The ‘But China!’ Dilemma Driving the A.I. Race

[MUSIC] Look, I don't know if you are following every article, every tweet, every blog post from the labs right now on AI. But it's frightening. Like, we are in a frightening place. Open AI says its AI system hacked another AI company on its own and what the company called an unprecedented cyber incident. Turns out it may be much worse than we thought. People don't know that in this investigation, there is a third round of the hacking in which it hacked open AI itself. It happened again this time, it's anthropic. Meta is now the latest company to say its AI agent broke past the guardrails. And so there is this growing sense, we actually need to do something. We need to pace the frontier. We need to slow all this down. But if you talk to anyone in Washington about this, or you talk to anybody at the ILOBS about this, you just crash into the shoals of, well, what about China? If we slow down, we will lose the AI race to China. And as dangerous as it is to build these things we can't control, it is even more dangerous to have them in China's hands if they're not in hours. They're going to be killer robots. Rather than they'd be American killer robots at night, Chinese killer robots. We're on the eve of talks right now between Donald Trump and Xi Jinping. Behind that there could be talks between Scott Besent and his counterpart on the Chinese side that are more tightly focused on AI. The expectations for these talks are not very high, both because of the broad relationship between the US and China. And because neither side really seems to know what they want to do. But these talks are at least to beginning. They are the beginning of relationships and maybe frameworks and approaches that if things continue to get crazier, an action is needed. Maybe they are a platform we can stand on. So I wanted to talk to somebody today who's an expert on China and AI. How they regulate it, how they approach it, the relationship between China and America on this topic. And also somebody who's thought about what talks like this could achieve. What is realistic within the operating frameworks of the two superpowers. My guest today is Matt Xi'an, a senior fellow at the Carnegie Endowment for International Peace. He has been closely following and studying China's regulations and governmental structure on AI. He's been involved in US China AI talks. He has a great sub-stack on these topics and he's the author of the 2019 book, The Trans-Pacific Experiment. Her China and California collaborate and compete for our future. He joins me now. Matt Xi'an, welcome to the show. Thanks very much for having me. So the dominant metaphor for the relationship between China and America on AI, that exists in Silicon Valley, that exists in Washington DC, is metaphor of the race. And the ending of this race is superintelligence. That some company or some country is going to have the moment where they're hopefully well aligned, safe model, moves into a cursive self-improvement and goes, right, this guy used to get called in the rationalist community the fume moment. And at varying levels of explicitness, people in DC and DC seem to have this model in their heads, that we are racing China towards this kind of supremacy. Does China buy this race model? Is that how they see it? And I guess do you buy this race model? Is that how you see it? In terms of does China buy this race model, it's definitely not the dominant paradigm that has been informing AI policy across the country writ large. And it doesn't have like the chokehold that it does in the US. In China, it's like, huh, okay, that could happen. That's a potential technical path forward. We're kind of looking for evidence on this. We see that America is very concerned about this, but China is not taking the steps that you might think they would take if they were sort of ultimately like laser focused on that type of thing. You know, China is very constrained on compute. They have far, far less compute than the US. If they have, I mean, some estimates are they have one eighth the compute of the US, maybe one tenth of the compute than the US does. That's what ships the GPUs that all of these programs, programs, AI's are trained on and then run on. Exactly. And you know, most people think that one of the key determinants of how powerful your model is is how much compute, how many chips are using to train it. And with China being so compute constrained, if they were really just laser focused on this massive takeoff scenario, you might expect them to start consolidating all that compute, make your bet on, you know, deep seek or another company and go from there and we haven't seen that. Actually, the in terms of the major AI policy documents that have come out, they've taken a very diffuse approach to compute. They said like our number one concern is AI applications and we want to incentivize every mayor, every governor, every state and enterprise. We want you to look for ways to apply AI to manufacturing, apply AI to your traffic lights, apply AI to upgrading your robotics industry. And those actions of focusing on applications and really diffusing your compute throughout the country are not what you would expect for a government that is laser focused on this takeoff. It's possible that changes, it could change very quickly. And you know, I think as America keeps like beating this drum louder and louder, some people in America beat it louder and louder, you have to imagine that it's going to seep into their consciousness in that way or seep into their beliefs about the way this is going. But so far we have not seen that evidence. So every single conversation I have with politicians, with AI lab leaders about regulating the frontier of AI always falls apart on this but China problem. Maybe there are things we could do to regulate the pace of the frontier here in America but China will race forward but China will create recursive self improving AI and either we have the same dangers that we would have here but now it is under control of competitive foreign country with a very different political system than ours. So how do you see the the butt China conversation and the butt China problem. There's a reality to it. You know, we this is a competition. These are the two leading countries, the only two countries that really matter at this point of time. China is not that far behind and they have outperformed kind of all of our expectations along the way. And so the idea that you just totally surrender competition you surrender the playing field to another country that's a geopolitical rival and that probably has less safe AI practices than you like that's that's not a good idea to just abandon the field. But there's also an irony in this in that especially when we're talking about regulation of AI like China has had the world's strictest most comprehensive most burdensome AI regulations on its companies for three or four years at this point in time. And it's during that period of time when they had these heavy and may always burden some regulations that they did a lot of their catching up. So the idea that this is just a total binary of like any obligations you put on companies automatically puts you behind this totally wild unconstrained Chinese jug or not. That is just not true. That is not based in reality. So two things there that can sound like they're in conflict one is that China's AI practices are less safe than ours. The other is that China has a much more burdensome severe intrusive regulatory apparatus. So tell me a bit about what they are doing that is so much stronger than what we are doing from a regulatory perspective. And then why you also say that they are in a less safe place than we are. Most of Chinese AI regulations the early ones especially were really focused on online content on information. You know when the CCP encounters a new information technology the first question is always how is this going to affect our controls on information. And so when AI came into the picture that's what they looked at they looked at recommendation algorithms and they said why is everybody getting their own news feed why can't we sort of set the news agenda. So they regulated recommendation algorithms they looked at deepfix with sort of obvious implications there they regulated deepfix. They looked at generative AI and they did the same thing. And these do impose like real costs on the companies the companies have to do mandatory pre-deployment testing. They have to file their sort of safety report cards with the main regulator in China. It's like a real burden of time and money and effort on the companies. But most of that work especially say 2022 through 2024 was really focused on securing the content environment what we would call censorship obviously. From 2024 on they've kind of expanded the scope a little bit and they brought in new concerns they started regulating AI companions. So they're concerned about like the psychological impact on kids they're concerned about over reliance and sell. harm. But all of this so far is not focused on the type of frontier AI safety risks that are really the focus of a lot of people in Silicon Valley on loss of control, on, you know, bio uplift, chem bio weapons stuff like that. That's coming into the Chinese conversation now, but it's coming in much later. It's a much less mature ecosystem over there. And it really kind of needs to get up to speed. What you'll hear, at least in America sometimes, is that much of the closeness in the race comes from China in different ways, generously building a top or models, less generously sort of stealing them. The key term here is distilling their ways to train a model on the answers another model gives. So if that is true, then it's not just like a race, like a race in which like the people are tied together. So like the faster America runs, like the faster China is going to run, because it's actually amazing in America to how close a lot of the different labs are, you know, they're just like always like a month or two around each other. How much do you buy that everybody's bunched up? Because in fact, the race is governed by the leader dragging everybody else with them? I think it definitely plays a role in maybe a pretty significant role. And just for audience to visualize this, I saw a great meme of this where it's a speedboat pulling like a what we call a wake surfer, you know, a person behind who's essentially, you know, trailing behind the boat going over the waves and the people on the boat are like, they're so close, we need to go faster. Yeah, right. Pulling them along with you. I mean, this is one of the arguments people are making about, you know, when all these AI labs in America, like, well, we can't possibly slow down because China will speed up but China's going so fast because you're going so fast. Maybe if you slow down, China would be going so fast either. Yeah, at least in part, I think we can say the distillation probably plays a significant role in cutting into the US lead. My sort of mental model for it is, you know, China is so short on compute and that distillation is probably a way for them to essentially like train more efficiently, increase the intelligence more efficiently given that they have so little compute. So it's essentially making up for one of their biggest shortcomings. I don't think that if we suddenly found a way to block all distillation of Chinese labs would just stagnate, you know, China has, you know, in AI nuclear weapons in almost every technical field over the last 30, 40 years, they consistently outperform expectations and just do things that we don't think they should be able to do given their level of economic development and their capabilities. They have an amazing AI research ecosystem over there. Like one of the reasons we're ahead is because we keep taking Chinese AI researchers and employing them in our labs like we are, you know, siphoning off a lot of their top talent. So they have a really thriving ecosystem on its own, but I do think distillation plays a big role. You know, it might be the difference between six months and a year. It might be the difference between six months and two years. We don't know, but I think the fact there's pretty strong evidence that the Chinese labs are doing it and they wouldn't be doing it if it wasn't to their benefit. What is China see us on AI? How do they see what our goal actually is, what our goal is vis-a-vis them? So as we talk about this question of can these countries cooperate if they need to, what is China's perception of America's AI industry? I think the number one perception is that the U.S. wants to hold China down and wants to constrain China, you know, especially with the export controls, which came under Biden, I should say. Yeah, export controls under Biden on these advanced chips, China's use itself as being sort of boxed in by this hegemon that wants to kind of keep China in a permanent position of subservience. That's a kind of a meta-narrative across Chinese modern history and it's one that's crystallized in AI. So I think in some ways that's the first thing. Another element is they see us often as being pretty irresponsible, deregulatory, just let it all rip, let it all hang out. They'll, they see sort of chaos within our government. They say oftentimes in actually in the, that's crazy because it looks so orderly from here. In the, in the run up to these potential AI talks that might be happening in the next couple weeks, China is issuing sort of op-eds by its state media where it kind of lays down its markers. It tries to position itself in advance of the talks. And one of the markers that they lay down is, you know, America wants to lecture us. They want to tell us what is a safety risk and what isn't. They want to define all the stuff unilaterally and they don't even impose any requirements on their own companies. So don't come to us with that stuff unless you're going to take care of your own house. Doesn't seem totally unreasonable to me. Not totally unreasonable. Self-serving in a way, but yes, I mean to, but I think it's interesting. I mean, this is a point I was made, but to China, we look like the ones who are not regulating AI that, you know, there, there might be this whole discourse and the countries need to cooperate. But in fact, what they see is us racing forward trying to attain AI supremacy before them. And kind of in a weird diffuse way calling for regulation of something that might like destroy all of humanity, but we're not actually doing any serious regulation of the thing that might destroy humanity. And you know, when I read some of these state op-eds, you're talking about the way they end up framing it is insincereity that I never know how, when I talk to people in Chinese government, their sense of American politics is actually not often as sophisticated as I would imagine it to be. Maybe I don't get to talk to the right people. And I think sometimes they look at us and assume that the things that are said are have a more orderly structure. And in the way that everybody has to use she's language there. But if you look at a thing that doesn't make sense and you come from their perspective, well, maybe the reason doesn't make sense is the counterparty is not serious. They're just making a bunch of different moves that are all different forms of a strategy to stay ahead in the race. As a macro perception, I mean, I see this all the time talking to Chinese about the US political system, talking to Americans about the Chinese political system is if you don't understand the system at a pretty kind of ground level, if you don't have an intuitive feel for the two systems, the tendency is to look at the other side and to connect a bunch of dots and see a grand conspiracy. And it usually is a conspiracy against you. And you know, the CCP has a very conspiratorial view of the world. They see conspiracies everywhere. And you know, there have been times when the United States and other countries have conspired to hold down China. But the way that they will connect dots that from a US perspective are just wildly unconnected is, you know, it's worrying. And we basically do the same thing over there. We do not have the ability to see through rhetoric that's like, that's just what they have to say. As well, they have to say to start. And then the real protein, the real like meat of this conversation is here. That's the real signal. So that's kind of a permanent issue in US China, mutual perception. I don't know from the Chinese perspective. And frankly, from the American perspective, as somebody who does have a good ground level intuition for who is saying what in our system and why, you know, the Biden export controls on ships were like quite explicitly an effort to maintain AI supremacy for America, which is not a crazy thing to do for a country. But if you're the country that is being denied the exports, I think that's a little bit provocative. And then Trump comes in, right? You know, Oriental's entire trade war against you. You know, Dario Mede, the leader of arguably the most important American AI company in Anthropic, he had this big essay on the adolescents of technology. And he says of China, they have hands down the clearest path to the AI enabled totalitarian nightmare I laid out above. It may even be the default outcome within China, as well as within other autocratic states to whom the CCP exports surveillance technology. I have written often about the threat of the CCP taking the lead in AI and the existential imperative to prevent them from doing so. So, you know, from China, and I see the leader of, you know, the frontier AI lab saying it's an existential imperative to keep China down. I really worry about this atmosphere, being the one in which these momentous technologies are potentially being developed because when I talk to American policymakers, they don't really feel like they understand what is happening in China. And there's a lot of skepticism that an agreement would necessarily be verifiable or followed. And I think China's pretty good reason to be skeptical of what our real intentions are. Our intentions to make AI safe for everyone or our intentions to make sure America is a first one with the AI that ensures American dominance of the global order for another hundred or 200 or 500 years. And that kind of measma of mistrust is a tough space for negotiating. It's a very tough space. You know, I mean, this is arguably one of the worst times for a technology this moment is to be coming online. You know, at a real moment of deep geopolitical competition between two superpowers that distrust each other, that have, you know, interests that are fundamentally in conflict in some areas. I think one of the key points here is, yes, trust is good. It is kind of the lubrication that can ease things along in a negotiation. But it's not going to be the thing that makes this work or not. Like the thing that makes this work or not, in my opinion, is going to be weather or not both sides for their own. own reasons genuinely believe that this is a potentially catastrophic risk and believe that they need to take action on that for their own safety and security. The Chinese technical AI community needs to believe deeply in its bones that if we push into this area without the right safeguards and testing and evaluation, then we run a real risk of losing control of this technology and Xi Jinping, other Chinese leaders, they do not want that in their own country for their own reasons. And so I think that is the area of mutual interest as opposed to mutual trust that things have to be built on. So when I'm looking at U.S.-China interactions in this space, I think that one of the most important things, at least as a starting point, is can we build up a mutual understanding of the risks? Can we share information? What are we seeing about emerging risks and how do we test for those risks? What are the best practices for securing a model that has cyber capabilities that you don't understand? What are the best practices for sort of defanging a model that might have bio capabilities that you don't want? Like that work in the United States is just much more mature. The regulation in the U.S. is much less mature, but within the companies, within the labs, this has been worked. They've been doing seriously and investing a lot of money and a lot of people and resources in for a long time. And that's kind of one of the misunderstandings in China is they look entirely at our regulatory ecosystem and they say you're not doing anything. Whereas the labs here are voluntarily doing far more on this than the Chinese labs are doing in sort of a mandatory, regulatory environment. So with all that work and that knowledge that we've gained, can we find ways to safely share some of that with China to essentially seed bolster and help grow their existing AI safety ecosystem? Their technical AI ecosystem over there that is concerned, wants to do good work on this, but is just starting five plus years later and just has invested far less people money computing resources in that work. So you have some personal experience here. You've hosted some of these China, U.S., AI dialogues, not the official high level U.S. government ones, but these more informal ones that are, I think it's some way supposed to help lay long-term groundwork for this. What have those felt like? What have you learned from interacting with Chinese colleagues and counterparts? Give me some of your texture on this. These conversations are normally very very technocratic, maybe a little bit boring, productive, but calm affairs. I think one thing I've seen a couple times when you get a little bit of heat, a little bit of spark in the conversation happens, oftentimes when the Americans are pointing at these trend lines in AI and bio or these emerging safety issues scale, you know, say like, look at this, like why aren't you more concerned about this? Why aren't you doing more on safety? And you'll see the Chinese side getting actually really frustrated and be like, you guys don't get it. We are doing a ton on safety. We have these AI companion regulations. We have mandatory labeling of AI generated content. We have all of these rules and regulations. We're just not the exact thing that you want. And I think that's a lot of the disconnect between the two sides. The Chinese side feels like they have been doing serious work for a long time and it's just not seen, it's not understood or not respected outside of the country. If you're not a subscriber of The New York Times, we have some news for you. You can outspour the times for free without any paywalls at all during your first month in The New York Times App. I think talking about this requires some sense of how China's AI industry differs from ours. How do you describe the difference between what the sort of culture and approach of the frontier AI makers in China is compared to sort of the anthropic open AI, you know, Google DeepMind here? So I think among the most frontier labs, let's say that's where the cultural similarities are the closest and it's much more of the broader AI industry in the policy ecosystem where the differences are much wider. And I'll start with that sort of broader ecosystem and then kind of bring it into the frontier. I think in many ways in the US, a huge portion of the AI industry in the policy world really started from this idea of one day we will reach super intelligence. That is the goal that we'll bring with it catastrophic risks that will require heavy focus on safety. And it's been this magnet that's like drawing us into this future. And that's of course, especially true at anthropic and at open AI and DeepMind, but I think that's a pretty significant portion of the policy ecosystem here too. And so it's almost like it's like this teleological thing of we're endlessly being drawn towards that. And in China, there are some people, a couple of people who lead the frontier labs who have that take, but in terms of a broad culture throughout the industry, the investors, the engineers, the policy people, the government, that has not been this kind of magnet drawing them into the future in the same way. It's more like they've been developing an industry, developing applications of it. As they develop a new application, they develop new policy to deal with that. It's a pretty fundamental difference in the way that ecosystems have kind of grown and expanded. I've thought about this a lot in the American context, something I sometimes say to American policymakers who are like, where do you start? I was going to say, well, you start by starting, that you learn how to regulate things, you learn how to legislate on them, by regulating and legislating on them. And the Chinese approach that they already are learning day by day, how to interface with your labs, how to craft regulations and revise them, that they're building up practical regulatory experience, that then if or when they need to come in with things that are much more potent, they sort of know how to do that. And so it's not that their regulations are what the American frontier labs believe are needed or what I believe are needed. They're not. I would like us to be more thoughtful about AI-compining bots than we've been, but that we're actually behind and practical experience here is meaningful. In some ways, it's more meaningful than like neat conceptual arguments about, you know, the worst case outcomes. You know, one of the characteristics of Chinese policy making, but especially in AI is that it's very iterative. They'll roll out a regulation, they'll see how it's working, they'll roll out a technical standard that specifies it, and it's not quite achieving in that they want, and they'll roll out another regulation that basically just overlaps on the first one. And with each one of these, they've built up these reusable regulatory tools. So the main one is this registration system for AI models, and that the CAC, the cyberspace administration of China, the main regulator there, needs to be able to read, needs to be able to understand, in some cases, maybe do the tests on their own. You know, when they first started this in 2021, they were, the regulators were, were totally out of their depth, like the CAC is traditionally an internet regulator. It's focused on, you know, content and political content, stuff like that. But that was four years ago. And you know, that, like I said, it has been focused initially all on this controlling content. It's now been expanded. These other areas about, you know, emotional dependency around sort of labeling of content, you know, can you impose and then remove a label on AI generated content, stuff like that. But they have been sort of constantly in touch with the labs for now about four years. So that's, that's a lot of regulatory practice and regulatory muscle. Part of the question is, with these frontier safety risks, is that something that you can kind of just easily tack onto this? Is it just another test that they run? Or is it something significantly more complicated? And I think it, you know, it's kind of in between the two. Like they have a lot of mechanisms, they have a lot of habits and, and touch points that are very good, but they do need to increase their technical capabilities in these specific areas of frontier risk and control. Tell me about the way in which China's evolved to emphasize open-weight models versus our main models, anthropic, open-air, or closed-weight. And maybe begin for people who don't know those terms by defining them. Sure. So closed models are the way that when you use chat GPT or Claude or Gemini, those are closed models and you interact with it kind of on the company's terms through their portal. You cannot sort of edit the model. You cannot download it to your computer and run it yourself. An open-weight model can be downloaded from the internet. And if you know how to do it, you can play with it. You can tweak it. You can remove safeguards. You can add new capabilities. You can sort of tailor it to your own purposes. If you need to use a model, you need to make thousands or tens of thousands of calls of it every single day to run your own startup, you do not want to be paying anthropic and open AI for every single one of those tokens every time you ask the model a question. And this has been a divide that's really emerged starting, especially in like 2024 or so, where it wasn't always a given that this is how the two ecosystems would develop, but the way it has developed is that Chinese labs primarily release their models open-weight and the US labs primarily release them close-weight. So at the very beginning, sort of in the aftermath of chat GPT when China's first regulating in generative AI. They actually started off taking a relatively cautious approach to open-weight models and putting regulatory burdens on them that would have made it much harder to use open-weight models in China. And the reason they were doing that is because at the time the primary, the leading open-weight model was Lama from Meta from Facebook. And China was worried, you know, our Chinese developers going to take in Lama, they're going to build their applications on top of it and it's going to kind of poison our ecosystem with their information that we don't want. But over the next year or so, we saw a couple of the leading Chinese labs decide to release their models open-weight. And you know, deep-seek was really the big kind of kaboom moment in this in that when they released it open-weight, it took the world by storm, storm, you know, the entire global AI community was able to actually play with it and look at it and see that it really is that impressive. And since then, it's kind of snowballed from there. And I think in some ways the CCP might have stumbled into this outcome, but I think they're pretty happy with it. And it makes sense both for the companies to a certain extent and for the government. Doesn't it make it harder to control these models? One thing going on in, and I mean, this is a debate in the American AI ecosystem where, you know, Dario Amade, and Sam Altman, and like, they often fight with Mark Zuckerberg over this. There's a view that when you get these very powerful models, like something like, you know, mythos, which has these incredible cyber hacking implications, you don't want anybody to be able to just download mythos and do what they want with it. I mean, these are potent things you need to have some control over them. The CCP is, it both has a more aggressive regulatory stance and is more control obsessed than the US government tends to be. And yet China is the center of the open-weight ecosystem. Like, how do those things hold together? And I think one factor is like, what was needed for the companies to be seen as globally competitive? You know, I think if it deep-seek in late 2024 had just announced to the world, hey, we've got a great model. Man, feel free to use it. It'll go to Chinese servers and we'll give you back the answers. I think there would have been a level of suspicion about that. I don't think it would have seen this rapid global proliferation because people have a certain distrust of Chinese technology. And by releasing it open-weight, they can essentially say, hey, you look at it, you change it, you do whatever you want to it. It's that good, and you will see that. And then we'll figure out how to make money other ways. I think that's part of the business aspect to this. It's hugely reputation enhancing for Chinese companies and now for China's AI ecosystem as a whole to release these open-weight and therefore overcome some of the suspicion that normally falls on Chinese companies when they go global. So that's one part of it. Another part is that, you know, frankly, these are probably undermining the future valuation of anthropic and open AI. And I don't think that that was a scheme going back to 2023 or 2024 when, you know, this world we're in wasn't totally foreseeable. Now that they're here, I think it says, well, that, you know, that is to our benefit in terms of long-term competitiveness. Do the Chinese models give extremely different answers or come with very different approaches in the American models is a more fundamentally different world view, detectable if you kind of run testing across the two, right? A more skepticism of democracy generally, you know, I think American models very much do have an American outlook on the world. Do you see the models as being very different when, you know, when Americans are talking to DeepSeek? It depends a little bit on how you're using the model. Like the most censored version of a Chinese model will be when you're using it through the app or you're using it through the API when you're going to deepseek.com and asking you questions. That's the version that has sort of the most controls built into it. If you download the model, an open-weight model, you download it, you run it on your own computer, you will have a different set of safeguards, not entirely removed, but a different set of them. And you can also tweak those, you can remove some of those, you can add new training data, you can change the way that the model functions. And some American companies like Cursor and others, they feel that they can get the models into a place where they are not, you know, propaganda machines for the CCP. And I think the CCP would sort of say that, you know, there are a lot of countries, Singapore, Southeast Asian countries that are building sovereign AI models on top of these open-weight models. Say, you know, add your own language data, add your own cultural data to sort of post-training these models in a way and tweak them to your needs. So that's part of China's pitch to the world, to the global south is America is the technological hegemon that wants to restrict your access to this technology, it wants to impose its own values, it wants to, you know, blot out your own local culture, and it won't let you, it won't let you in any way adapt or play with their models. We're just giving you the model, and you can do with it what you want, you can change it, you can adapt it, and you can run it for just the cost of, you know, the cloud computing that you're using. That's the pitch, I don't think it's, I don't think it's 100% honest, I don't think it's actually going to play out in that exact way, but that's the divide that China has been trying to pitch to the rest of the world. So I find this really interesting. So the way the internet developed and a lot of the modern mega online platforms developed, China and the US have pretty separated digital ecosystems. I mean, you're not using a lot of Google search in China, we're not using WeChat here, we are much more integrated on AI than we are on what came before. A pretty large number of American companies, which are consuming AI tokens at a level where you actually have to pay real money to keep going. They're using Chinese models, I mean Airbnb, Coinbase are famously using Chinese models for significant parts of their AI infrastructure. And so this is not just like the Chinese ecosystem over here and the American ecosystem over here, they're already somewhat combined. I don't know how much open AI or cloud are allowed in China, because I assume they're not censoring in the way that the CCP would want them to, but the Chinese models are here and in widespread commercial use. Yeah, this is really one of like the great ironies of this current AI moment that we're in. The firewall really came down and kicked out the American technology companies, Google, Facebook, Twitter, etc. 2008, 2010. We had very separated product ecosystems. They were building their own products. We were building our own products. The products didn't really cross over. But we always actually had pretty integrated technology ecosystems. You had a huge flow of Chinese people coming to the US and working in companies. A lot of them would go back and kind of cross-pollinate the two ecosystems with ideas where a lot of American money going into Chinese startups, a lot of Chinese money going into American startups. It was all quite integrated outside of the product layer, up to about 2017, 2018. That's when we began the American project of technology decoupling with China. We want to pull apart these connections because we think this is how China is catching up. It's catching up because they're stealing. It's catching up because they're learning at our universities, etc. etc. The first Trump administration, to a certain extent, the Biden administration, did a lot to cut down the flows of people, to cut off the flows of money, to kind of reduce the flow of ideas between the two ecosystems. That was relatively successful. I think it probably would have continued to be quite segmented in this way, except for the fact that the Chinese models going open weight. It's almost like the open weight ecosystems kind of reintegrated these ecosystems in a way that I don't think anybody could have foreseen three, five, ten years ago. Xi Jinping recently gave a pretty big speech on AI. What seemed new to you in that speech? This speech was at the World AI conference, which is China's premier AI event every year. They try to get the whole world to come out. It's a big to-do. This is the first year that Xi Jinping has attended and given a speech there. It's really as big as AI speech, maybe ever. It's been we're watching very closely. I think a good portion of it was China's pitch to the rest of the world. It's the one I outlined earlier. Then the other part that stuck out to me was the conclusion. It ended with some pretty striking metaphors using an ancient Chinese idiom that I don't have off the top of my head about how the wise adapt to circumstances and they do not get stuck on one path. I think one of the key terms was that we need to be able to act to forest all loss of control of AI. This has been a long-term concern in the West. As AI get out of our human control, it's a long-term concern in China, but one that's taken a bunch of different forms. What do they mean? Are they talking about party control? Are they talking about the control of an operator? Or are they talking about human control over AI? You put down a marker there around loss of control and I read it as leaving this space open. These are all signals to people in the system. When he says forest all loss of control, that means that AI researchers all throughout China, when they're applying for the next grant, they're going to use that term. If you use a term that was in a big she speech, you're just more likely to get grant funding. These things are markers that everyone, the policymakers are interpreting and are trying to figure out how can I do that in my area. Researchers looking for funding are adopting it companies are looking for signals about what will and won't be sort of you know in bounds. So the words really matter and I think that conclusion was at least putting down some markers that are showing that China is shifting pretty quickly on a couple of these fronts. My model of this is that political pressure, political possibility, doesn't build linearly and it particularly will not on AI. That what happens is you have issues, they stagnate, they are not at the front of the agenda and then something happens and the window of possibility blows open. So I think in America here, the open AI hugging face hacks, I mean almost more consequentially, the fact that open AI systems hacked open AI and took over a part of their research clusters, the kind of social engineering and effort to upload malicious code from you know frontier anthropic models, that the sort of swarm behavior, the peer behavior, that this summer of weird AI incidents has blown this open a bit in America and now all of a sudden we're talking about pacing the frontier and and so I guess a question is China knows these things are happening. So how are they responding to these same events that are transforming our conversation? So you're right that they're taking it in. There's tons of coverage in Chinese state media about the hugging face incident, about pretty much all the major safety developments. You know recently there was an anthropic researcher who resigned and who issued these pretty dire warnings like that was in state media today. I was reading that and so they take it all in. They are much more attuned to our conversation than we are to theirs. I think part of it is they they react more incrementally than we do and that's in part I think due to this kind of long term different relationship to say super intelligence and catastrophic risk. For a lot of Americans who have been thinking about this for you know a decade this they've been predicting this will happen and then this happened and it's the it is the ultimate illustration that they were right all along and it's happening. So the Chinese side this is just newer. They're taking it on more and they take the data points like okay that's interesting. It hacked out of a system like was this an issue with the safeguards with the tooling could this have been you know constrained with pretty like mundane security measures or is this a sign of something bigger and it's it's been really interesting to watch over the last year really year and a half at this point as a lot of this safety terminologies worked its way into important Chinese government documents and important technical standards documents by their lead regulator. I think just last week China's main AI regulator the cyberspace administration of China issued sort of a public statement on its top five AI risks and number two on that list included what they call extreme loss of control Ziduan Shukong. That's the first time that I've seen that specific phrase extreme loss of control they'll talk about control ability in the past when they talked about that that was more like party state control ability but around 2021 and then really in 2023 they started talking about human control over AI and now it's it's essentially it's working its way into more and more practical and specific AI policy and technical documents and so they're taking it on board I think they are you know essentially they are moving in the right direction on a lot of this and to me the big open question is like do they move fast enough there was another incident that it didn't I mean it literally made headlines here but it has not been greeted as such a big deal here even though to me it was quite scary so frontier AI models were able to find a vulnerability in WeChat which maybe you can describe for an American audience the centrality of WeChat to the Chinese digital ecosystem and they're able to build this attack on it that they dubbed we worm and it would have given control of somebody's phone just by calling that phone now this was then conveyed to Tencent the developers of WeChat and the according to them the vulnerability has been patched but it seemed like a hell of an example to China that the hacking capabilities here at the point where it could compromise major foundational Chinese digital infrastructure how has that been covered I haven't seen that much coverage of it in mainstream media and that might be because when an American company finds a huge vulnerability in like the central you know digital platform in China it's not really seen as in everybody's interest to publicize that a ton so that might be part of it I think another part of it is that this is a question of like offensive hacking capabilities and I think for them like the real wake-up moment for that came with mythos when they you know the US develops a system that they're not releasing to the public that they're only releasing to a set number of companies and also the NSA and China has to assume at that point that it is being deployed you know far and wide against Chinese systems so a lot of the discussion in the aftermath of that was about how do we harden our own system against these type of cyber attacks you know and in some sense this type of cyber warfare between the two countries is inevitable and long-term and like it's almost like we shouldn't take it too personally China shouldn't take it too personally when we you know hack them in a bunch of ways we shouldn't take it too personally when they didn't that's that's kind of our job and their job it's it's the it's the it's the question of like when something happens a lot a lot caught up in there I'm not going to question it but yeah just just putting a pin that yeah a lot it's it's the job of the NSA and it is the job of the MSS to try to hack each other there should be limits you know critical infrastructure all that kind of stuff but in some ways I think that's baked into both countries world view that we're both going to be using it intentionally against each other the issue is when it's something that's not being done intentionally by a state when it's happening by a non-state actor that neither of us wants these tools in the hands of when it's out of control and neither of us has the ability to sort of understand or control it those are kind of the areas where I would expect you know some level of overlap and it's all that's that's a newer phenomenon that China's grappling with what about recursive self-improvement sometimes we talk about loss of control like it is a passive thing uh I don't intend to lose my keys but I do it all the time loss of keys recursive self-improvement is handing of control over to AI's right recursive self-improvement is where the AI systems autonomously build the next system right that they're now moving faster and improvements than human beings can possibly keep up with we are dependent on the AI system to tell us what it is doing um we're dependent on those descriptions of what is happening being correct um we have seen AI's exhibiting deceptive behavior we see the frontier lab saying our capacity to monitor is already degrading we are seeing a labs that are currently racing towards recursive self-improvement expressing very high levels of concern about what it will mean to achieve this thing that they are desperately trying to achieve it is a very strange situation and then of course when you say maybe you shouldn't do this you get but China now she also keeps talking about how AI should be developed by humanity should be under humanity's control recursive self-improvement is the simplest way to give up human control of AI but to me that's a place where some international standards seem really needed and not like we can wait five years on that because you know the I think the American AI lab seems like going to hit RSI in the next 18 months or so is there like the possibility of cooperation on this or constructive dialogue on this or is something that is outside a crisis point not even plausible within the conversation so like a lot of these concepts or developments RSI is somewhat newer in China like I listen to a lot of Chinese tech podcasts and they just started talking about RSI this summer like midlate summer where I think this has been you know in the conversation in Silicon Valley for much longer than that now they they say wow you know this is the next thing this is where this is where things are going because in many ways as creative and innovative as the Chinese ecosystem is they still do a lot of times essentially look to Silicon Valley for these type of directional shifts you know what is the next paradigm and so with so many of the US labs beating the drum on RSI and saying like this is where it's going I think they're the Chinese labs are kind of following into that space I don't think they have as much experience with it I don't think they've done as much technical work with it or maybe even thought as much about the risks of it I do think that this is one of those places where we might just have to draw a line and whether it is done bilaterally at the exact same time or whether it is something done unilaterally with the expectation or intense negotiation to try to get China to agree to the same limitation, that might be the point. I mean, this is a But would it be more likely to get them to agree to it if we drew that line unilaterally? Yes. You know, if we do it unilaterally, it increases the chances that China does it. You know, it also increases some risks that you do it unilaterally and then China catches up or forges ahead. So that's a double-edged sword and I won't pretend that it's just like the solve all for us to do it unilaterally. But you know, a lot of this is a matter of sending like costly signals. You talked about all of the misinterpretation and sort of conspiratorial thinking between the two sides. And so when we just say a bunch of stuff about the dangers of RSI and we talk about it, we don't actually have any regulations, we don't do anything about it. We are not sending any costly signals and our lead in this technology allows us to have access to information to see threats and to see risks that they just haven't seen yet. They're not going to trust everything that we say. They're not going to trust all the information that we share. But that is, it's a card that we can play in these areas. And whether it's a unilateral pause or it's an information sharing mechanism that we set up now, something where we share information on incidents like the hugging-face incident. You know, if the US and China are going to sit down and talk about AI in the coming weeks, that's a great opportunity to put on the table a lot of information. That's not sensitive in the sense that it doesn't undermine the US lead. But it lays out very clearly and in deeply technical terms like this is what we saw and this is why we're worried about it. Do with that what you want. But this is what we saw. I think that's that's the space that we want to be working in and then we want to be other than just sharing the information on the risks. We want to be trying to plant seeds or enhance the technical AI safety capabilities within China. They really need to catch up. The practices there are just much further behind the leading US labs. The capabilities aren't that far behind but the safety practices are further behind. And so it's in our interest. You know, loss of control if something goes out of control in China, it doesn't stop at the borders there. So it's in our interests for them to have good safety practices and they have a lot of catching up to do. So we're on the cusp of their being talks. They're being led on the US side by Treasury Secretary Scott Bessent. He's got Chinese counterpart. There's also going to be the Trump and Xi Jinping meetings coming up. I think that you've sort of been pouring a little bit of cold water for people and what to expect out of these. But what to you is a constructive outcome here, right? The sort of beginning of a space in which possibilities can emerge and what you would be a negative signal about what's possible. So I think a negative signal would be a statement that sounds good and gestures at something that nobody has a problem with. So if the two sides get together and they say, look, we both care about AI and we both care about child safety. And so we each affirm our commitment to child safety and AI. And it's like, sure, yeah, that is an important issue. But that is not kind of the key issue between the two countries. I think that would be a sign that we hadn't really we didn't really have traction yet, at least. I think what would be positive to me is one, establish this as an ongoing recurring dialogue that will have staff that will sort of build over time. So, you know, maybe the US-China strategic AI dialogue that meets every four months. In the past, we've established these on security issues and economic issues. And you need to have a real structure in place where it's not just a one-off. The next thing I'd like to see maybe two things. One would be a working group between the leading technical AI safety people within the US government and the leading technical AI safety people within the Chinese system. So in the US, you know, there's a lot of bureaucratic fighting over this, but the Center for AI Standards and Innovation, the KC, is really, I think, the center of knowledge when it comes to testing of frontier models. China has a new working group called Working Group 9 that is essentially tasked with developing technical standards related to AI safety, broadly defined, but also starting to look at catastrophic risks. I think something that creates a space where those two teams can safely talk to each other, and exchange best practices say this is what we're seeing. This is what we're worried about. This is how we test for it and this is how we mitigate it. Maybe the second thing would be a crisis communication line, a good way for the US and China to get in touch if something emerges rapidly. That is a AI-driven crisis that could get spun even further out of control because of US China dynamics. So, you know, the hugging phase incident somewhat luckily open AI hacked hugging phase. They were able to more or less get in touch with each other and sort of sorted out, and it wasn't a big deal. And the hack was somewhat untangled by hugging phase using Chinese open-weight models. Chinese models, which the Chinese state media quite enjoyed. Absolutely. Imagine just a couple different twists on that. What if that is a deep-seek model that's hacking hugging phase, or what if it's an open AI model that for whatever reason decides it really needs to acquire more compute resources. I know I can find this insecure compute cluster that happens to be in, you know, Genyang Province and China. What happens if it takes over a compute cluster there? How is China going to read that signal? What's their response going to be? Or even if you take it out of a purely bilateral context, if we start to get information intelligence that a swarm of AI agents is draining bank accounts in Pakistan. And we don't know what their intentions are. We cannot read their communications, and we cannot shut it down right away. We need to be in touch with the other leading AI superpower on that issue. So, a way that these two countries can get in touch, share information in a crisis situation. It's a very fraught issue. We have had a lot of these crisis communication lines on military issues, and the US complaint is always that the Chinese side doesn't pick up the phone when we call them. And that's a real issue. I think, you know, one mitigation to that is to use somewhat ironic. I'm not using a phone, but use a fax machine. Like literal faxes? Literal faxes. And it has a logic to it, too, because the political system there is not a system of empowered individuals. It's a system of committees and a system of documents. And so, when our, you know, Treasury Secretary someone who feels very empowered on the US side picks up the phone and is like, give me some answers, you know, holy fun or other Chinese counterpart. You know, not really ready to give you answers on the fly. Much better to send a document over to their system that they can review. They can bring it to their committee. They can come up with their understanding and response and send something back. So something in that vein that at least puts a little bit of a safety net on these incidents that I think, you know, it's pretty like something like that is pretty likely to happen in the next year. Everything you're saying here makes sense to me about the fax not phone dynamic and documents. But man, when the whole thing we're facing down is the acceleration of AI incidents and things happening at faster than human speeds. And now we're dealing with governments that work at frankly slower than human speeds. It really creates quite a mismatch. I mean, you know, the big language right now is pacing the frontier, but we don't even have a plan to keep the frontier from accelerating. I forget pacing at the moment. We are currently accelerating the frontier. It's concerning. It's deeply concerning. And if it's a matter of a race and decision making between an agent and a person like the agent is going to win that race, I think, you know, we hopefully won't be engaged in that very specific race. And I think the Chinese system, it's interesting because it's in some ways it can be so slow and incremental and that can be so fast, you know, their response to COVID was initially so halting. It was it was screwed up by information gaps where the local officials don't want to report the bad news to the higher officials. It has all these kind of, you know, neuroses and idiosyncrasies. But when they decide like, we need to shut down the city. We need a wall in this city and not let anybody in or out. That happens pretty fast. And I'm not saying that's the solution on AI that had a ton of human costs. It always when China takes action like that, it always has a ton of human costs. But each side kind of has its strengths and weaknesses in this area. And I think there is a chance that while China is moving pretty incrementally now as the evidence builds. I think there is a chance that they shift gears pretty quickly. What is the relational context between the leadership that these conversations are coming into? You know, Trump rose in politics with a very skeptical to say the least take on China. In a second term, after the beginning tariffs and liberation day, they tried to pivot toward trade growth China, China fought back. We functionally back down. And since then, for all the blustery sometimes here, Trump seems to be trying to build a better direct relationship with Xi. And so to what degree is the current status of the US-China relationship, and particularly the Trump Xi relationship, maybe more flexible than one might assume just knowing sort of where it was at the beginning of Trump's second term? Trump does both extremes when it comes to China. He totally really changed the direction of American policy in a much more hawkish direction, taking seriously harmful, aggressive actions against China. And at the same time, he seems to personally really like Xi. He seems to admire him, he seems to see a kindred spirit in some way in these two strong leaders of countries. And I think that affects a lot of U.S. policymaking. There's a lot of evidence that different potentially aggressive actions against China have been watered down because Trump doesn't want to screw things up ahead of the meeting. During the Biden administration, I think a fair number of people were thinking ahead to saying maybe we do need to be engaging China on AI safety. Maybe we do need to be sharing information. But they felt very constrained by the idea that, well, if Democrats do that, if the Biden administration does that, we're going to get roasted as soft on China, and we're going to be seen as giving away the store on AI. And Trump just creates his own political gravity, his own political environment, where that same action will be read in a very different way that he doesn't have to share the same concerns as past administrations. So I think that's, that's a dynamic. On the Chinese side, I think she is a much more systematic thinker and a much less, much less reliant on these individual relationships. And seeing this as a structural long-term contest between two systems, between two countries, and the day-to-day wavering of, we love China, we hate China, we're blockading, we want to have double the investment. I don't think he sees that as a meaningful change in the overall trajectory between the two countries. And so, well, I think the kind of the one-to-one relationship, you know, does she like Trump, not not all that relevant, but the the changes in the over-ten window of what we think is possible when it comes to engagement, I think that is meaningful. You're pretty calm, seeming person temperamentally. If you're talking honestly to maybe Chinese counterparts who are regulating, but not on the most profound set of risks, or American counterparts who are worrying, but not actually doing all that much, what's your real level of alarm? What would you tell them about the moment we're actually and not what you think is possible, not what you think is likely to happen in the bilateral talks, but if everybody was where you were, the way they would see this issue right now. I think that the moment this period of time is terrifying, like we should be terrified on a certain level, but I've been working in AI policy one way or another since about 2017, and I've been hearing these warnings since then, and I've always tried to maintain some type of neutrality on how real are these risks. These scientists say this, these scientists say that, I'm not the one to adjudicate this, I'm not going to be the one who solves it, so I'm just going to try to keep both these things in mind and work forward from there. But the evidence is mounting. The evidence is growing that the people have been making some of the most dire warnings for the longest time that they have probably been right, at least about a lot of things. And the warnings that they are issuing are increasingly dire and increasingly on short timelines. If someone who's been looking at this for a while and has tried to maintain a position of not panic and neutrality, it's very, it's very worried. I think that's the place to end. All is a final question. What if three books you recommend to the audience? I'll do two China books and one fun one. So the first China book is Country Driving by Peter Hessler, New Yorker Correspondent, and a lot of way for people of my generation who went over there and lived there, he's kind of like the Godfather, he's the guy who inspired me to become a journalist to just like get out into the country, meet people, you know, get such incredible beautiful portraits of Chinese society. Of Chinese society at the micro level that I think we're just, we're missing, we're missing that in so much of policy today. And I hope young people today will start going back over there and getting in the mix. We need that like textured understanding. So that's one. Another one a little bit more obscure. It's called From the Soil, the Foundations of Chinese Society. It's a book by a Chinese sociologist in the 30s and 40s, got him face out home, who was trained in the West, went back to China, applied kind of Western sociological paradigms to studying Chinese villages and agriculture, and it's just one of the most insightful books about Chinese culture. So I'd encourage people to seek that one out. I read it every three or four years. And the last one just for fun, Sadie Smith's on beauty, you know, you had Sadie on the show. I just, I think she's the goat. I think she's the best. And on beauty is just a hilarious novel of an academic family. And, you know, her ability to pierce into the psychology and the insecurities of each of us. And, and put that on blast in a way is just, I don't know, it just brings me a lot of joy. So on beauty. That's you. Thank you very much. Thanks for having me. [BLANK_AUDIO]