I can conclude that C is true.

So this is a first-order logic, so it’s not quite the same as what we’re talking about in Lean. You can do more advanced types of logical statements there. But ultimately, that’s what’s happening.

I think it’s going to be hard to…

Essentially, the next step beyond that is just getting to Lean and the calculus of constructions and these axioms.

So there is one thing when I learned it. There’s actually…

People are also exploring use of Lean to teach math. And I think now it’s sort of like practical at the high school level, but you could see a world where it extends to like middle school and maybe even younger if someone’s precocious enough. But I think mathematics education will go from sort of like the chalkboard to the computer lab.

So there’s this thing called the natural number game where you learn Lean by deducing properties of like multiplication and addition basically. So for example, the commutative law, which is basically that

A plus B equals B plus A, right?

Or the distributive law, right?

A times quantity B plus C equals A times B plus B times C.

So you can sort of like discover and prove these fairly basic facts just using the core axioms and the Lean language.

So that’s a good way, you know, if anyone just wants to like, all right, what is this Lean thing? Why is it useful? But I’m not a research mathematician. Dip your feet into it. I think I would recommend that.

And that’s been extended to harder things too. I think there’s like the real analysis game now, which is if you want to learn real analysis, it’s very proof based. And it’s basically the foundation of calculus.

You can start with like basic facts about:

• What’s a sequence.
• What’s a real number.
• How many of these numbers are there.
• How big are the sets.

And then you can kind of keep proving more and more complex things.

That’s a great tip. I’m definitely going to bookmark the real numbers game and see if I can get my soon to be seven-year-old into it.

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And we haven’t really talked about Mathlib, but the Lean kernel is quite small. There’s an open source project called Mathlib, which you can kind of think of as the largest digital repository of mathematical knowledge.

So all of the, a lot of the famous theorems and results can be found in Mathlib, and those give you almost like additional complex moves or algorithms to prove your thing. So you can apply a theorem, and it’s almost like applying a function from a library. That can help you get to the goal.

Yeah, I think that people can understand what it is better. Just think of it like every math textbook in the world merged into one in a self-consistent way. So eventually, all of mathematical knowledge will be in this one repository.

And if you hit build on your computer, you’re going to be able to check it all from the foundations. If you have any question about any math concept, you just search for it, you click on go to definition, you can jump around. It’s really going to be the new foundation for math in the future. It’s pretty exciting.

I think mathematics is certainly going to change fundamentally—like how it’s done, how fast it moves. And I think to a large degree, it already has. And AI is just going to accelerate it.

The great thing about our timing is Harmonic really started when both of these things matured to a level of capability where you could start doing interesting stuff.

• Lean basically went from being essentially beta software, like not appropriate for real mission critical use case, which was version three, Lean three to Lean four.
• That was about the same month we launched the company.
• And also GPT-4, which you were starting to actually see glimmers of it being really, really good at synthesizing information and the starting points of reasoning, came out around the same time.

I think both of these matured to the level where you can start putting them together and doing really cool things. And I think we were just the first to see that. That’s how we came up with this concept of mathematical superintelligence, which really means the combination of formal verification and formal tools with artificial intelligence.

Funny story, as I was using Aristotle a little bit to try to wrap my head around all of this, I don’t have the sophistication to pose any really interesting problems. So one challenge that I gave it was to prove that two plus two equals four. And then I had to laugh when it came back, just citing something from Mathlib that was like, “this is already proved in Mathlib” for the, the theorem is literally like the two plus two equals four theorem. So I was like, it’s done. And I was like, yeah, that’s not exactly what I was looking for, but I guess I kind of got what I deserve there for asking it such a basic question.

Did you use the, were you using the web interface or the terminal UI? I started by having cloud code installed the terminal and then was using that a little bit. And then somehow it tipped me off to the fact that there was a web interface. And so I, then I, after that I’ve moved over to the web interface. Yeah, that came to the last week and they’re probably a little bit more appropriate for those types of questions.

I think we wanted to roll it out for on terminal, because I think it makes it a little bit more clear what the tool is great at. I mean, lots of things can answer two plus two equals four, but even I can answer that in the calculator.

Yeah.

And I think, I think for a while we were talking about like, how do we describe this, this, like what Aristotle is? I mean, it’s, it’s kind of like an amazing calculator where you can imagine you could just talk to your calculator. So it has:

• both the reliability, like, you know, if your calculator gives you an answer, it’s correct
• but it’s not very expressive at the same time

You know, something like ChatGPT or Claude are very expressive, but sometimes you have to double check its work because it doesn’t always, you know, it doesn’t have the verification. But really the intent is to put those together.

And it turns out that the things, the first things that people really want to be sure about and to verify are like more complicated things. So I think the, you probably found this out, but the complicated things I think is where you really start to have aha moments when you’re using it.

Yeah, let’s get into Aristotle. And I appreciate the time spent in remedial education. I think it’s beneficial, not just for me, but hopefully everybody will now be able to kind of grok what we’re about to get into much better with that foundation that we’ve laid.

So Aristotle has three core parts. I’ll just kind of sketch them and then you can, you know, give me the double click on them.

First, there is this Monte Carlo tree search type thing. I kind of think of that as sort of an AlphaGo-like structure where we are systematically exploring the space of moves. I guess that’s where I got the chess analogy, right? Is that I kind of was making this equivalence between Aristotle, at least that part of Aristotle, and AlphaGo.

And so it’s kind of maybe I can make this move. And then there’s this learned scoring function that’s like,

“Okay, does that move seem promising? Does this path of, you know, does this branch of all possible moves that I could make, does it seem promising? Do I seem like I’m getting closer to my goal?”

And with that, you can kind of grind things out, run deep tree search, right?

The second part in some ways to me jumped out as even more interesting and kind of, I really want to dig into the metaphysics of it a bit, because this is the lemma-based informal reasoning system, which I take to be sort of saying,

“Okay, if I have some really big mountain to climb, and it’s maybe so big that I can’t just grind my way… it’s maybe becomes impractical to grind my way through like all these small localized steps.”

It’s sort of guessing like what’s the base camps that I would want to get to along the way that are like the really good way points such that if I can get there, then I know I’ve like, I’ve made it somewhere.

But that’s really interesting because it sort of strikes me a little bit more like a, like it behaves, it seems like a little bit more like a language model where it’s kind of guessing and not so formal. I mean, it says in the, in the technical report that it is an informal reasoning system.

And then there’s a third part, which we maybe don’t have time to go as deep on, which is specifically dedicated to geometry. Again, in the technical report, you described that as being like AlphaGeometry, which I think DeepMind developed.

So correct any misconceptions that I have there and give me the double click on what, like what more I should understand about how this thing works.

Sure. I think, I think you covered the components pretty accurately. So one thing I have to say is that, you know, we revamp our systems pretty often here. So I think Aristotle now looks quite different than Aristotle for the IMO. You know, I think a lot of things are consolidated and improved.

I think that you made this point about the Monte Carlo tree search being more of a grinder. I wouldn’t quite characterize it that way.

So the Monte Carlo tree search is actually doing a lot of inference on its own about high-level steps. So the levels that we’re talking about, they’re much closer to solving a challenging math problem than they are to prove that two squared equals four. So there’s a lot of reasoning that goes into them.

In some sense, it’s grinding once you get low enough in the search tree because you’re just closing out cases or easy subproblems. But it’s really solving harder problems on its own.

And so when we combined it with the informal reasoning system, you could almost think of it as a form of context management, actually. So ultimately, you need to end up with a lean proof, and that’s going to involve big steps and small steps. And it’s helpful when you’re focusing on the smaller steps to not have to remember the entire context of the bigger steps.

And so it turns out the informal reasoning system itself actually makes enormous quantities of mistakes. So one should not think of it as,

“oh, it’s a really smart human that’s laying out the steps to base camp.”

It’s more like a system that can propose lots of things that are wrong and don’t have to be formalizable or even correct. And you kind of try to assemble things from that.

So you can think of both of them as kind of doing the same thing, just at slightly different scales and complementing each other. And they’re actually all LLMs. So as we described in the tech report, the tree search itself is driven by language models.

Part of the language model is proposing steps. Part of it is scoring steps. But they work in concert to solve the lemmas and then eventually the full problems.

And as you mentioned, alpha geometry, it’s a slightly different system. We’re exploring kind of high-level steps and then trying to use an algorithm to grind through the rest of it. I think if we’re talking about systems grinding through a lot of math, I would say alpha geometry in the deductive reasoning system is really a grinder. So it’s really trying to find every possible conclusion of a geometry diagram.

I would say there’s not too much pattern recognition intelligence going on there. And that’s because geometry, if you think about it, is more constrained. You basically have points. You can basically start with points.

If you have three points, there’s only so many angles involved. Obviously, if you go to like 10 or 15 points, things blow up pretty quickly. But it also then becomes hard for humans to solve. And I think that’s why geometry was among the first class of competition problems to fall to AI and automation.

I think there’s also a couple of other components that might seem simple but are non-trivial that Aristotle, the system, does and are independently improving.

• One is auto formalization: taking input that you provide in natural language and faithfully translating it into Lean in the best possible way. And I think, relative to our competitors, at least, I’m not aware of anything that’s as good at that as we are.

• And also theory building: sometimes in the way of solving something, you have to create new theories and new structures that might not exist in mathlib. Aristotle has the capability of actually building that on the fly and incorporating that into the proving process.

Another funny anecdote, so that you’re referring to what I discovered is informal mode, right? Where I can provide—I think real users would not do this—but you can provide anything, any natural language input, just something that the system will then try to prove.

I asked it to prove all is love. And it came back and said,

“this is a philosophical statement and outside the scope of the Lean formalizer’s ability to prove.”

I also asked it to prove

“Epstein did not kill himself.”

And it came back and said,

“this is a statement about current events. And again, it’s sort of outside the Lean formalizer’s ability to prove.”

But yeah, I think this kind of gets back to this sort of metaphysical question that I find so perplexing around that translation from the messy real world of human affairs and intuitions to the formal definitions of,

“okay, this is actually the thing that we would want to prove.”

I did find that very, very interesting that you had such a thing at all. And I guess, well, do you have a sense for— I also do want to get into a little bit more details of just like technically how you created the models and all that stuff. But, you know, on my spectrum from 2 plus 2 equals 4 to all is love, is there, how do you think about the intuition for what the boundary is of what is inside the, what, because I, because I, again, when in listening to your previous interview with Sequoia folks, it seemed like you had the sense that eventually as the system and systems like this get capable enough that more and more things that are of interest every day people will start to become the sorts of things that they can do.

So like, how do you think about that boundary and how does that boundary expand over time?

I think the, the ultimate boundary of a system like Aristotle is in reasoning through any problem where people can also agree on what it means to be a valid sequence of reasoning steps. So right now you have math. That’s one obvious one. When we talk about mathematics being the same as reasoning, that chess example you gave is a perfect one. So you can express the logic of a chess game and then check it, right, and then reason about it.

I think one area that’s really going to touch a lot of people’s lives is it turns out you can use the same reasoning approaches to think about software. So when people write software, they write these things called:

• Unit tests
• Integration tests

And it’s kind of having the computer just run the program and check the output against what they expect. But that’s what they do after they’ve written the code.

It turns out that when engineers are writing code, they’re thinking logically:

“Okay, if I have this range in my input, I can think okay, as I go to this for loop at these if statements, it implies certain things about the output.”

And that itself is logical and mathematical reasoning. So we’re starting to see API users reason about programs in the same way that they can reason about math. People are writing cryptography implementations and then checking:

• Is there any possibility that two inputs might give me the same output, which would be violating a certain principle of the crypto algorithm?
• They might be implementing a controller for an autopilot and saying, is there any sequence of inputs for which I’ll have an unstable dead zone or something?

I think the same kind of input that will go to software will help take us to a bug-free software future.

Now, Vlad and I disagree a little bit. It’s not clear to me if we’ll be writing history essays or something — maybe there is a way to value them objectively. But I think the boundary is really in anything that’s quantitative and logical in nature.

Yeah, I think in the first version of Aristotle, it would actually formalize and build a theory for your all is love example. And it would give you a correct proof that it’s probably true.

I think it surprised us. People were asking all sorts of questions. We had people asking:

• Biology questions
• Medical questions
• Economics and finance questions

And Tudor mentioned computer science. So I think it’s actually surprised us how broad of a set of things it can successfully create a theory around and formalize.

I think the constraints we put were just, you know, when you’re building a product, you want to make sure that you deliver value. At this point, I don’t think we provide the most value if you want to write a history essay.

So we’re trying to nudge people to the point where they can discover what Aristotle is really, really good at as quickly and simply as possible. I think over time, you should expect that the surface area increases.

We start formalizing things. And I don’t think it’s inconceivable that at some point it pulls current events and news from the internet, puts out the axioms, and can sort of fact-check and make conclusions based on real-world events.

Not our focus right now, but I don’t think it’s a crazy thought.

I mean, I ask a question sometimes. I’m interested in astronomy, right? And I wanted to know:

“When’s the next full solar eclipse that I can see from within 50 miles of Palo Alto, California?”

The models usually struggle with this type of stuff because nobody’s asked that identical question out on the internet, so they can’t pull it. You actually have to do some math.

So you can imagine there’s a spectrum and there are questions like this that a model that can reason actually from first principles is going to be way better at.

Okay, let’s talk about just how you created this thing a little bit and how your experience, lessons learned, et cetera, kind of relate to some of the live questions more broadly in the AI space. I think you can take on faith that folks listening to this show will be familiar with things like reinforcement learning from verifiable rewards and stuff like that and certainly understand kind of how the ability to generate synthetic data feeds into a system like that and that’s, I’m sure, part of what you’re doing.

What more can you tell us in terms of like, would it make sense to start training something like this from some off-the-shelf pre-trained model or does that messiness that those, you know, LLMs start with corrupt or pollute your, the purity of the mathematical reasoning too much? Can you tell us anything about size of models, which could be parameters, could be tokens, whatever?

I’m interested in things like also, is there any role for taste in this process? Obviously, like mathematics, mathematicians are very interested in correct proofs, but they’re also interested in these eureka moments and the sort of sense of elegance of the proof, right? There’s a sense of the beauty that, you know, matters as much, I think, to many people as the correctness or maybe not as much, but, you know, it’s certainly heavily weighted.

And then I also noticed there’s test time training that’s part of this, and I think that’s, you know, a huge trend that I’m kind of watching in general.

So, you know, you can swing or take any of those pitches, but what do you think are kind of the most interesting next level of depth that people can use to inform their own AI worldview with?

Well, first, I have to say that if your audience knows about reinforcement learning from verifiable rewards, you’ve got a great audience.

“That’s not betting data.”

Yeah, that’s not. So, I think that is a safe assumption. Nobody was talking about that stuff, right? It was like science fiction almost, but it’s cool to see it entering the popular consciousness.

I want to address the taste question, because that actually, you know, strikes at a key thing that, you know, companies can decide on.

So, we get gold performance at the IMO, we have a very powerful system, and it was obvious we had to give it to people.

And there’s two ways you can do it.

• One way is you can say, well, we’re going to keep this in-house.
• We’re going to recruit some great mathematicians to come in-house and work in secret on problems.
• As they make progress, we say, well, Aristotle’s now done X and Y and Z.

That’s one way of expressing taste in the research map.

The other way, which we ultimately decided to do, and we think it’s been great for the community, is we said, well, we’re not going to be the ones to decide what’s important in math. We’re going to make Aristotle accessible to everyone.

And so, we opened up the API, the web interface, there’s a lot of great features coming.

And then, in this scenario, taste is expressed by the community by the revealed preference of what they submit to the API.

So, we don’t choose what kind of math they do.

We’re not saying, hey, Navier-Stokes is more important than P versus NP.

It’s the mathematicians that have the credits on the API to say, well, we care about X or some other thing.

And that’s why we’ve seen so much interest in:

• computer science
• crypto
• certain branches of number theory

And for a while, there are people doing a lot of interesting conjectures in graph theory on the platform.

And I think that that’s actually the right way for companies to engage with the community.

You know, you open the system and you let the people decide, you know, where they want to allocate those computer resources.

So, I think that’s an important decision. We’ve come on one side of it, but I think that’s the right long-term approach.

I think there’s a philosophical question there, too, which is, are we headed for a future where the AI labs themselves are going to generate all the discoveries?

Will the cure for cancer or diabetes look like a giant AI lab with a two gigawatt data center just churning on this problem? And then, you know, it comes out and they capture all the value?

Or does it look more like millions of people empowered with these tools working independently and collaborating and, you know, in that world, they’ll get the credit and the value will largely accrue to them?

And I think we believe that the second world is more interesting and it’s probably the one that’s more likely.

The first one is rather dystopian and less likely.

And I think we noticed that because when we rolled out Aristotle, you know, we had one view of what people would use it for, but then we started getting all of these, you know, Erdős problem results and things like that.

And it’s like, we’re not going to run on all the Erdős problems. We’re not going to do like computational learning theory, formalizations in house.

So I think the amount of cool things being done with it just explodes if you put it, if you make it generally available. So I think it’s not only right from a business strategy standpoint, but also like, I think that the world that we built, assuming this path, is a better world that I would like to live in.

So that speaks to taste in terms of problem selection.

But I was also just thinking in terms of, as you’re training the model, you’ve got the correctness signal, but maybe one sort of heuristic for elegance would be like just brevity.

Which is maybe one kind of way of trying to send an elegance-like signal through a deterministic mechanism. But I would be very interested to know if there is like a panel of mathematicians that you guys have reviewing solutions for elegance to try to make sure that this thing is not just a pure grinder long-term, but really has a more eureka flavor to it.

Well, brevity—if brevity is the definition of elegance—then our two plus two equals four proof probably takes the cake, right?

“I can’t get any shorter than that.”

I would feel bad for any mathematician’s job of us to compare AI proofs. That’s certainly not the job I’d want.

So we, we have never. It’s a big business these days across all domains:

• Many billions spent on expert validation of AI outputs.

Yeah, we have done essentially zero of that in the two years we’ve been around.

I think the metric we optimize for is the net present value of future proofs or computational costs of future proofs. And so that guards very naturally against certain phenomena.

When you’re solving easy problems early on in reinforcement learning, you absolutely can solve them with grinding. So you can say,

Let me just do brute force.

But you know that if you do that, it’s going to cause issues later because you haven’t learned how to do more complicated things.

In contrast, if you’re given two proofs that are not grinding, but one is drastically longer and more inefficient than the other, you prefer the more efficient one.

So there’s a tension there because you can get more efficient by grinding, but that messes you up in the future. So it’s a balance that our AI researchers strike based on their intuitions about what’ll be helpful long-term.

But we have never had panels of mathematicians do testing on proofs or anything like that. Really, you want to give your system as few priors as possible and just run reinforcement learning at scale.

There’s a famous essay called The Bitter Lesson, which I’m sure your viewers are familiar with. We really believe in that at Harmonic.

To get to your question about how we started: sometimes we’ll start from pre-trained models. Ultimately, you want to do whatever optimizes that net present value of future cost of proof. So pre-trained models are great for that.

I think at some point you might ask the question,

“Is that going to bias you too much towards how humans do math?”

And so you want to mix in reasoning systems that are not trained from human knowledge, right? They have more entropy and more complementary knowledge.

That kind of thing we always play with, but it hasn’t really been the living factor so far. I think that pre-trained models are a great starting point.

Cool. I guess one thing: Goodfire just announced today that they raised a bunch of money at a unicorn valuation. I was a very small-scale supporter of theirs, and it got me thinking.

This also connects to Vlad’s comment where you said the system can sort of invent new theory.

Obviously, one big thing people have said AIs can’t do, or AIs can never do—which is always a dangerous position to take—is that they can’t come up with new abstractions.

Sure, they can learn from what we have done and what we’ve encoded into language, but will they ever come up with their own abstractions? I think that’s not a very strong, increasingly hard position to defend.

But what is so interesting with Goodfire is they’re now starting to look at model internals and unlock new kinds of understanding based on looking at what the model has learned.

The famous one they just put out is like new markers of Alzheimer’s that people didn’t know about, but the model was able to figure out, and they were able to figure out what the model had learned by looking internally.

I’m kind of wondering:

• Have you guys done any interpretability work on your models?
• Do you think there is a different kind of latent space that you are tapping into?
• Do you see sort of hybrids as part of the future? Because one thing I could imagine happening is starting to stitch together a mathematical superintelligence with a more, kind of fuzzy, associative, understand-the-world superintelligence, perhaps like later in the training process to try to get the best of both worlds.

I mean, one of the things that I’m very excited about is eventually Aristotle powering a spacecraft, right? Much like HAL 9000, but a benevolent one, one that doesn’t go crazy. So, yeah, I think eventually you’ll see it expanding into more real-world things.

I think the… I don’t know if you’re as excited about that. A safe HAL 9000. A safe HAL 9000, I think, would be very valuable.

You know, to your question on interpretability, I think that interpretability is often used as a proxy for trustworthiness. So, a lot of the reason that people explore interpretability technology is that they can make sure that the system does the right thing or aligns with the user’s intent.

So, when it comes to trustworthiness, we made the explicit decision at the very beginning of the company to focus on Lean. By outputting our reasoning in a formally verified way, that is the most interpretable possible output. So, the computer can check it. If the human wants to understand how the proof works, they just keep hitting “go to definition.”

It’s almost like navigating through a code base. There’s no more interpretable way to output math than in Lean, really. That’s the maximal version.

So, now the question is, okay, well, how interpretable is the model? I think, in the context of the bitter lesson, we just focus on letting the system do whatever it can to optimize for computationally cheap proofs of more and more complex things, with a caveat that it has to output in a way that’s verifiable.

I think down the road, we’re very curious, how does it do math? How is it so smart? And we’ll look into that. But for us, we’ve solved the trustworthiness question upfront by focusing on formally verified output.

Yeah. Okay. That’s quite interesting.

I do sort of feel like, I have this one kind of mental—mathematicians are famous for visualizing things—my kind of visualization of what is happening in a large model is sort of like shrink-wrapping reality.

Like, you’ve wrapped in plastic all of, you know, all internet data or all the kind of whatever domain it is that you’re trying to learn at scale, and you’re just sucking all the air out of it and gradually shrinking down to whatever, hopefully, is kind of the true structure.

And it strikes me that in math in particular, that structure might be amazingly simple. Or, there might be really interesting things to learn by running that process and then kind of cracking it open and seeing what is inside.

I would expect it to be maybe a lot more interpretable internally than something that has had to learn all internet data and can recite Wikipedia and all that sort of stuff.

I actually think that what these models are doing is interesting because they’re smashing together all of the techniques that all mathematicians have done before.

And so, while I haven’t seen the spark of superintelligence yet where it’s some breakthrough eureka idea that’s incomprehensible, I’d say that if you push it in, learning how the models do things, you kind of ask it to solve more and more complex problems and just see, like,

• How did it pull together these three subfields of math in a way that no human has done before?

I think that’ll be a lot more interpretable and comprehensible than trying to dig through the way it’s structured—I might be wrong, but that’s probably where I’d start to interpret how it does things.

Yeah.

So does that mean maybe we can kind of look at different levels of difficulty of problem?

We’ve got the Erdős problems.

There’s definitely a phenomenon happening right now where people are using either Aristotle by itself, or—I’ve also seen a lot of examples, not that many, but increasingly more, of GPT 5.2 Pro to sort of generate a proof in token space, then bring it over to Aristotle for formalization.

Then there’s, of course, the IMO.

If I understand correctly, everybody who—and I think it was just three, right?—that you guys, OpenAI and DeepMind, got the gold level performance. I think everybody missed the same one question, which is really interesting to me.

I’d be interested in your thoughts on,

“why that—why so consistent?”

And then, of course, we’ve got these extreme problems where you would need this sort of move 37-like moment to solve them.

So maybe kind of sketch out,

• Where are we on this curve of problem difficulty?
• Do you think that we’re just going to ride a smooth exponential, meter-task-length style, all the way up to Millennium Prize problems? Or do you think that there are going to be breakpoints of some sort where you might need a new architecture, a new insight, a new learning method to get from one range of problem difficulties to something that’s qualitatively different?

I mean, I think – so on the IMO, the three labs that announced gold medal performance—us, DeepMind, and OpenAI—all missed question six. And I think that it wasn’t super surprising to us because question six is probably, I don’t know, 5x harder even for humans, right? It’s just a more complex question with lots of steps, and it requires this type of spatial reasoning that right now is more difficult to encode in formal systems.

We were running our system on it quite a bit, and we felt like we saw signs of life. So it’s definitely not inconceivable that before too long, question six is going to fall and be gobbled up just like the other questions. I mean, even one year before, questions three and five would have probably been well beyond reach for most of the models. So I think it does appear to be more or less a smooth exponential.

Yeah, I agree with that. I want to highlight that there’s two aspects of this.

• So I think we’re continuing to see a smooth exponential in terms of AI capabilities in math.
• What I think is a little more interesting, actually, and was less predictable before, was that there – I think there’s now definitively been a phase transition to formal math.

So I think years ago, if you had asked someone, “Hey, could you automatically formalize a number theory paper in Lean or Rock or Isabel, these other languages?” you’d have been laughed out of any room of mathematicians you’d be in. And today, we are seeing people upload the full text of a math paper and run Aristotle a few times. We’re thinking of adding a Ralph button to just keep going, keep going, keep going. And then you get a formal version of it.

I think that phase transition has essentially come and gone now because of Aristotle. So in the next couple of years, as AI keeps improving, the fact that we can now formalize the AI’s arguments obviates the need for the humans to just be the verifiers, right, just sitting there and checking if some output is correct, to ones being the tastemakers. So we’re the ones setting what problems to work on if we’re happy with the techniques used. So that, I think, is the interesting transition that’s happened. So smooth exponential capabilities, but I think we’ve gone zero to one on verification.

I think that’s such a great point because I think there was some debate about this at the beginning.

And in a way, if you look at DeepMind, they started with formal, with AlphaProof, which was the silver medal-winning model back in 2024. It was a great result at that time, and that was a formal model. And then they went back to informal for Gemini this year, and I’m sure they ran AlphaProof. Maybe it was just that AlphaProof didn’t do as well. OpenAI, obviously, informal.

But if you think about, okay, let’s say we go to a world five years from now, and the autonomous math being done by AIs increases. Instead of five to ten-page proofs, you’re starting to produce 5,000-page proofs, which you should assume, right, as these models can autonomously reason more and get more efficient, they’ll produce longer and longer output per unit time. It’s going to be a proxy for complexity.

Who’s going to review that? Nobody’s reading a 5,000-page math proof. So I think it’s becoming even more clear that the future is formal because you have this problem of someone having to validate it and check it. And we want to make sure that the time to validate it and check doesn’t actually grow linearly with the complexity of the proof.

Yeah, that was really the founding thought experiment of harmonics.

So we asked ourselves in 2023:

- These models can do high school math poorly, but they could do elementary school math poorly a year ago.
- What happens in 10 years if we ask it to prove the Riemann hypothesis?

Any model will make an attempt at it and give you 100,000 pages of output, which you might as well throw in the trash for two reasons:

1. There’s probably a mistake somewhere.
2. You can’t process it. There’s just nothing to do with it.

No, you just can’t wrap your head around what is going on in that proof.

And so there were two hypotheses, both of which have been proven out:

• First, outputting math formally makes it digestible for humans, and there’s a high level of certainty and trust.
• Second, it’ll lead to more efficient ways to do reinforcement learning for math, which is what we saw proved out.

If you compare the resourcing we’ve had compared to the big labs, we’re punching well above our weight at the IMO. So I think, in our view, the debate on formal versus informal is settled. I mean, clearly, it’s going to be formal.

One can debate, okay, what’s the most efficient way to train a model? There’s some aspects to informal that are helpful, but I don’t think we’re ever going back to a world where we’re like, “oh, it’s just going to be informal from here on out.”

I think the interesting question, though, is to extend this to software, right? Because the same things actually hold for software that hold for math.

Let’s say AIs are getting to the point where they can autonomously work and create a software project over a period of a week or multiple weeks. You know, who was it? The cursor team ran this and generated like a Chromium-compatible browser, right? It was something like one and a half million lines of code. It was incredible.

So who’s going to read that code and find all the security vulnerabilities and the bugs? And is that code in the future that’s generated by AIs going to be in Python and Java anymore? Like, why would it be in Python and Java? Those are just languages optimized for human readability.

And, you know, if the answer, we think, to humans reading and trusting something or even an AI that the model is collaborating with checking something is the same. You want to make the cost of verification as low as possible. And that makes us believe that the future of software is formal as well. And more and more software will be written in formally verifiable languages.

Yeah. And I think, you know, Lean is our favorite language. It would be amazing if everyone can write in Lean. I think that as AI writes more and more code, it will be easier for people to accept that. But we’ll see.

And I’ll start with mission-critical, important stuff where bugs are much more serious and much more costly. And there’s a bunch of domains that already are doing formal verification for software, but they’re doing it in a very artisanal way.

You know, they’re hiring Lean or Rock or Isabel experts and kind of painstakingly formalizing stuff. So I think you’ll start to see it accelerating the work of those people first, but then it’ll just diffuse and you’ll see, like, formal vibe coding before too long.

Yeah, I love the term vibe-proving, by the way. Yeah, I think that vision is an incredibly compelling one. And, you know, it’s also one that I’m still kind of wrapping my head around.

For listeners who haven’t already heard it, I did one episode with Kathleen Fisher, who was at DARPA, and I think now has just moved to ARIA in the U.K. to lead their whole operation. And Byron Cook, who’s like a legend of the formal methods field at AWS. And, yeah, they’re kind of right there with you, you know, envisioning this world of basically totally verified, bug-free software, starting with mission-critical stuff, but potentially extending to everything over time.

I guess one – so I think that is super compelling.

The one kind of nagging – I don’t know if it’s a worry that I have or what exactly, but I’ll just frame it as a question – is, like, if we are training an AI to be superhuman at formal reasoning, within the formal reasoning system that we have,

how do we get new abstractions from that or how do we get a sort of Einstein kind of moment where, you know, like, it seems that at some point we all sort of thought the world was just naturally 3D and that was, like, obviously intuitive.

And then it’s kind of come to light, obviously now, that, like, well, that was an adaptive understanding of the world that served us well as monkeys, you know, and allowed us to survive. But it was at – at the end of the day, we now know that it’s, like, a lossy approximation of true physics.

And so I’m kind of like, do we have any room for doubt or worry that the math that we have now, as sophisticated as it has become, might also at some point prove to be not quite the right paradigm? And is there any way – if you’re training in this, like, purely formal way, is there any way sort of to punch your way out of the box as an Einstein did, right? He seems to have –

“The fourth wall.”

So he broke the fourth wall conceptually, but the key thing to remember is that he was able to describe his theory rigorously and formally in the framework of differential geometry.

So the point I was making earlier about math being reasoning is the point I’ll appeal to now, which is to say that no matter what complicated theory somebody might come up with to explain how the universe works in the future,

If it’s going to be based on a series of logical deductions that can be explained to someone else and checked independently, that is itself a logic that can be encoded with Lean or other languages like Lean and then verified. So, again, the axioms that Lean is based on are so minimal and just expressing just the most basic possible common sense about how reasoning should happen, like, one thing might fall from another, or if two things look the same, they are the same.

That’s the level of axiom we’re talking about. So I really don’t think there’s any conflict here. I think that one should just think about formal reasoning as an especially detailed version of informal reasoning that a computer can check automatically. There’s no limitation to it. Sometimes it might be a little more verbose than you’d want, right? So you want to write tactics and things to cut down on that, but there’s really no fundamental tension to turn into.

And I think there, you also, you know, might be thinking about Gödel’s incompleteness, like the fact that in any sort of axiomatic system, there’s statements that are true and unprovable. And there’s also statements that are undecidable, right? And independent. So there’s sort of like a bunch of edge cases here, but I think it doesn’t prevent us from making a lot of progress and proving actually the lion’s share of useful things. I mean, there could be things that are unprovable but true that are very, very useful to know as well. But, yeah, no way to know unless you explore the frontiers.

Do you think there’s always going to be a role for entropy of some sort in these systems? I mean, I think hallucinations are a key part of a reasoning system. Hallucinations are what allow a model to explore something that has never been encoded by a human before.

So, you know, when we run Aristotle, whether it was at the IMO or noun, it makes a lot of mistakes. It tries a lot of paths that don’t work. But that exploration is the very thing that lets you get to the right answer after enough attempts. So entropy is crucial. I think this whole notion of seeking fundamentally hallucination-free LMs doesn’t really make much sense.

Now, of course, you want to pair them with a system like Aristotle that can verify things in 10. But, no, I think entropy hallucinations are a key part of the training process for models like this. You’ve got to be able to pose false statements in order to prove that they’re false. Learn like humans. You know, you try a lot of room for humans. Some of the most creative humans are the ones that hallucinate the most.

So what’s kind of the latest progress on the path to superintelligence? You said you, and I think this is true of all good frontier AI companies, whether, you know, at the application layer or the model layer or anything, any hybrid of those, you know, you’re updating your systems frequently. It sounds like there’s kind of a convergence of some sort going on between the tree search part and the informal lemma guesser that you described in the technical report. What can you tell us about kind of what the trends are right now?

I think a lot of the—well, just to review the progress, right? So we started in 2023 and then in 2025 goal performance, the IMO, we topped out this Verena benchmark at the end of the year with our public API users started solving Airdish problems, right?

• Which were unsolved for what, 30, 40 years.

So I think there’s a very clear trend, right? And, and capabilities. I think the phase transition I mentioned has also happened.

So I think what’s next for harmonic and for the field at large is, you know, a couple of things.

Well, we can expect math live to grow. So math live is the, think of it like the Wikipedia for math that’s computationally certified. So as Aristotle makes it possible to auto formalize a lot of math, you can expect that users will start contributing a lot of pull requests to math live. And that makes it possible to solve more and more problems on top of that base.

I think when we look at how mathematicians are using our API, certainly people are starting to work on more important unsolved conjectures that a lot of people would care about.

So you can kind of think about conjectures as like,

“Okay, there’s a conjecture that’s technically been open, but nobody really cares about it.”

So it’s not like people are trying all the time, but now you might have some conjectures that, yeah, like a mathematician might try it once or twice a year, just take a shot at it. Maybe a hundred mathematicians would.

And then eventually, but the millennium prize problems where, you know, any mathematician would be happy to spend years on it if they might be able to solve it. So I think what you can expect from Aristotle and other systems is, you know, more and more problems get picked off. So it becomes easier to use it extends to software, as I mentioned.

So we have users using it to check, say, decretable software, whether in Lean or other languages.

And overall, if I had to pick out just one trend, it’s really just that formal reasoning goes more and more mainstream. So as more stuff is produced with AI, I think you’ll see complementarily more formal reasoning to kind of verify all of it.

And I think on the product side, we’ve gotten a lot of feedback coming in from the folks using it. Obviously, whenever you’ve got customers that are using a technology like this, they’re very passionate.

So there’s lots of ways in which they’re still complaining about things and improving the ergonomics of it, making it so that people don’t have to hop between so many different tools. And we could just solve their problem as simply as possible and at the lowest possible cost. You should see that continue to improve.

There have been updates to the system pretty much on a daily basis. Maybe you’ve seen some of them just as you’ve been kind of experimenting yourself. But that is going to continue. And you should expect that it gets exponentially more useful over time.

So maybe a good place to close is kind of the vision for what that looks like as you succeed. I mean, obviously, one thing is solving Millennium Prize problems. But I’d love to get a little bit more of kind of an intuitive understanding than that.

I mean, one dichotomy that kind of comes to mind is this very formal reasoning-based paradigm versus what I think of as intuitive physics. It does seem like models are very good at developing intuitive physics in kind of any number of spaces.

Right. Like folding a protein with a model is not something that’s done in a formal way. It’s just kind of something where whatever kind of mess of heuristics they learn, they can do a protein fold orders of magnitude faster than we would be able to do it.

And if we were going to do it through a sort of physics-based simulation approach, when we think of no limit to math and what a mathematical superintelligence looks like, I also think Eliezer, once famously—or at least famous to me—said:

“A real superintelligence in his mind could look at one still image and deduce all of physics from just the information contained in that one still image.”

That kind of also connects, I guess, to test time training.

What is your vision? You can bounce off any of those concepts, but what is your vision of how this thing evolves? Is it an ever bigger tower of formal statements? Is there some role of new kinds of intuition, new abstractions that emerge out of that that aren’t so strictly defined but potentially useful?

You know, what is this thing doing in 2030 once all the Millennium Prize problems are solved?

I think that by 2030, we will have theoretical explanations for everything, basically.

I mean, if you look at the history of science, there’s leaps of intellect and leaps of data:

• The microscope comes along, suddenly you build a lot more theories of biology.
• Now the electron microscope comes along, you can build more theories like chemistry.

Right now, there’s really been a shortage of people that are able to reason logically at the highest level.

So when you think about unifying general relativity and quantum mechanics, it’s just a very hard thing to do.

I think what you’ll see is really like anything that can be posed mathematically, which is what underlies all of science, we’re just going to get theories for everything that are self-consistent and make sense.

I think we’ll then go back into a regime where we’re data limited. So, we might have maybe five theories that unify QM and GR, and we’ll have to run very high energy experiments to figure out which one is right.

We’ll have to wait a while to build those colliders. But at the very least, we’re not going to be bottlenecked anymore on wondering, “Can we explain something?”

We’ll have a system that can explain anything perfectly correctly. So it really will be a renaissance of science. You just remove the intellectual bottleneck in everything.

So do I understand that correctly? Basically, you’re envisioning:

• Multiple grand unified theories that all explain all the data that we have,
• Then it becomes a problem for the collider experiments to figure out which one of these is in fact right.

Yeah, because AI is not omniscient. Whether it’s our model or others, they’ll be able to reason about anything they can kind of ground in their own logical deduction rules.

But ultimately, there are aspects of the universe where you just have to run the experiment and find out how it really works.

Wow.

Just to be clear, I think there’s a lot of utility before you get there. If I have to analyze asymptotically where we get to that, that’s my point. Well, I mean, that’s, we’ve heard about centuries of scientific progress collapse into five years. That sounds like more like a few thousand years, perhaps, of scientific progress.

Also, that’s left will happen, and then you just have to get more data. But you’ll have a superintelligent system that can help you. Wow. Okay. That’s about as grand of a vision as I’ve heard anywhere.

Do you guys worry about the safety of these systems? It sounds like we haven’t talked about that really at all in this context, but I’ve done many explorations of different safety concerns.

You know, Eliezer, when he described the model, whatever AI he was kind of envisioning, when he described it, understanding all of physics from a single image, he also thought that was going to be super dangerous because it would be so powerful.

How do you guys think about that aspect of this whole, I mean, we’re talking about a lot of stuff in the next five years.

I mean, I think right now we’re not so worried about it because the outputs of our system are constrained.

I think that you’re likely to see, like, the first dangers will probably look a lot like cybersecurity incidents, right? Because, you know, you have the models that are making API calls and running autonomously, interacting with other systems.

So that both creates API level cybersecurity holes and the mechanisms to exploit those. So I think you’re likely to see a lot of those.

I think for our model, since it’s basically just the interface to the outside world is tightly constrained, and it’s not just going to fire off a request to your Gmail account or the iMessage APIs, we’re a little bit further away from that. But, you know, you can imagine we’re going to have to start taking that much more seriously when we do get to a point where we’re connecting the model to the outside world and it’s, you know, speaking in the interfaces are not just sort of like lean files being outputted.

Yeah, I do think constrained action space is certainly one of my favorite paradigms for keeping things under control. But I mean, there’s a full like molt book, molt bot thing that has been fascinating to watch. And, you know, I think we’re entering a strange new world for sure.

And I think the benefit is we’re probably not at the danger frontier. So we’ll have the opportunity to learn from others’ mistakes, and hopefully they don’t screw up too badly in order for us to learn.

Yeah, okay, fascinating stuff. This has been fascinating stuff, guys. I really think the approach is really interesting.

The vision for how far we can expect, or even somewhat entertain the possibility of being in 2030 is arresting, and both inspiring and for me, a little bit scary.

Anything else you want to leave people with before we break?

I think for me, and you kind of see this in the values that we put on our website of what we care about:

• We believe in a future where humans are going to be at the center of all this progress.
• We will definitely accelerate it, but the humans should be in charge and calling the shots.
• That’s also why we care so much about putting this into people’s hands and making them use it—not just be a lab that runs things secretly and makes big proclamations.
• We believe humans need to be at the center of everything and still calling the shots.

You know, that’s what we believe in and in the world that we’re helping — the future that we’re helping bring to life.

Yeah. And I think just to add to that, for me, when I started using Aristotle, it was very different to have an experience where the output’s always correct. And so I think if people haven’t experienced that before, they should just try it out. It’s a free to sign on for.

Cool.

Well, there’s, I’m sure there’ll be plenty of ways to monetize mathematical superintelligence when the time comes. We might do ads, you know.

Yeah. I can’t wait for that.

All right. We’ll do those anthropic ads to life.

Fascinating stuff, guys. I really look forward to watching your progress. Thanks for both the remedial education and a grand vision today. It’s really extraordinary. What a time to be alive.

Vlad Tenev and Tudor Akeem, co-founders of Harmonic. Thank you both for being part of the cognitive revolution.

Thanks for having me. Pleasure to be with you.

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The programming language after Kotlin – with the creator of Kotlin

Why would anyone create a new programming language today if AI can already write most of your code? Andrey Breslav has an interesting answer.

Andrey Breslav is the creator of Kotlin, a language that runs on billions of Android devices and is one of the fastest growing languages in the world. Today we cover how Andrey designed Kotlin by deliberately borrowing ideas from Scala, C Sharp, and Groovy, and why he considers leaving out the ternary operator one of his biggest regrets.

We also discuss why making Kotlin interoperate seamlessly with Java was a gigantic undertaking, and what it took to get it done. Kotlin adoption went through the roof after Google announced it as the official language for Android, in a move that even took Andrey and the Kotlin team by surprise.

Andrey’s new project, CodeSpeak, is a new programming language built on English, designed for an era where AI writes most of the code. If you’re interested in the future of programming languages from someone who built one of the most loved languages of today, then this episode is for you.

This episode is presented by Statsig, the unified platform for flags, analytics, experiments, and more. Check out the show notes to learn more about them and our other season sponsors, Sonar and WorkOS.

Andrey, welcome to the podcast. Hello. Thank you for having me.

It is not often that I meet someone who designed such an influential language across mobile and backend. So let’s start with: how did it all start?

Okay, so that was a little messy because I went to school back in St. Petersburg, studied computer science, and I didn’t really know exactly what kind of programmer I wanted to become. I knew I wanted to be a programmer. At some point, while I was still at the university, I started teaching programming in school. It was a big, passionate hobby of mine.

At some point, I got a job with Borland and worked in some developer tools. That was awesome. Borland was a very big name, though they went under pretty soon after I joined. I hope it wasn’t because of me.

I worked at the tail end of the UML era, doing developer tools in the UML space. That was very interesting. I learned a lot. But then Borland went under, and I went back to teaching full-time. Then I started PhD school. All that was kind of not really planned out.

In my PhD, I was working on domain-specific languages (DSLs), and generally, I was interested in languages. I was curious about typed languages specifically. I was always curious about how these things worked, but never really serious. When I started looking into DSLs, it was slightly more serious. Although my PhD was a mess and I never defended because of that.

At some point, someone reached out — he was actually a person who was in charge of Borland’s office in St. Petersburg. By that time, he was already at JetBrains. He reached out to me while I was in Tartu, Estonia, where I was a visiting PhD student for a year. It was a lovely time.

He invited me, during my next visit to St. Petersburg, to visit the JetBrains office and talk about something related to languages.

What I thought was that it was about this project called MPS (Metaprogramming System) that JetBrains had. I knew about it. It’s about DSLs. I worked on DSLs; it was plausible they wanted to talk about something like that.

But I was completely wrong.

What they wanted was to start a new programming language.

I was completely unprepared for that. I had never thought about doing something like this. My first reaction was:

“You don’t do new language. You don’t need it.”

The basic pitch was that the Java ecosystem needs a new language. Java is outdated, so on and so forth. We can talk more about this.

It was 2010, I think. I said, “but there are other languages. Everybody’s doing fine. Why do you need to do that?”

Then this conversation was actually very insightful because the guys at JetBrains explained how things actually were. It was a big problem by that time.

So Java didn’t really evolve and hadn’t been for a long time.

What was the reason behind this? Can you take us back for those of us who are not in the ins and outs?

Yeah. So the last major version of Java by 2010 was Java 5, released in 2004 — a six-year-old language. Since then, there were updates. Java 6 made no changes to the language at all. Java 7 made minor changes. In parallel, other languages — especially C Sharp — were progressing very well. And by 2010, C# had all the nice things. There already were lambdas, like header functions and all that nice stuff. There were getters and setters and many other things that made the language much nicer. And Java was felt like it was standing still. There was a project to work on lambdas for Java, but that was in the works and had been in the works for a long time and only came out in 2014. So that was the situation.

And, you know, the ecosystem didn’t stand still in the sense that other people were building languages. And there was Scala, there was Groovy. And, of course, people at JetBrains knew both Scala and Groovy. They built tools for them.

It’s traditional to build your tools in the language you’re building the tools for. So the Scala plugin was built in Scala. And there was a lot of Groovy used in JetBrains as well. So they knew what the issues were with the language. And both languages are very interesting and very good in their own ways.

But they saw an opportunity in the market because basically Groovy was too dynamic and too far from, you know, hardcore, mainstream, large-scale production. Because dynamic languages are not for that, basically.

What are dynamic languages for? What are their strengths and best use cases? The trade-off, I guess, if you look at a statically-typed language like Java, Kotlin, and Scala, for example, versus dynamic languages like Python, Ruby, JavaScript, and Groovy:

• In dynamic languages, it’s very easy to start and build something working very quickly because basically the language is not in your way as much.
• There’s a saying that “nothing limits the imagination of a programmer like a compiler.”

And this may be changing nowadays a little bit. And this is in part what I’m working on now. But back in the day, it was completely true. The whole art of making a good language was to restrict the user in a good way.

Yeah, but in any case, the situation with dynamic languages is that they are much more user-friendly in the beginning. But then when the project scales, you’ll have trouble making large refactorings. You have trouble making sure that everything works together. You need to do a lot more testing and rely on other things like that.

As opposed to static languages where you have precise refactoring tools and other things that can make sure that at least a certain class of problems just doesn’t happen. And, you know, this is why, at least in our mind back then, it was absolutely clear that if we’re building a language for large projects, big teams, so on and so forth, it has to be a static one.

So with Groovy, that was a big issue of performance as well, because Groovy was building a dynamic language on top of a very static runtime. So there was quite a bit of tension there.

That wasn’t the Groovy side and the Scala side. Scala is a wonderful static language and incredibly powerful and with tons and tons of good ideas. But it had its own problems. It relied very heavily on implicits, for example. And I have a history of debugging one line of Scala for an hour to try and figure out what it does. Just because it was pretty complicated.

Also, the compiler was very slow and there were issues of stability, and many, many things were just not accessible enough for a lot of engineers. So from the experience of using Scala, JetBrains, my colleagues basically understood that it’s not what’s going to change the industry. Although Scala got a lot of adoption.

And again, like Martin Odersky, he is a great language designer. And I think one of the biggest use cases was old Twitter. A lot of it was built on Scala and they scaled to massive scale, etc. And I think LinkedIn as well.

So in any case, these were, you know, it’s always very nice when other languages kind of pioneer things. And then you can build on top of their successes and failures. And we were in that position, basically.

So the argument that people at JetBrains were making was basically that there is a window of opportunity. People need this language. We, JetBrains, are the company who can actually put out a language and make it successful because:

- We have access to the users.
- We have their trust.
- We can make good tools.

And it was another issue with Scala, for example. It was very difficult to build tools for Scala back then. Now Scala 3 is more tooling-friendly, but back then it was a nightmare.

Like, I said that, you know, if you have a static language, you can’t have precise refactorings if the language is too complex. And some languages are particularly challenging. So Scala back then and C++ were incredibly challenging to make precise tools for.

So, and that was the basic pitch. And I quickly understood that, yeah, they were right. And this was something that was worth a shot in the sense that it was not completely hopeless, not completely dead in the water. I had no idea if we could pull it off.

It’s, it was then when we actually sketched some initial features on the whiteboard.

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So everybody I talked with was deeply in the weeds with IDEs and everything in new programming languages very well. We had a very technical discussion.

I don’t remember exactly all of the features we were talking about, but the current syntax for extensions in Kotlin was already there. I don’t remember why exactly we focused on extensions, but it was there.

So, from day one, we’re basically building on top of ideas from other languages, like extensions obviously came from C#.

Yeah, so it was a very exciting conversation, but I didn’t make a decision then because I was in Tartu and I needed to finish there. It took me a few months to finish.

Then I came to St. Petersburg for one month because after that I had an internship scheduled with Microsoft Research in Redmond. I was going to Seattle to stay there for about three and a half months.

I said, “Okay, guys, I have this month. I can work in the office and we can try to sketch things, but then I’ll go into Microsoft and then I will decide whether I commit or not.” Which in hindsight, I made the right decision in the end.

I had a great time for this month or so. I worked with the guys in the office — it was mostly Max Shafirov we were working with and it was incredible. We had such great discussions and I actually saw Max this morning and it was like, it was great time.

So then I went to Seattle, did something completely different. There are Microsoft researchers, some really great researchers working there, actually was exposed to the top notch level of academia for the first time — was very insightful.

But after that, I kind of realized what the question was: whether I want to try to pursue an academic career, which I didn’t feel like I was really built for and was not sure whether I can be a good researcher on my own or I’ll have to follow in somebody else’s footsteps.

So for those of us engineers, which will be the majority who have not built a language from scratch, how do you start with it? Like, speaking for myself, I know how to:

• write code
• open editor
• write Hello World
• write a more complex app
• even write a more complex one

How does a language start?

In our case, we basically talked a lot for a few months. I think not everyone is like that, but I think the best when I’m talking to people.

This was the ideal environment because we were basically discussing things with the Macs constantly for many months. There were a few internal presentations that I made at JetBrains and some of the slides survived.

I can see, including my spelling mistakes in the slides — my English wasn’t as good then — and you can see some of the evolution through those slides. I think there’s a recording of one of those presentations.

So we were basically doing whiteboard design for some time. And the great thing about doing this at JetBrains was that there were a lot of people with opinions about not so much how to make a language, but what problems do programmers face and what they like and don’t like in other languages. So I had tons and tons of input from other people and very good people. So that helped. And I really, I don’t think I realized how special that environment was back then. Like I was 26, to be clear. And I had no idea how things were done in general. But somehow these people just trusted me. I’m not sure it was very rational on their part. It worked out. But I’m not sure I would recommend anyone to do this.

And so in the first few months, I understand that you kind of whiteboarded and wrote down how you want this language to evolve. You kind of, you know, like wrote out like,

“We’re going to have these features. Or how can we imagine?”

So I guess the easiest way to explain this would be like this. It basically went off what the pains were with Java. And there were quite a few. And there was a lot of experience of using Java across the community and inside JetBrains. And we kept making lists of things we wanted to fix.

I came up with some ideas and some other people suggested other ideas about how things can be fixed, what is an actual problem, and what we don’t care about, and so on and so forth. For some time, I was just, you know, pieces of the puzzle basically laid out on a table without fitting together. And then at some point, we started fitting them together. I was just doing a lot of that in my head, which is not the best way. But this is how I knew how to do it.

There were also some crazy ideas that we thought were important back then. For example, I wanted to implement multiple inheritance, fully-fledged multiple inheritance, which was a dumb idea. And multiple inheritance meaning that a class can inherit from like several classes, and you have to take care of like conflict resolution and all sorts of edge cases. Right? Yeah.

The actual challenge is not so much conflict resolution in terms of methods, but initialization of state. Constructors are really hard. And it was actually someone outside of Gibbons who explained to me that was a very bad idea. And I’m very grateful to them. Yeah. So, you know, there were crazy ideas as well. And some of them just fall off over time as we were discussing or prototyping.

I think I started writing code maybe six months in or something like that. Maybe a little earlier than that. I started with a parser. And it was actually a very unique way to start a language because the idea was to start not with a compiler, but with an IDE plugin. I have it in the editor first, which is, you know, an IDE plugin shares a lot with the front end of the compiler, so it’s not absolutely crazy. But I was just relying a lot on the infrastructure that was available in IntelliJ IDEA.

All the parsing infrastructure, and it was awesome. Like, the parsing infrastructure in IntelliJ IDEA is better than anything else in the world because it’s the heart of the IDE. It has to be incredibly fast and very robust and so on and so forth. But then later, someone who knew the infrastructure a lot better than I do had to factor that bit out to make the Kotlin compiler autonomous. And it was Dmitry Zemirov who did that. And he’s an awesome engineer. Like, he’s probably one of the best people to refactor a large code base and, like, take this one bit out of something that was already 10 plus years old back then.

So we started with this IDE plugin. I think Max wrote the scaffolds and I actually plugged in the parser and everything. And that was an interesting start because it was very interactive. So I could show off the language as if it existed because it had some tooling. But I couldn’t compile anything in the very beginning. And that was actually a very good way to experiment with the syntax.

But then soon after, I started working on a full-fledged front-end and on some translation. And Dmitry and Alex Kachman were working on the back-end. Everybody was part-time.

When you say you work on front-end, and they work on back-end, in a language context, what does that mean?

It’s slightly different in different languages.

Basically, the front-end is what deals with the:

• syntax
• checking
• understanding what the program means

And the back-end is what translates to the executable code.

In our case:

Front-end:
- reading the text
- parsing
- doing types
- all that

Back-end:
- generates Java bytecode

And Kotlin has multiple back-ends for different target languages:

• Java back-end
• native back-end for iOS and other native platforms
• JavaScript back-end
• WASM back-end

At that time, nobody was full-time working on this project. Even I was part-time, a PhD student, part-time Kotlin developer. And it was the very early days.

Then, at some point, I gave up my PhD and focused 100%. Which was also, like, isn’t it a weird decision to start a new language part-time? Yeah. Looking back, I was young and stupid.

There’s a saying that we didn’t do it because it was easy. We did it because we thought it was easy. Absolutely that. I didn’t realize how hard the problem was. I also had an unreasonable amount of hubris. I just thought I knew how to do everything. I didn’t. But it worked out in the end.

So, when the language started, what did you call it internally? There’s always internal code names, right? Right, yeah.

So, I don’t think there was a discussion of this first name at all. It was generally understood that the language will be named Jet. And it was logical. We had all the code base using the name Jet. We had:

• JetParser
• JetEditor
• JetHighlighter, something like that.

Then someone realized that the name was trademarked by someone else. It was actually people we know there in Novosibirsk in Russia doing something. It’s not a language, but it was a compiler, and we couldn’t use it.

This is when we started looking for another name. It was very painful — looking for names. Guys, this is so bad. It’s one of the worst things because you never know what name will work unless you want to do an extensive study.

And then all the good names are taken, of course. Then some of the names that are not taken are not taken because they’re not really Google-able.

Some people are just very brave. People who named their language Go. This is why people now call it Golang because otherwise you can’t identify it. It’s a verb in English, a very common word.

Yeah, so we had weird options. In one of my old presentations, I found a list of early names:

• Robusta (a flavor of coffee)
• Up
• G
• Something else like that

And those weren’t great.

By that time, other languages were popping up. One of the alternative languages was called Ceylon. The logic was: Java was the island of coffee. And Ceylon was an island of tea.

Dmitry Jemerov basically looked out of the window and said,

“OK, we have an island here in St. Petersburg. In the Gulf of Finland, there’s a big island called Kotlin.”

And it’s a good name in the sense that it’s very Google-able. Nobody uses it for anything. It’s very recognizable. It’s not super smooth for many languages, but it’s kind of OK.

Nobody was in love with that name and we were kind of hesitant.

You know, “Kot” means a bad thing in German. Also, there is like some negative connotation in Mandarin, I was told, or something like that. You know, it’s always some language has some nasty association with any word.

We basically were super hesitant. So when we announced, and we had this deadline, that we were basically putting this off, when we announced, we were still not sure.

So we called it, we decided it would be a code name. We called it Project Kotlin to have wiggle room to later replace the name — but it stuck.

The first thing we did was put out basically a Confluence page with a description of the language. It was just a bunch of wiki pages and there was no compiler available then, I think.

There, the word Kotlin appeared many, many times. I was like,

“My God, this thing doesn’t, like, I can’t do search and replace and then change the name everywhere.”

So the workaround that I came up with was to create an empty page called Kotlin. And so it had a name. And then everywhere else, you mention it as a page. When you rename a page, it gets renamed everywhere.

This is why there was an empty page called Kotlin in that documentation. But yeah, the name stuck and it turns out to be not a bad name.

So, when it started, what were the main differences with Kotlin compared to Java? Because Java was, what was the big one? How did you explain to developers who initially started onboard or wanted to give it a go?

Yeah, I guess there were a few major selling points. Then there were other things on top of that. When we started, like in the very beginning, we didn’t have null safety in mind. Null safety came a little later.

After one of the internal presentations, it was Max Shafirov who invited Roman Elizarov, who later was the project lead for Kotlin. Roman came and listened to the presentation, gave some feedback, and said something like,

“Guys, if you want to do something really big for enterprise developers, figure out null safety.”

And we did. It took a while.

So in the very beginning, it was the general idea of what makes Java feel so outdated. There were a bunch of things. Lambdas were very big. The general, like, the general feeling from Java back then was it was very verbose. It was called the ceremony language. A lot of people were grumpy about too many keywords, like public static void main is something everybody was really grumpy about.

But also, there were getters and setters for every property. There were constructors and overloads and all that stuff that looks like boilerplate because it is. Yeah. It’s super annoying to type out.

The problem with boilerplate is, on the one hand, it’s annoying to type out. But tools can generate it for you and fold it and so on and so forth. But the bigger problem is always readability. So reading is more important. Reading code is more important than writing code. We do a lot more of that.

And with boilerplate, it’s terrible because if some tiny thing is different in the middle of completely standard boilerplate code, you’ll miss it. You’ll become blind to it and you can debug for days not seeing that. So, you know, that was the point of sort of modernizing Java, making Java programs be more about what they do and less about the ceremony of making the compiler happy, basically.

And, you know, type inference was also a big thing because Java was repeating types a lot and many other things like that were, like, semicolons. The modern languages of the time already got rid of semicolons. And so in Kotlin you also got rid of it?

Yeah. So we got rid, basically, in terms of syntax, we got rid of semicolons and duplicated types. And that was a lot of noise across the code.

What does it mean that Java had duplicated types?

So in that version of Java, when you declare, say, a local variable, you say it’s a list of string called strings equals new array list of string.

Oh, yes. I remember this one.

Yes, yes. You need to type it out twice. And if you get one of them wrong, compiler, et cetera.

Right. So, and at best, you could omit the second mention of string by using a diamond operator, but that only came later, you know. Basically, it was very verbose, especially if your types are long.

• Like, if it’s just a list of string, it’s sort of not so bad,
• But if it’s a map from something to a list of string, for example, that’s already really long and you don’t want to read that.

So, and a bunch of things like that were really annoying to a lot of people, especially compared to C# or Scala.

So, we did all of that. And then, on top of that, there were other value-add features and null safety was a big thing that we spent multiple years actually on implementing. And I think it’s one of the main differentiating factors now for Kotlin alongside of with extensions and other things. But null safety is one of the core features.

And can we just spell out why null safety is so big?

I mean, I just today I came across a bug on the, I couldn’t send a package because in JavaScript on the Dutch post website, there’s a null issue happening in production.

But, you know, before Kotlin and a lot of languages, why is it such a big problem?

It is.

Yeah. So, dealing with null references is a big hassle in most languages. And I think it was Tony Hoare who called it the “billion-dollar mistake” at some point because, like, introducing, I think it was about introducing null pointers to C or something.

So, basically, when we look at all the runtime errors that we have in Java code, I think null pointer exceptions will be at the top. So, you know, the type system of the language is supposed to protect you from those unexpected errors.

So, there are errors you’re designed for and maybe errors that are not even your fault, like a file system error or something like that. But there are also errors that should be prevented by the compiler. So, for example, class cast exception or missing method error are things that the compiler is trying to protect you for. It’s trying to make sure that this never happens in your program unless you switch off the check by making an enforced cast or something.

And with nulls, it’s not a thing in Java. Like, anything can be null, and if it’s null, it will just fail. Yeah. It throws an exception and the program dies. So, it’s a very common thing.

So, a lot of people are kind of used to it, and there are different ways of being disciplined about it and so on and so forth. But, basically, this is a plague across any code. You know, there are different approaches to this.

And in Kotlin, we took the approach of:

- A: enforcing it in the type system,
- but also making it free at runtime.

What does that mean, that you made it free?

So, one very common way of dealing with nulls is to use something like an option type, where you have a box, which might be empty, or might have an object in it.

No. And that box is not free. Like, you have to allocate it, you have to carry it around everywhere. And, this easily creates a lot of objects in the old generation for the garbage collector, so it can be challenging. What we did was just have a direct reference at runtime; our nullable or not null reference is the same as Java’s reference.

All we do is compile-time checking and some runtime checking when we cross the boundary. But that’s a lot cheaper than allocating objects. Although the runtime is getting better, and they can optimize some of those objects away, it’s still an overhead.

What are features that you took in from Kotlin that were inspired by other languages that you admired?

A lot of them. I have an entire talk about this. It’s called Shoulders of Giants. We really learned from lots and lots of languages. And it was always the point. Andre just mentioned how Kotlin was built on top of the shoulders of giants, taking good ideas that existed, not reinventing them. This was one of the reasons Kotlin succeeded as much as it did.

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With this, let’s get back to Andre and how Kotlin was standing on the shoulders of giants.

So the slogan for Kotlin was “pragmatic language for industry.” The pragmatic bit, which is a nice rhyme with your podcast, was kind of coming from the experience with Scala being called an academic language. A lot of people had trouble getting their heads around many of the very smart tricks in the design.

And so our idea was:

“We’re not doing academic research here. We’re not trying to invent anything. If we don’t get to invent anything, it’s a good thing, not a bad thing.”

From the engineering perspective, it’s generally a good idea to do this. Usually, you end up making something new, but most of what you’re doing shouldn’t be very new because you want familiarity. You want people to easily grasp what you’re doing. This has to be familiar from other languages.

Also, if you’re building on top of the ideas of other languages, you benefit from them having tried it already. You can look at their designs, their community’s reactions, and the implications all over the place. That gives you a huge benefit.

So we did a lot of that.

I think the language that influenced Kotlin the most is, of course, Java. Because the entire runtime of Kotlin is the JVM, and we depend on that.

Apart from that, Scala had a huge influence. We used many ideas from Scala, including:

• Primary constructors
• Data classes
• vals and vars
• Interesting tricks about how generics work, for example, variance declarations — a great idea of Martin Odersky.

It’s a huge pity that this didn’t make it into Java design. It was flipped at the very end of the design process to what Java has now. The Martin Odersky idea was much better.

We had to fix this problem on the Java boundary and figure that out.

There were many ideas we took from Scala, and that was very helpful. We usually transformed those ideas a little bit to adapt to our setting and to build on the knowledge of how it actually works in practice. We left some things out. We simplified some things.

For example, Scala had traits. Traits are a very powerful construct, like an interface where:

• You can have method implementations
• Also, in Scala traits, you could have fields or properties

What you couldn’t have were constructor arguments. You always have a default constructor and can initialize all your fields.

It’s not as bad as multiple inheritance in C++, but it’s still a little complicated when it comes to the order of calling constructors. We decided we don’t want to deal with that. It’s a complex algorithm and hard to explain. Let’s just get rid of the state in interfaces and only have method bodies. And I think it was a good compromise. Especially given that Java ended up in the same place. It was easier to integrate.

Yeah, so Scala was a big influence. C Sharp was a very big influence. Extensions, of course. And we learned quite a lot from how C Sharp compilers do things.

There, there was also one particular trick that makes Kotlin syntax a lot nicer, nicer than Java’s and nicer than Scala’s, that we’ll learn from C Sharp. And it was actually my colleague who worked on the C Sharp IDE who told me about this, which is basically a super pragmatic thing they do in C Sharp.

There is like, when you call generic functions, you use angle brackets inside an expression. But the thing is that there is no such thing as angle brackets. There is less and greater. Right? And the parser can easily get confused and think that this expression, since we’re not in a type context, it’s an expression context. This expression is a comparison. It’s not an inequality, right? It’s not a call. And this is mathematically unresolvable. It’s an ambiguous grammar.

Yeah, look, you can do anything about it. And the way other languages handle this is:

• Java, for example, when you’re passing type arguments to a call, it has to be after a dot. So you say collections.<Type>functionName(). Really awkward. Which is kind of weird.
• And the way Scala deals with that, they use square brackets for types. And then arrays can’t use square brackets, so they use round brackets. Which is unfamiliar, like, it’s not the end of the world. Scala is doing fine, but still.
• And C Sharp uses angle brackets because there’s a hack in the parser that basically disambiguates ad hoc.

And we did the same or something very similar, and it just works. And the syntax is very familiar and very intuitive, and we’re very happy about that.

Yeah, because when you read it, as a person, I never get confused. Like, this is not a smaller sign. Like, I know it’s a generic. Yeah. Yeah.

Okay. Wow. Most of the time, it’s not a practical problem. And there is a way to disambiguate, if you like. So C Sharp was a big influence.

Groovy was a big influence as well. JetBrains used Groovy for build scripts. And there were incredibly useful patterns in the Groovy syntax that they call builders, which is not about building programs, but, you know, building objects.

And this is what inspired something fairly novel that we did in Kotlin, which was typed builders, where we had the same syntactic flexibility, or almost the same syntactic flexibility, as Groovy, but it was all typed. And we could make sure that all the arguments matched and so on and so forth.

So all that side basically was inspired by how Groovy people did this and reworked into a typed setting. And this is why we have, for example, extension function types. And this is why we have dangling lambdas and other things that are actually very nice syntactic constructs.

So, yeah, many, many things came from different languages.

A less known language called Gosu, I think it was what inspired us to do smart casts.

What are smart casts? Oh, yeah. So, I think smart casts are one of the nicest things a compiler can do to a developer. Because it’s a very common situation when you say:

If x is a string (so you do an instanceof check), then do something with x.

The annoying thing is that in a lot of languages, you have to cast x to string again. Like, you’ve done the check. After you’ve done the if, you know it’s a string, but then you need to write it out again.

Yeah, so you’ve just done the check, but you have to say string again to make the compiler happy.

So, smart casts basically get rid of that. So, that cast gets figured out automatically. So, if that’s a string and then inside the bracket, you can now use it because it’s a string. Yeah, you can use it as a string.

And isn’t it an easy thing, right? So nice. Yeah, it’s a very nice thing.

Yeah, it’s a pretty complicated algorithm. Because, you know, variables can change values and the check that you’ve just made can go stale. And, you know, there’s a bunch of algorithmic trickery around this.

And you can’t do a smart cast on any expression. It has to be a certain type of expression that can be stable enough and so on and so forth. But, you know, it’s a very nice thing. And you can get rid of so much noise in the code because, like, all the code in the world is riddled with this instanceof cast. instanceof cast.

So, we wanted to get rid of that. And it worked. And it was fun to implement.

What were things that you looked at other languages, you considered, maybe we should bring it in. But you, after debate, you’re like:

“No, let’s just leave this out.”

Like, not all of them, obviously, but some of the big ones that kind of came close. We had a design for pattern matching in Kotlin that was inspired by functional languages like Scala and Haskell and others. But at some point, early on when I was still working on the parser, I just realized that this is a huge feature.

So, when I was sketching it out on a piece of paper, it looked like a very useful thing, just another feature in the language. But then when I started working on the parser, I realized it’s an entire language in size. Like, you have to create a parallel universe in syntax for pattern matching. And I was like, okay, this will be a lot of work. Let’s postpone it.

Later on, when we were doing review for 1.0 or maybe a little earlier than that, I just realized that smart casts plus we have something called destructuring together give us like 80% of all the good things pattern matching can do to normal developers. Then there is another group of developers that can be very vocal, mostly compiler developers and people super into functional programming. They have a point, but that point is only relevant to them, and there are not very many, so we decided to not have pattern matching back then.

And, you know, maybe there comes a day that pattern matching gets added to Kotlin. And pattern matching is, is it in the case? Yeah, it’s the… So you can have, like, a lot nicer case statements, a lot more expressive ones, right? Yeah.

Generally, Kotlin has this compromise where you have our version of switch case, which is called when, and you can have smart casts there. So you can say:

• when my expression is a string, then use it as a string,
• or it is a pair, and then you can use it as a pair.

That kind of gives you a lot of the niceties of pattern matching, but some things you can’t express like that. And that was, I think, a good compromise because it’s a really big feature. It’s hard to design well. There would be a lot of work on the tooling side. But maybe it gets in the roadmap one day. I’m not sure.

Java is trying to get towards pattern matching, so we’ll see. Maybe they kind of make it more mainstream.

Why did you omit the infamous ternary operator, which is when you write out something with the question mark and the colon, and it confuses new developers every single time if you’ve not seen it before? Yeah. Was it for readable reasons?

This is the saddest story I think in the design of Kotlin. I didn’t realize how much people liked it. The reason was, Kotlin used this principle from functional languages that everything we can make an expression is an expression. So if is not a statement, and the ternary operator is sort of a patch on the design of C and other C-like languages that makes an if expression, basically.

The logic was:

okay, we have if as an expression already,
can we just get rid of this extra syntax construct,
especially given that it's using very precious characters?

Like, there is a question mark and a colon, and we might find some other use for that. So we decided to not have it. We used question marks for nullable things and the colons for types and so on.

But it turned out that if as an expression is pretty verbose; people don’t like it. I resisted for some time, and then by the time I agreed, it was too late because you can’t retrofit the ternary operator into the current syntax in Kotlin—it just doesn’t agree with how other operators have been designed.

So you’re actually sad about it not being there a little bit? Yeah, I think in retrospect, it was a mistake because pragmatically, it’s more use than harm to have it. But we just can’t retrofit it.

What are some other interesting features that you like about the language that you added that we could explain for those who are not familiar?

Okay, so the good ones, there’s quite a lot of them. One feature that is not a traditional kind of language feature is Java interoperability. That’s probably the single thing we spent the most time on. And I always say that if someone offers you a job to create a system that interoperates transparently with another huge system you don’t control, ask for a lot of money. It’s a very tricky deal to figure this out.

Interoperability means that from Kotlin, you can invoke Java, and from Java, you can invoke Kotlin. You do a bunch of work there, but it just works in the end as a developer. You don’t need to think about it.

The idea is whenever you have a Java library somewhere in the world, you can always use it from Kotlin. It was a big selling point because if you start as just a language in a vacuum and you don’t have any libraries, that’s not a good start.

In this direction, definitely, it was an absolute requirement for Kotlin. But also, we had the requirement to go the other direction. In an existing project, you could just rewrite parts of your code from Java to Kotlin, and everything keeps working. And some libraries actually did that. Many projects started using Kotlin bit by bit.

A lot of people started with just writing tests. But then, you start adding things in Kotlin, new things, for example. And all the Java code around that has to transparently use the Kotlin code. So we put a lot of effort into that. And that was fun.

Can you explain to us as engineers, like, it sounds like it was a friggin’ big project. What is the work, right? Because from the outside, again, I’m just being your average developer, where I’m invoking a Java class.

And things I can think of are:

• Maybe Kotlin or Java doesn’t support things in a certain way.
• Maybe it’s not that hard.

What is hard? Tell me, tell me. I’m dying to know.

So one thing to note here is that we don’t control the Java compiler. We somehow need to make it work so that in your Java code, you make a call into something that only exists in the Kotlin source. And the Java compiler somehow agrees to call it to begin with. It’s not a Java file. It doesn’t know it exists.

So the way it actually works is: when we build a mixed project, what we do is we first compile all the Kotlin code. That can depend on the Java sources in the project. So we have a Java frontend baked into the Kotlin compiler so we can resolve everything in the Java code. Then we produce class files, binaries for the JVM that the Java compiler can read. So when Java compiles, it takes Kotlin sources as binaries. And this is how it works.

We would have to implement a Java compiler otherwise. Fortunately, Java has separate compilation, so this works.

This trick means that whenever you have tooling, like in your IDE, for example, when you navigate from Java sources to Kotlin sources, it has to be a special trick. Someone needs to go and teach the Java world to know about the Kotlin world.

Of course, the IDE doesn’t do the compilation to navigate. But at compilation time, we don’t control the compiler. So we did our own IDE. This way, we could do something about the Java tooling, but we couldn’t do anything about the Java compiler. So that’s trick number one.

Then, when it comes to incremental compilation, it becomes even funnier because Java incremental compilation is a complex algorithm on its own. Now we are incrementally compiling two languages at once. And that’s fun.

Incremental compilation algorithms are generally a very messy, very complicated heuristic with tons of corner cases. So, that’s like one example.

But then you start making interesting new things in Kotlin. You need to expose them to Java. You need to make sure that whatever fancy thing you have, Java can actually interoperate with that.

One example would be Kotlin’s approach to making Java collections nicer in Kotlin without rewriting the collections using the same library. Java collections are what’s called invariant because they’re all read-write. So if you have a list, it always has a set method.

That’s a little bit of a problem because whenever you have a list of objects, you cannot assign a list of strings to that. That’s annoying because you want to be able to represent a list of anything, and for that, you need to play with question marks, wildcards, and stuff like that.

It would be very nice if we had a read-only list interface that doesn’t have a set method. Then there is no problem in assigning a list of subclasses to a list of superclasses. But this interface doesn’t exist at runtime, right? We can’t just invent it. Or can we?

So we actually can. No.

In the Kotlin compiler, we have this layer of trickery specifically for Java collections where Kotlin always sees Java collections. If they come from the Java world, they are read-write, mutable collections, we call them. But mutable, right? Yeah.

So the Java collections are always mutable or platform mutable. I’ll talk about that later. But when you do it in Kotlin, you can actually distinguish between read-only and mutable collections, and it’s all very nice on the Kotlin side.

But then when Java sees the Kotlin collections, they are normal again. When we expose them through binaries, the Java world always sees them as normal collections; they’re mutable for Java, and it’s all right.

Okay, I’m starting to see why you said you need a lot of money for this because this is just one of many things. But this itself sounds like, I don’t know how you solve that.

Yeah, so just to add a little bit of detail to this. So the nice thing about those read-only collections is that you can pass a list of string for a list of object, right?

Wouldn’t it be nice if a Kotlin method that takes a list of any could accept a list of string in Java? But aren’t we erasing all the Kotlin nice stuff? We are, but we know that this list is actually what’s called covariant. So we can expose it to Java as a list of question mark extends and not just list of objects. So, you know, it becomes covariant for the Java world as well. And that’s like one hack that makes it a little more transparent.

And there’s a bunch of that. So, you know, so that’s another thing that we had to play with. But the biggest thing is, of course, nullable types. And actually, we handle nullable types and these things with collections kind of similarly, which makes the whole typing layer of the interop quite interesting.

But basically, so Java doesn’t know anything about nulls, right? Well, it knows about nulls, but not about nullable types. It does not exist. Yeah, Java doesn’t know about nulls at compile time. So in terms of types, it’s just not represented. So technically, every Java type is a nullable type.

And this is where we started. We said, okay, so Kotlin types can be not null and it’s very convenient. And when you have a not null type, you can just call a method on it normally, right? But if something is nullable, you can’t just dereference it. You have to first check for null and then use it, right? Or if there is a safe call operator, well, just propagate null is on the left-hand side.

So we started with saying,

“Okay, all Java types are nullable, which is a conservative, like very mathematical way of treating it.”

This is correct, right? Yeah, you’re not going to be wrong with that. Yeah. And we implemented that and we started using it inside JetBrains. And the feedback was horrible. Like your code is plagued with those null checks and you know that they shouldn’t be there because you can’t express anything on the Java side the right way.

And there were like, we had some annotations for the Java side. It was also brittle and not always worked because, you know, there can be long chains and stuff. And some libraries just don’t have the annotations. And we struggled with that for a long time.

And basically we realized that this assumption that everything in Java has to be treated as nullable just doesn’t work. This was a turning point where we sat down and reimagined the whole thing.

And we worked with a great type theory type practice, I would say, guy from, I think it was back then he was in Cornell, Ross Tate. So Ross helped me figure out the sort of mathematical side of how you can represent those types that come from Java and should be, like we should be aware of that they are from Java and can possibly be nullable.

But we shouldn’t treat them as nullable because it was very inconvenient. And Ross put together a very nice sort of calculus about those.

And when we started implementing it, all the nice things are gone. The mathematical beauty is completely gone from all that. And I think we took the general idea of sort of splitting a type in two and everything else is just very messy industrial kind of thing. That’s not sound, but it works well.

Okay. And interoperatively sounds like it was a journey, but a necessary one.

How long did it take? Can you give me just a sense of like how many people working on it? How much, because I think in traditional projects we can get a sense, but I have no idea with the language. How does this work? And how long did you think it would take versus how much it took?

Yeah. So let’s start with that.

• Every time I was asked when we were going to release Kotlin, I would say one year from now.
• And, you know, this is, this is not a plan. I had no idea. I had no idea.
• I also had the illusion that the initial version I was building was a prototype and we would write everything.
• And I’m sure a lot of people out there have been there.
• I think that prototype has been written more or less completely now, but it took six years, something like that. Yeah. So maybe longer, actually.

Yeah. So, so I had no idea. And I always said like, okay, a year from now feels far enough. We’ll probably be done by then.

In practice, we started in 2010, yeah, autumn of 2010, basically. And we released in February 2016. So, you know, it was a long time, five-ish years. And that, you know, in part was just because I didn’t know how to manage projects.

And my initial team, the people who worked full-time on the project, I looked up on GitHub to verify that. Everybody who, almost everybody, who joined JetBrains to work on Kotlin was a fresh graduate. Because I used to teach and I had some good students and I knew how to work with students. And so basically everybody on the team was a student, apart from a few veterans from JetBrains who were helping, not all of them even full-time.

So we started getting experienced engineers on the team a bit later. And, you know, to be fair, a lot of those people, people who are following Kotlin know those names. People who are core contributors, who built out, like, absolutely foundational parts of Kotlin, joined as fresh graduates. And they became great engineers.

But I think I overdid it a little bit. So it’s great to have, you know, younger people have no fear. And that’s wonderful. But, you know, the balance was not right.

And how big was the team initially and then towards the release?

So we started out basically with four people part-time. And, yeah, we went like that for maybe a year or something. So the initial prototype was built like that. And then people started joining in. By the time we released, I think it was around 25 people or something.

And the team grew quite a bit. So by the time I left in 2020, it was about 100 people on the team, 70 of them engineers. So it became a pretty big undertaking.

Can you tell us about the development process inside language?

I think a lot of us are used to building, you know, like services, backend services or products or mobile apps, etc. They typically have a release process. How does this work inside a language? Like, what is your release process and what is the, I guess, best practices?

Like, do you even do code reviews or, you know, like how can we imagine? Because, again, it feels such a rare project. There are people building languages, but not many of them.

Yeah, so one peculiar thing about building languages is what’s called bootstrapping when you write your compiler in your language.

Oh, nice.

Which means that, you know, to compile your code, you need a previous version of your compiler. And you better agree with your colleagues which version it is. It can be really tricky, especially when you do things about the binary format. And there is, like, quite a lot of bootstrapping magic going on.

And I don’t think you can reproduce the Kotlin builds from scratch. Because, you know, if you just take a snapshot of the Kotlin repo, you can only build that with a Kotlin compiler. And I don’t think we kept all the bootstrapped versions. So it might not be really possible without a lot of manual intervention to rebuild all the sources from the very beginning and reproduce all the versions.

Because sometimes, you know, we had to, like, commit a hack into a branch and use that branch as a bootstrap compiler for the next build and then throw the branch away. So that was, like, a one-off compiler used to facilitate some change in the binary format or syntax or something. So that’s a separate kind of fun.

But generally, I mean, many practices are very similar. Like, we had code reviews pretty early on. It’s my personal quirk, again, that I like to talk to people. So in code reviews, I often just sat together with someone and either they reviewed my code or I reviewed theirs. But this is, you know, I can’t argue that it’s much better or worse. It’s just how I prefer it because I like talking to people.

So code reviews, yes. And, of course, we had an issue tracker like everybody else. Ours was always open. So everybody can submit bugs to the Kotlin bug tracker, which was very helpful. It’s hard to manage because there will be, like, with usage, there will be a lot of bugs and a lot of feature requests and all kinds of stuff. But it’s worth it. You have a communication channel.

Release cadence is a very difficult thing to figure out for such projects. Because one big consideration you have for languages is backwards compatibility.

In part, this is what delayed 1.0 because we wanted to be reasonably sure we can maintain compatibility as soon as we call it 1.0. In part, because it was the expectation, especially Java is incredibly stable and very good with that until Java 9 came about. And also, Scala had a lot of trouble because they were breaking compatibility a lot. And the community was struggling, really. So we really didn’t want to repeat that.

But, you know, it turns out you can even break compatibility Python 2 to Python 3 and survive.

Barely. Barely survive.

They’re doing very well. Now they’re doing well, yes.

Yeah.

So we were really serious about that. But basically what it means is you start doing interesting things like deprecation cycles. So we actually invented an entire tool set for compatibility management.

So before 1.0, we tried to help people migrate. So we had those milestone builds. Embarrassingly, we had 13 of those.

And, you know, when we broke the language in major ways, we tried to provide tools for automatic migration.

That’s nice of you.

Which was, I don’t think, a standard practice in the industry back then. Now people are doing it more. So I’m very happy to have sort of popularized this idea. And then when we were preparing for 1.0, we did a major review of everything and took a year to sort of review all the design.

What we’re doing is basically trying to anticipate what changes we might want to make or what new features will require. And to basically prohibit things that might block that. So we tried to make sure that the changes we were planning were guarded well by compiler errors to make sure that users don’t accidentally write anything that looks like a new feature. And that was fine.

We had design meetings, I think, every day at some point—basically working on that, like, “okay, let’s outlaw this. Let’s prohibit that.” And we prohibited a lot of stuff correctly and some stuff incorrectly. But, you know, generally worked out. So this compatibility thing was a big deal.

But there’s also a lot of stuff that we didn’t anticipate. So we had to figure out ways to manage this. And there is something in Kotlin compiler called “message from the future,” which is basically when in a newer version of a compiler, you introduce something that the old compiler doesn’t understand.

We have different options. And one option a lot of languages go for is:

• The new kind of binary is completely unreadable for the old compiler.
• So the version is higher.
• I don’t read it.
• That’s it. I bail.

But it’s a little hard for people then to manage their versions because new libraries, new versions of libraries come with new compiler expectations and you have to migrate your entire project to do that. It’s a little annoying. And if what you’re adding is like one method that basically invalidates the whole library for an old compiler, that’s not great.

So what we’re doing is a newer compiler can write something into the binary that tells the old compiler, “okay, this method is what you can’t understand, but everything else is fine.”

Wow, that’s smart. Yeah.

So we call this a message from the future and like it can provide some details. So there’s that.

And there’s also the discipline of experimental features, which is incredibly helpful. And I am very happy to see other languages doing it now. And even Java does experimental features now, which is wonderful.

Andrei just talked about experimental features in programming languages and how that used to be rare back in the 2010s. What this reminded me is that running experiments in production used to also be rare. Not because teams did not want to do it, but because doing it meant building a lot of internal tooling around it:

Assignment, rollouts, measurements, dashboard, debugging, the whole thing.

For a long time, only a handful of companies really pulled this off at scale. Companies like Meta and Uber.

Which brings me to Statsig.

Statsig is our presenting partner for the season. Statsig gives engineering teams the tooling for experimentation and feature flagging that used to require years of internal work to build.

Here’s what it looks like in practice:

• You ship a change behind a feature gate and roll it out gradually, say to 1% or 10% of users at first.
• You watch what happens. Not just did it crash, but what did it do to the metrics you care about?
  • Conversion
  • Retention
  • Error rates
  • Latency.
• If something looks off, you turn it off quickly.
• If it’s trending the right way, you keep rolling it forward.

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To learn more and get a 30-day enterprise trial, go to Statsig.com/pragmatic.

And with this, let’s get back to Andre and experimental features in Kotlin.

So we did quite a lot of work when you’re doing something experimental. This is something that’s supposed to break, and you want to emphasize this to make sure that the user is aware that:

“this is something we are not promising to keep compatible. This is something we’re going to break.”

We used to put the word experimental in package names for people to understand that this is going to be renamed. And warnings when you use language features, and we require compiler keys to enable language features and stuff like that. It kind of helps. So we did quite a lot of that.

All this is an extra layer. And unlike a SaaS system, for example, a compiler leaves behind, but not behind, creates a lot of artifacts that pin down its history in the world. There is source out there and there are binaries out there, and you’re guaranteed to encounter them every time anyone hopes that

“this is an obscure case. Nobody will ever hit that.”

With enough users, you hit every freaking case. And this is so surprising.

I discovered this fairly early on. I think before 1.0, when we had a few thousand users, I realized that

“if something’s possible, some person out there will actually do it.”

So you got 1.0 out. Can you tell me how Kotlin grew in popularity? When you released it, what was your target audience? And then how did Android happen?

Okay, so that’s a complicated story. Let’s try to not get off track, because this has a lot of sidetracks to it.

When we started Kotlin, we were not really very aware of Android. And I mean, we knew that that was a thing called Android.

Kind of ironic.

Yeah.

From now, message from the future.

Right.

Yeah.

So basically in 2010, we were focused on the majority of Java developers that was all about the server side.

• The majority of Java developers were server side.

Clear.

Yeah.

So the most money IntelliJ was making was on Spring users. And, you know, everybody knew that this was what the Java platform was about by then. So we were targeting server side developers, basically.

And also desktop developers, because JetBrains had the, probably the last desktop application written in Java, or at least in Swing.

So that was the target. It was initially not even a plan to do Android.

Kotlin got some usage for the server side. And, you know, it’s still there and it’s growing there, not as fast as on Android, but still has quite some representation on the server side.

But then a few years in, some person on the Internet asked us whether Kotlin works on Android. And I was like, I heard Android uses Java, so Kotlin should work. We’ll never try. Go and try.

I think it was either the same user or a different user who came back and said

“the toolchain crashes.”

And it wasn’t even Kotlin toolchain. It was the Android toolchain that crashed. And, you know, we looked into it and it turns out that some tool in the Android toolchain that’s written in C just fails with a core dump. And it’s not very clear what’s going on.

We later figured it out. It turned out that the Android developers and the people who built the Android platform actually read the spec of the JVM, unlike the people who implemented the Hotspot VM. Because the Hotspot VM, I suspect, came before the spec. So it was the reference implementation, but it was actually specified after it was built.

The Hotspot VM was super lenient to weird things. Like, there would be, if we put a flag on a class file that was not allowed for classes, Hotspot wouldn’t care. And we ran everything on Hotspot. So we thought everything was fine.

But then on the Android side, those were the people who actually read the spec and implemented it. Yeah, they would complain about everything.

This is why we used the Android toolchain as a testing environment basically, because

“this is how we could get rid of stupid things in our bytecode.”

They helped us a lot with validating everything. But, you know, there were some gotchas there. Some legacy stuff nobody cares about in mainstream Java just were faithfully implemented on the Android platform.

That was fun.

So, you know, at some point, pretty early on, I had this realization that Android was a growing platform. Which, to me then, I didn’t have much understanding of the dynamics of markets, but it meant that there would be a lot of new applications.

And it’s much easier to start completely anew with a new language.

So, I made sure, at some point, that we worked well on Android. It was already after the lawsuit.

So, the big context to all this was that when Oracle acquired Sun Microsystems, they sued Google for billions of dollars for using Java.

And I think that is settled.

It was settled in some way, yeah.

And then everyone could go on their own way.

Right.

But yeah, it took years and years to settle.

Back then, it was very much a thing. And, you know, that dispute was somewhere in the background.

But yeah, so basically, we saw that a lot of people on Android really liked Kotlin. They loved it.

Yeah.

As soon as it was stable, pretty much. I mean, I think for all the things that you mentioned: it was just so much nicer than Java. Easier to write, easier to read, lots of nice features.

So, you know, you use Android as a way to actually make sure that Kotlin compiled correctly.

And then, why did it take off on Android?

Yeah, so the situation in Android was pretty interesting because unlike Java server side that is kind of under control of the teams that develop on it. In the case of Android, there are devices in the pockets of people, right? And when you have billions of those devices, and those devices don’t always update the virtual machine.

So, people on Android were basically stuck with old Java. And even when Java started progressing, and, for example, Java 8 came out in 2014, it was very difficult to roll out this new version of Java across the entire Android ecosystem because it required updates to the virtual machine.

There were workarounds, and Retro Lambda really helped, and so on and so forth. But there was still a lot of people stuck with really old Java. So, Java wasn’t on par with Kotlin or C Sharp in 2014. But it still was much better, and solved the major problem. But it was not available to the Android people.

So, there was a lot more frustration with Java in the Android community.

And also, there was Swift on iOS. Where it was a real example of a big ecosystem transitioning from a really dated language to something really nice.

I think compounding these two things were the major factors. Also, we made sure that Kotlin worked well on Android.

Very fortunately, at some point, Google switched the developer tooling from the Eclipse platform to the IntelliJ platform when IntelliJ was open-sourced back in, I don’t remember, 2013, I think.

So, we had a nice plug-in because everything worked on the IntelliJ platform, and the same plug-in worked for Android. Many other things were just very smooth. Well, very smooth—there were a lot of bugs, but reasonably smooth.

So, it felt like a very good match, and a lot of people appreciated that.

We really wanted to somehow draw the attention of the team at Google to maybe talk about it or something, but it just didn’t happen.

We released in 2016, and there was some communication with Google in general, but there was no interest on that side. They were like, okay, we, I guess we’ll just keep going as we do.

Some people were already building Android applications, and some people were building production applications in Kotlin before we released 1.0.

Kudos to the brave people because they gave us indelible feedback. But you guys are too brave.

So, it just grew organically.

When we started, in the very beginning, I set this internal goal to myself, that if we get to 100,000 users, it’s a success.

I’ve done well enough if it gets to 100,000. Of course, it’s hard to tell how many users the language has, but you can kind of estimate that.

I think we were on track to get to 100,000 users during 2016 because it was growing, it was in the tens of thousands, it looked good.

Then, some people from Google reached out and said they wanted to chat.

It turned out they wanted to chat about announcing official support for Kotlin at Google I/O 2017, that would be in like three months from the time of that conversation.

They said, “yeah, sure, let’s do it. What do we need to do?”

It turned out we had to figure out quite a few things, but we managed.

I think it was a heroic effort on the side of the Google team. They did amazing, impossible things.

I have good friends among them now.

It was really, really close. Like, we could have missed the deadline, but we figured it out.

On our side, we had to make many things work and figure out how we interoperate with Android Studio better, and how to set up processes and everything.

But there was a big legal thing around it. This is when the Kotlin Foundation was invented. We had to design the protocols for decision-making in the Kotlin Foundation.

Google owned the trademark for Kotlin for one year because of legal things. It was basically a guarantee from the JetBrains side until the foundation was set up.

You can look up the public record:

Google was in possession of the Kotlin trademark for a year.

But then the foundation was set up and the trademark was transferred to the foundation.

It was fun. It was a pretty crazy time.

But it was amazing to see how happy people were at Google I/O when the announcement happened.

Then usage must have skyrocketed. You probably blew past 100,000 pretty quickly.

Yes, I think we went into millions that year.

So this was basically the moment happening.

I knew many years before that the easiest way for a language to succeed is to be part of a platform.

For example:

• C was part of Unix
• C Sharp was part of Windows
• JavaScript was part of the web platform

And I knew that Kotlin had no platform. So it was supposed to be a much tougher time for Kotlin than for some other languages. But, yeah, the platform came along somehow.

Jumping forward to a lot more closer today, you left Kotlin in 2020. Later, you left JetBrains. What are you doing right now?

Yeah, so I’m also working on a language right now. But it’s sort of a different kind of language because the times have changed. And, you know, you can look at it from a similar perspective. Like, in Kotlin, we wanted to get rid of boilerplate. We wanted to make programs more to the point. And less of a ceremony.

And I think this is where we, today, we have a great opportunity to do the same thing at a different level. Because of AI, right? Because of AI. It’s all because of AI.

Yes. AI is great because many things that are obvious to humans are obvious to LLMs as well, which closes this gap between what the machine can understand and what a human can understand quite a lot. Which means we might not need to write dumb code anymore. That would be very nice.

So, on the one hand, you know, the entire history of programming languages is going from lower to higher levels of abstraction. We started with machine code. And then assembly was a step up, actually.

• Assembly language is a higher level language.
• And then machine code.
• And then C was a high-level language back in the day.
• Managed languages like Java were a great step up and made programming a lot more accessible.

Teams could grow and you didn’t have to be a super competent programmer to build working software. And then, you know, things like Kotlin built on top of that success. And we raised level instructions some more, but now we can do even better in the same domain.

So, you can imagine a normal program, some application code. A lot of the things in this code are obvious to you and to me. So, if you ask me to write this code, you don’t spell everything out. You explain what the program needs to do and I can implement it. And it will work the way you want.

There are, you know, it depends on how detailed the specification is. But you can tell me a lot less than you would have to tell a compiler.

And so, this is the point with Codespeak. We want to basically shrink the amount of information a programmer needs to tell the computer to make the program work. From my current anecdotal experience, you can shrink a lot of the code about 10x.

Which means that a lot of projects out there can be a lot smaller. And it will be a lot easier for humans to deal with that and a lot easier to read — and reading is the most important bit — and a lot easier to navigate.

It becomes, you know, the essence of software engineering. When you are not dealing with a stupid compiler, you’re not restricted by that anymore. What you’re expressing is what only you know about what needs to happen because everything else, the machine knows as well.

So, can you tell me a bit more on what Codespeak is or what this language is? Is it designing an actual, like, in a formal language, just simpler? Is it using, of course, we know that AI and LLMs and agents can do all the funky stuff. Where is this? What is this?

Okay, yeah, so I’ll try to explain this.

So, I think the best way of thinking about Codespeak is it’s a programming language that’s based on English. It’s not a formal language or not an entirely formal language. But it’s a programming language. It’s a language that’s supposed to be used by engineers. But it uses LLMs heavily.

And this is like the way new languages will be. Because, you know, you can think about the ultimate language of today as a normal programming language that uses an LLM as a library.

You know, there was a time where NPM was wonderful because, you know, it’s a huge repository of all kinds of JavaScript libraries. It’s the node packet manager, one of the biggest package managers in the world, right?

Right, yeah.

So, you have:

- a huge library out there that you can call,
- but now you have an even better NPM,
- The LLM that has seen all the code in the world,
- and if you're inventive enough, you can fish this code out of the LLM.

Yeah. You need to know how to prompt.

Right.

And the trick is, like, it would be really nice to have a programming language that has the entire LLM as a library or as a bag of libraries, right?

The trick is to take anything out of an LLM, you have to use natural language. So, the query language to this incredible database of all the knowledge is informal. And there is no way, at least known today, that you can make it formal.

So, inherently, this ultimate language of today has to be, at least in part, informal. And this is what we’re working on.

So, it’s still in the air, like, how formal can we make it? And, you know, it’s not the goal to make it super restricted. But the goal is to leverage all the power and support the user. You know, we need to rule out stupid mistakes and things like that. And we’re still working on that. But the basic idea is, if you, instead of spelling out every line of code and every bit of your algorithm, you can basically communicate intent the same way I can communicate it to you, you will just get there much faster.

So, one question that I asked Chris Lattner, which I’m going to ask you as well, you’re talking about designing a language for software engineers to build software more efficiently, maybe more concise, in a new way, and it sounds super exciting. But going to the other side, we have LLMs. Do you think there is a need to design a new type of programming language for LLMs to use more efficiently?

That’s a very interesting question. And I had a few discussions about this. My position is it’s probably misguided because of a number of things.

So, one, to get an LLM to understand some language well, you need a huge training set. And with the new language, that training set is not there. You can try to synthesize it and so on and so forth, but it’s not going to be as good as other languages. Like, for example, right now, the newer languages are just harder for LLMs than the more established ones.

• Any LLM writes Python better than it writes Rust or even Kotlin.
• Even the LLMs that write Java very well won’t write Kotlin as well because it’s not as present in the training set, because it’s younger.

And, you know, there are ways around it. I think the later models added some more Kotlin into the RL sets and it’s getting better. But still, it’s pretty hard. And so that’s challenge number one.

Also, challenge number two, I don’t think there necessarily has to exist a language that makes it better because LLMs are trained on human language. Their knowledge of programming languages is part of that. Their power is in having been exposed to all the code in the world and its existing code. And inventing a new language for that, I don’t know how promising that can be.

You can do another thing, which is an interesting research project. You can sort of extract a language from an LLM because, internally, it has some intermediate representations of what’s going on during inference. And maybe you can sort of extract the optimal prompting language.

It’s not guaranteed to be intelligible to humans. And there are some experiments that show that you can create completely unintelligible prompts that give the same results as normal human prompts, but they will be shorter.

You maybe can do something like this. I don’t know if it will help a lot. But what we’re doing in code speak as part of working in this language, we need to really nail down this query language capacity.

What we’re doing now is we are looking at existing code, and we’re trying to find the shortest English descriptions for this code that can generate equivalent implementations—not necessarily character to character, but they have to work the same way.

That’s an interesting exercise because you need to figure out how to represent the ideas in the code in a way that:

• You can generate the same kind of code.
• The ideas are represented much more compactly.

But also, this code you represent evolves over time, right? So you have a commit history on top of this version. Going forward in time, you need to be able to represent all the changes in your code speak version.

You need to make sure that when it’s a small change in the original code, the change in the spec is smaller. That’s an interesting challenge. So in this way, we’re sort of discovering code speak as a language, or at least parts of it, and not really designing that bit of it.

You know, it’s a very new world in the sense that, nowadays, if you work with AI, everything is a machine learning problem. That means, back in the day, if you had a very smart algorithm on paper, you could just implement it and make sure it works. Nowadays, whatever algorithms you have in mind, you need the dataset.

First of all, like if you don’t know how to collect a dataset, don’t even start. And, yeah, this is what we’re doing.

So just taking a look at, you are using these tools day in, day out. I mean, you’re building with them. How do you think programming as a whole, or I’ll say software engineering, is being changed by AI? And how do you think the future is starting to look? Especially thinking about software engineers. You’re a software engineer yourself. You’ve written so much code in your life. And are you still writing code?

Yeah, I’m writing some code, yeah.

Sorry, typing or prompting?

I’m doing both. Sometimes I’m just typing. More often, I’m typing with cursor tab completion. I’m doing quite a lot of prompting as well. And that’s a combination of all this. But cursor’s completion is really a step up from traditional IDEs. And I think the IntelliJ side has something similar now. So it’s like a lot of coding, but in a very different kind of mindset and a different tool set.

Yeah, so in terms of what’s happening to programming, I think we are in the early days of the new era. So, you know, it’s only last year that we figured out that coding agents are good. No. Cloud code and cursor agent and so on and so forth. And I think this is a very early step.

Right now we are in this phase where a lot of people are in love with agents and it can be very useful and I use them every day. But I think there are inherent problems with the model, with how you interact with a coding agent because it’s a one-on-one chat. And as a human, I talk to the agent in human language. So I’m communicating my intent on a high level.

And that intent gets translated into code and it’s the code that I commit to the repo and it’s the code that my teammates will see. So my chat history is lost. Big problem.

Yeah, so it turns out I’m talking to a machine in human language. But the way I communicate with my team is the machine language. That’s kind of backwards.

So, yeah, so what we’re trying to do in the Codespeak is to elevate everything to the human language level. So this is where we start. We say, okay, we have this incredible tool. We can prompt agents to implement code for us. And we are just picking it up. So I think a lot of teams haven’t yet realized how difficult it is to review the code.

And I’ve talked to people who are like,

“Maybe we can just not review this code.”

I’m like, yeah, I mean, you can for a couple of days and then it just collapses. And I think another big theme of today is that we’ll be doing a lot of testing.

And like, you may not need to review the code if your tests are really good. You need to verify it, right? Yeah. That’s what you’re saying is verifying might not mean reviewing. Right. Or it could not mean. Yeah, depending on the domain. Of course, of course.

You might get by without reviewing the code as much, but being sure somehow either reviewing the tests or somehow else, making sure that your tests are good. That’s a trend. And we are putting a lot of effort at Codespeak into automated testing and making sure the tests actually check the right things and that they check all the code and all that stuff.

It’s very interesting computer science. And also, it’s now a question of, especially in the case of Codespeak, and I think for other agents as well, like, yeah, reviewing code can be too much, but can we present the tests we generated to the user in a way that actually verifies that we did what was to be done?

It’s tricky. Some tests will be just very long and tedious to read and, you know, but we’re working on that. And that’s where we are.

And I think we’ll see a lot of development in terms of power of the models and we’ll get some quote unquote obvious things implemented in agents. For example, the agents are just starting to use like language servers and basically all the stuff that we’ve always had for code is not very utilized.

And, you know, if you compare like IDE-integrated agents like Cursor or Juni at JetBrains, you have a lot of like code navigation capability and, you know, databases of code is indexed and you can navigate it very quickly. You can find things very quickly.

When you run cloud code, for example, it might not have that and use grep and it will be as successful, but take a lot longer and burn a lot more tokens.

So, you know, I’m sure this year all these tools come to most agents and we’ll have a lot more sophisticated scaffolding around the models.

So that’s one thing. But then, you know, my question is always what’s going to happen in the endgame or in further future. And there it’s very hard to predict. And we can assume that models will become much smarter. But an important thing is that humans will not.

So one thing I know about the future and it’s hard to know the future, but this thing I do know about the future, humans will be as smart or as dumb as they are today. And if we have incredibly smart models, what we will be doing is constrained by how humans are and this is one of the reasons why I’m working on Codespeak because Codespeak is a tool for humans, not for models.

Yeah. And humans, I know I can build a tool for them.

I guess an important footnote is that many people will say things like,

“If we have smart enough models, they can review the code themselves and they can test the code themselves.”

But then my question would be like, who’s making the decisions here?

You know, if all the software engineering work is done by models, it means humans don’t have any say in that. And this has a name. It’s called technological singularity.

Yeah. When humans are not making decisions, it means we’re not in charge.

Yep. So this is not the future I’m building Codespeak for. Nobody should build any projects for that future. In that future, we’re gone. Your projects don’t matter.

But so my assumption when I’m talking about the future is that the technological singularity is not happening. And so the basic assumption is humans are in charge.

And if humans are in charge, it’s their job to communicate intent. So we have to say what kind of software we need to build. And when we’re talking about serious software, it’s always complex. There’s no way there’s some very simple thing that will make a difference.

And when we talk about this complexity, this is what our jobs will be, like dealing, managing this complexity, figuring out what we actually need to do. And this is absolutely engineering. There is no way someone can tackle huge amounts of complexity without an engineering mindset. It can be called software engineering, can be called something else, but you will have to do it. You will have to navigate this complexity, organize this complexity, figure it out.

And I’m not talking about the complexity of many, many layers of implementation. Maybe not. Maybe that is what’s called accidental complexity, something that happens or arises from how we implement systems. But there is also essential complexity. How we want it to behave is complex enough that we need to figure it out.

And this is why I believe there will be teams of engineers working on systems like today. Maybe they will be a lot more powerful teams. Maybe fewer people can deliver a lot more software. Yes, but still teams of people working on organizing complexity.

And this is what Codespeak is for.

Going back to where we are today with what the models can do today, what do you see with developer tools? It feels a little bit of a wild, wild west right now, very much so. I mean, there’s a lot of, obviously with Cloud Code, with Curse or with others.

But what are areas that you think we will see, we will have to see new, different, better tools to actually just catch up with how we can generate? And what parts feel the most messy and the most interesting? Especially because at Kotlin, you have, and the team has built so many tools for developers.

Right. So I think, as I already mentioned, this year will be the year of making developer tools available to agents.

There are some technical challenges, but you can’t figure it out. The people will be doing that.

There’s also a surprising advantage to using a good UI for your agent. It’s very nice to have everything in your terminal, in one sense. But then you can have a lot better user experience if it’s a dedicated environment.

The terminal tools, especially Cloud Code, are amazing. And it’s a complete breakthrough of what you can do in a terminal. But generally, you can do better in a specialized environment.

So I think we’ll see more of this integration into development environments or just new development environments built from the ground up to work with agents primarily. So that is an important thing.

Since we are putting a lot more emphasis on review, there should be new tools for review. And I think we can do better than what we’re doing now in many respects.

I don’t expect many breakthroughs in testing this year because it’s hard. I’m doing it right now. It’s hard. It’s not going to happen this year. But maybe some advances will arrive this year.

But generally, I think the big lesson of the last couple of years is that all the things that were, quote unquote, obviously needed and, you know, the idea of connecting agents to developer tools was absolutely the trivial thing to think of two years ago. But they take a long time to happen because it’s hard.

And, you know, nobody in this industry is lazy. Like everybody’s working their asses off. But it just takes time. You need to figure out the basics before you can do advanced things. So, you know, all the straightforward ideas will get implemented at some point.

I think there’s been this massive jump with AI, especially over the winter break, where the coding agents, the CLIs, have become a lot more capable.

And I know so many developers who are actually just prompting most of their code, if not all of it. It’s just a massive, massive jump. I don’t think we’ve seen anything this fast.

I see a lot of engineers scared because it can shake you to the bone. You know, it took 10 years to get really good at coding. And the writing the code part feels that it’s kind of going out, you know, the trash can.

You yourself have coded for a longer time. What would your advice be for developers who are feeling like this, that they’re feeling, you know, it is scary.

I think we, and I talk with some folks, a lot of people message me as well. How are you thinking about this specifically these last few months? It’s really hard to give advice.

There are a few ideas I can share. So one thing is there’s a lot of hype and a lot of it gets to the management and a lot of people make suboptimal decisions. But that will go away.

So, you know, there’s more and more news about people not hiring junior developers, for example.

• This is dumb.
• It’s stupid.
• This is dumb. This is not going to stay for long. I mean, it’s hard to tell how long this can go on. But people will figure out that they need new people in the industry.

And a lot of other things can be really stressful in the moment, but some of them will be rolled back. So that’s one thing.

Another thing, it’s absolutely worth it to invest your time into learning these tools and getting good at it. There’s a lot of skepticism around in the developer community about how useful it actually is. And, you know, I tried it on my project and it’s no good.

There is quite a bit of skill to using these tools. Unfortunately, it’s not super formalizable. At least so far, nobody figured out a really good, clear way of communicating how to do it well. But there are people who can do it much better than others. They not always can’t articulate why their prompts work better. But, you know, you can learn it. You can get a lot better at it.

And, you know, not necessarily believing everyone on Twitter. Some people claim crazy things, but you can be very productive with these things when you use them well. And it’s absolutely worth investing into that.

And yeah, so as I mentioned before, in the future, it will still be engineers building complex systems. So keep that in mind. It’s not like we all go to nothing.

And for new grads, people coming out of university, what would your advice be for them who are like determined, like, “all right, I actually want to be a standout engineer. Maybe with these tools, I can do it faster.” What would you advise them to focus on either skills or experiences to get?

I guess it’s a matter of what your inclinations are.

• If you can just become incredibly productive and put out a lot of working code that is really robust and you can evolve it for a long time, get good at that. And, like, there is a lot to be done there.
• If you can or like to do harder things, go into the most hardcore things you can and get good at that because it will be your rare expertise. It will be marketable. Even if that very thing goes away, you will just become a lot smarter through that.

So, you know, generally, if you have any inclination in looking under the hood and figuring out how things work, go as deep as you can. As a younger person, you have a lot of mental capacity for that. And this helps a lot. You become a very good expert in very wide fields, just through drilling down on many things.

That’s closing. I just wanted to do some rapid questions. I just ask and you shoot what comes next.

What is a favorite tool that you have? It can be digital. It doesn’t have to be digital.

Well, I love my AirPods. They’re incredibly convenient. They fit under my earmuffs.

Well, another tool would be earmuffs.

Earmuffs. Incredibly good.

Yeah, I saw you wearing it. I’ll take that one, Earmuff.

And what’s a book recommendation that you recommend and why?

There is this classic that’s been recommended across the tech community for many years. It’s called Zen and the Art of Motorcycle Maintenance.

I heard that recommended.

Yeah, it’s a very good book. I mean, there is a part of it that’s about technology and how to deal with the real systems and others, but it’s also a very good novel. I really like it.

Well, Andrew, thank you so much. This was very interesting and I think inspiring as well.

Thank you very much. It was great to chat.

It was great. Thank you.

The thing that struck me most from this conversation with Andrey was his observation about how we work with AI coding agents today. You talk to an agent and play in English. It generates code. You commit the code. But that conversation, your actual intent, it disappears. You communicate with the machine in human language, but with your teammates in code, in machine language.

Whether or not CodeSpeak becomes the answer, what is sure is that we’re missing an intent layer. And someone is going to figure out how to preserve it.

If you enjoyed this episode, please do share it with a colleague who’s been thinking about where programming is headed. And if you’re not subscribed yet, now’s a good time. We have more conversations like this one coming.

Thank you and see you in the next one.

Uneasy Calm: Ryan Hass on Three Pathways for U.S.-China Relations Under Trump

Welcome to the Sinica Podcast, the weekly discussion of current affairs in China. In this program, we look at books, ideas, new research, intellectual currents, and cultural trends that can help us better understand what’s happening in China’s politics, foreign relations, economics, and society.

Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China. I’m Kaiser Guo, coming to you this week from my home in Chapel Hill, North Carolina.

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As we move into the second year of Donald Trump’s seemingly interminable second presidency, U.S.-China relations have once again defied easy characterization.

What began as a return to tariff escalation and hardball trade tactics has somewhat unexpectedly given way to a period of relative strategic calm marked by:

• Pauses
• Truces
• A noticeable softening of tone at the very top

Even in the national security strategy and the national defense strategy that was just released.

The once dominant language of great power competition has definitely receded, and many of the most vocal China hawks who shaped Washington’s approach for the past decade appear to have been sidelined.

In their place, we’ve seen a policy posture that reflects Trump’s highly personalistic approach to foreign affairs and emphasis on leader-to-leader rapport.

“Xi Jinping’s my friend,” deal-making over doctrine, and a willingness to bracket or at least downplay ideological disputes in favor of transactional progress on trade, technology, and risk reduction.

Trump’s repeated praise for Xi Jinping, his apparent sensitivity to certain of Beijing’s red lines, including on Taiwan, and his apparent comfort at treating China as a peer rather than a civilizational rival mark a sharp departure from recent bipartisan orthodoxy in Washington, if you indeed believe that it was a bipartisan consensus.

Supporters argue that… This shift has lowered the risk of conflict and delivered tangible gains. Critics, though, counter that the United States is conceding leverage without securing durable returns. Either way, the result is a relationship that feels less confrontational for now.

In my private communications with certain among my more panda-hugging friends, there’s this sort of bewilderment. It’s like, we kind of agree that Trump is awful for this country but not so bad for U.S.-China relations, right? But beneath the surface calm lie unresolved structural tensions, deep mutual dependencies, of course, that can be weaponized, and parallel efforts in both capitals to reduce those vulnerabilities.

So, what comes next? Are we headed toward a genuine lasting stabilization or a familiar snapback to the acrimony that once dominated, once our expectations collide with reality? Or a more ambiguous middle path, one in which both sides buy time, avoid escalation, and quietly work to insulate themselves against future shocks?

Well, to help us think through all these questions, I am joined by Ryan Haas, director of the John L. Thornton China Center at Brookings, and one of the most clear-eyed analysts of the U.S.-China relationship working today. Ryan has just published an essay on the Brookings website laying out three plausible pathways for the relationship under Trump scenarios ranging from:

• a soft landing
• a hard split
• the most likely outcome: a period of uneasy calm in which both Washington and Beijing seek stability, not out of trust, but out of mutual constraint.

He joins me from D.C. And Ryan, welcome back to Sinica, man. Thank you, Kaiser. It’s wonderful to be back with you.

So Ryan, like I said, you’re joining us from Washington. Let me start there. One of the strengths of your piece is that it treats leaders as not free agents but constrained actors. From where you sit in D.C., what are the most powerful domestic forces that are shaping the U.S.-China policy right now? And which of them do you think actually matter to President Trump?

Well, it’s a really interesting question. I have to say, sitting in Washington, D.C., one thing that is very palpable is a hope, a wish among many inside the beltway that we will soon snap back to the way things were before—that this one to two-year window is just sort of a brief pause from the long-term trajectory of intensifying competition and confrontation.

I’m a little less confident of that. In fact, I’m fairly skeptical that’s where things are headed, but that’s certainly a palpable sense of mood within the beltway.

To your question, I actually think that President Trump is fairly unconstrained in terms of his approach to China. I believe he is pursuing the approach that he thinks will yield the best benefit for him personally and politically, but also for the country. The basic contours of it, to the extent that you can assign strategy to what President Trump is doing, are:

- Trying to lower the temperature of the U.S.-China relationship through direct engagement with President Xi.
- Showing tremendous respect to President Xi and, by extension, China in service of that effort.
- Building deterrence in Asia militarily.
- Reducing dependence upon China for critical inputs to the U.S. economy.
- In his own way, trying to rebalance the U.S.-China economy.

That’s the direction he is trying to take things. I don’t think he surveys the landscape of the U.S. political class and finds too many threats to his vision and approach to the relationship. But he’s thinking about midterms, he is thinking about 2028, and he’s thinking about affordability and things like that.

I mean, is that part of the logic that’s driving him to soften things with China right now—to hit pause?

Yeah. I think that there are a few things causing him to do that. First, he believes that China has us over a barrel in terms of their control over earth and other critical inputs. Until we get out from under the sword of Damocles that the Chinese have above our head, I don’t think he sees much value in taking the U.S.-China relationship toward head-on collision.

He also recognizes that he’s managing a lot of other problems around the world simultaneously. Adding to that list with intensifying confrontation with China may not be wise or prudent.

But I think he also recognizes that there isn’t a ton of appetite in the United States among the body politic for head-on confrontation.

This is something, Kaiser, you have written about and talked about—the vibe shift in the United States. President Trump, one of his unique strengths is… His reptilian feel for the mood of the American people. And in this regard, I think that the president reflects what he can sense from the American people in terms of what their expectations are for the U.S.-China relationship today.

Well, that’s comforting. The other questions, industrial policy coalitions used to be, at various times, a ballast for stability or even an active force for improved relations with China. Are they acting on him today? Is there business pressure somewhere? Is Jensen Huang a major force in his thought these days?

Well, I think that President Trump operates much differently than traditional U.S. presidents, in the sense that he is not sitting in the Oval Office waiting for his staff to bring him options for him to decide upon as it relates to China. As we’ve talked about before in Berkeley and elsewhere, he is his own China desk officer. He takes his own responsibility for calling the shots and setting the direction of U.S. policy towards China.

And in doing so, he is not informed by stale, turgid intelligence briefings that stone-faced people deliver to him early in the morning. He is talking to a range of people in and outside of government. He’s talking to people he treats as peers and considers as peers, including Jensen Huang, but not just Jensen Huang. He is basing judgments upon the body of inputs he’s receiving, which are far broader than a traditional U.S. president would.

So if he is so unconstrained and if his policy toward China, as with all things, is such a function of his just idiosyncratic whims and his character, is this current pivot away from ideology credit where it’s due? It’s something that I’m really happy to see. Is this something that could survive Trump or is it inseparable from his personal instincts and his incentives?

Well, I’ll try to take this in two parts. The first is that I think Trump is in a category of one amongst the U.S. political class in his willingness and tolerance to affect the change in America’s overall orientation towards China. And you noted this very articulately in your introduction, that he has moved the United States away from sort of an emphasis and a framing of great power competition as the sole lens through which to view the U.S.-China relationship to something that’s much broader.

I think of it as sort of non-conflictual coexistence, a more pragmatic, realistic appraisal of the nature of the U.S.-China relationship than preceded President Trump. But it does raise the question, I think a very legitimate question that you’re asking, which is, is this just something that will perish when President Trump departs office?

I can’t tell you. I honestly don’t know. But my instinct would be that no, this has the potential to outlast President Trump. However, for it to do so, a few things will need to happen:

• First, President Trump will need to demonstrate return on investment. Over the next couple of years, he will need to demonstrate that this less harsh approach to the U.S.-China relationship yields tangible benefits for the American people and American workers.

• Secondly, whoever succeeds him, whether Democrat or Republican in 2029, will need to be able to make a case for what America’s national goals are and how China relates to them.

It’s impossible to know how those two variables will play out, but it is certainly a possibility that we could see an elongation of this period beyond just Donald Trump.

The ball then is sort of in Beijing’s court. They need to pay a return on that investment, and I think if they want it to endure beyond Trump.

But speaking of Beijing, let’s flip the lens to Beijing. Is Xi similarly unconstrained? Is he a sort of singular determinant of Chinese policy toward the U.S., or does he have domestic determinants of China’s policy toward the United States at this point?

I mean, and if they are, is it like economic stabilization in the post-COVID period? There’s plenty of things that bedevil the Chinese economy right now.

Is it:

• elite risk aversion among his broader circle of elites?
• concerns about regime stability?
• his longer-term project of technological self-reliance?
• something else?

What are Xi’s considerations as far as you can tell?

Well, one of the unique aspects of this moment is that we are in a situation where the two countries are driven by very personalistic leadership styles. There are some, for me, uncomfortable similarities now in the way that the two countries are sort of operating.

I don’t think that Xi is perfectly unconstrained. I’ve never subscribed to the view that he has a monopoly on power in China and that he alone can determine the outcomes for 1.4 billion people. But I do think that there are certain things that… He is very invested in and that his brand is associated with, his political brand. One of them is making progress towards greater self-reliance and less dependence upon the United States and the West for China’s future growth, innovation and technological breakthroughs. And this period of relative calm in the relationship, I think serves that purpose. It gives breathing room and space for China to make progress down the path of greater self-reliance.

The second is being able to give proof to the narrative that time is on China’s side, that China has “winded its back” and that it’s the United States that on a relative basis is declining. And I think there are plenty of proof points that President Xi and those around him can point to, to build that case persuasively inside China today, which I think also gives some momentum to the current direction that we’re in.

I mean, I know it’s hard to say with any certainty, but is it your sense that there’s debate within the Chinese system about how hard or soft to lean into this current period of calm? Is this something that, you know, is he facing opposition? In other words, are there people who are saying,

“Hey, America’s showing weakness, time to press our strength,”

or does it seem to be, you know, Xi’s calling the shot in that case?

Ryan Hass: You know, it’s a good question. My latest sort of touch for that is a bit dated. I was last in Beijing and Shanghai in December. So I’m a month plus out from my last contact with people who are in policy circles in China.

But based upon that last round of conversations, my view is that many people recognize that this moment is serving China’s interest well, that China’s goal is to try to relieve pressure and sort of unblock the path to China’s continued rise.

To the extent that President Trump is willing to play a role in that by relaxing pressure upon China, whether it be through:

• reducing tariffs,
• lowering export controls,
• reducing strategic pressure on China,

I think those are all sort of indicators that this is working to China’s long-term benefit.

Kaiser Guo: So Ryan, a central claim or assumption in your essay is that both sides, Beijing and Washington, are behaving less out of mutual trust than out of mutual sense of vulnerability. That, I think, isn’t a claim that many people would challenge, actually, and I wouldn’t.

To what extent do you think that policymakers in both capitals genuinely understand this as kind of a negative sum dynamic? And to what extent are they simply discovering through painful trial and error that they are mutually vulnerable and that they need to chill out?

Ryan Hass: Well, I have a pretty high degree of conviction around this point, but I don’t have some smoking gun evidence that I can point to to prove it.

My sense is that both leaders and those around them have come over the past year to recognize that the other side is capable of doing immense harm to itself.

And I think that this has been a revelation, more so on the US side than the Chinese side. The Chinese side has been well aware for a long time that the United States is capable of being a dangerous superpower that can do immense harm to China.

But when President Trump and Secretary of Treasury Besant and others entered office last year, they entered office with a certain degree of bravado and hubris. Secretary Besant famously said that

“China is holding a pair of twos in terms of, you know, the cards it has in its hand and the lack of leverage it has over the United States.”

No one is talking like that anymore.

Through painful trial and error, both sides have come to realize that they are each capable of doing harm to the other. And that if one side initiates action against the other, it should expect painful retaliation response.

And so I don’t think that President Trump and President Xi over the past year have developed some like brotherly friendship where they decided not to do harm to each other.

I think they both come to recognize that if they take actions that are harmful to the other, that they will get hit back in response. And that it will hurt.

And that was the whole lesson in 2025 leading up to Busan, right?

Kaiser Guo: And you know, your trip may have been a couple of months ago, but that was still in the post-Busan era. So I think you have a probably quite accurate read of how they’re feeling right now. Not much has changed since then, so.

Ryan Hass: Right.

Right.

Yeah. There haven’t been many major ruptures or fluctuations from then till now. Except the rupture that, you know, Mark Carney spoke of.

But so Ryan, let’s jump in with your first scenario, the soft landing. In this pathway, both leaders:

• invest in improving the relationship,
• maintain regular contact,
• lower barriers to trade and investment,
• and move toward a narrative of peaceful… Coexistence or managed competition. What would actually have to go right on each side for this to move from a theoretical possibility to a durable trajectory? I mean, you could point to a couple of things that say, well, this step actually does seem to have been taken.

I mean, you know, they’re really talking about investment right now. We’ve got Ford talking about working with Xiaomi possibly, according to the FT, at least on a battery plant, right?

Yeah, you’re absolutely right. I think for this scenario, the soft landing scenario to take root, a couple of things would need to happen.

• The first is that both leaders would need to discipline their systems to actually prepare thoroughly and meticulously for leader-level engagement so that they yield durable breakthroughs and not just ephemeral headlines. This is sort of the challenge of the personalistic leadership style of both countries. More so in the United States and China, I think that President Trump doesn’t really want to be particularly constrained by the preparatory process. He wants to have room to maneuver and decision space to be able to get in the room with President Xi and sort of work things out.

So that’s the first prerequisite.

• Second, both sides need to take costly signals to invest in durably improving the relationship over the long term. The types of things that you’re pointing to — if the United States became more welcoming of Chinese investment, that would be a costly signal.

I think one of the things that some people point to who are advocates of this approach would be some type of grand bargain.

So we know that President Trump is planning to travel to China in April. If that visit were to yield a sort of significant breakthrough on a contentious issue, most people would identify Taiwan as the candidate, Taiwan combined with some type of transactional benefit for the United States and its workers. Then that would give momentum or solidity to the idea that we could travel down this path.

But short of that, I think it’s hard to imagine both sides really sort of believing and acting in ways that both leaders believe they can sustainably improve over the long term of the nature of the relationship.

What makes that costly from the American side?

• In the case of inbound investment, it could potentially displace entrenched interests in the U.S. economy.
• It could invite criticism of President Trump and his judgment that he is growing too soft and giving away the store to China in service of soybean sales or whatever it is that he’s setting up.

So you say that it would require both sides to send costly signals. What sorts of signals are we talking about from Beijing, and what would be costly about those? How hard would they be to deliver domestically in Beijing?

It’s a great question. I think in the case of China, there is a certain degree of skepticism about whether the Chinese leadership would be comfortable seeing some of its companies and crown jewels invest or produce outside of China. We see this in particular with Meta’s efforts to acquire a Chinese-origin AI company that relocated to Singapore.

Meta’s, yeah.

Another area, in the Taiwan context, would be if President Trump were to alter longstanding declaratory policy toward Taiwan, would China reciprocate by:

• Agreeing to withdraw its military actions to its side of the center line, the unofficial center line of the Taiwan Strait?
• Making a reciprocal statement about its commitment to resolving cross-strait differences without use of force?

These are the types of questions that sort of point to costly signals that each side would expect the other to give if they were to give it themselves.

I have trouble seeing that is costly to China compared to the electoral costliness of signals from America. So it feels like China can ram this through; Trump faces electoral pressure.

Yeah, he might. But let’s keep in mind, he’s never going to be on a ballot again for the rest of his life.

That’s true.

And so, President Trump has never shown a lot of conviction about election outcomes that don’t involve his name on the ballot.

Ryan, looking back over recent U.S.-China history, is there a precedent that you can point to for restraint for restraint actually holding for any decent length of time?

I can’t think of anything off the top of my head right now that would give a lot of confidence to the notion that restraint for restraint is a time-tested and well-established trend. This is the critique that I think people of the soft landing approach would make, is that the soft landing would The discussion involves the United States making concessions to China without receiving reciprocal benefits in return. There’s a pretty calloused skepticism that has built up over years, including within the Trump administration, as a consequence of the underperformance of China in the phase one trade deal.

Obviously, you floated this possibility that something like a fourth joint communique on Taiwan could anchor the sort of soft landing you’re talking about, the grand bargain.

What problem would such a document actually be trying to solve? What would be the content of a fourth communique? And is Taiwan ultimately the issue that makes this scenario maybe politically untenable, even if both leaders are inclined toward restraint? I mean, is Taiwan going to flummox this?

I think it will be very difficult. The idea would be that the last time that the United States and China had a communique was in 1982. A lot has happened in the last 40 plus years. A new framework that sets out a baseline of understanding for how both sides will approach cross regulations may be a useful stabilizing mechanism.

I’m on the more skeptical end of the spectrum on this question. I don’t think that the challenge is a lack of understanding about the nature of cross-strait issues. I think that there are just competing interests involved that need to be managed.

In Washington, it’s treated as sort of a foregone conclusion that Beijing is desperately seeking a fourth communique or some type of new understanding related to Taiwan with President Trump. There are a few factors that may mitigate against that as a foregone conclusion:

• It’s not entirely clear that on a day-to-day basis, President Trump has absolute control over his bureaucracy. His bureaucracy does things that surprise the Chinese and surprise the president on a not irregular basis.
• President Trump changes his mind often. He is adaptive, flexible, fluid in his thinking, as seen with Greenland and other issues. If he agrees on the spot with President Xi that he has adopted a new way of thinking about Taiwan, will that survive contact when he returns to the United States?
• The Chinese have to ask themselves whether or not this will be an ephemeral understanding that exists between President Trump and President Xi. Trump has a shelf life of three years in office.
• If the Chinese reach an understanding with Trump over Taiwan, will that trigger Congress to become more active and engaged to try to counterbalance whatever concessions members of Congress believe the president has made in return for some type of commercial transaction?

Yeah, yeah, yeah. Just to remind everyone, this is your most optimistic scenario. And in this most optimistic version, there is still a sense that the soft landing would be kind of inherently provisional, something closer still to a pause than to a full reset.

I am ineradicably optimistic but still have trouble seeing either polity really arriving at some kind of durable modus vivendi right now. There’s just no trust. There are many deeply entrenched habits of mind on both sides.

But there are other scenarios that you posit here. The second scenario is the one I sincerely hope to avoid: a hard split.

You frame this as a familiar arc: Trump starts conciliatory, grows very frustrated, and then swings really hard. We’ve seen this many times. What are the most plausible triggers that could push the relationship down this kind of path toward a hard split?

Well, there are a few ways we could get here:

1. There could just be a misunderstanding on what each side agrees to. President Trump comes to the conclusion that the Chinese are under-delivering on their promises. He grows frustrated, angry, and we find ourselves back following the same cycle as we did during the first term, where:
   - The first three years focused on negotiating a phase one trade deal.
   - The fourth year focused on letting it rip because the president was so angry and frustrated that COVID had spread and undercut his reelection prospects.
2. China takes actions against American allies that involve **use of force** and puts the United States in a very difficult position of deciding whether or not to employ force against China to come to the defense of their allies and uphold **Article 5 commitments** or traditional understandings of security commitments.

Examples of such allies include The Philippines or Japan. Right. Right. Right. And then what some people in Washington would say is that as the midterms get closer, the political incentive for President Trump to become harder and harder towards China will grow, and that the political imperatives of President Trump wanting to hold off Democrats gaining control of the House and relaunching impeachment probes against him will compel him to grow tough.

This is the hope, I think, of a lot of people in Washington who want us to get back into the business of great power competition. And I’ll just offer just a quick caution, Kaiser, as to why I’m not yet convinced that this is the natural course of events that we’re going to find ourselves in.

First, you know, the president has demonstrated that he is very sensitive about America’s dependence on rare earths. That dependence is not going to change in the next 12 months, 18 months, even two years.

• The magnets.

Yes. The second is that President Trump just genuinely is not activated by the military threat or the ideological nature of competition between the United States and China. But he’s much more focused on economic and tech issues. He wants to make deals that he can point to and tout his as successes and breakthroughs.

And having a hostile relationship with China would sort of move against that objective.

I also think that President Trump is pretty comfortable with the status quo right now. He doesn’t face immense political pressure at home for where the US-China relationship stands. He also likes to brag privately with his colleagues and counterparts about how much tariff money he believes that the United States is generating from tariffs on China, never mind the fact that it’s US importers that pay the tariffs.

And then lastly, I think that President Trump is very focused on legacy and blowing up relations, burning down the house with China is not a legacy enhancing exercise. Putting the relationship on a new plane potentially could be.

So, I mean, the fear of a blue wave in 2026 in the midterms, I mean, I get that. But part of him also has somebody’s got to be showing him these polls that say,

“there’s just not a lot of appetite right now among voters for tough on China. It’s not a winning campaign strategy right now.”

I mean, poll after poll after poll is showing that that is fundamentally weakened vibe shift once again.

Right.

So, I mean, hopefully that’s a mitigating force.

Yeah.

And traditionally, midterm elections are not animated by China or by foreign affairs. I mean, there really isn’t any empirical evidence that going tough on China improves the odds of House and Senate candidates getting elected.

So, from Beijing’s perspective, I mean, it’s pretty easy for us to think of what kinds of U.S. actions would collapse strategic calm and force Beijing to take a harder line that would be reciprocated by Washington. I mean, all sorts of triggers, right?

• Taiwan
• Rare earth exports
• American export controls

But where do you think miscalculation is especially dangerous? What are the areas where you think that crossed wires and signals misinterpreted are particularly dangerous?

I would suggest, just as a hypothetical scenario, if the United States became more aggressive with other countries about urging them, insisting that they adopt America’s AI tech stack—

Right.

—and conditioning security support for them doing so. That could be an example of how things could go off track.

And if there were further actions like we saw last fall where the Department of Commerce rolls out something in an uncoordinated fashion, the 50% rule, the affiliates rule.

Right.

Something along those lines that the Chinese perceive as violating the truce, the understanding that was reached between both leaders—that could compel the Chinese to reciprocate and retaliate.

Well, that problem may be solved. Trump has apparently neutered BIS, right? So we’ll see.

One thing that struck me is how much this scenario depends on momentum, on anger compounding on anger. Once the relationship starts moving in this direction, how easy is it to reverse?

• Are there off-ramps?
• Does it become just like self-reinforcing super quickly?

I ask because this isn’t the first time either Beijing or Washington has seen things go sideways. And you’d think that both sides might have learned something about how to manage that sort of crisis. And at least sometimes they’ve managed to get the relationship back on track.

And we saw that with the taco meal that resulted in Busan.

Has there been any learning? I mean, do you think that there’s enough sort of wisdom on either side to avoid that kind of scenario?

Well, I think that the key to avoiding that scenario is the two leaders. When things begin to veer off track, it’s the two leaders that usually put things back on track. And the challenge, the structural challenge, is that the Chinese traditionally, historically, are pretty reticent about requesting calls from President Xi to President Trump.

So if there is an incident that is an unplanned encounter between naval vessels or whatever it may be, and things begin to sort of go off the rails, pressure builds. We have a spy balloon-like dynamic emerge inside the United States where there is just boiling angst and anger about something that China has done that violates American airspace or hurts American sailors or whatever it may be.

When the Chinese do not appear to be reaching out to President Trump personally, we could find ourselves in a tough spot. And if the Chinese are perceived to be the instigator of this downward spiral and they don’t communicate directly with President Trump but try to operate through intermediaries, I think that President Trump could find himself both humiliated and offended in ways that could sort of compound the initial problem.

So that’s scenario two: one where there’s a hard split, not an optimal outcome at all, obviously.

You, fortunately, ultimately judged scenario three, which is about buying time and building insulation, as the most likely path. I would certainly concur. But what, in your mind, makes this outcome more resilient than the other two? I mean, because it seems sort of inherently unstable, right? It’s provisional. It’s about sort of just playing for time. And so it feels very impermanent.

But why do you think this is maybe more durable than the other two possible outcomes?

To me, Kaiser, and this is unscientific, this is just sort of a feel, it feels like the most realistic scenario. I don’t think that either of the two leaders is prepared to sort of make significant lasting concessions to the other. I don’t think that either country is prepared to accept a subordinate status to the other.

I think that both countries, in their own way, are able to tell themselves a story that time is on their side. And if they just regenerate or strengthen themselves, that they will be able to outlast and outpace the other.

And so this third scenario of sort of buying time and building insulation, it’s most appealing to me because it works for both leaders and how they describe their intentions and their goals.

• President Trump is clear.
  • He does not want a war between the United States and China.
  • He wants to make the United States less dependent upon China.
  • He wants to rebalance the relationship between the United States and China.

This scenario allows him to make directional progress on all those goals.

Similarly, for President Xi, I think that there’s a fairly mirrored set of objectives.

President Xi is very committed to strengthening China’s self-reliance and moving down that path. He certainly, in my mind at least, does not seek a confrontation or conflict with the United States. But he also isn’t interested in making any significant gestures or major concessions to the United States either.

I think that the Chinese believe that they have momentum behind them. And the wave of leaders that have come to Beijing over recent weeks to visit President Xi, I think, have reinforced that perception.

So a core insight of your piece, Ryan, is that both sides are constrained by deep mutual dependencies. I think most people who are listening are aware of some of these and can rattle them off:

• China’s dependence on advanced semiconductors
• The U.S. dependence on Chinese processed rare earth elements

But what do you see as underappreciated vulnerabilities on each side that might reinforce this uneasy equilibrium? Are there things that we’re not talking about enough where there is mutual dependence?

Well, I’ll offer a few.

When I was in China last December, I was discomforted to be reminded in almost every meeting about America’s dependence upon active pharmaceutical ingredients from China, APIs. And I don’t think that that was just sort of a stream of consciousness idea that bubbled into the minds of everyone we were sitting down with. It was a reminder that rare earths aren’t the only source of American dependence upon China.

Similarly, I think for China, they are painfully aware of their dependence upon the United States and the West for:

• Airplane components and parts
• Everything related to the advanced semiconductor manufacturing, ethane, plastics

But also at a more intangible level, access to America’s higher educational system. This is something both from the students themselves and their future contributions to Chinese society, but also Chinese leaders’ ability to keep that door open for students, the children of their peers, is critically important. And if the relationship were to deteriorate, we’ve already seen that this is something that the Trump administration has considered using as a retaliatory tool.

• Rubio’s sudden announcement about, banning all Chinese students at one point.
• To President Trump’s credit, he basically called bullshit.
• He said that that isn’t where he wants to go or what he wants to do.

Now he’s talking about 600,000 Chinese students in America. I guess maybe he thinks about them as a service export rather than as human beings who contribute to the flourishing of our academic community.

But whatever the case, I think that having Chinese students in the United States enhancing the education of classrooms that they’re a part of is a net benefit for the American people.

So, Ryan, in this scenario, you kind of suggest that the way we score this is by measuring who reduces dependence faster. I mean, if we look out five, ten years from now, which side do you think is better positioned to actually succeed in reducing those dependencies? I mean, who’s working hard at this?

• We talk a lot about reindustrialization. Is that underway?
• China talks a lot about technological self-sufficiency.
• There’s ample evidence, to me at least, that that is well underway, that it is a serious priority, that they’re putting the effort and the brainpower into that.
• I think there are probably things happening in America right now with rare earth elements that should give people comfort.

But what’s your assessment of this?

Well, we have a tendency to swing from one extreme to the other in the way that we talk about this in Washington. A few years ago, Kaiser, you and I were talking about peak China, whether it’s a serious thing, how should we think about it? Everyone was focused on all of China’s weaknesses, vulnerabilities, and soft spots.

In recent months, it feels like the pendulum has swung to the other extreme where China can make everything. China can do anything. Ten foot tall again, right?

The world is sort of gravitating towards China. The United States is in dire straits. I’m uncomfortable with either of those extremes.

I think that China does have profound challenges, but it also has immense strengths. Neither of those are going to go away anytime soon. We have to get comfortable to be able to look at both of those side by side.

And the same can be said of the United States.

I will just make one observation that I hope is in service of answering your question, which is that I am deeply uncomfortable with the direction that our country is headed in certain respects. I think that right now the social fabric of the country is tearing, and national unity is the foundation of national strength.

No country can be stronger on the world stage than it is at home.

What we are watching in Minnesota and elsewhere is deeply troubling, both for me from a spiritual standpoint, but also just from a civic standpoint, and also in a measure of national power.

Secondly, I worry very much about America’s alliance network fraying and unraveling. Alliances traditionally have been a force multiplier of American influence on the world stage. Now, I think that our alliance network exists more in name than function.

This is going to be a long-term cost that the United States is going to pay for the moment that we find ourselves in.

But more fundamentally, and this I think, speaks most directly to the question that you’re asking, I worry that America’s economic competitiveness is eroding somewhat.

• We see manufacturing declining.
• Consumer confidence is at its lowest levels since the shadow of the global financial crisis.
• Talent is being turned away at our borders.
• We’re forfeiting on clean energy.
• We’re losing ground on biotech.
• We’ve put all of our bets on racing to the frontier on AI.

I just feel like at a certain level, President Trump is pursuing a 19th century strategy of assuming the control of natural resources will be the source of national power. We find ourselves in a different world today.

I think that his resource obsession is a strategic distraction.

For me, the goal needs to be to stimulate growth.

Growth comes from productivity. Innovation and diffusion come from:

- Talent
- Ideas
- Efficient allocation of capital
- A transparent and predictable legal system

This is how America gains strength.

The further we turn from that, the more that I fear we will lose our ability to achieve the sort of escape from dependence that your question was anchored in. Yeah, I mean, it’s so frustrating to be, this is a man whose favorite metaphor is cards, but, you know, he’s talking about who’s got the stronger hand, you know, who holds more cards.

It feels like somebody’s got to be able to convince him that what he’s been doing by, like you say, turning away talent at the border, by destroying those things like predictability, rule of law, alliances, all these things, you know, that act as force multipliers for us.

He’s plucking valuable cards out of his hand and, you know, lighting them on fire to light his cigars. It’s just bizarre.

I mean, I feel like at this point, Beijing must look at, you know, the hands that each side holds and conclude that there’s some very pronounced asymmetry here.

I feel also like that could really make this equilibrium that you described in scenario three more fragile. I mean, if one side succeeds faster than the other in reducing vulnerability, and right now it looks like China’s succeeding faster in reducing vulnerability, that actually seems like it would destabilize this equilibrium.

I agree with you if the equilibrium is measured in bilateral terms only.

And I thought that Adam Tooze made a very important point in the interview that you flagged to his with Ezra Klein after Davos, which is that if we are thinking about the world as undergoing a power transition from the United States to China, it is going to trigger all the anxieties, insecurities, and antibodies in the United States about China’s rise and compel us to try to suppress it.

And if we rather think about what’s going on in the world, not as a power transition, but as a power diffusion, where the United States is not significantly declining, but power is growing much more diffuse in the international system. The international system is splintering. It’s growing more disordered.

Then the nature of the challenge shifts, and the way that we think about and address and respond to it also evolves.

I am much more inclined to the latter view, that we’re seeing a splintering and a diffusion of power rather than a transition in power. But this is going to be, I think, sort of a core aspect of the debate that will be underway about the way that America relates to the world for the next couple of years.

Yeah, it’s interesting. I seized on that metaphor that Tooze used, too.

And I started thinking about that kind of moral panic securitization that we’ve seen in this country as an autoimmune response.

“You’ve got to take some goddamn antihistamines and chill.”

I agree with you that this scenario, this third scenario that you describe, is probably the most likely.

Does this framework, just stepping back, suggest that we’ve entered a phase right now where U.S.-China relations are less about, you know, trying to build trust or establish shared norms and more just about engineering resilience under assumed conditions of enduring mistrust?

I mean, where each side, you know, we’ve got a hand on the other’s choke points,

• they’re grabbing our oxygen tube
• we’re grabbing their oxygen tube.

It’s, you know, I guess it’s structurally analogous to, obviously not identical to, kind of, you know, mutual assured destruction during the Cold War.

If that’s right, how should it change the way policymakers even think about stability?

Well, it’s a great question. I am inclined to your second scenario that you just described. I do think that we’re both sort of holding each other’s oxygen tubes to a certain extent.

I don’t think that there’s any outbreak of goodwill or warm, fuzzy feelings towards each other right now. And I also think that we’re in a pretty fraught moment. Both countries believe that they are gaining a certain degree of advantage over the other or that they can do immense harm to the other.

But on top of that, if you look at, you know, social science work and some public polling data,

• the Chinese public feels pretty triumphal and nationalistic right now.
• The American public feels pretty beaten down, distraught, and just sort of beleaguered at the moment.

And so this isn’t the time. We are not at a moment where there’s going to be some grand breakthrough in the relationship.

I think that if we manage it well through this coming period, we will have done a service as stewards of a long-term relationship rather than as authors of some concluding chapter to it.

Well put. Beautiful.

A final question to you. I mean, if listeners wanted to just cut through the rhetoric and only watch for just a handful of real concrete indicators over the next, say, 12 to 18 months, what would you tell them to focus on to assess which scenario we’re actually in or which we’re careening toward?

I would encourage people to watch the frequency of interaction between the two leaders,

- how often they talk on the phone,
- how often they acknowledge exchanging views through each other as ambassadors or intermediaries.

I would pay attention to the degree to which both sides are preparing for engagements, direct face-to-face summits between the two leaders, whether this is a professional process or just sort of a slapdash trip across the ocean. I would watch to see how well the United States is doing in terms of building or stockpiles, reducing its sort of vulnerability to shocks in the industrial supply chain system from China.

And similarly, I would watch to see the degree to which China is sort of making progress and innovating around some of the export controls and other obstacles that the United States has put in its development path.

So how important are atmospherics going to be around the April Trump visit to Beijing? Well, I think it’ll be significant.

You know, it’s somewhat ironic, Kaiser, because traditionally, the United States trades form for substance. You know, we decide to negotiate away different sort of bells and whistles of a Chinese leader’s visit to the United States in exchange for substance. Because we know that the Chinese leader cares deeply about the imagery that comes out of such engagements because

it bestows respect and gives people inside China pride that their leader is being treated with dignity on the world stage.

Now, I think we’re in a moment where sort of the roles are reversing, where it’s President Trump will be committed to the trappings of dignity and respect, and we’ll want something grander and more dramatic than what he experienced with the state visit plus in 2017 or 18. 17 it was, yeah. I expect that he will probably go to a second city this time as part of his trip.

And so how he is received by the public, but also, you know, the imagery that comes out of that will be important to him. But ultimately, I think that the measure will be to what extent has his travel to China benefited the American worker and the American people. And, you know, we’ll have to see.

Well, I will be there on the ground in Beijing in April. I’m leaving very soon. In fact, just two weeks from now. And I will report faithfully. I’ll do a couple of shows about, you know, preparations for the Trump visit and see how that plays out. Because I think that is a very, very telling indicator.

And I think you’re absolutely right. We are in this world right now where the Trump presidency cares very much about all the symbolism, the pageantry, all the sort of etiquette and the formalism of it. And I think Beijing knows that. Beijing knew that before November 2017 when he went. They sort of turned up the flatterometer to very, very high. They know how to do this.

Well, I will be listening carefully to your reporting from on the ground, Kaiser.

Well, thank you, Ryan. Make sure to read the piece. It’s on the Brookings website and everything else that Ryan writes because it’s all super, super good.

Ryan, thank you so much for taking the time to chat with me. Let’s move on to paying it forward. Do you have a younger colleague or somebody who you’ve been working with who deserves a shout out here on the show?

I do this selfishly because, you know, I’m looking to cultivate, you know, new guests to bring on. I would point to Audrey Wong, who is an incredibly thoughtful, talented researcher, writer, public intellectual, who is doing tremendous work explaining China’s economic orientation to the world.

Fantastic.

And we can find her stuff on Brookings?

Audrey, I believe she’s at USC right now.

Oh, okay. Cool. Excellent. Audrey Wong. I will look out for her.

And what about recommendations? As you know, we do a recommendation every week. What do you have for us? You got a book or a film or some music, a travel destination, something that you want to recommend?

You know, Kaiser, I wish that I had something super cool to share. I’m going to just default to a book recommendation from Robert Sutton. He wrote The Conscience of the Party, the biography of Hu Yaobang.

And it’s as much just a gripping human story about Hu Yaobang, the last reformer in China, as it is a sort of an x-ray of the Chinese Communist Party system and the way that it operates and how it operates. So it’s for anyone who’s sort of interested in the functions of the party. I think that Robert’s book is a tremendous starting point.

That’s been on my list for a while. I really need to finally get around to reading it.

That’s an excellent recommendation. Thanks, Ryan.

So I’ve got a book as well, as well as a couple of China-related things. But my book is just for fun. I’ve been reading the long-lost final book that Alexander Dumas wrote. The English translation that I have is called The Last Cavalier, but it’s also known as The Knight of Saint-Hermain. The French title is Le Chevalier de Saint-Hermain.

But either way, it is a really fun bit of Napoleonic-era historical fiction in which actually Napoleon himself is a major character. And Dumas gives him a really kind of believable personality. I mean, much better than Ridley Scott gave him in that lamentable film, which I hope none of you had to suffer through.

But there are loads of fascinating characters. Many of them are historical. It sent me skirting to Wikipedia many a time just to sort of look these people up. But it’s also just got a ton of historical material mixed in. It’s got letters and decrees and courtroom proceedings, all kind of jumbled into the fictional stuff.

I mean, the story, the plot is a bit of a shaggy dog. It’s maybe, you know, 40% fewer total tangential plot lines might have made this book a little more sort of readable. But it’s still worthwhile if you’re interested.

Dumas actually writes himself or his father. I mean, he does this sort of breaking the fourth wall thing where he suddenly starts talking to the first person and then talks about his father, who was this Napoleonic general, who’s also Alexander Dumas.

It’s anyway, great stuff to take your mind off the world as it is. But still, you kind of get to scratch this itch for, you know, political turmoil and intrigue. If you’re listening to this show, you probably have such a niche.

For a couple of quick China-related recommendations, some really good sense-making of the Chinese economy has dropped just in the last couple of days for the day we’re taping. Check out the Asia Society conversation led by Lizzie Lee, who listeners will know, of course, from her many appearances on the show.

She’s joined by two of my faves:

• former World Bank country head for China, Bert Hoffman
• Gerard DiPippo of RAND, formerly CSIS, also just one of the smartest dudes on the Chinese economy.

It’s about the challenges of rebalancing the Chinese economy, but it goes way beyond that. It goes, you know, into the – obviously, you know, the problems of the property market and much else. It’s as good as you would expect with these three all taking part.

Related to that is the latest outstanding Trivium China podcast, of course, which you can find on the Sinica Network. It’s hosted by Andrew Polk, and it is just a banger of an episode.

Joe Peisal, who heads macro research at Trivium, is the guest for the first half, and they do this thing that they’re going to be doing every month or so, just looking at the macro numbers. But this one sort of looks at just – not just macro numbers for Q4, but for the year. And it’s a great survey.

The second half, though, features Danny McMahon and Corey Combs, who are both absolutely brilliant.

• Danny McMahon looks at markets mainly.
• Corey, who is – they’re so lucky to have this guy. Corey covers – he does strategic minerals and supply chains for Trivium.

They are both really brilliant. It’s on, you know, why China is facing headwinds on boosting capital expenditure, which, if you follow the Chinese economy, you’ve probably heard, dropped really, really precipitously in the last quarter. So check out those shows.

I’m a neophyte soul when it comes to the Chinese economy, but I’m always interested in learning. So these guys have taught me just enormous amounts.

Anyway, Ryan, great to have you on again, man. And this is going to be a very Brookings-heavy month because I’m going to be talking to your colleagues, Kyle Chan and Patty Kim about the work of theirs recently.

“Delighted to hear it, and thanks for having me on, Kaiser.”

Thank you. You’ve been listening to the Sinica Podcast. The show is produced, recorded, engineered, edited, and mastered by me, Kaiser Guo. Support the show through Substack at SinicaPodcast.com, where you will find a growing offering of terrific original China-related writing and audio.

Email me at SinicaPod@gmail.com if you’ve got ideas on how you can help out with the show. Do not forget to leave a review on Apple Podcasts.

Enormous gratitude to the University of Wisconsin-Madison Center for East Asian Studies for supporting the show this year. And, of course, huge thanks to my fabulous guest, Ryan Haas, who is always a favorite, fan favorite, my favorite.

I’m really – thank you, Ryan, once again. Thank you, guys. Thanks for listening. We’ll see you next week. Take care.

Why the Belt and Road Is Back in a Big Way

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Hello and welcome to another edition of the China Global South podcast, a proud member of the Sinica podcast network. I’m Eric Olander. Today, we’re going to get an update on the state of the Belt and Road Initiative.

Now, for the past several years, we’ve been hearing that the BRI is spent. The Chinese have run out of money and Global South countries that were the destination for so much of that investment simply can’t afford to take on more debt. And even the Chinese themselves have tried to change the narrative to make way for what was supposed to be a new, more austere era. Remember all of that talk about small yet beautiful? In Chinese, it’s called Xiao Er Mei (小而美). That was the line that they told everyone about smaller, more affordable, less risky BRI projects around the world.

Well, the data tells a very different story. BRI engagements last year actually reached an all-time high of more than $200 billion. Construction projects increased by 81% and investments surged by 61% compared to 2024. Energy engagements, especially in the fossil fuel sector, were very, very hot in 2025. And while the U.S. may have soured on Africa, Chinese investors haven’t. The continent was the top destination for BRI engagements anywhere in the world last year.

All of this comes from a new report published by the Green Belt and Road Center at Fudan University in Shanghai and Griffith University in Australia. Our old friend, Christophe Nedepil, is the man in charge of the project and joins us today from his office at Griffith University.

Good morning, Christophe, and welcome back to the show.

“Good morning to you, Eric. Great to be here and good to see you.”

It’s wonderful to see you and to get these surprising numbers because, again, we had heard that the BRI was all but done. Even the Chinese themselves were trying to brace us for a much more austere era. Your data says otherwise. What do you explain for this big surge of construction and investment by Chinese stakeholders?

“Yeah, I think that was a very big surprise already. When we were tracking the data, this level of commitment that we’ve seen in 2025, it’s something that we hadn’t expected and obviously hadn’t seen before.”

We’re at levels of BRI engagement and engagement, again, there’s construction contracts and investments—construction contracts where Chinese construction companies, like particularly state-owned enterprises, take the lead in implementing a large project. And investments are much more where the Chinese are investing their own money through equity investments, so they take ownership.

Now, these levels are more than double from the COVID years. So it is quite impressive. And you mentioned the energy engagement over $90 billion, and that over $90 billion is more than we’ve seen. Just kind of only the energy engagement is higher than we’ve seen during the COVID years. So this level of engagement is really something that is quite surprising to us.

I think there are a couple of explanations, of course. In the COVID years, 2020, 2021, and 2022, there was this whole idea of Xiao Armei, like small, yet beautiful, which was, I think, very logical. There was a lot of global risks. It was difficult to make deals. It was difficult to travel. And so the projects overall, the project volume decreased.

And now, really, this uptake in a still very volatile world, but with massive deals, scales more than $10 billion for single engagements.

So I think the largest one, also outside the BRI, is $37 billion by TikTok in Brazil. But we’re also tracking outside BRI. So we’re not just tracking BRI. We’re reporting on BRI, but in all the massive engagements:

• Nigeria: $20 billion for gas industrial park
• Kazakhstan: $10+ billion for mining and metals related engagement

And this $10+ billion engagement, we’ve not seen before. This is a new level of BRI engagement that I think is quite interesting to observe, and we’ll see whether that continues over the years to come.

So a lot of us were surprised, not only because of the size of the numbers but also the timing of it. Coming in 2025, when Donald Trump comes back into power, the international system goes into disarray, is there any connection that you can see in the data between the events that have been happening, say, within the new Trump era—that is the disruptions? Do they see an opportunity to move as the United States is pulling back from the world? Or are these just more coincidental in terms of the timing?

“I think there’s both. These projects take a while, particularly large-scale projects, take a while to negotiate.” This is not something that the Chinese are able to do with their partner countries in months. This is usually maybe a year in the making. So not everything that we have seen in 2025 was agreed to in 2025.

Now, what we know, of course, that over the last years, and this is not just the Trump era, this is also Obama era. And there was supply chain diversification, supply chain de-risking, with manufacturing plants being constructed in countries outside of China in order to reduce the tariff burdens from exporting directly from China, so rather exporting from other countries.

And there, of course, then came Liberation Day in April 2025, and with the massive increase of tariffs around the world to the U.S. And again, some of the Chinese companies have actually reacted quite quickly and also, for example, scrapped investment decisions for manufacturing from Vietnam and brought them to Morocco or other countries that have lower tariffs. So there’s still a lot of movement around in terms of the investment decisions, and that is also driven, of course, by geopolitics.

Let’s go back to energy. You mentioned that that was one of the major investment surges of last year. In fact, it was the highest of any period since the BRI’s inception at $94 billion, more than double what it was the previous year back in 2024. Give us the profile of these energy investments, because we had heard that the surge in Chinese investment overseas was in solar panels and new energy. But it seems to get these numbers at $94 billion, you’re going to have some of the older energy modes in there as well. Tell us a little bit about what happened in the energy sector.

Yeah, so I think kind of one of the quotes that have been picked up, it’s like

“2025 was the dirtiest and the greenest year in terms of energy engagement.”

And that’s true in absolute terms. So the overall engagement, as you said, increased quite a bit. It is particularly driven by oil and gas related engagement. These, for example, in Nigeria, the gas energy industrial park, there are a number of other fossil fuel engagements across the region.

So fossil fuel engagement actually has taken by far the majority, I think 75% of the total engagement is related to fossil fuel. And that’s a very high emitting energy engagement. And this is, so I remember in 2020, we celebrated that green energy or renewable energy has broken the 50% mark of the total energy engagement. And we’ve been backsliding since then in terms of the share. So that’s a worrying trend in some ways, particularly if we want to talk about a green Belt and Road Initiative and China’s green engagement.

Now, at the same time, the green energy engagement also increased to record levels. So that’s why we can also say it’s been the greenest year. And so that’s particularly in solar construction, but also in solar and wind construction, as well as in battery storage. As a broader kind of engagement portfolio that the Chinese have compared to previous years.

What’s important to note here, and I think we’ve also discussed this previously, Eric, is that we’re not looking at exports. So China’s green energy-related exports, solar panels and wind, whatever Pakistan, for example, imports 19 gigawatt of rooftop solar, this is not captured in the data. But because this is just pure export, we’re not capturing export. We’re capturing construction engagement and investment. And again, in the export space, China’s green-related exports, of course, are also increasing. And there’s great other reports out there that look at that.

Do you get a sense that in the fossil fuel sector the Chinese are building infrastructure and connectivity for exports from other countries to China? Or is this building coal, gas and oil infrastructure for these countries to use themselves or a mix of both? How does that break down?

So we don’t know exactly what it is used for each single project. What we see is that a lot of the fossil fuel engagement is indeed through construction contracts. So where Chinese construction companies just have a very strong expertise in

• building processing facilities,
• building extraction,
• building storage facilities,
• building pipelines,

where Chinese companies potentially, either through a government-to-government contract or even through open bidding, have offered the most competitive price and therefore get chosen to lead this implementation.

And it might come with some Chinese financing, but it also might just come with local financing. What’s interesting for the Chinese construction companies is that a lot of these projects are very well-financed because you have the fossil fuel that in the end generates revenue. So you can be pretty sure that you’re going to get paid back for the construction that you do. And that’s different, for example, probably we’re going to talk about it in road infrastructure, which is public infrastructure, where there’s not such a strong revenue model. And therefore, the risks for the Chinese construction companies are much higher.

Again, fossil fuel, very clear. You’re going to sell the fossil fuel. You’re going to make money. And then you can pay back the Chinese construction companies. And so it’s a very lucrative business also for the Chinese.

That seems to be one of the trends that you’ve been following over several years now: the types of infrastructure that the Chinese are financing and building that used to be railroads, roads, things that we would call public goods, are less prominent today as opposed to telecommunications networks, fossil fuels — things that the moment you turn on, revenue starts coming in.

So that debt sustainability issue becomes paramount in what the Chinese are funding because, obviously, a lot of the countries where they’re doing these activities are having debt issues. So they’re looking for projects that are revenue generating right from the start. Is that a fair assessment?

I think that’s a very fair assessment.

So in 2019, the Chinese published the debt sustainability guidelines. That means for companies to evaluate whether they’re going to give a loan or work with a country to build an infrastructure project and look at the country’s profile, whether they are able to pay back the debt and whether they’re actually exacerbating the debt issues of that country.

Since 2019, this debt sustainability framework exists, and so that was before COVID. Then we saw during COVID that a lot of the global South countries were subject to a lot of sovereign debt issues. That impacts Chinese construction companies quite severely because, in the end, it’s the construction companies, if they took out loans for building, let’s say, a coal-fired power plant or a road project, and whatever country — Pakistan, for example — does not pay back the loan or does not pay back the loan in time, who’s going to be paying the loan?

And in the end, it is actually often the construction companies that have to shoulder some of the banks necessarily, but it’s the construction companies that have to shoulder the risk.

So there’s a very clear risk management necessity to understand:

• Am I going to make my money?
• Am I going to earn my money back?
• Or is it too risky and I’m going to stay away from it?

I was surprised that Africa turned out to be the top destination last year for BRI engagement, $61.2 billion, an increase of 283%, largely by this big project in Nigeria that you referenced, $24.6 billion.

Just to be clear, is that project in Nigeria:

• an MOU?
• a committed project with contract signed, money transferred, already building it?
• or is that something more aspirational?

Because sometimes it’s not clear.

It is so true, Eric. So we are trying our best in our data to distinguish project-level commitments. I think every database is running into the same issues. We don’t track money. We track announcements of projects by two independent sources where possible, or a stock market announcement. So we try to be as rigorous with our methodology as possible.

There are different levels of commitments that we track. This one is, I think, more than an MOU. It’s agreed. We have the location. We have the amount. We have both sides’ agreement.

In the end, I believe this project will change and will evolve. It’s not kind of… The design phase is definitely not finished from what we can see. So there’s a lot more work that needs to come to make this project actually real. But the commitment is quite explicit from both sides and confirmed. And so that’s why we were willing to include it in the database.

Yeah, the report also said that part of the reason for this surge in Chinese engagement in Africa was because of potentially, again, just a theory, because of the lower U.S. tariffs that African countries received traditionally through the African Growth and Opportunity Act (AGOA), which is now in the process of being renewed through Congress.

By the way, something very interesting on the renewal of the African Growth and Opportunity Act.

• AGOA is making its way through Congress, but it’s only going to set the tariffs back to the Liberation Day tariffs, not to zero tariffs. Very important distinction there.
• So really, AGOA will not be a tariff-free entry into the United States. It will be Liberation Day tariffs — so on April 2nd, whatever Donald Trump announced for those various tariffs.
• So it’s not going to have the tariff advantage that a lot of regions had, or at least that Africa had, that other regions suffered.

But you said that there might be some connection between lower U.S. tariffs and the surge of Chinese BRI engagement. Tell us a little bit more about that. If a Chinese company wants to export to the U.S. and is in the process of making an investment decision, the logic is, of course, that the Chinese company will look at countries that have a tax regime that is favorable or a tariff regime that is favorable to them to be competitive against other competitors. It might be sitting in a country with a high tariff regime. So these investment decisions are just normal. I don’t think that any country or any company would not make those.

What’s interesting with the Chinese, and I think there’s an upside and a downside to that, is that China’s speed, making quick decisions, being able to build factories very quickly, and to churn out the products very quickly, is an opportunity, I think, also for host countries, for BRI countries, to attract specific types of investment.

The downside is that, and the downside is that, once the regime changes, and maybe there’s some issues, and maybe another opportunity for the same Chinese company, there’s also a risk that the facility will be abandoned very quickly.

Now, early on in the BRI, back in the 2013-2014 era, it was a lot of Chinese state-owned enterprises backed by Chinese policy bank loans that were going out and doing these big deals, these huge projects. We saw that run-up of lending that peaked in 2016, and that’s gone down. And the Chinese private sector back then played a secondary role.

Over the past couple of years, as we’ve talked to you, one of the things that we’ve noticed is that

• the private sector is playing an increasingly prominent role, and
• the state sector is actually pulling back.

Are you seeing that in the data for 2025 as well?

Yeah, so definitely for the investment side, it’s mostly private companies that are leading the fray, and it’s interestingly also a lot of these new tech companies that are both in kind of the IT tech, like TikTok and Alibaba, as well as in the green tech space, like Jinko Solar and other green tech companies that are leading the way.

These are private companies that are interested in:

- being closer to their customers
- diversifying their supply chains
- de-risking their supply chains
- going abroad

And that’s also now the capacity, management capacity, and technological leadership to be actually a really, really attractive partner in host countries to set up factories.

And that’s not just in BRI countries, that’s also in a lot of developed countries, and those are trying to attract battery manufacturers from China, because this is state-of-the-art technology, very different from the early phases of the BRI, where such technological leadership just did not exist.

From that perspective, I’m always very, very impressed, and I think the rapid emergence of these technological leaders in China over the past couple of years has very much flipped kind of our logic, what type of investment we want to attract.

It was at the beginning, of course, the Chinese wanted to attract Western technology, and now it is often the case that everybody wants to, particularly in the green space, attract the Chinese technological leaders to set up shop.

In the construction engagement, it’s still a lot of state-owned enterprises that are very engaged abroad. So, these are real leaders in driving these construction engagements.

What’s, I think, also clear is that state-owned enterprises have a different mandate, particularly of spending their own money. Now, construction engagement really brings them in money. That’s just revenue. You’re a service provider.

For investment, you have to, of course, use your own money, and Chinese state-owned enterprises might have a mandate to also invest domestically to create jobs. And it also, kind of, their financing modalities are quite different. Their approval processes are quite different from private companies. And so, their ability to invest abroad has also changed over the last year.

So, that’s, I think, why we’re also seeing less state-owned enterprise investments compared to the previous years.

As we look forward to 2026, which obviously is now underway, some of the trends that we should watch out for are probably:

• more fossil fuel engagement
• more activity by the Chinese private sector

And mining is something we didn’t talk about, but that was one of the areas that was also showing a lot of activity.

Do you expect Africa to continue to be a main focus, or will the Chinese look elsewhere to spread out some of those investments?

Man, that’s always the golden question, looking into the future. Obviously, we don’t know. I always start with that.

The trends that we’ve seen over the last years, I think, can continue. So, I think we’ll see even more tech-related engagement.

And we’ve seen this tech-related engagement, not just in developed countries, but really in emerging economies. I believe that this will continue. There are a lot of opportunities for the Chinese to set up shop. There is a lot more capacity for the Chinese in the management skills to do so, to manage all the local staff. So, I think this is really a learning. And therefore, I think this trend will continue.

In terms of the mining, there’s a very clear engagement across the world to kind of own more mines, to kind of also use this accelerated need for a lot of the transition minerals to utilize on this trend. And it’s not just the Chinese. It’s also, of course, the Australians and other countries that are trying to get their mines and their processing in order.

In terms of regional engagement, there will be a lot of kind of up and down. So, I always believe that kind of one year does not give a trend. And so, this year, of course, we saw a lot of Africa engagement. In the previous year, we often saw a lot of Southeast Asia engagement.

I think the only one that has been very constant over the last years is actually the Middle East, where there has been just a very strong engagement across a number of different sectors. It includes:

• Energy
• Manufacturing
• Real estate

So, I think this Middle East engagement has been very strong. Also, in countries that are often not seeing a lot of Western engagement, it includes Iraq, Afghanistan, where the Chinese had some good engagement.

Again, Africa, Southeast Asia, I think this is always kind of up and down. I’m not able to see a very clear trend where it will go over the next year.

Okay. The report is the China Belt and Road Initiative (BRI) Investment Report 2025. It is by far the most authoritative report on the trends related to the BRI, where the money is going, what they’re doing with it, and who is actually engaged.

It was prepared by Christoph Nedepil, who is the director of the Griffith Asia Institute at Griffith University in Australia, and the acting director of the Green Finance and Development Center that’s part of the School of Finance at Fudan University in Shanghai.

Thank you so much, Christoph, for letting us know about everything that’s going on. We’re looking forward to talking to you later in the year to get an update on how things are going in the first half.

You do these reports every, I think, two or three times a year, correct?

“Every six months.”

“Every six months.”

So we’ll talk to you over the summer to get an update on how the first half of 2026 is going.

Thank you so much for joining us.

“What a pleasure to be here again, Eric.”

“Always a pleasure to see you.”

Thank you, Christoph, and thank you, everybody, for joining us today.

We’ll be back again next week with another edition of the China Global South Podcast on behalf of everyone around the world at the China Global South Project.

Thank you so much for listening and for watching.

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How South Asian States Navigate Rivalries Between the U.S., China, and India

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Hello and welcome to the show. I’m Eric Olander. Today, the fallout from Canadian Prime Minister Mark Carney’s speech at Davos is still reverberating.

If you recall, he declared that the old U.S.-led international order is dead and called for middle-power states to work together to form a new coalition.

Not surprisingly, his remarks were not well-received in the United States, but they sparked a lot of conversation in wealthier middle-power countries like:

• Germany
• Australia
• South Korea
• France
• and others.

We’re reading a lot about that right now in international media coverage.

But we haven’t heard much at all about what all this means in smaller, lesser-developed countries in Asia, Africa, and Latin America. The dynamics are very different and oftentimes because they are much more vulnerable due to their size. Oftentimes, it’s poverty or weak governance that are factors that play into all of this.

So we’re going to focus on a fascinating report that came out last October focusing on how small states in South Asia are navigating this new multipolar world that we’re in.

What’s interesting is that the dynamics of what’s happening over here in Asia are very similar in many ways to the challenges that smaller countries and other developing regions are also confronting. There’s an opportunity here to apply learnings from one region to another. But of course, not in all cases, and there are a lot of differences.

The report I mentioned looks beyond just the U.S.-China competition, but also includes India in the mix as well. And that’s something important in certain parts of the world.

I’m thrilled to have two of the lead authors of the report join me today for our discussion.

• Sagar Prasai is an independent advisor for international development agencies and joins us today from Kathmandu, Nepal.
• Mandakini Suri is an independent consultant who spent more than 20 years doing development work for government, NGOs, and think tanks.

Zagar and Mandakini, thank you so much for taking the time to join us today.

Thank you for having us.

It’s great to have you today, and what a great time to have this. When you wrote the report back in October, you could never have foreseen where we are today.

Before we get started looking into the report, I’d like to get both of your perspectives, both from India and from Nepal, on the Carney speech. Whether you think the message he signaled is as important where you are as it’s being discussed in Europe and parts of industrial Asia.

Sagar, let’s start with you, and then Mandakini, I’d like to get your take on that.

Yeah, so it’s like that moment when somebody suddenly screams from the sides, you know, the emperor has no clothes, right?

And so, in that sense, the existence of the U.S. hegemony was well understood at all levels, at political levels, at sort of in financial domain and otherwise.

And the average Nepali cannot buy or sell anything without first changing their currency into dollars. And so, the presence of the dollar is quite overwhelming everywhere.

But for the immediate stakeholders, which is the foreign policy establishment in Nepal and those who keep track of the issues like these, it was like, well, we all knew. It’s just that there is an open admission.

And in that sense, even in that speech, the precursor was that, well, we all knew, but, you know, at the same time, we never quite mentioned it or openly confronted the U.S. in this fashion.

And so, there was some, let’s just say, a quiet celebration that the truth is out, right, from that angle.

But for countries like Nepal, you know, which is right in the middle of India and China, it’s got only two neighbors, China to the north and India to the south. Both are emerging giants, disproportionately larger than what Nepal is.

And so, therefore, it lived in a different geopolitical setting where the U.S. mattered, of course, because it overwhelmingly matters everywhere, and to a certain extent, particularly as a sort of developing partners, and Europeans also mattered.

But beyond that, Nepal has always a predominant concern about what happens in China and India rather than elsewhere.

Mandakini, the India reaction has been very interesting in part because India has seen this dynamic play out before as well. India, during the Cold War, very skillfully played both sides.

And so, I’m wondering if the reaction in New Delhi was similar to what Zagar was hearing in Nepal.

Well, I think for one thing, I’m not sure that it actually made the frontline news. I think it was buried somewhere in the newspaper. And, of course, I heard about it and was very curious to hear what he had said. And when I heard it back, I was actually a little underwhelmed. Underwhelmed in the sense that what he was saying was not really new.

I think countries, developing countries, middle-income countries, countries which are kind of small island countries, have been talking about the structural inequalities that they have been seeing in these international processes, whether it’s the WTO, the World Trade Organization, or on trade, or financing for decades. And I’ve been calling it out quite vociferously.

And I think India has been one of those countries, South Africa, Brazil. You know, the Prime Minister of Barbados, if nobody heard, is absolutely fabulous. I mean, I think she calls out…

Hypocrisy, she calls out quite a bit. She calls out the hypocrisy.

I think what was interesting was the fact that, as Sagar said, for the first time, you had a Western democratic leader actually calling it out and saying that,

“Oh, you know, the post-World War institutional structures, this rules-based international order that has been shoved down the throats of many countries is unfair in many ways.”

And that larger, more powerful, more financially powerful countries for years have been pursuing their own foreign policy or diplomatic economic imperatives with a lot of impunity. And it’s been the kind of hush-hush secret that everybody has kind of gone along with.

So I think it was a bit of… Yeah, I think that the reaction, I think, just not only from India, but many countries in the global South was,

• “Well, yeah, we told you so.”
• “You just weren’t paying attention until it’s come to bite you and affect you, our country.”

So I think, for example, just to give an example, with the rise of China, as Sagar mentioned, the concerns about China’s expanding footprint across the world has been…

It was such big news for the last decade. It led to the Indo-Pacific becoming a new geographic construct. The Quad alignment between India, USA, Japan, and Australia came as a result of that. All a bit focused around very much controlling China’s strategic rise. And in fact, even Canada came up with an Indo-Pacific strategy for China. And now you have Mark Carney saying, okay, you know, we’re willing to talk to China.

So, I think India very much on…

It took over to your point around the Cold War, which is, you know, when you had the US-Russia, the tensions rising, particularly of the last couple of years. Trump wanted India to stop buying Russian oil. He still wants us to stop buying Russian oil. And I think India has been more muted about it now. But the foreign policy position was like,

“Look, we’re going to exercise our strategic autonomy and buy oil from where we can, because we’ve got, you know, our economy needs to grow.”

And India has actually done a lot to respond to Trump’s demands as well. But yet now we have some of the highest tariffs being imposed of all the countries in South Asia. So I think calling out that kind of double standard is something that countries have experienced for a long time. But now that it’s coming to bite the West, I think there is more open acknowledgement target.

Yeah. And he even acknowledged that they knew that this was a flawed system, but went along with it.

Just very quickly before we get on, I mean, you were being very polite that it was saying it’s from a wealthy or G7 country. Is the fact that this is coming from a white man different?

Yes. Because we don’t, I mean, the whiteness matters here.

Oh, it does. I was trying, I was wondering, should I say it or not? But yeah, it does matter.

No, no, no. Let’s kind of be, take, you know, be as direct as you can.

I mean, 100% it matters. I think, you know, the fact that a white person who is, you know, the leader of a G7 country saying,

“Oh, you know, it’s unfair and it’s unfair to Canadian people.”

You’re like, well, what about the millions of people south of the equator who have been saying it’s been unfair for generations?

So I think there is definitely a factor. And I suspect it would not have made such mainstream headline news had it not been a white leader who had said it, a white male leader. I mean, if Modi said it or if Modi or she said it, people would have been like, yeah. And they have. Modi, she, the prime minister, Barbados, the BRICS countries, all of them.

If you Google it, ChatGPT, you will find statements from them going back decades, which would have said something to the effect that the existing world order is not fair.

You know, there’s a similar phenomenon going on in the United States where white people are shocked, shocked that the police are abusive and that even video recordings of police brutality… Against white protesters in places like Minneapolis and killings now of white people and brown and black people, many have been saying this for decades, for centuries actually, that the police have been impartial. So again, this is a reckoning happening both inside and outside the U.S.

As much as I’d like to continue that line of our conversation, I want to get back to the report that you guys worked on last year. Now, it focused on three countries in particular: Bangladesh, Nepal, and Sri Lanka. You also had some insights included in it from Bhutan and Maldives as well.

Sagar, let’s talk a little bit about the understanding that a lot of countries have where we hear the top line, which is they don’t want to take sides between the various powers. And as you pointed out, in Nepal, we cannot make this only about the U.S. and China. Obviously, India plays a very important role.

You also wrote in the report that they don’t follow the textbook strategies for hedging because there’s the impact of domestic politics, there’s regime survival, all sorts of other factors. Let’s start at the high level about how these three countries in particular are managing these rivalries and what we should take away from it.

What we are essentially bringing out in that paper is that, look, countries are—it’s difficult to say countries are rational actors because countries are only as rational as their ruling establishments are rational, right? And it’s like what you see in the U.S. right now.

Like you can’t call the U.S. behaving rationally or irrationally. It’s more like Trump and his coterie behaving rationally and irrationally. So that happens in smaller states too.

You’ve got the ruling elites who have a particular interest. They would want to extend the legitimacy to rule as much as possible. And in that process, if China is a resource, if China’s influence is useful, then they would be more than happy to take it.

You see this in countries like Maldives, where Maldives has periodically, election after election, either become very close to India or very close to China. Other states have sort of, in some ways, tried to balance it.

But what we are arguing is this balancing act is really, really difficult because it’s never—the foreign policy positions are never derived from a broad, national, consensus-based interest determination.

These things happen at the will of the ruling elites, and it can go in any direction. That makes it all the more risky.

Mandakini, Sagar gave us a really nice kind of setup for this. One of the things that we’ve seen is that in Sri Lanka, Maldives, Bangladesh, and certainly Nepal, there’s been this flirtation with the major powers in the region to varying levels of success.

But again, talk to us about this question of the interests that Sagar brought up. Sometimes the ruling elites’ interests are not necessarily aligned with those of the population or the foreign policy. And as such, they don’t necessarily behave rationally.

So if we want to look at how these countries are managing these rivalries, give us a little bit of your insights of what you found on the research.

Well, I think it’s useful to think of it in sort of like an analogy, right?

• Geographically, South Asia is one geographic unit, but the Himalayas is a natural boundary, and of course, you have the oceans.
• Historically, there have been very civilizational legacies – the Ashokas, through history, the Mughals, et cetera, and then the British who kind of knit it into one administrative unit.
• But that administrative unit fractured during partition, and you had the creation of these different nation states.

I think we often forget how strongly that legacy of partition—both in terms of the division of land, people, and resources—has truly affected the way in which states in the region actually see each other and are able to engage with one another.

So it’s kind of like when you divide land amongst your, if you were to divide land amongst, you know, your five brothers of five men and women. And it’s been fundamentally unequal in some instances. Some geography was traded, some people got left to Bahrain on one side.

Those wounds, I don’t think, have ever really knitted.

So India has, and the region has a baggage which it carries, which I think very often plays very emotionally into foreign policy decision-making.

And very often, by the political parties in different countries, in particular moments of either political upheaval or economic hardship, it plays into decisions that they might take with respect to:

- Choosing a particular infrastructure project from India versus China
- Taking a particular line of credit or a particular loan

So what I’m trying to say is that engagement with India always comes with a certain degree of historical baggage, one of which also is this idea of it being a regional hegemon and behaving like a big brother. It’s something that India has been accused of for decades, and I think justifiably so.

But at the same time, it’s kind of like that big brother who you hate, but you love to hate. And we all know we love to hate him. But in a time of crisis, you know that big brother is the one that’s going to come.

So in the instance that during the COVID pandemic, when the whole world locked down, it was India that actually manufactured vaccines and was the first to provide them to a lot of countries in its neighborhood. But to be fair, until India shut down its own vaccine manufacturing, the rest of Asia could not get drugs from India. So there are limits to that. And that exposes the risks, though.

So we in Vietnam were counting on India to provide vaccines to us. Now, the West hoarded all the vaccines for themselves. But when India made a decision in its own interest, at the expense of everybody else, it exposed the asymmetries in these relationships.

Can you speak a little bit to the imbalances that exist in these great power rivalries?

When you’re sitting in Nepal and you’re relying on India, you’re up to the whims of what happens in New Delhi, and that’s it. Like the vaccine during COVID. I mean, I think it’s not just the vaccines, right?

• Sagar will speak about the 2015 blockade of the border between India and Nepal, which had serious implications on Nepal’s economy and fuel access.
• Then it’s actually very often, like I said, India’s high-handedness in moments of crisis for other countries very often has also pushed them to seek alternative options as they should.

And I think would be a rational policy choice for any government in that moment to diversify options.

But I think what the paper is also showing is that those decisions sometimes are genuinely reflective of what the country needs at that point of moment. Sometimes it’s to do with just servicing the interests of the ruling political elite, for example, right?

So that hedging sometimes works and sometimes it doesn’t. That balancing works sometimes, but it doesn’t.

The lesson I think for countries like India is that, you know, also the geography and South Asia has changed in the sense that, very often you’re looking at a population of 2 billion people. The median age in India is 20, not India, in the region is 27. That means that’s a young, very young population, all of whom are looking for jobs, all of whom have social media, and they’re seeing a lifestyle which they all aspire to.

So there’s a lot of pressure on local governments, on countries in the region to provide for their young voting elites and middle class a lifestyle that they aspire to. And the question is:

• Where is that going to come from?
• Where will the jobs come from?
• Where will the market come from?
• Where will the goods be sold?

And India, unfortunately, has done a terrible job of opening up its markets to its neighbors. And so they will look for markets elsewhere. They will look to send their labor elsewhere because India, I mean, the region is famously called one of the least integrated regions in the world, right?

• Trade is very hard.
• Transit is really hard.
• Making a phone call is very hard.
• Getting visas is really hard.

So unlike ASEAN, which is quite a well, you know, really well-functioning, to some extent, regional unit and political bloc, mobility is really hard in South Asia. You know, people can’t even visit relatives across the border.

And what you’re saying, I think, will resonate with a lot of people in Africa where mobility is also an issue and also a very young population that is looking to upgrade their lifestyles and certainly against what they see in TikTok, but also just in absolute terms as well.

Sagar, this question of the great powers, the U.S., China, and India, and how they’re being perceived. When we were in Indonesia a couple of weeks ago, we met with some senior stakeholders and they explained the relationship that they have with China as one where Indonesians don’t look at China as an ally or a threat, but an opportunity. And what they said was,

“This is basically a conditional relationship. The moment it ceases to be an opportunity, they will look somewhere else for opportunities.”

How do you think the smaller powers in South Asia, especially in places like Nepal, look at the major powers, all three of the major powers, in that same way as Indonesia? Or do they see it differently?

It is more or less the same as Indonesia. China is an emergent actor here. And then it comes with all these goodies. It’s an opportunity, right? But what the Chinese trajectory is of a kind that will probably not stop being an opportunity for some time to come, right? And that’s largely because how well it has established itself in the technology front, right? Like you, in the whole world as some anticipation that AI, for instance, would be part of their economic engine or a sort of a new window for innovation in all economies.

But look at how AI is developing, right? If in the entire world, there is this particular space in the US, in Silicon Valley, where seven companies have invested more than a trillion dollars in that technology. And for that technology to become ever affordable or for any other country to sort of think of coming up with their own AI ecosystems is completely impossible from cost-wise, talent-wise, and so on and so forth.

So while there is almost a preoccupation among the seven giants as to who beats who, China is quietly putting ecosystems, the entire AI ecosystems, that’s the hardware and the model and, you know, lock, stock, and barrel ready to be sold at much lower prices to any buyer in the global south, right?

So that’s what they did with the cell phone industry. That’s what they’ve done with the EV. So if you just look at these two products with which we already have prior experience, which is EV and cell phones, now think about if at a much lower cost, companies, governments, militaries across the world can buy Chinese-produced almost break the seal, open the package and start running kind of AI modules, right?

• At absolutely low cost.
• At low power and low cost.

So there are opportunities like that, right? And even in terms of financing, right? So it’s easy to say we can live without the U.S. But the reality is, U.S.’s current annual budget is 1.5 times larger than the Indian economy, right? So you can’t escape the influence that comes from that kind of money, right?

From that angle, and then for most South Asian countries, India’s market is as in, why is the EU in India today? And Canada as well was there as well. Canada as well. So they’re in India. And that fact is not unnoticed by India’s neighbors. Like, what about us, the little guys here, right?

So from all of those considerations, I don’t think China is, at least not foreseeably, going to weaken in terms of what goodies it has to offer. And from that angle, the balancing, edging, sort of thinking about what lies ahead in future will continue to make geopolitical calculations difficult.

Mandakini, the points that Sagar raised on AI and the goodies relate to oftentimes infrastructure. And infrastructure becomes a very important part of the dynamics of great power management in these parts of the world.

The U.S. has sought to become a bigger player with its various initiatives that it’s brought out over the years with the DFC and others to counter the Belt and Road. India is a big infrastructure builder in the small states that you guys covered in your report. And of course, China with its Belt and Road initiative, particularly in places like Bangladesh and Sri Lanka.

Talk to us about the importance of infrastructure as a vector of the great power competition in this part of the world.

I think infrastructure is a really big one. And of course, India cannot hope to compete with China in terms of the scale and the number of projects with the BRI, the Belt and Road Initiative. Of course, India is very concerned about things like CPEC, you know, the China-Pakistan Economic Corridor, because that has a certain, you know, and the border roads construction happening around India’s northwest and western eastern frontier.

But I think when it comes to the small states that we looked at, obviously, whether if you’re a small island state like the Maldives, which is basically a bunch of island atolls, which are very inaccessible, I mean, inaccessible either by flight or by boat, right? So, infrastructure for them is a real need.

And I think it’s interesting to see how the Maldives has been very effective in kind of extracting infrastructure contracts or getting infrastructure investment from both China and India, and also by successive governments.

So a few years ago, the government of, I think it was Mohamed Yameen, had investment that he brought in from China. And then subsequently, the following prime minister brought in, president brought in investment from India.

So I think also you have this flip-flop very often between competing opposition political parties where, you know, one is openly pro-China, while they’re in government, they’ll bring in Chinese investment, that the person in opposition will be like,

“No, no, China out, India in.” And when they come into power, they bring India in.

And of course, a recent president of the Maldives came into power on a very anti-Indian stance. He wanted India’s defense support to the Maldives. We had some troops stationed there for them to leave. He came into power, the troops left, and then the following year, he came to India seeking investment.

So also a lot of these decisions are politically expedient and demonstrate certain optics to your domestic constituency, which is also important. So verity is very important to small states. The optics of being seen as being neutral, non-aligned, not pro-one party or one power or the other is actually strategically very important to them.

So I think to the point around infrastructure, I just want to make one point, which is, I think it goes without saying that, if you go to Sri Lanka, for example, China has built the most amazing fall-in highways.

• The feedback from the ground is China comes in with its own engineers, its own equipment, but they deliver the goods in record time very efficiently.
• And it’s built to last, whereas sometimes India’s own track record of delivering these large infrastructure projects is not as good on the ground because of bureaucratic inefficiencies or maybe some issues in terms of contracting, etc.

So I think India needs to do better if it hopes to compete with China. But it is in many ways it can’t because of the scale, the sheer proficiency with which China has been building roads and infrastructure around the world. Africa is a good example: I had a friend who was posted in Sierra Leone, six-lane highway in like a couple of months. It’s very impressive.

I’d like to close our discussion looking forward a little bit. You wrote this report back in October of last year. And again, the world has changed dramatically since October. We see a breakdown of the international system and also of the institutions themselves. The United States has all but quit the United Nations. The United Nations is doing significant layoffs now of its staff.

What does it mean for these kinds of countries when the institutions and the systems that have been in place for 70, 80 years are not there anymore? It’s obviously a risk, but is it also an opportunity?

So for the small states, it’s a risk. It’s a risk because the number one issue comes from the fact that small states as such couldn’t or never did have much of a voice in actually making these rules in the rules-based order. But anything that promises to treat everybody equally is always good when you are a geopolitically weak actor.

And so there is a natural leaning towards a rules-based system in small states. And that being shaken is a serious problem. Because now the middle powers jostle. In the sense that when the Canadian prime minister spoke about it, it sounded good. But then there is internal competition between the middle powers.

• In the 1990s, both China and India were considered middle powers.
• China is in a different place today. That’s a different story.
• The India-China competition was felt by these smaller states, even them.
• And now you have Europe coming in and so on and so forth.

So there’ll be a lot of jostling. And then the smaller states have a more heightened risk of being squished in one direction or the other.

The third thing about the upheavals that we’ve seen is this whole jeopardy on development financing stream. America withdrew lock, stock, and barrel. Europe, because of its own war in the backyard and failing economies and now that it has an issue with tariffs with the U.S., its biggest trading partner, the European outlook economically isn’t good.

So whatever they were able to do through EU or at a bilateral level, particularly U.K., Germany, France—France in Africa, others elsewhere—that development financing stream is also in some ways being compromised. And then now the latest news is Japan is being shaky. Japanese bonds being as cheap as they were, borrowing from Japan was a great advantage for very many developing countries in Asia where Japan has some degree of focus:

- India has borrowed heavily.
- Sri Lanka has borrowed heavily.
- Nepal has borrowed heavily.
- Bangladesh has borrowed heavily.

That’s because the interest rates were so low. Now the Japanese interest rates are growing very rapidly high.

Because of all of these changes, it’s like just because the dominoes started falling from the U.S., it has sort of taken the whole world in a sweep. And so all of those development prospects, financing and so on and so forth has become a problem for smaller states. Mandakini, what do you think?

I think it’s, you know, it’s sort of like you may, we all may have known that the rules of the game were not fair, but at least we knew what the rules were. I think now when you’ve thrown the rules out of the window, it is a situation of, it’s an unknown situation of just not knowing what will happen, what you’re going to wake up to and read in the papers tomorrow, right?

I mean, for a large country like India, yes, certainly it’s a concern. You never know whether the tariffs will go up or down tomorrow, what Trump will tweet about overnight.

And I think for small states, the existential anxiety will probably be even more. And I think one underestimates the power of a single vote in the UN, right? So even a small island state, like a small like Nauru or Kiribati or one small little island in the Caribbean, that vote really mattered in the UN.

So if the devaluation of that UN vote, I think is significant. Equally, the fact that, you know, the UN has been passing all these resolutions on whether it’s Ukraine or on Gaza, and none of them have been backed. You know, if a country like Ukraine or, you know, a large political, a big political conflict like Gaza, no one is going to come, essentially, the message is:

“No one is coming to our rescue,” right? And no one is listening.

And I think that’s very disconcerting.

I think in terms of an opportunity, you know, with this whole Don Roe or Monroe doctrine of America wanting to kind of withdraw and create its own sphere of influence in the West, that means it’s going to create a vacuum, right? Now, who is going to fill that vacuum?

• Russia
• China
• India, to some extent

It’s a player, but not in the same way. It doesn’t have that kind of military capability. But I would suspect that there’s a lot of head scratching and thinking going on in countries in South Asia, whether they are small or big about, you know:

• Who are our friends and who are our allies?
• What kind of new alignments do we need to be thinking about?

I think we’ll see the rise of more minilaterals or trilaterals, you know, triumph groups of two or three countries trying to come together. But as Sagar said, you know, economics matters, and they will be looking at how do they shore up their economy so that you don’t see the kind of domestic political upheaval you’ve seen in Bangladesh, Sri Lanka and Nepal, right?

So it’s going to be a very tough balancing act and also maintaining your own strategic integrity as a country, you know?

Yeah. And we also didn’t touch on it, but there’s going to be bottom-up pressure as well from Gen Z where if a cigar, I mean, we can talk about that at some other future time, but, you know, Nepal was ground zero for one of the most violent uprisings of Gen Z that expressed their frustration.

So these governments are going to be facing:

• Top-down pressure from the major powers
• Bottom-up pressure from their own huge population of young people who want a better life.

And we saw these same pressures in Nairobi and in Jakarta and in other parts of the world as well.

Absolutely fascinating to start thinking about this because we’re in a whole new world now. And it is, as you pointed out, maybe this is something that, you know, many of these countries expected because they’ve seen the hypocrisies for so long, but actually talking about them now is so important given that it’s being discussed in Berlin and London and Brussels and Washington, but it’s not necessarily being discussed as much elsewhere.

So we’re happy that you both were able to join us.

Mandakini Suri and Sagar Prasai are both independent development consultants who’ve been in this business for a very, very long time. They did some fascinating research on how small powers in South Asia are dealing with this new world that we’re in.

Now, again, they wrote it last year. The new world is even more new this year. And so we’re happy that you were able to join us.

Thank you both for taking your time today to share some of your insights. We really appreciate it.

Thank you so much. Thank you, Eric. Thanks, Eric.

And we want to thank everybody for joining us today for another episode of the show. We’ll be back again next week with another edition. And we hope that in the meantime, you’ll check out everything that the team at CGSP is doing around the world in French and Spanish and English.

Go to ChinaGlobalSouth.com and you can get all of the coverage that really is the only place in the world that we’re doing it at this level. So we hope that you’ll give us a look and check us out.

So for the entire team around the world, thank you so much for joining me today and for watching and for listening.

The discussion continues online. Follow the China Global South Project on Blue Sky and X @ChinaGSProject or on YouTube. At China Global South, share your thoughts on today’s show or head over to our website at ChinaGlobalSouth.com, where you can subscribe to receive full access to more than 5,000 articles and podcasts.

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Thank you.

Afra Wang on “The Morning Star of Lingao” (临高启明) and the Rise and Reckoning of China’s “Industrial Party”

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Welcome to the Seneca Podcast, a weekly discussion of current affairs in China. In this program, we look at books, ideas, new research, intellectual currents, and cultural trends that can help us better understand what’s happening in China’s politics, foreign relations, economics, and society.

Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China.

I’m Kaiser Guo, coming to you this week from my home in Chapel Hill, North Carolina.

Seneca is supported this year by the Center for East Asian Studies at the University of Wisconsin-Madison, a national resource center for the study of East Asia. The Seneca Podcast will remain free, but if you work for an organization that believes in what I’m doing with the show and with the newsletter, please do consider lending your support.

You can reach me at sinecapod@gmail.com. And listeners, please support my work by becoming a paying subscriber at sinecapodcast.com. You’ll enjoy, in addition to the pod:

• The complete transcript of the show
• Essays from me
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And of course, you can bask in the knowledge that you’re helping me do what I honestly believe is important work. So, do check out the page, see all it is on offer, and consider helping me out.

Today, my guest is Afra Wong. I suspect many of you will already have come across her work through her podcast, through appearances on other China-focused shows, or through the many provocative, beautifully written, and fascinating essays she’s published.

Afra is a writer working between London and the Bay Area, currently a fellow with Gov.ai, and previously with the Roots of Progress Institute. Before going full-time as an independent writer last year, she spent six years in Silicon Valley covering AI and crypto, running newsrooms, building developer communities, and absorbing the Valley’s growth logic from the inside.

She writes about China and about Silicon Valley — the latter sometimes metaphorically — but about neither of these places ever as mere abstractions.

She writes about them as overlapping systems, how China’s technological interiority shows up in Western debates about AI, industrial policy, and even progress itself.

She’s also the host of the Chinese language podcast Pipei Jiao Wah, Cyber Pink, and part of the Baihua podcasting community.

We’re talking today about her recent Wired piece on what might be China’s most influential science fiction project that you’ve never heard of: the Morning Star of Ling Gao, or Ling Gao Qi Ming, and the worldview behind it, something known as the Industrial Party or the Gung Yedang.

If you haven’t read that yet, click the link, read the piece. It’s one of actually several China-focused pieces in this issue of the magazine — some really good stuff. Come back when you’ve finished. We will still be here.

This isn’t just going to be a conversation about time travel sci-fi — though that would be a lot of fun — but actually about:

• Interpretations of history
• Emotion
• The national story
• Power

About how a country explains to itself why it fell behind. and what it thinks salvation looks like.

Afra Wang, a very, very warm welcome to Seneca.

Oh, wow. Thank you so much, Kaiser.

When you were describing my work experiences, it’s almost like I’m reliving my past life, especially my time doing a lot of growth stuff for tech companies and crypto. And actually, I discovered the Morning Star of Ling Gao, or Ling Gao Qi Ming, as a collective science fiction novel writing project from my crypto phase.

Really?

Yeah. I was told by a lot of nerdy technologists, people who are Chinese cypherpunks, saying there is the greatest DAO experiment ever, which is a sci-fi story collectively written by many people, like hundreds of thousands of people. I was like, “wow, what do you mean?”

Because DAO in crypto represents decentralized, autonomous organization. Referring to this science fiction writing as a DAO experimentation is really fascinating. It also sort of reflects on the demographic — the people who are reading this story, right? Who are reading the Morning Star of Lingao? Who are reading Lingao Qiming? And it turns out to be:

• STEM people
• Technologists
• Developers
• Programmers

Yeah, not surprising at all. A lot of overlap with sci-fi.

But before we get into sci-fi and about that essay, this is your first time on the show, so I’d like to give listeners a chance to get to know you a bit better.

You describe yourself as a kind of cultural in-betweener, and that really resonates obviously with me. For people who move between China and the West, especially when writing about technology and about power, translation isn’t just a linguistic exercise. It’s actually epistemic, but it’s also moral and maybe even aesthetic. I mean, it covers pretty much all of philosophy.

One thing that struck me reading your essay is how effortlessly you seem to do this, just to kind of code switch, not just in language, but also in your moral and emotional register, especially when you’re writing about something as charged as the industrial party. Is that something you experience as deliberate, or does it feel almost second nature to you at this point?

I think probably I am a somewhat open-minded and perceiving person, so I don’t know, people have been telling me that I tend to kind of like be able to make friends with all kinds of people. I think that’s, in a sense, like a good trade for me to be a more discerning writer because I think I’m really sensitive to vibes.

Also, I like to use the vibe because this is how I feel. I’m really sensitive to the aesthetic, the sensations when I encounter something, for example, the Silicon Valley mental model versus the Hangzhou-Shenzhen-Beijing mental model, right? I was really fascinated by the sort of the cognitive infrastructure, like the intellectual backbone of the Chinese version.

So I, you know, last year I wrote something called the China Tech Canon, which is a response.

Yeah, that was great. Thank you so much.

Yeah, I think it’s like, it’s all come to the sense that I want to like deeply, contextually translate certain, you can say:

• lore
• myths
• mental frameworks
• cultural influences.

I want to translate something to the Western discourse, but in a much more like humanistic and personal way because I think I am somehow constantly digesting cultures from both sides. I am native in Chinese, but I feel really native in English as well, in the Silicon Valley discourse as well. So I think that I’m just kind of like naturally juggled in between.

Do you go the other direction as well? Do you translate the Silicon Valley kind of tech canon into Chinese as well? Or do you find yourself doing more sort of the explanation in the direction of explaining China to the West?

Yeah, so not about technology, but I’ve been doing this Chinese language podcast for many years with my amazing co-host. I think all of us are cultural in-betweeners and we actually translate the Western popular culture and then talk about those Western popular culture in Chinese language. You know, for example, the popular movie Hamnet is a golden global hit. And we recorded a podcast about Hamnet in Chinese language, but the whole context, the theme, and the reaction, the catharsis we experienced — we were basically discussing this movie in Chinese language, although it’s a quintessential English movie.

Yeah, I read the novel. I have not seen the movie yet. Is it good?

Oh, it’s absolutely good. It’s so moving. It’s very touching, and you do experience this Greek tragedy style catharsis at the very end because it’s like a movie to Force you to confront a lot of eternal questions like death, like loss. Like, yeah, it’s such a layered movie, I can’t really explain it. It’s beautiful. It absolutely changes some part, like, deepest part of you.

So do you ever find yourself judging things differently depending on which context you’re inhabiting? I mean, because, I mean, not because you think one side is right, but because, you know, different histories seem to demand different weights, different priorities. You know, I mean, this is something I’m constantly wrestling with.

How conscious is that process for you when you’re writing? So, you know, you might have one view of the industrial party, say, as a Chinese person living in China and another entirely looking at them from the outside and talking about that to Americans. So, do you find yourself sort of having different standards?

I think I do. I think I’ve been having double consciousness since I grew up as a kid in China. I have double consciousness in a sense that a lot of stuff can coexist although they look like contradict to each other but they could both be true.

Like, you know, in a sense I went through the whole Chinese education, right? I finished high school in China and then I only went to U.S. for college. And I think, I guess, like, accepting a lot of contradictory views and philosophies, as you said, abstemious knowledge systems is part of reality to me, I would say.

But I still think the Chinese Chinese me and English me or the sensible me and anxious immigrant me, when they’re coexisting, I think there is a converging aesthetic standards or sensibility that I uphold. For example,

• like, you know, when something is well-written, it is well-written, right?
• when a movie, when there’s a John E. Moe movie from the 1990s, when the international acclaim, it is good to me, right?
• Like, I wouldn’t denounce it because John E. Moe later turned into a state spectacle propagandist.

I think there is certain sensibilities and aesthetics that’s always true and always, I could always try to stay true to that.

Wow, that sounds so healthy and grounded. That’s fantastic. It seems like you experience this kind of ability to code switch and to experience sort of two whole different moral and epistemic systems as more of a freedom than a burden, then.

I would say so. Like, for example, this piece for Wired, it’s about Industrial Party, it’s about this poorly written, crowdsourced science fiction writing. I do not like reading this piece. I do not like reading this story at all because it’s so poorly written.

But at the same time, it gives this energy and spirit of what people are actually craving for in the rapidly developing, urbanizing China and why people feel so strongly about this developmentalism. And in a sense, maybe U.S. needs more poorly written collective science fiction like Lingao because U.S. right now kind of needs some industrial party people.

I mean, I hate the story. I hate the, you know, like the greatest Chinese science fiction as the title of this Wired piece is actually an irony, right? It’s not actually greatest because it’s like honestly really bad, but it speaks to so many things that I, yeah.

We’re going to get really deep into Ling Gao in just a second here, but there are a couple other things I still want to ask you about because there’s another divide that I see you moving across really fluently and that’s the one between STEM and the humanities, between, you know, the engineering ways of seeing the world and the more humanistic or cultural ways of seeing the world.

So reading your work, I get the sense that you’re genuinely at home in both of these registers. You’re able to translate between them without, you know, romanticizing the one or condescending to the other. Is that, again, something that you’re conscious of when you’re writing or does it feel like a natural part of, you know, how you make sense of tech and society?

I’m not sure if I’m really fluent in the STEM language. First, I am not a technologist. I don’t code except, you know, like right now, live coding makes everything easier. Everyone’s doing that.

Yeah, everyone’s doing it. Not me.

Yeah, I honestly don’t think I speak the KPI coded language, like optimizing everything, improve everything, because I do have a lot of friends that are like that, but I do think working in tech company gives me a sense that an entire corporation, like hundreds of people could just like grind really hard, iterate the product really hard just to like improve

- 2% of user retention or
- 1% of daily active user

because I’ve been there and… I was one of the people who were trying really hard to retain users, study the users, or try to improve the recommendation algorithm, so our app has more revenue that day. You can see this is all correlated, right? I was a content manager, a growth manager during my first job. When you put out a certain content or adjust the algorithm a little bit, there’s an instant bump in your revenue that day. It’s almost like it’s extremely correlated.

If you spend more money on Facebook’s advertisement, you will just get more new users. It is so direct in the tech world, and I do think I understand that eagerness or straightforwardness in the tech landscape.

This divide, though, between the STEM view and the humanities view, do you feel like that divide is even more acutely felt in Chinese life than in the Western context? I mean, the gap between the engineering dude and the artsy fartsy literati type—do you think that’s an outdated caricature by this point, or is that still something very much a dividing line in Chinese life?

I think China’s society logic was dictated by the STEM optimization logic, or like industrialization logic for a long time until the young people are so tired, people are so tired, and then this sort of optimizing bubble bursted.

So back then, maybe 10 years ago, optimizing everything — trying hard. There was an internet slang for people trying too hard, trying to get promoted, make a lot of money during the economic boom — during the Chinese economic boom and internet attack boom. This was admirable.

But right now, this bubble bursted, so people proactively do not want to participate, such as Nuli lore, Nuli fairy tales. Instead, you see China’s today’s mainstream sentiment is:

• How to lay flat
• How to dodge more work
• How to interact with your demanding boss without being fired
• How to still get paid but do less job

This is the current mainstream. I would say China is a post-industrial party society now.

Oh, good, good. I’ll feel more at home there because I’m a good old Gen X slacker, so I know all about avoiding work.

I mean, it’s interesting to me because I feel like I agree. There used to be a period where one side of that divide was absolutely treated as:

• more legitimate
• more serious
• more responsible
• naturally the steward of China’s future

and the other was just written off. But yeah, I’m glad to see this swinging back.

Before we get into what Lingao represents, I think it’s worth situating it a bit. For listeners outside China, it’s almost completely unknown, as you said. How widely known is it inside China, especially among communities that care about:

• technology
• history
• national development?

Is this like a cult classic or is it something closer to shared cultural infrastructure?

I don’t think it’s widely known as a popular cultural product like a movie or Journey to the West. This is basically the most common vernacular day-to-day language.

But I think Lingao is very popular, very influential in a niche community. This community itself is what I would say the elite class of technologists, the STEM people who see themselves as pillars of China’s urbanization and industrialization, and predominantly male.

So Lingao, to be completely honest, strikes me as a semi-misogynist, misogynist novel because a lot of plots imply many things towards women.

But Lingao is a cult fetish. It is a Bible for the industrial party, this loosely connected intellectual group in China.

Yeah, I definitely want to ask you about the gendered nature of this book and about science fiction more broadly. I remember reading Senti, Three Body Problem and just being shocked. There’s stuff you could not get away with in America today, just the level of misogyny that was in there.

But how did you come across it yourself? You told me that you heard about it from Crypto Bros in the Valley, right?

Yes. Chinese Crypto Bros. I heard it from Chinese Crypto Bros.

Yeah, that’s hysterical.

What finally got you to read the thing? I mean, what did it just keep coming? And before you actually surfed over to it, what did you think it was? What kind of reputation did it have in your mind before you actually read a page?

I actually didn’t know anything except it is like a DAO experiment. It is a crowdsourced sci-fi lore. And like, to be honest, when I read anything that’s Chinese internet native, I tend to have lower expectations because I know some of the products, some of, especially those fiction writing stuff, is almost like Harry Potter fanfic, right?

It’s not written by J.K. Rowling herself, but written by the fans who just spend an afternoon, put a lot of scrappy plots together, and then you have a fanfic. So I tend to treat Ling Gao as an interesting phenomenon, like a part of the deeper corner of the Chinese intellect as a lore instead of as a serious science fiction. So I kind of had a lower expectation entering this novel.

And it turned out to be, yes, it is very scrappy. It was written by so many people to the point they started collectively writing it since 2006. And then people just keep writing and piling up and piling up.

A few years later, people were like:

“Okay, now we have too many things. Like the plots are going to multiple directions. We need to sort of come up with a kinetical plot together.”

So someone came up and compiled the storylines together, which creates the sort of, quote-unquote, kinetical Ling Gao timeline as we see today. But you can guess the nature of collaboration is:

• If this person is free, this person can be in charge of this part of the chapter.
• If that person is actually creative, then that person can start a newer plot about building a chemical factory in Ling Gao.
• Some female writers joined later and wrote a lot about gender issues.
• Some history people later joined and wrote about the Ming dynasty bureaucratic system.

There are thousands and thousands of different branches. When I was reading it, I couldn’t really tell which part is the kinetical story and which part is the fanfic back then. But because it’s well written, it’s sort of merged into the kinetical.

Is it because they’re all very put together and scrappy? It doesn’t read a thoughtful thing but reads like a collective stream of consciousness. There are these people who did the actual organizing, who actually decided what is canon and what is sort of peripheral.

What do we know about these people, about these principal writers? Who are they? What kinds of backgrounds do they come from?

They all use pseudonyms online, but we know some phenomenon writers sort of emerged out of the Ling Gao scene, later became the influencers or the writers for Guancha Zhe Wang (观察者网). And Guan Cha Zheu Wang is inseparable from Ling Gao’s collective writing.

Give us a sense of what Guan Cha Zheu Wang is. I mean, they have a certain political slant, a certain reputation. Why don’t you explain what Guan Cha Zheu is?

Okay, so in the Wired piece, I told the readers that Guan Cha Zheu Wang is almost like Chinese breadboard, but I think it’s less like breadboard because it doesn’t punch up. It kind of only punches west.

So Guancha isn’t that up though?

Yeah, so it is, I would argue, a more thoughtful patriotic or nationalistic collective online magazine delivering a lot of pro-industrial policy, pro-state opinion pieces, and some of the pieces are quite persuasive.

You know, I used to be a reader of Guancha when I was in college. Guancha reached its peak in the early 2010s. The founder himself, Eric Lee, I think he studied at UC Berkeley.

Yeah, I think he was the same year as me, in fact.

I see, I see. Yeah, he studied at UC Berkeley. It seems like he made a lot of money and he sort of diverted his money into this collective intellectual body building and started Guan Cha Zheu Wang.

It’s like a think tank and online publication, but it really represents a cohort of writers who, just like Ling Gao, have a strong stamp background, very pro-China, very pro-industrialization, and very anti-West.

And early on, a lot of their pieces are similar to a little bit like today’s narrative on how to establish a strong national, industrial national identity, and unapologetically loving China and being patriotic.

Yeah, so it’s very, how to say, very rad, very internet native. I would argue they’re very internet native because all of them know how to talk. They’re actually… Really good writers. You know, I mean, Eric X. Lee, the person who’s really sort of at the heart of it, as you say, you see Berkeley graduate and a venture capitalist of some success, very, very wealthy guy. He, in fact, is very well-spoken and quite persuasive in some quarters. You know, he has this famous TED talk in English. I agree. You know, he gets this gigantic standing ovation from him. I’ve described him before as sort of the first sword of China apology. He’s very gifted, I mean, in that sense. Yeah.

Let’s get back to the 共业党. Yeah. You know, it’s often spoken of in juxtaposition to the so-called 秦华党, which I’ve seen translated variously as the sentiment or the sentimental party. Does this 秦华党 actually have a representative online novel or a body of literature associated with it, you know, like we see with Lin Gao or is this just a straw man? Is it a real thing even?

I think 秦华党, if I understand correctly, is like the basically the civic space existed once on Chinese internet and I would say they no longer exist. I can say Chai Jing would be seen as a 秦华党 by industrial party standard because Chai Jing is this Chinese journalist who would make a documentary about air pollution, you know, she would Under the Dome. Yeah, Under the Dome. Like she would make a lot of influential documentary or journalistic pieces to remind people that the human cost of China’s rapid development load, you know, she would care about the migrant workers’ rights. She would care about the people who are dislocated because of the deformation of the city, because of the reconstruction of the city. Xi would, you know, care about air quality, right? Yeah.

So anything that’s been negatively affected or left behind by China’s headlong rush toward industrialization, right? Yeah, I would say that both the party and industrial party, I mean, industrial party doesn’t have the power to purge the sentimental party or, you know, the humanistic, the free journalist, the China’s civil civic space, but industrial party justifies for the state to marginalize and purge what they call sentimental party.

But I think sentimental party is actually a core part of my formative experience because I was growing up in China where the internet was a place to discuss real things from political reform to rule of laws to freedom expression to many things. I remember reading a lot of absolutely brilliant investigative reports about

• coal mine abuse
• labor rights
• construction companies not properly paying those illiterate migrant workers.

I remember reading so many great stories about the one-child policy, about how this one town in China has forged some ties to systematically trade female babies to have them adopt in the U.S. A lot of the stories like this couldn’t exist in today’s China because of the demise of a sentimental party, because of the state’s effort of eradicating them.

So the industrial party in a sense doesn’t have any real political power, but I think they are a collective unconsciousness of the regime, of what CCP really prioritized or really think about.

Just to be clear, when you eradicated them, it’s not like they were rounded up and locked up. You’re talking about censorship, you’re talking about all sorts of different lawfare efforts, pressures to, yeah.

Yeah, I mean, when I was in high school, when I was in middle school, I could go on Weibo and read about Han Han’s pro-democracy essays, and those are really bold, quite fundamentally radical essays, if you see them now. I would be reading Chatter 08, written by Liu Xiaobo. I would be reading a lot about Arab Spring. I mean, a lot of the content sort of existed inside of the Great Firewall. It’s a beautifully diverse, chaotic, steaming, intellectual space. I kind of grew up in this internet.

People in those internet forums seriously talk about civic stuff, seriously talking about can China have a political reformation in the future? Because those possibilities were so real back then.

I think when you talk about the industrial party, you need to sort of dial your clock back to the 2000s and 2010s, it was because the tension between sentiment party versus industrial party were really real. I wrote in the piece, I think the signature event was the Wenzhou high-speed rail crash, the train wreck. I still remember vividly where I was that day and how I felt because I was about to board the high-speed rail from Beijing because the high-speed rail finally because I grew up in Shanxi.

Shanxi is an economically backward… Province, so I was really excited to see Taiyuan finally had a high-speed rail connecting to Beijing.

Instead of staying in the old train to take an overnight train to go to Beijing, you can actually spend only three hours to go to Beijing now.

Beijing as a cosmopolitan city, in my mind back then as a high schooler, it’s so close by to me, I can just go there. I was so excited and then the story burst out about the terrible train wreck in Wenzhou.

• 50 people died.

I remember there was a huge debate online about who was guilty, right? Like, where is the weakest link in this? If you dig way back in the Seneca archive to July or maybe August of 2011, you’ll find the show that we did about that.

Yeah, so I remember back then, all the public intellectuals were still active, their other accounts are still not banned. So a lot of people online writing lengthy articles or posting online about the liability of the authority that didn’t have a proper monitoring system.

And so basically, the thing is because a certain signal was missed, two high-speed rail trains basically crashed face-to-face. It was basically a pure human mistake. It was because a certain message didn’t send to the other side, so the tragedy happened. It was pure human mistake.

But anyways, I remember so many people writing about it online and there’s this one piece basically crying for China to slow down. And it was like,

“slow down China, wait for people.”

Implies don’t let such bloody train crashes happen again because this is a price we cannot afford just to aimlessly progress.

And this is a moment when industrial party people came and then they took the stage. They organized a systematic rebuttal against the humanistic sort of pro-slowdown discussion.

Because the industrial party intellectuals have a lot of advantages for knowing so much industrial knowledge because they are the ones building a lot of Chinese infrastructure. For example, I featured this one intellectual, his name is Ma Qianzu, one of the authors.

• He’s one of the authors of the Lingao story.
• He is a bridge engineer, right?
• So he really knows infrastructure, not just from a witnessing perspective, but he is the engineer, he is the builder.

So the industrial party people organized a big rebuttal and they systematically published many articles to not justify this accident but saying we should take this accident seriously, but this shouldn’t be the reason for China to slow down its building on the high-speed railway infrastructure.

And yes, I think the industrial party and the development logic won in the debate and so the result is China didn’t slow down.

I mean, like I think retrospectively if it slowed down maybe China wouldn’t have such a convenient, vast, amazing network of high-speed rail today. But I think back then if China should develop was a real and very visceral and painful question to confront.

A lot of people’s idea is no, we really shouldn’t progress like this:

• Cities being demolished
• People being forced to relocate
• Factory workers suffering from poor conditions

Like, are we, like, why are we allowing ourselves to be the colony for development?

But I think right now we’re basically sitting in the future to meditate on the dispute and one could say of course development is China’s thing, is what China always wanted.

But no, like, you know, there are people strongly against a lot of things the government proposed. There are people interesting to ponder that alternative.

Yeah, but that’s what this itself is — it’s a, you know, Lingao itself is pondering an alternative.

Now I haven’t read it myself, not one bit of it, I’m probably not going to, but I’m hoping you can give us kind of a controlled spoiler.

So a wormhole opens to 1628 from our present, or from the present of the time you know, 10 years ago when they were writing this.

So how does the alternate timeline then unfold? What kind of society do these guys end up building in Hainan? How different does it end up looking from our own history? How much do they change history in this project, in the book?

Yes, so okay. So reading this book is very interesting because the plot evolves as the people who write the story evolve. So like, and also a lot of the writers would write themselves in. The story features a captain—like a captain of the ship that would transport the 500 time travelers back to the Ming dynasty. The captain himself, his real name and real-life nickname, became known as Captain as well. At a certain point, the boundaries between past and present, fictional and reality, kind of blurred.

The same happens with Ma Qian Zu himself. He is one of the main people in the novel. So, it’s Qian Zu and Qian Zu—they almost spell the same in Pinyin, but one is Ma Qian Zu and the other implies humbleness. Ma Qianzu means you stand next to the horse to serve, but fictional Ma Qianzu is arrogant. You are Qianzu: you can see a thousand miles away. Zu means seeing.

I think things like this are very interesting. The basic premise starts from a simple thought experiment: what if you can travel back to the Ming dynasty with modern knowledge and equipment? People started writing about it without character building or discussion. The first 30 chapters are all about people getting together to think about what equipment they should bring to the Ming dynasty.

You will see this laborious preparation list, almost like the list a very organized person writes when packing for a long trip. People spend 30 chapters to prepare for this list.

Then, around chapter 37, people finally get together to board the ship that will take them to Ling Gao. You also see this immense obsession with details:

• How to keep the ship safe from Ming dynasty coastal guards
• What kind of soil Ling Gao county had 400 years ago
• The geography: was it a deep-water pier, deep-water port, or shallow-water port?
• Transporting heavy materials
• Details about geology and soil chemistry
• Natural resources available in Ling Gao back then
• Ming dynasty guards present in Hainan
• Risks of Japanese pirate attacks

They conduct serious, detailed risk assessments. It’s really first principles thinking—almost like an action manual. If you really had a wormhole to travel to the Ming dynasty, you could simply follow it.

This is because a lot of the knowledge is factual. Professional people research and fact-check it themselves and each other in a peer review process ensuring scientific accuracy. People are thinking about how to bootstrap an industrial revolution on this island—what do we need?

• People
• Resources

But, let’s get to my question: how far do they take it? Are we talking decades of institutional development, or does it mostly stay in an early building and consolidation phase? Do they change history profoundly? Do we even know what history looks like now as a result of the changes they make?

The story kind of progresses as the current time progresses, I would say. Everything stays in the Ming Dynasty—there is no fast forward to the Qing Dynasty or the Republic period. The time flow of the Ming Dynasty basically matches today’s pace.

Because the story has been written for about 20 years, a lot has changed:

• Female servants start earning for their own workers
• Stories about certain political reforms
• People leave the Linggao Island to travel to the mainland and interact with Ming officials

There are also plots, like some fanfic, which are not part of the main story:

• People travel from Linggao County to North America
• They colonize North America, specifically the area of today’s Boston
• They see huge opportunities in the New World and decide to colonize the East Coast

There is also a story plot that diverges from the main story that… people ended up colonizing Australia, and they formed a huge sort of empire, almost like a British empire. In the 19th century, they forged a huge Linggao Australian empire across Australia, New Zealand, and Southeast Asia. The north part would be like Linggao county, like Hainan and Taiwan. Yeah, so like crazy stuff, really crazy stuff.

But what really strikes me about this, as you’ve described it, is this is an alternate history that doesn’t imagine salvation through new ideas, or a moral awakening, or the scientific revolution necessarily, but actually just through kind of competence, and very specifically through technological, technical competence.

There’s this like obsessive attention to getting the tech tree right, like:

• What materials come first
• Which tools unlock different forms of production
• How you get the logistics and the energy systems

It’s just like precise accumulation step by step, as you’ve described. But alongside that, there’s also, I guess, as you talked about in your piece, this kind of unglamorous work of building institutions that can sustain these capabilities over time.

They’ve thought through a lot it seems, seen that way. Linggao feels less like escape fiction and more like a thought experiment about governance and about why technocratic instincts have such appeal to China.

Let me ask about this because there’s this framework explicitly in academic terms. We usually talk about the Needham question and about things like Ken Pomerantz in his book The Great Divergence. These are ways of explaining why industrialization took off in Europe rather than in China when China seemed quite ready for it in some measures, by the Song dynasty.

We had the capability to do mass mechanized production in some ways. But again, I haven’t read it, but reading your essay and the broader discourse around the industrial party, it feels like this community has its own implicit theory of history.

How would you characterize the industrial party’s answer to the Needham question? What do they seem to think actually mattered in producing the divergence that we saw, that Pomerantz describes?

I think, I got educated in China. I think the sort of national scar, the hundred years of humiliation that China left behind, didn’t modernize until the European powers kicked your ass. Then China started industrialization.

This part of history has always been a sort of a collective scar, a wound, a true wound basically, among everyone that I know who received primary education in China.

Alternatively envisioning a China that started to modernize, started to industrialize at the pace of the European counterparts has always been, I think, a psychological comforting thought experiment.

I also noticed that sometimes the national consciousness in Linggao’s plot is really weak. Of course, it is a big part of almost like a salvation porn or like salvation.

“That’s a good way to talk about it: salvation porn.”

Part of it is salvation porn, but I realized a big part of it is the joy of meticulously planning everything itself. To the engineers, building itself is very joyful; it is beautiful, it is satisfying.

Because I observed this among not just Chinese engineers but among a lot of the western engineers that I’m friends with. They love YouTube channels like Primitive Technology. It is literally an Australian man using mud to build all sorts of tools from zero.

It seems like engineers really enjoy this sheer ability to transform the surroundings with the scientific knowledge they possess in their mind. It seems like totally my dad.

Like homo sapiens seems to us like we’re homo sapiens. We seem to really enjoy thinking about our ability to transform our surroundings.

I mentioned Robinson Crusoe. I think Robinson Crusoe is like the 18th-century Primitive Technology YouTube channel.

“For sure, for sure.”

When I was a kid, I obsessed with this book because I constantly imagined myself being this all-powerful human being, like going into a savage island and humanizing and civilized a place by my sheer intelligence, by the modern advanced knowledge I possess. And I think, thinking about this, it’s not just Li Gong Dan; it’s also Western engineers. I know, it’s also you, it’s also me. Very interesting, right? It’s like reading this makes you happy.

Seeing the primitive technology YouTube videos makes me calm. Like, I think as a hunter-gatherer, like offspring of a hunter-gatherer society, human being, I found this psychologically safe.

So I think a big part of Ling Gao’s dopamine hit comes from writing about technology and planning itself, writing about building the civilization itself, other than national, yeah, right, for sure.

I get that. I get that for sure. There’s something about—I mean, it’s a flex, you know? They get to show, look how I understand the very fundaments of the technologies that I deal with. But there’s also something like this kind of inherited historical vulnerability at work here.

You know what you talked about, this century of humiliation thing. I mean, not a grievance in a narrow sense, but just kind of a memory of how badly things can go, you know, when state capacity falters.

So I wonder, in addition to this satisfying kind of, you know, tech just tech qua tech, there is—I wonder if there’s this kind of implicit never again embedded in the discourse, you know? Not just about foreign domination but also about chaos, about fragmentation, about, you know, loss of national agency, right? I mean, that’s in there too. I wonder if that appeals to you as well.

I agree. I agree. I think this, we should memorize that engineering and industrialization and urbanization are the true things that truly gave the Chinese nation power. Like we shall engrave this in our bowl.

I think this is part of the message the Ling Gao Qiming Morning Star of Ling Gao has been sort of projecting. And it reminds me of—so there’s a scholar whose name is Wang Xiaodong, you’re probably familiar with, yeah, of course, who wrote, I think in 2009, China is Unhappy. I remember it was a big intellectual sensation.

Like he is the one who coined the term industrial party. So in this article that he coined the term industrial party, he stated very clearly that—I actually want to read this—he stated really clearly that:

“We must never envy the finance Hollywood, the Grammys, and NBA of the West. We would rather forge iron and smelt copper and let the Americans sing and dance for us because forging iron and smelting copper is the true—this is where true power lies.”

And I think this is a big—this basically crystallizes industrial party’s salvation arc, which is it is the industrial capability that made China powerful so other people couldn’t kick our ass again.

The true power, the true international strength that European countries wouldn’t bully us, like America and Japan wouldn’t bully us, is because now we can forge iron and melt copper. It is not because we can sing or dance, it’s not because we care about social welfare, it’s because we can build stuff.

I think industrial party has such a clarity about the importance of engineering and industrial knowledge.

I want to quickly shout out Fred Gall, who actually wrote another essay right after yours came out, and it happened that the very day that I read yours right away suddenly in my inbox there was Fred’s Substack. And he had actually written about it as well, and you know he definitely helped me to get oriented with this.

But what you’ve just described, it’s engineering then becomes an act of patriotism, right? It becomes synonymous with patriotism. Building is loving your country, and that connection seems to be quite explicit in the whole industrial party discourse.

I mean, building itself becomes a moral act. It takes on moral weight, which is a really interesting worldview.

Fred also frames this though in his writing as a generational revolt, especially against earlier, maybe more literary or humanistic modes of thinking about China, the China that you maybe described when we worried about the cost, we worried about the human cost.

I mean, it doesn’t describe this hostility exactly, but a sense of that those ways of talking had become just kind of unmoored from material reality.

So there is this tension between the When Yi Ching Nian phase and the Li Gong Nan dominance phase.

And, but I want to get to this gendered layer here that feels really important for me to acknowledge—that this industrial party worldview, this whole emphasis on engineering on… Discipline on technical mastery that to me feels very gendered in terms of who speaks with authority, what kinds of traits are valorized.

You’re somebody who identifies as a feminist and you work very fluently across technical and cultural domains. How do you read that gendered dimension to that, who gets to imagine the future in these narratives?

I think first of all Ling Gao Qiming itself is a piece of historical record because I think the collective writing process peaked maybe during 2011 to 2015, and this is the internet before China’s feministic awakening. So I would say certain feministic consciousness hasn’t arrived in China yet.

So Ling Gao Qiming is in a sense a product of its time — a pre-feminist cultural product — and people just really don’t have a lot of tools or instruments or frameworks to criticize it.

Just like a lot of women writers would participate in writing, they would probably feel extremely uncomfortable but they couldn’t name why they feel uncomfortable. But now, retrospectively looking at this text, looking at these primary sources, it is very much misogynistic.

It’s just like how much Liu Zixing’s Three-Body Problem feels extremely misogynistic when you’re reading in Chinese.

I mean Ken Liu did a great job in removing a lot of the poorly written female parts, it’s still in there, yeah, yeah. But you know like there’s definitely some plots in Liu Zixing’s work that would be like:

“Oh, you’re a woman but how can you listen to Bach, this German composer, like because Bach is such a representation of rationality, a rational music. How can women appreciate this beautiful, high class, high broad rational music?”

You know, such plots permit Lin Gao and the first 500 pioneers — like a very small group of them are women, predominantly men. And I think the made revolution is the part which is really fascinating because Lin Gao basically operates in the semi-military structure where the resource needs to be centrally planned and allocated to people.

It is a techno-authoritarian society where it’s also a little bit like plutocracy. I would say people who possess the most engineering knowledge have a better social status.

So at the time, there is this distribution of:

• female domestic servants
• some low status engineers
• some laborers who didn’t get female servants.

These people are very unhappy. I mean, they’re all fictional plots by the way, and those plots are the incels of Linggao — the single people from Linggao.

In the sense of domestic servants are also, you know, sex slaves, which is not being explicitly said but later you will see this Linggao society operating as a semi-feudal but techno-authoritarian style political structure.

Later, they recognize that:

• “Oh, you kind of need to give your female servants better hygiene.”
• “You need to give them better training in different things.”
• “You need to teach them how to read and write.”
• “You give them time discipline.”

This is all part of the modernization process.

China’s modernization success depends on female workers in the factory, so Linggao is like:

“Okay, if we’re rational enough to truly industrialize Hainan, to truly industrialize Ming dynasty, we shall truly give the female servants proper treatment, so we can properly…”

So it’s basically all rational, not like:

• “Oh, we love women.”
• “We want to respect them.”

It’s not moral—it’s rational.

So it’s rational for the Linggao community to progress to a female-male equality scenario, and then this is basically a historical fatalistic direction instead of out of, you know, humanitarian concern or out of cuteness or moral goodness.

Wow, there’s just so much to plumb here, and it’s sort of the theory of history that underpins this that I’m particularly interested in. Maybe I will at some point take a crack at this thing. I’ll be good for my Chinese anyway.

So let me shift a little way away from Linggao here.

I do want to bring it back in frame but this book Breakneck, by Dan Wong, which is one of the most talked about books of 2025. Dan, of course, as you know, describes China as an engineering state.

I mean, listening to you talk about Linggao and the industrial party, that phrase starts to feel less like an abstraction and more like an actual lived… Worldview, right? Does that framing resonate with how you understand what Linggao is imagining, or does it miss something important?

You have this book club where you have been talking about, reflecting on Chinese language discussions of Breakneck. You know, it’s called What? Reading Breakneck in China.

Yeah, reading Breakneck from China.

Right, right.

One thing that struck me in your book club reflections—I’ll link to that because you’ve written about it on your Substack—is that Chinese language discussions about that book seemed less surprised by that framing than English language ones.

So, I mean, did the idea of an engineering state feel like any kind of a revelation to Chinese readers, or more like seeing something familiar finally given a name?

I really appreciate Dan’s framing. I think Dan’s framing is at least to better capture certain reality in China. I honestly think the democratic versus autocratic binary is not helpful anymore. Like, if you look at the US, what’s democratic about the US, right?

I know a few Chinese, China-focused scholars who used to study the authoritarian regime of China and now all sort of pivot to study the US authoritarian term.

You know, I honestly think Dan’s framework can somehow better explain the reality and better get to the point. It’s really helpful, it’s really instrumentally helpful.

And then, according to Dan, he tends to be playful with this framework. He’s like not 100% serious about it, doesn’t want to challenge the status quo of democracy versus autocracy. But yeah, I’m going to borrow that cop-out from him.

I’m just being playful here, I’m not really—it’s a way to not commit completely, right? I mean, that playful is—it doesn’t have like, you know, we have generations of scholars studying authoritarian systems, right? But like in a sense, I don’t think Dan wants to challenge that.

I think he comes up with this framework just to better explain today’s China and today’s US.

Yeah, I think I do appreciate this framework, and I think the engineering state captures a lot of the developmental, the knee-jerking intuition for the Chinese society as well as the party’s industrial policy.

I think the industrial party ideology is reflected by the CCP itself as well, and I would argue this industrial development is the priority spirit, is a collective unconsciousness among so many powerful people, so many decision makers in China.

For example, Xi Jinping mentioned the new productivity force. I think new quality forces of production is very industrial party coded—it’s because this implies that China’s economy is stagnating; the growth is that as we don’t have the prosperity like the growth like before, how do we solve this problem?

Okay, let’s shift to this magical new productivity, new quality productivity force. Let’s do more engineering, let’s upgrade our engineering so problems could be solved.

I think there is this industrial party-coded naivety or innocence in it, and then I think a big part of the CCP’s decision makers still think they can engineer a lot of problems away. But in reality, it’s not true anymore because the industrial party itself has a lot of intellectuals start to have their own reckoning on a lot of China’s problems, and then they realize that a lot of problems couldn’t be engineered away.

So, Dan Wong’s book, do you feel like it hits differently between English and Chinese audiences when it comes down to their different lived experiences? How would you, if you had to sum up the difference between how your Chinese friends—many of them have maybe not spent time in the West—how that hits differently?

A lot of people are overly obsessed with if China is a real engineering state. For example, they would argue:

• If the Chinese authority are engineering minded, why would they do stupid things like zero COVID, right?
• Because zero COVID is essentially a political power test.
• It is an obedience test — it’s really about whether the officials are following the ultimate order from the overlord instead of rationally thinking about what COVID is and how should we deal with it.

So a lot of the Chinese language readers who are living in China would be dissatisfied with Dan’s engineering state verdict, because they would argue like, you know:

• Not a lot of CCP officials are actually stemmed from… Trained background like maybe Ding Shui is the only one who had an engineering degree, but none of the people from the Politburo are serious engineers in their career. So, people were overly obsessed with this, but I think I agree with Dan’s framework because I think engineering states basically summarize China’s logic. A lot of internal logic is like that.

I tend to think it’s very useful to accept it in a sort of provisional and playful sense. But there’s this irony I keep coming back to: it feels like it’s only just in the last year or so that many Americans have really fully become aware of the scale of China’s industrial might or industrial power in China.

Yet, in our conversation, it sounds like the industrial party worldview—the whole framework that helped articulate and legitimize this push from within China, this crazy breakneck, engineering-driven mentality—is already losing some of its explanatory force in China. It’s weird that Americans are only starting to believe this is the case at the moment when the industrial party logic has lost or is losing its grip.

I don’t think the industrial party logic has lost its grip in China. I’m pretty sure a lot of the industrial policy decision makers still very much adhere to the industrial party logic:

• “This development has solved everything, so let’s just keep building, building, building.”

But the intellectuals who were part of the industrial party movement in the early 2010s, I think they’re starting to suffer from China’s declining economy and, say, COVID. For example, Ma Qianzhu himself, an influencer in China with two million followers on Bilibili, is a very articulate writer. But his account was banned because he voiced certain issues during COVID. Ma Qianzhu himself got cancelled by the state even though he used to support everything for the state.

This brings us to the irony where the industrial party people, the engineers themselves, are very smart and aware of certain societal issues like:

• The slow burn of the Chinese real estate collapse
• Demographics
• The 996 work culture
• Care work
• The housing crisis
• Youth unemployment
• Meaning itself

These issues don’t necessarily yield to the logic of industrialism.

I’m curious about Fred Gao—I don’t know if you know him personally, but I’ve met him in Beijing. He’s a really nice guy and has been explicit about moving away from the industrial party orbit over time.

I wonder if this is a personal evolution or symptomatic of a broader shift in discourse. I think for many industrial party intellectuals, it feels like a personal evolution. They have kind of grown out of the industrial party phase. I would say they lost their innocence in believing engineering could solve everything. It’s not a magic potion.

Mai Tienzo himself definitely took some hits in life to realize that his youth was starry-eyed and innocent about many things. It’s called growing up. A lot of people I know had that kind of super faith in technology early on, and anything that didn’t surrender to the hard logic of mathematics and engineering was just worthless. They’d ask, “Why bother reading novels? You should be reading that kind of thing.”

People grow up, right?

It’s really funny because within the crypto community, I also met a lot of rational engineers—people who hang out in the rationalist forum community. I see them growing up as well, starting to learn that:

• Culture is upstream of engineering, product, implementation
• Culture is upstream of institution
• You can only understand culture to actually change society

I see them also sort of grow out of this obsessive, almost purity phase.

It’s funny like my tensile right now, he speaks out. A lot about the child supply, and he speaks out about local government debts and certain central-local relations. He also has an absolutely descending voice during COVID. Well, I mean, it’s comforting to know that it’s still possible for people to change.

Yeah, let’s go for one final question just to wrap this all up about what Lingao tells us about China today. If someone wants to understand contemporary China—not the politics necessarily, or the policies, or the political imagination—what should they take away from the Lingao phenomenon? What does it tell us about how China thinks about:

• time
• failure
• the future

What’s your big bottom line takeaway?

A big thing that tells us is maybe stories like Lingao are worth more attention. In a sense, it’s a more grassroots Senti—a Three Body Problem that’s more widely accessible. In a sense, it’s an egalitarian Liu Cixin collective building process. Like, you know, Three Body Problem’s Liu Cixin is representative, but I think Senti maybe speaks more to the unpolished, the authentic, the grassroots, the organic aspect of these things.

For me, reading Lingao is such a journey. It introduced me to knowledge I never really thought about. Part of the Industrial Party I constantly laughed about during the peak of their debate in the early 2010s: they constantly laugh at this humanistic journalist who would complain about the suffocating urban life and want to escape to the forest. As long as this journalist can take a hot shower and have access to the internet, the Industrial Party would laugh at this fantasy.

This escapist imagination ignored the infrastructure it needs to have a hot shower and wifi connection. The Industrial Party deeply advocates for the invisible wires buried in the ground. They advocate for the pipes that transmute the hot water to this escapist little Eden garden. This humanistic journalist would imagine oneself to be like this, but Industrial Party people are really making a lot of the invisible stuff visible to me.

In the process of US re-industrialization, such knowledge is revealing because I used to take hot water and electricity for granted. Then I learned that’s not true. China’s electricity supply is top of the world right now—the high voltage grids, convenient industrial basis—everything to fuel China’s innovation.

Yeah, I think Industrial Party really gives me certain knowledge that humbles me because I could be that ignorant humanistic journalist complaining about urban life. I want to take a hot shower in the forest and don’t reply emails, but I still want wifi. I could completely ignore the infrastructures—that’s like the iceberg under the ocean.

Yeah, I think, in a sense, Lingao is a textbook for me to learn about the industrial process at its very first principle. It’s not fun to read but also fun to read. That’s really an interesting take. I gotta wonder what these guys today would think of Li Ziqi.

I mean, you know, for those of you who know, Li Ziqi is a very, very popular video blogger, huge on YouTube and stuff like that. This woman is very attractive, who left her life in the city to go home and take care of her aging parents or grandmother in the countryside in Sichuan, and has made this enormous following because she’s so good. On the one hand, she sounds so far like that kind of journalist who wanted to flee as long as there were hot showers and internet.

But this woman also has mad skills. I mean, she crafts, she does, she’s a good asset on, you know, Hainan Island in 1628 for these guys because she knows how to build stuff, how to make stuff, and all these traditional crafts. I wonder what they would make of somebody like her.

She embodies, on the one hand, both what they don’t like and what they very desperately need.

Oh yeah, I think if I were a Lingao writer, if I were part of the Engineering Party, I would salute Li Ziqi because if I were them, I would meticulously break down the amount of planning for her to do in order to create. A seamlessly beautiful video like that—if I were an industrial party member, I would appreciate the engineering part of her production. I would be like,

“Oh my god, it’s because you did so much invisible infrastructural production work.”

So the 20 minutes—the visible time of you showing up on the screen—can look so effortless and seamless. I think, yeah, I generally think the Ling Gao people would appreciate her engineering skills in a sense—like production engineering and resource management skills for sure. Fantastic!

What a fun conversation this has been, and the time has just flown by. Afra, let’s move on now.

First of all, thank you for spending so much time speaking with me, and again, everyone’s got to go and read your piece if you haven’t done it already. It’s just a wonderful piece of writing. For me, I think it’s one of those things where this little slice, as you say, just this artifact of Chinese culture, made me think so much about the contemporary Chinese condition. It made me think so much about, you know, the mindset that really does—in so many ways—just sort of inform and shape the world that we inhabit today.

It’s become—it’s not just ideology, it’s more like infrastructure, right? The whole mentality, in many ways, has come to define the modern polity.

But let’s move on and talk about this segment that I call paying it forward. If you’ve got a young colleague or a friend or somebody whose work you want to call attention to, now is the time to do.

I think one thing I need to shout out is—I mentioned in a piece that there’s no English translation for Lingo, which is not true. So, two months ago, obviously a group of people took it as a passion project and translated the canonical version into English and made it a website.

• I can link the website.
• I can send you the link.
• You can link to the show notes.

They also basically have a GitHub commit about the tools they use to translate the piece. They use the GMLI 2.5 to translate everything.

Yeah, I’m just really glad that people are spending effort systematically translating Lingo into English, so I would recommend reading that. I think that’s the first recommendation.

Second is, unfortunately, if you’re not a Chinese language speaker or don’t listen to Chinese, you won’t get the great content. Baihua is this podcast incubator actually started by my friend Izzy. We’re all like sort of the founding members of Baihua, and we’re trying to incubate more Chinese language podcasts.

One of the podcasts I really like and really appreciate is called Xin Xin Renlei. I can also send the link.

“Please do.”

Xin Xin Renlei is a podcast hosted by three tech journalists who are also, like me, really bilingual and understand the tech world on both sides. They find some very interesting niche topics to discuss. For example, they would talk about:

• Elon Musk’s imagery evolution in China
• Burning Man and Burning Man’s evolution—how Burning Man is perceived by different generations
• Their obsession with web novels
• AI

Yeah, so highly recommend Xin Xin Renlei. The English name is Pixels Perfect.

Pixels Perfect, Xin Xin, Xin Xin, Xin Xin.

Okay, well excellent, excellent—that’s fantastic. Now, I don’t know whether that was your paying it forward recommendation or your actual recommendation recommendation. I distinguish between them, but did you have a book or something that you wanted to recommend?

Yes, I actually read voraciously. I do have a lot of books I would recommend. One would be, I think, it’s edited and written by Carrie Brown. It’s called

China from European's Eyes: 100 Years of History

I think that book, to me, is—

You know, like we always talk about how China is the foil and mural for the West’s imagination, and people’s obsession about China—the way people project China as a beacon for technological advancement today—is actually a sense of otherness, right? Like other in China.

So this book illustrated that this phenomenon is not new. It has been existing for 800 years. You know, many European intellectuals have been portraying China as the otherness projection—like it’s elderly, alien, different—but it… It could be either really beautiful or really ugly. It could be elderly powerful or elderly powerless. The reason why China couldn’t develop modern technology and modern systems, Hegel would argue, was because the Chinese language, the characters, are so laid back.

Basically, Cary Brown, as a historian, compiled 16 or 18 permanent European intellectuals on their takes of China. So the people from like Voltaire to Hegel. Yeah, so I think it’s a fascinating intellectual genealogy. I would recommend it.

Yeah, that sounds great. I mean, I have all the time in the world for Cary Brown. I think he’s wonderful, brilliant, and a fantastic writer. I don’t understand how he writes so much—like he’s gotten a new book every six months.

Oh, I have another one I really must say is Ilin Liu’s upcoming new book. It’s called The War Dancers. It’s coming out, I think, at the end of February, and this is a book about the history of the Chinese internet in the past 30 years. I think you’re going to be interviewing her.

Yeah, I read it. It’s absolutely such a craft—it’s a beautiful craft, so well written. She’s a great writer. Oh, she’s such a great writer. Honestly, as her friend, I really admire her craft. Such a role model.

Yeah, we know each other socially as well, and I am going to have her on the show to talk about the book. So yeah, I mean, it’s great because the book is really well written. I read that book—it’s called The War Dancers. I couldn’t remember the full title, but I have it. So I’ll make sure to put the title in there, and it’s an excellent recommendation.