The United States and China are really the only two countries that matter right now in shaping the A.I. future. As President Trump and President Xi Jinping meet in Beijing, there’s a kind of Cold War atmosphere, with people talking about an A.I. arms race. But who is winning? Are we even in a race at all? Kyle Chan, a foreign policy fellow at the Brookings Institution, says it’s hard to call it a race because the U.S. and China have very different A.I. goals.
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Ross Douthat: Kyle Chan, welcome to “Interesting Times.”
Kyle Chan: Great to be here.
Douthat: So at the moment, there are really only two countries that matter for the A.I. future: the United States and China. Their leaders are meeting in Beijing this week, and the atmosphere is sort of similar to a kind of Cold War atmosphere, where people think and argue and talk about them being in a kind of arms race.
You are an expert on China and A.I., and we’re going to talk about that race: who’s winning, what winning even means, whether it even makes sense to talk about the U.S. and China in terms of a race.
But I want to start with a basic question. How is China’s current approach to A.I. different from the American approach?
Chan: It’s quite different, actually. In the U.S., there’s a particular focus on A.G.I. — artificial general intelligence — and to create something approaching an artificial superintelligence, some kind of almost machine god that can do virtually everything that any human can do ——
Douthat: And more!
Chan: And more. That’s right.
Douthat: You want to get more. That’s the “super” part.
Chan: [Chuckles] Absolutely. And you can see that the amount of spending, the amount of investment, the amount of effort that the American Big Tech companies and their quote-unquote “start-ups,” like OpenAI and Anthropic — which are now close to $1 trillion each — are pouring into this is an indication that they’re making a big bet that they can get there at some point, maybe in the near future. That’s the race to A.G.I. in the U.S.
China is running a different kind of race. I would argue they’re running multiple races. On the one hand, they are trying to produce better and better A.I. models. They do want to try to keep pace with their American competitors, but that’s not all they’re focused on. They’re also focused on efficiency, making these models smaller, cheaper to run, easier to deploy. That’s one area.
Another area they’re focused on is diffusion — trying to get A.I. into the hands of as many users as possible — and part of that strategy involves open source. This involves kind of giving away your models for free. And that allows other people around the world, including in Silicon Valley, to download Chinese models and to also customize them and tweak them based on their own data, and to make them work in a way that’s more tailored to their own needs. That’s the advantage of open source.
Another major area that China’s focused on is applications. Specifically, robotics is a huge area of focus, both for the government and for Chinese A.I. companies.
But you don’t really hear so much about A.G.I. You might hear some of the Chinese tech founders talk about this, and they sometimes sound a little similar to their counterparts in the U.S. But overall, they’re much more focused on the nuts-and-bolts uses and applications of A.I. in people’s daily lives. That’s the key priority.
Douthat: So if I went to Shanghai or Beijing right now and spent a couple weeks there interacting with physical reality and digital reality, do you think I would notice a big A.I.-driven difference versus life in the United States? Just describe the everyday experience of this strategy, to the extent that it makes a difference in how people are living.
Chan: Yeah. So in the larger cities in China, you might see autonomous delivery robots dealing with package deliveries, food deliveries. In a restaurant, you might see a waiter robot bringing your food. This is not super, super widespread yet, but it’s starting to come about. Hotels, rather than having room service be delivered by a person pushing a cart coming up the elevator, it might be a delivery robot. You have of course self-driving cars. You might even have drone delivery for coffee or food.
But it would be a subtle but probably surprising difference to what most Americans experience in terms of their interaction with A.I. in the physical world.
Douthat: Let’s just pause for context, because you talked about the government versus the Chinese A.I. companies. I think most viewers and listeners are accustomed to the American situation, where you have a set of big companies, they have been extremely, lightly regulated by Washington, D.C., and just in the last year, we’ve started to get into dynamics where the Pentagon especially seems concerned about their national security implications. There’s talk about regulation, screening of models and so on, but basically it’s been a very traditionally American capitalist environment. Not a Manhattan Project or anything like that.
To what extent is China similar or different just in the relationship between the companies and what is obviously a much more powerful and often repressive state?
Chan: In China, the state is in charge. Or specifically, I should say the party state. The Chinese Communist Party and the various government agencies that they oversee, they’re the ones who set the rules. They’re the ones who ultimately are shaping the trajectory of China’s A.I. industry.
They have quite strict regulations — for example, requiring A.I. models to be registered in advance. They have certain content and censorship rules that must be followed. They have a whole host of ways to enforce their rules and have leverage over Chinese A.I. companies. And there are echoes back to a previous era where Chinese regulators cracked down on Chinese internet companies, for example.
That’s the overarching relationship, but that doesn’t mean that the Chinese A.I. labs themselves are just in lock step following whatever Beijing says. Ironically, China tried a more top-down model to technology in a previous era, and that failed miserably. It did not produce the kind of innovation and flexibility and agility in the marketplace that you would need to have cutting-edge technology.
Douthat: What era are we talking about with the more top-down approach?
Chan: That was, I would argue, going back to the Mao era. This is the classic ——
Douthat: Pre-Deng, roughly pre-1980s?
Chan: Exactly, yeah. That almost Soviet command-economy-style approach.
So what you have is sort of a hybrid model in China, if I could characterize it in a single word — a broader direction and guidance and certainly support from the central government in China as well as local governments on the one hand, but then also trying to create space for competition and innovation from the Chinese A.I. labs themselves, whether you’re talking about China’s equivalent of the Big Tech companies, like Alibaba or Tencent, the maker of WeChat, the popular super app, or you’re talking about China’s own A.I. start-ups, like Z.ai or Moonshot AI, which have actually become quite popular around the world.
Douthat: What are the Chinese equivalents of an Anthropic or an OpenAI right now?
Chan: That’s a good question. So maybe DeepSeek would be the closest. And then you have the smaller start-ups. And by smaller I mean like on the order of $40-to-$50-billion market cap. And those are some of the more successful ones.
But it’s hard to find that kind of middle ground. DeepSeek now is preparing to take in outside investment. Remember, they were actually not originally an A.I. company. They were part of a hedge fund, actually, that was trying to use A.I. to develop more sophisticated financial models. So they’re sort of a category unto themselves.
Douthat: And all of these companies, though, are operating under some basic constraints that don’t apply to U.S. companies right now, mostly around chips. Can you describe the landscape of constraint in China and what it means?
Chan: Yeah. I had mentioned earlier that Chinese A.I. companies are trying to run different races, and one of those was efficiency. Part of that is in response to the constraints that they’re under, in particular around compute and chips.
So remember, right now the U.S. has export controls on our most advanced semiconductors, basically made by Nvidia, and we stopped those from officially being sold in China. We allow the sale of watered-down versions, but the idea is that we keep the best and the most advanced chips for American A.I. companies in the United States and for allies and partners.
For China, that means that they don’t have access to the most cutting-edge A.I. chips. They have some Chinese domestic alternatives — and this is a big part of the story. One of the leading players in the space is Huawei, the heavily sanctioned Chinese tech giant that rose first in the telecom space, branched into smartphones and is now in pretty much every other industry — electric vehicles, clean technology and certainly now A.I. and chips. So China’s trying to build up their own capacity for developing A.I. chips on their own, not just designing them, but actually producing them.
The problem is, they’re just not quite as good as the Nvidia chips. And without that, it does put a lot of constraints on what they can do. So they’re trying to squeeze more out of very limited compute resources.
Douthat: Why aren’t their chips as good? I know this is a simple-minded question. Is it just that Nvidia is so awesome at engineering and China’s engineers, even if they have a Nvidia chip, can’t quite get there themselves? Talk to me like a non-chip specialist.
Chan: This is the $5 trillion question, which is currently, I think, roughly the market cap of Nvidia today.
There are a couple different aspects to this. One is actually the chip fabrication that is producing the chips. Remember, Nvidia doesn’t make their own chips. TSMC in Taiwan, they’re the ones that make the chips.
Douthat: Conveniently located not that far from China.
Chan: [Chuckles] That’s right. To the consternation of probably a lot of folks in Washington and maybe other folks dependent on those supply chains.
But TSMC has been pushing the boundaries for increasingly advanced semiconductors in a whole range of areas, and that includes A.I. And Nvidia, by partnering with TSMC, can combine some of the best design work out there with some of the best production capabilities.
For example, ASML, a Dutch company that maybe some people have heard of, it’s actually one of the biggest tech companies in Europe now. They make these extremely precise, extremely expensive lithography machines for printing chips, basically. And they’re the only ones in the world that can make this kind of machine.
They sell those to TSMC. TSMC can use that cutting-edge technology, combined with their own cutting-edge manufacturing processes, and work with Nvidia to produce these incredible state-of-the-art chips that keep getting better and better.
Douthat: So essentially, when we talk about the U.S. not allowing Nvidia to sell to China, we’re effectively talking about the U.S. cutting China out of a larger supply chain that runs through Taiwan, through the Netherlands, all around the world?
Chan: Absolutely.
Douthat: OK. That’s interesting and very helpful. What does China have going for it then, in terms of A.I. build-out, that the U.S. doesn’t have?
Chan: Energy is absolutely huge in China. If you’re thinking about the broader A.I. stack — that is, not just the chips or the models themselves, but deeper down on the layer — energy is perhaps the most important and least talked about.
For the U.S., this is a major bottleneck. It’s very hard now for data centers to build out the power capacity to power all those chips that they’re putting together.
In China, interestingly, they’ve been building out energy at a very rapid pace — clean energy like solar, wind, batteries. They’re trying to leverage that ongoing energy build-out to feed into their compute build-out, which then feeds into their A.I. development.
So you see really interesting sort of strategies that the Chinese are taking. For example, they have this effort to try to build data centers out in the western provinces, away from the high-population urban areas in China.
At first, that might not make any sense. Don’t you want to have your data centers close to where people are actually using them? Don’t you want to have that low latency, high response time?
What China’s trying to do is they’re trying to leverage a lot of their renewable energy resources out in those further-off regions.
They’re also trying to just do sort of good old-fashioned geographical redistribution, always concerned about having these poorer provinces remain poor while the high-tech Shenzhens and Shanghais speed on ahead.
So this is another area where they’re trying to leverage some of their strengths to feed into areas where they’re maybe weaker.
Douthat: China is, to simplify, imagining a future where they’re only a little bit behind the U.S. and — actually, say what that means. People talk about the best Chinese models being three months behind the U.S. or six months behind the U.S. How far behind are they, and what does that mean in practice?
Chan: Overall, I think the consensus is that Chinese models are somewhere between three, six, to nine months behind, depending on the time of year, and which was the latest model that just came out.
What that means is that when you look at specific benchmarks, specific evaluations for trying to understand how well these perform on, say, math or coding tasks or even new agentic tasks, the Chinese models that are released today are starting to get close to the American models that were released a couple of months back. That’s what that lead time means.
The thing is, it’s not just about having the absolute most cutting-edge model. You can have very, very strong models that can do a lot, that can do a lot of useful agentic tasks, like maybe create a whole PowerPoint presentation for you and do all the research and analysis that goes into that, or answer your emails.
There’s this strategy, I think, right now in China where they’re hoping that it’s not just all about having the very best models. It’s about trying to figure out where to make this work, and also to build the broader ecosystem for deploying these models, to integrate them into more and more services, like food delivery or ride-hailing or, again, much more practical real-world applications.
Douthat: In the U.S., obviously there’s just a lot of anxiety around A.I., to a greater degree than any big technological change in my lifetime, certainly. There’s apocalyptic fears, there’s economic fears about job displacement, there’s social and cultural fears, there’s people who just don’t want data centers built in their backyard. So there’s a whole range of different moods.
If you were going to try and distill the mood in China, the public mood around A.I., how would you describe it, and how is it different from the U.S.?
Chan: I think the biggest anxiety right now in China is around falling behind on technology. I think in the U.S., there’s a lot of worries about job displacement, of A.I. being a net negative force in society. In China, there are some of those concerns — and I can come back to that — but I think right now, the fear among individuals and companies and workers is that they’re not keeping pace with A.I., that they’re not using it enough, and they’re not savvy enough with this new technology, so they won’t be competitive enough in the labor marketplace.
It’s interesting: This anxiety at the individual level kind of mirrors China’s anxiety at the national level. When ChatGPT first came out — and in fact, you can even go back to when AlphaGo first defeated the human world champion in Go — there was a lot of anxiety in China among China’s A.I. industry and among policymakers in Beijing. They were worried that China was also falling behind, that they were not making the most of this new transformative technology.
So it’s interesting to see this kind of mirroring where it’s not about how do I keep out this technology from my life? It’s about how do I bring it in even more and integrate it and give myself that edge in a very, very crowded marketplace?
Douthat: I see that attitude in the U.S., but it is a very Silicon Valley and tech-adjacent attitude. It’s spreading, but you see it in a pretty confined zone of the American economy. But are you saying that in China, it is just much more widespread? That you don’t have to be working for DeepSeek or working for Alibaba or something to have this “Am I falling behind? I must add A.I. protocols” mind-set?
Chan: That’s right. It’s interesting that A.I. is hitting at a time when China was already experiencing a whole bunch of anxieties around labor markets, especially for young college graduates.
For example, the unemployment rate for young people in China is basically double what it is in the United States. It’s somewhere close to 17 percent, which is extremely high. The number of new college graduates hitting the job market this year alone is 12 million-plus in China.
These are all people competing for many of the same jobs. They don’t want to work in the factories. They don’t want to have those blue-collar jobs or delivery jobs. They want, in their minds, the “good jobs.” And they’re worried that if they don’t keep up with A.I., they might not be able to get those.
So it’s a longer-standing concern about this hypercompetitive environment in China that has been there as long as I’ve been going to China, but A.I. really sort of amplifies and accelerates those anxieties.
Douthat: And part of the debate in the U.S. has also been about the welfare state, and you have tech leaders talking about how the welfare state has to adapt if there is A.I.-driven unemployment. You have Elon Musk promising, not universal basic income, but “universal high income” — I just like saying that.
China does not have a safety net to any degree like the United States, or like Western Europe. Is there a welfare state debate in China, a U.B.I. debate — anything like that?
Chan: Increasingly so. The great irony here is I was speaking about the Mao era earlier. That is the era of the “iron rice bowl,” the idea that you were a worker at a state firm, at a state organization, and you basically had your job for life. And this idea of job security is no longer there in China, unless you’re working for, again, a state-owned enterprise or within the government.
So that concern is coming back. And there’s actually more discussion now, including among policy folks in Beijing, about the potential issues related to A.I. job displacement and what China should do about it from a welfare and policy standpoint.
Douthat: Are there actual policy ideas in the wind? Is U.B.I. under communist conditions?
Chan: [Laughs] It’s still early stages.
Douthat: “From each according to his ability, to each according to his need” makes a comeback.
Chan: That’s right. [Laughs]
Douthat: To get rich is glorious, but also …
Chan: [Chuckles] But also … they are the Chinese Communist Party after all.
I think it’s still early days for that discussion. And there’s still a pivot that’s happening from the sort of all-in “hit the gas pedal on A.I.” progress, including from the policymakers, where they were emphasizing all the new jobs that would be created by A.I. — “don’t worry about those other jobs that might be affected, that’s part of the industrial revolution that’s happening now, Industrial Revolution 4 or 5.0” — but now that conversation’s starting to shift.
Douthat: And what about the central government’s concern about social effects of A.I.? One notable thing in China — you mentioned earlier the crackdown on internet companies — there was and has been a deep anxiety about the internet’s effect on social life. There have been attempts to crack down on video-gaming among young men. All of the things that American commentators worry about at a speculative level have actually sometimes been actual policies in China.
And this is connected to the reality that China has a bigger problem than the U.S. with falling birthrates, falling marriage rates. Are China’s leaders looking at A.I. through that lens and worrying about the A.I. girlfriend, A.I. boyfriend future?
Chan: Definitely. They are very worried about that. And in fact, they are already rolling out policies and regulations around A.I. boyfriends and A.I. girlfriends.
It’s so funny. They have a very negative view of wasting time, basically, of what the folks in Beijing see as nonproductive activity. And in that earlier era of a tech crackdown, they saw video games as not really part of the Chinese vision for a high-growth, technologically powered future, when everyone’s at home playing video games.
They also cracked down on the education market. So there was a lot of private tutoring — ed-tech start-ups were sprouting up — and they also saw that as wasteful because it was sort of a race to the bottom in terms of preparing for exams and feeding into that kind of cutthroat academic environment.
Right now, I think we’re seeing something similar happen again, with worries that A.I. companions could end up being a big time sink for Chinese youth when they should be engineering the future and building out the start-ups and the future Chinese versions of SpaceX, for example.
Douthat: But is there also a sense that this is the solution if China never fixes its birthrate, that robots are just the way that aging low-birthrate societies compete? Is that also part of the theory or the mind-set?
Chan: Definitely. That’s a big part of the story.
So China has a shrinking work force — I think their labor force size actually peaked over a decade ago — and they’re heavily dependent on manufacturing. They don’t want to let that go. They see that as the engine for the whole economy. So how do you reconcile those two factors — when people don’t want those factory jobs anymore, and young people want different jobs — and there’s just not enough people to fill the factories?
One solution is robots. One solution is to increasingly automate factory production, to put robots of many different kinds, whether they’re your classic six-axis industrial robot arm that can lift up a car in one go ——
Douthat: The classic six-armer!
Chan: Or now this big push with humanoid robots is seen as being yet another potential solution, if not a perfect solution, to this ongoing labor issue.
So China wants to continue to become more and more competitive, to move up the value chain and to make better and more high-value stuff, but they don’t have the work force so A.I. and robotics is seen as the way to fill that in.
Douthat: It’s interesting, you mentioned robot waiters. One thing that has been sort of encouraging, I think, to people worried about job displacement in the U.S. is the extent to which robotics in restaurants, fast food places, supermarkets and so on has not, so far, radically displaced human workers. In fact, places like McDonald’s and Starbucks that have tried to really move to kind of automatic ordering and so on have often found themselves maintaining human staff beyond what they expected — or even expanding human staff.
In a context, though, where the Chinese birthrate is maybe two-thirds the U.S. birthrate at this point, depending on which stats you look at, you’re just in a different landscape. Maybe you’re worrying less about whether the robot waiter displaces workers and more about whether you have a waiter at all, and so the robot waiter is welcome and necessary?
That seems like it could be a big point of divergence, ultimately, between how the U.S. and China relate to robots.
Chan: Yeah, definitely. You’re going to have to err on one side or the other. You’re going to have to err on the side of going too slow, and then you may not have the ability to do all these things because there’s not enough workers there. Or you might err on the side of going too fast, and I feel like that’s more of the concern in the U.S.
Douthat: Let’s go back to the A.G.I. superintelligence question. How do you think China’s leaders actually think about the American fixation, or the tech world’s — Sam Altman, Dario Amodei — fixation on A.G.I.?
Two options — you can tell me if there’s a third. One option is that the Chinese basically think that our tech companies are high on their own supply. That there is never going to be some insane return to superintelligence, and it’s always going to be fine to be three to six months behind, but then you catch up.
Another option would be that China is actually worried about superintelligence and is basically trying to figure out what their contingency plans are if the Americans seem to be pulling much further ahead.
Does either of those describe China’s mind-set, to the extent that you can read the tea leaves in Beijing?
Chan: Yeah. One interesting corollary question is: Is China trying to do an A.G.I. Manhattan Project somewhere, buried underground in a bunker, with data centers that can’t be seen by satellites and are powered by ——
Douthat: Yes.
[Chan laughs.]
Douthat: Yes. Are they?
Chan: And my inclination is no.
Douthat: Do you think they could do something like that without the U.S. being aware of it?
Chan: I don’t think that they would be able to do that without the U.S. being aware. I think that it would require such a scale of production, of amassing resources and construction, that we would detect something, and we would start to wonder what is going on.
I mean, we’re already watching everything about the nuclear weapons build-out, for example, in China. I would be very doubtful that we would miss something of that scale, because you really would need a massive scale, in terms of compute and energy, to power something that would be like a Manhattan Project for A.G.I.
Douthat: So they’re not secretly trying to win the race. Whatever they’re doing, they are sort of accepting this position of being in our draft on the racetrack, or whatever metaphor you want — for now. But is that just making a virtue of necessity, or do they think that we’re deluding ourselves in our race to superintelligence?
Chan: [Chuckles] I think they just see the technology quite differently, and they just don’t have that kind of transcendent view of technology.
I think that you can see this in other approaches that they’ve taken to the internet, or to the I.T. revolution, which they were obsessed with as well. They were really focused on just trying to integrate the internet and I.T. infrastructure into basic services — education, health care, government services — and I think they see something similar with A.I. now.
One kind of thought experiment I often think about is: What would be the signs that they were trying to do a secret A.G.I. program? And one of the signs I think would be about those Nvidia chips that I mentioned earlier.
Right now, Trump has relaxed some of the export controls and allowed H200 Nvidia chips to be sold to China. Those are better than what China had gotten before, but not the very best. And China has basically said, “Thanks, but no thanks.”
The A.I. companies in China, to be sure, really, really want those chips. But here’s the divergence: Beijing doesn’t necessarily want to be dependent on the U.S. They want to bolster their own semiconductor program.
So if they were really sprinting today for A.G.I., I think they would’ve gobbled up those chips as quickly as possible, not knowing when that window might close. That is one indicator that they are kind of seeing this as a medium- to long-term bet.
Douthat: So there might be people at DeepSeek who believe in the superintelligence future more strongly than people in Beijing?
Chan: Yes.
Douthat: The closer you are to the machine god, the more its voice whispers in your ear.
Chan: [Laughs] That’s right. Yeah, I don’t think that Beijing is A.G.I.-pilled.
Douthat: What about espionage? Which obviously played a big role in the early Cold War arms race, with nuclear secrets. Is there an equivalent spy-based solution for China if the U.S. seems to be pulling too far ahead?
Chan: So there is something called distillation. That’s where you take a weaker model and you actually train it on the outputs of a stronger model. Distillation is a common practice for A.I. developers when it’s done with full knowledge and full disclosure and total authorization.
What seems to be happening now is that some of the Chinese A.I. labs seem to be distilling on American A.I. models without authorization, and they’re using a number of different proxy accounts so they can get around efforts to block these campaigns.
Douthat: But that doesn’t require stealing secrets from Anthropic. It just requires using the Anthropic model in a way that you’re not supposed to be able to use it.
Chan: That’s right. It’s sort of its own category. It’s not quite outright I.P. theft — it’s not like taking the source code from Anthropic or OpenAI. It harks back a little bit to an era where Microsoft was always trying to cut down on black market copies of Windows and Microsoft Office.
Douthat: Does it work in the sense that you can have a Chinese Claude distilled that works as well as Claude?
Chan: It can help somewhat, but you need to have that foundation to start with. I think that this is probably one area where it’ll still be hard to get concrete data on exactly what the net effect is.
I would say, if you or I were building a model from scratch, we would not be able to use distillation as a way to catch up to the frontier. But if you were one of the better Chinese A.I. labs, you might be able to use some of this to improve your model, especially on areas where you’re weaker. On coding, for example, you might be able to use Anthropic’s Claude models to support your long-term coding capabilities.
So there is that aspect to this whole A.I. race.
Douthat: In a world where there is some kind of take off, one of the theories that animate the American A.I. companies is the idea that at a certain level, the A.I.s start training the new A.I.s, and you get this acceleration where suddenly, being three or six months behind, it becomes impossible to catch up. Again, this would be the theory.
Suppose that takeoff starts to happen. Does China just invade Taiwan?
[Chan laughs.]
Douthat: Well, seriously. It’s just a fascinating circumstance in which you have a kind of arms race. Maybe China doesn’t think of it as an arms race, but it is sitting next door to a central hub in the supply chain that makes the arms race possible. Is that the natural Chinese move in the event that they seem to be falling incredibly behind?
Chan: Ironically, if that were really starting to happen, taking over TSMC would be a move too late. The chips are already made and installed and are already running and training the models and feeding into this feedback loop in the United States. At that point, all bets are off, and you’re kind of out of options for what to do.
The big question here is how fast that can happen and whether this could happen without being detected. There’s always speculation about whether there is a version of the latest A.I. models that hasn’t been shared or even disclosed to the public in, say, the U.S. or maybe even in China, where they have gotten the inkling of this recursive feedback loop that will lead to this superintelligence explosion. So that question is sort of hard to know, and then, how quickly can you actually get there?
Douthat: I want you to be prescriptive for a moment, because we’re having a summit. We’ve been talking about what China is doing, how China is thinking, and so on. What does all of this mean for the United States in terms of our policies? Does it mean that we should treat China as a fundamentally more benign actor than our current policy treats them? Or is it an indicator that, in fact, our policy is working by shaping a Chinese perspective that is not as engaged in the race as it could be?
Chan: I think at this point, what we should do is take a step back from this all-out-race framework, because right now, that race mentality is driving a kind of recklessness, I would argue, from the American side.
To bring up the threat of Chinese A.G.I. — we should think about that, but I don’t think that that’s what they’re so focused on. But if we’re only focused on that, that means we need to get rid of the guardrails. We need to not bind ourselves. We need to not have any kind of regulation or restrictions. We need to have as many data centers as possible everywhere.
Right now, that approach is starting to run into some problems in the United States. And whether you’re talking about the backlash to data centers, or you’re talking about some of these models now getting so capable that they might not be at whatever A.G.I. level, but they are at the level of potentially causing greater damage, either in terms of cyberattack capabilities or maybe even in terms of augmenting what a relatively unsophisticated group could do with bioweapons.
There are all these sorts of questions that the A.I. community has been talking about for a long time. But certainly, for the Trump administration, if you recall JD Vance’s speech last year, where he said basically we should not have hand-wringing over A.I. safety slow down the progress of American A.I. development. In other words, in this trade-off — and he viewed it as a trade-off — we should err on the side of going faster rather than putting on a seatbelt.
Now we’re reaching that point where we need to think about still making progress as fast as possible, competing with China, making sure we do have the best A.I. models — we can keep that. But does it have to come at the expense of wearing a seatbelt or having some basic safeguards?
Douthat: Would you also suggest that the U.S. should adopt a more Chinese vision of the goal of diffusion and building the best possible A.I.-enabled technology right now?
A different way to frame this is that the U.S. and China are in a race, but China thinks it’s running a race to build the self-driving cars and the robots that every single country in the world will use. And the U.S. will be stuck sitting here with its pretend machine god while China sells to India, Africa and Latin America successfully.
Do you think the U.S., in being less breakneck, should also be pivoting to a strategy of, essentially, integration and sales?
Chan: Yes. I think we need to focus a lot more on deployment. One of those areas is actually open source, which, because of the commercial incentives, is not a high priority for the top American A.I. labs. They’re focused on selling access to their models through subscriptions, through A.P.I.s.
The thing is, that open source approach has been really, really powerful for these Chinese A.I. models to gain adoption — not just in China, but around the world. So it feels like right now, the U.S. is ceding a really important channel of competition.
When it’s so expensive, it can be the most powerful A.I. model, but you don’t want to pay for it. That can put limits on your growth.
Douthat: Do you think you get that shift organically if there is a slightly stronger regulatory hand? Again, the U.S. has “industrial policy” — I put it in quotation marks — but we don’t have the kind of steering of economic strategy that China has.
So it’s not like you can say, “Oh, the United States should be more focused on deployment,” and there’s a button to push in Washington, D.C., that makes that happen. But do you think it would happen naturally if it was a little bit harder and a little bit more challenging to maximize compute and capacity for existing A.I. companies?
Chan: I think there’s a way to tweak the incentives in a way that is not like the Chinese approach, that is not about a top-down steering of the whole industry, but is more about trying to create maybe some of that commercial or even research space for, say, open source models.
You can think about a number of different markets where this is happening, where there’s a focus on the high end of the market, on consumers or businesses that are willing to pay a lot, but there’s less focus on mass adoption and that broader marketplace.
And we’re seeing some of this. I should be clear that Nvidia is trying to release open source models. They have a commercial incentive because the more A.I. gets adopted, the more their chips are needed. So there’s that closed loop there. And Google DeepMind has some relatively good open source models.
But the commercial incentives as they stand are not quite there.
Douthat: Do you think we should sell more chips to China as a sort of token of a different model?
Chan: It’s a very difficult topic because anyone who tells you yes or no on chips to China is really flattening the whole story.
On the one hand, you do have real near-term effects on China’s ability to produce the most cutting-edge A.I. models. So by limiting chips, that does slow down China’s A.I. development in the near term. And that can be useful, for example, for giving our companies that edge in cyberattack capabilities. With Mythos coming out, even a few months of being able to test on our own systems first is very useful, versus a Chinese model having this capability and they’re testing on our systems. So that’s important.
At the same time, there’s the other side of this whole equation, which is accelerating China’s own chip development. That’s an area that they’ve been really focused on, and they’ve been focused on because of our export controls. So it cuts both ways.
In the near term, it will slow down their A.I. development. In the longer term, it could speed up at least their ability to have a more resilient, self-reliant semiconductor supply chain that is not as affected by U.S. actions.
Somewhere in there is a sweet spot, and it’s really about where you draw the line rather than just saying more chips or less chips.
Douthat: And also, how short timelines are overall.
Chan: Absolutely.
Douthat: And I’m just going to make the hawk’s case against your case and see how you respond.
The hawk says: Look, we’ve been at this for an incredibly short amount of time. Since ChatGPT appeared during the pandemic, there’s been tremendous acceleration. The people who have predicted acceleration keep being vindicated.
And yes, if you’re talking about a 20- to 25-year time horizon for the point at which you sort of hit maximum superintelligence capacity, then yeah, you have a lot of room to figure out the optimal regulatory balance and all of these things.
But if you’re talking about two to four to six years, then maintaining a three- to six-month lead over your leading rival — who, by the way, is an authoritarian government — seems like it may be really, really, really important. And the slowdown that you’re advocating is one that could give up that advantage.
So how would you respond to that kind of argument, which seems to be the mind-set that certainly not just people at the Pentagon but a lot of people in Silicon Valley have?
Chan: So that timeline comes up again and again in so many different debates within the U.S. as it relates to the U.S.-China A.I. competition. And fundamentally, it’s impossible to say how that timeline will play out.
So, for example ——
Douthat: I’ve discovered that in interviewing people.
Chan: [Laughs] Yeah, people will squirm on the timeline question.
Douthat: Yes, it is impossible to say.
Chan: I mean, then it really boils down to what your views are about this A.G.I. timeline and how likely this is to happen.
Another factor that I will throw in there, as a thought experiment, is: Imagine that China did have access to the most cutting-edge American A.I. chips. Would they be more A.G.I.-pilled? Would Beijing be more A.G.I.-pilled? Forget about DeepSeek or the actual tech founders themselves.
Even on that, I’m not so sure that they would be so A.G.I.-pilled. My guess would be that they would certainly try to deploy better models, but they would basically run their current playbook, just amped up a whole bunch.
Douthat: But even their current playbook includes cyberwarfare. You just mentioned the fact that an advantage of just three months in the deployment of a cyberwarfare-capable model like Mythos makes a big difference. So it’s not as though the current Chinese playbook is sort of innocent of conflict with the U.S.
Chan: That’s right. That’s why I see it as different sets of risks. One is this A.G.I. risk that you’re talking about. That, I would argue, has been sort of overblown.
What I don’t think has been overblown — and in fact, maybe even underestimated up until recently — is the cyberrisk and the biosecurity risk. It’s kind of crazy to say this, but those are more medium risks relative to the A.I. catastrophic total takeover by superintelligence.
Those more intermediate risks I do worry about, and I do worry about U.S. competition vis-à-vis China. That would be, in my mind, a reason for maintaining the export controls that we currently have, and not fiddling with them or agreeing to these side deals with Xi Jinping, for example. So that’s why I try to find that balance.
But in terms of the A.G.I. question, that’s where I’m just less convinced that we’re really all in this sprint — that China’s really all in this sprint — for A.G.I.
Douthat: But even on the medium risks — which I agree seem to be the most plausible risks — you are then making a calculation where you’re saying: What am I most afraid of? Am I most afraid of China with the capacity to do unprecedented cyberwarfare against the U.S., or a rogue A.I. or disastrous A.I. model that crashes the entire U.S. power grid for some inscrutable A.I.-related reason?
It’s that balance that you’re worrying about.
Chan: Yeah, exactly. And it comes to this question, too, about how the U.S. should engage with China about A.I. If we are focused just on China’s cyberattack capabilities relative to our own, then you might say: Don’t bother engaging. We’re both in this arms race, essentially, on cybercapabilities.
But if you’re thinking about the rogue agent or, say, a nonstate actor using either a set of American models, a set of Chinese models, or maybe they do arbitrage — I mean, this is sort of like 4-D chess, where they are deliberately playing this geopolitical competition against each other and trying to distribute an attack across all these different models in order to disguise their origins.
Those are areas where I do think that, one, it would be useful to talk to the Chinese side about these, and two, where I think it would be in the U.S. national interest. It wouldn’t just be about binding ourselves and slowing ourselves down relative to China. It would be about this extra third factor that we want to take seriously.
Douthat: And this is a good place to end, because a lot of people in Silicon Valley will say: Oh yeah, in theory, we could engage with China and negotiate a sort of mutual A.I. slowdown. But in practice, either it’s not clear that China wants to do that, wants that kind of negotiation, or it’s just unimaginably complex to verify some sort of A.I. control agreement in the way that we did with nuclear missiles during the Cold War.
Do you think a Cold War-style ongoing A.I. control negotiation with China is possible?
Chan: I think we should not have high expectations. I certainly don’t.
I think that we should start by talking. We should start by sharing our approach to A.I. safety and A.I. risk mitigation. We should try to convince the Chinese to take this more seriously — and they are starting to take this more seriously.
We should also have a discussion about open source models, actually. As those get better, on the one hand, we want those to diffuse more, but on the other hand, they could also pose a risk if they get into the wrong hands.
So we can talk about all those areas, but I would be very hesitant, certainly at this stage, to even think about binding constraints, verification agreements, a kind of arms control treaty for A.I. between the U.S. and China.
At this stage, it’s way too early. Let’s just start talking.
Douthat: If it’s too early for that, is it just because of the sheer difficulty of imagining such a thing? Or is it a dynamic where precisely because Beijing’s attitude is that we’re not in some Cold War-style race, they’re actually less interested than they otherwise would be in that kind of negotiation?
Chan: I think overall it really boils down to one thing, which is an extremely low degree of trust between the U.S. and China, and an unwillingness for either side to subject ourselves to invasive verification, monitoring and surveillance by the other party.
And yeah, there could be interesting technical solutions that would make that more feasible, but it boils down to this geopolitical reality where we don’t trust them, and they don’t trust us.
So we might be able to make progress on areas that affect both of us, but when it comes to letting, say, Chinese regulators come into the U.S., or letting American regulators go inspect data centers in China, I think that is pretty, pretty far out there at this stage.
Douthat: Do you think that that only changes on the far side of some disaster, conflict, some sort of event? Because one theory that I don’t just toy with, but I guess I hold, is that a lot of the negotiations around nuclear weapons were only possible because they’d been used, and people were aware of how destructive they are. Is there a world where the only way that the U.S. and China come to terms is a world where something tragic has to happen first?
Chan: Yeah, that’s a scenario I think about too. And I think about what would be the level of incident, and what could the response be. You can think about a most extreme case where you have some major cyberattack incident, or even a bioweapons incident related to A.I., where there are real lives at stake, for example. That could cause both countries to just unilaterally put a pause on all of their A.I. development because they realize that this is such a big issue with such huge risks. That is possible.
So I do wonder and I do worry that we might be waiting for that incident to happen before we take action in advance, before we even start to talk to each other about how to take action.
Douthat: All right. On that somewhat dark note, Kyle Chan, thank you for joining me.
Chan: Thank you.
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