Hello and welcome to the Vergecast, the flagship podcast of the model wars. I'm your friend David Pierce, and today on the show we're talking about the AI race ongoing between the US and China. For really the last several years, it's been pretty apparent that most of the best models and most of the most ambitious, most impressive AI has been coming out of Silicon Valley. Google, Anthropic, OpenAI — this handful of companies has been really driving the AI revolution. But then it has increasingly become clear that China is catching up quickly. We had a big moment a year and a half ago where DeepSeek all of a sudden showed up with a vastly more efficient, vastly cheaper model that was almost as good as some of the frontier stuff. And that became a big deal. Then just this past weekend, two different companies, Moonshot and Alibaba, made very clear that they have built frontier models that they believe to be just as important as anything else coming out of Silicon Valley. There is a race happening between the US and China to win at AI, whatever that means. It's becoming a huge tech story. It's becoming a huge policy story, and I want to figure out what's going to happen if and when China catches up and even passes what's happening in the labs in Silicon Valley. So that's what we're going to talk about. Lauren Feiner and Hayden Field are going to come. We're going to do it. But first, here's everything else happening on The Verge today. This is 90 seconds on The Verge for Monday, July 20th, 2026. The Odyssey is a huge hit. It made $264.1 million worldwide this weekend, which is both Christopher Nolan's biggest opening ever and one of the biggest movies of the year so far. I haven't seen it yet. I'm hoping to wait for a 70 mm IMAX screening, but those are apparently sold out like forever. In general, this has been a big summer for the movies. You have obvious wins like Toy Story 5 to surprise breakouts like Obsession. Both great movies, by the way. It's shaping up to be the best year for movies since before the pandemic, all the way back to 2019. And if going to the movies isn't dead after all, that might be really great news for Hollywood, and maybe bad news for the streaming services that were pretty sure they'd already won this war. Meanwhile, the European Commission just levied its biggest fine ever against the platform AliExpress for failing to prevent illegal, unsafe, or counterfeit products being sold on the platform. Honestly, if you've ever been on AliExpress, none of that should surprise you. The commission specifically mentioned things like unsafe toys and dangerous cosmetics and said that AliExpress has until October to come up with a plan to fix it. Temu got a similar fine for similar reasons this year. I don't know if the end of these ultra-cheap, relatively unmoderated shopping platforms is coming immediately, but there is a big fight coming and fast. And finally, a leather jacket once worn by Nvidia CEO Jensen Huang was bought on auction for $960,000. I'm not saying this is the moment to call the top on AI and tech and the economy and everything, but boy is it getting weird out there, my friends. I don't know. You can read more about all of this at theverge.com. That is 90 seconds on The Verge for Monday, July 20th. Joining me now, two people deeply qualified to make this make any sense to me. The Verge's Hayden Field, our senior AI reporter. Hi, Hayden.
And Lauren Feiner, our senior policy reporter. Hello. Hi. All of us are just like deep in China is the bad guy in bizarre and confusing and unknowable ways. So we're going to try to make sense of this along one very particular axis today, and that is AI. And Hayden, I kind of want to start with you because I remember you and I talked on this show a while ago about this sort of truism in the AI community that China is six months behind — that whatever happens here in the US, whatever new frontier models, China and its homegrown companies are running about six months behind the state-of-the-art in AI. Is that still sort of the generally considered idea about US versus China right now?
Six months is basically the best-case scenario in terms of, you know, the slowest they could possibly be. It may be that they're already caught up, and it may be that it takes one month, but six months is like the max. That's kind of the consensus right now. And you know, all of those types of hypotheses were basically made around Mythos and, you know, the cybersecurity aspect of things. So everyone said, you know, they're about six months behind max when it comes to cybersecurity capabilities that Mythos and other models may have. It seems to be that it's even less for, you know, the models we use every day. For a lot of them, they're either equal now or they will be very soon.
Okay. And Lauren, give me a sense of how much politically that is a thing people are talking about. I mean, we had David Sacks a year and a half ago as the White House czar trying to figure all this stuff out, yelling about, like, you know, American AI supremacy. This is a thing Trump in particular seems to care about on cybersecurity grounds. Like the sort of US versus China AI race — is this relevant politically at this moment in the same way that it is to the people in the AI industry?
Yeah, I think the US versus China race in tech, AI specifically, is perennially a big topic in Washington. I think how it ends up playing out has changed over the years a little bit. You know, we started with the Trump administration kind of being more or less in line with the Biden administration on things like export controls. Those have kind of changed over time. So I think the way that it's gone about has changed a little bit, but overall it's always something that I think a lot of people in Washington care about and care about keeping up with and beating China in this race.
And Hayden, it seems like the big question right now is about distillation. Everybody's all up in arms about distillation. Anthropic is yelling to Elizabeth Warren about distillation. OpenAI has been complaining about it for like 18 months. It turns out everybody's very upset about distillation. Can you just explain what this thing is? And actually, it seems like kind of the main problem all of these frontier labs in the US are looking to the government to solve. What is going on here?
Yeah, that's a great question, especially because, you know, it's come up a lot in high-profile lawsuits recently. We found out that Elon Musk's Grok distilled on OpenAI's models, according to the lawsuit.
And his response was basically like, yeah, everybody's doing this.
Exactly. And he kind of tried not to say it, and then he had to because he was under oath. It was a whole thing. But yes, so distillation is essentially kind of you having a smaller AI model or a newer AI model learn or extract knowledge from a larger, more established one, a better-trained one, in order to, you know, become better itself. That's kind of the simplest way to put it. So it's something that, you know, smaller or newer AI teams, sometimes with fewer resources, will do because it's an easy way to kind of make your model better very quickly and use fewer resources and just, you know, kind of speed everything up. So, you know, if you see an AI company come out of the woodwork, start really soon, and then really soon after that they have a new model and it's just amazing and everyone's talking about it — a lot of times they use distillation to kind of, you know, bridge that gap. Essentially what happens is companies will collectively generate, like, you know, up to millions of exchanges with, you know, another chatbot. So one example is Anthropic put out a statement recently that it believes that three Chinese companies — DeepSeek, Moonshot AI, and MiniMax — all distilled on Claude, and it said that, you know, collectively all three of them generated like 16 million exchanges with Claude or something like that from tens of thousands of fraudulently created accounts. And so basically they just, you know, collect all those responses and, you know, use them to make their own model better, whether it's for direct training or for reinforcement learning where, you know, the AI models may learn certain decision-making techniques through, you know, this type of process. So yeah, it's basically just kind of a get-rich-quick scheme, but instead of getting rich quick, it's learning very quickly for an AI model.
Right. It's like a catch-up-really-fast shortcut. It seems like you can just basically ask the model questions, take the answers, and sort of reverse-engineer your way into understanding how it works.
That's why a lot of people thought that DeepSeek sounded a lot like ChatGPT at the time.
Interesting, including OpenAI thought that, and I think continues to think that. Speaking of DeepSeek, Lauren, I'm struck by the fact that we — like, what, beginning of last year — there was this DeepSeek moment where all of a sudden it came out that, like, China's tech is here. It's moving quickly. China has figured out a way to make these models in a way that is much more efficient. It's much cheaper. And all of a sudden they have, like, competitive technology. And the fact that it wasn't the very best model actually sort of became less and less important over time. And what has the government done between then and here? Obviously, we're in this, like, insane mess of AI regulations that we don't have nearly enough time to try and piece apart, but it does seem to me that, oh no, China's AI is catching up and we think that's important is a thing people in the government have been saying a very long time. Have they done anything about this in a meaningful way yet?
That whole moment kind of undermined this idea that, you know, if we just make it harder for Chinese tech firms to access advanced US chips, then we can help slow down China's ability to advance in this race. And if DeepSeek was able to get to where it was without, you know, needing the most advanced chips or with, you know, so much less compute, it really kind of undermined that whole theory. So, you know, I think we've seen, you know, a different approach to export controls. The Trump administration at one point allowed for, you know, greater sales of chips to China. Now, there's some bills in the NDAA that could strengthen certain export controls on chips to China. So I think there's still not a ton settled about what's the best way to go about this. But I think it also makes clear that, you know, just cracking down on chip exports might not be the only way to, or might not be as effective as maybe we once thought it was, to slow down China's ability to compete here.
Yeah, I've enjoyed doing the research on this subject because on the one hand you get a bunch of people who are like, well, it's China, so there's essentially nothing we can do. And this is sort of a long-running thesis that actually all of our legal and regulatory fights with China really don't ever amount to anything. So just full nihilism, who knows what'll happen. And then on the other side, I've heard a theory that I think kind of borders on conspiracy theory that what we've seen, like with Mythos recently, where they're actually taking steps to not release these things to the public in the same way, is actually a way of keeping them out of the hands of these companies that want to do distillation. My sense is none of that is actually what's going on, and that in fact what's happening is everybody is still desperately trying to figure out how to do anything here. Is that fair? Or is there some four-dimensional chess thing in progress, do you think?
I mean, it's hard to say. I think I feel like I tend to agree that we don't really — it's hard to know what any of these actors are going to be doing in the next six months, one year. You know, we're really at the start of this race, and it's just becoming more and more clear how many different ways all these companies are going to have to compete, and that, you know, the path to getting to the finish line here, so to speak, could look a lot different than we once thought. So it's really hard to predict or really know, like, who really has the best strategy here or what's going to pan out. So I feel like everyone is kind of making their bets on what's going to work, and it'll remain to be seen what actually does.
Okay. So I have the same question for both of you that I want you both to answer from sort of very different perspectives. And the question is essentially: what happens if China starts to win? Right. I think at this point, Hayden, like you said at the very top, we're at a point where China is either a little ways behind or barely behind. But I don't think there's anyone who would really try to make the case that China's AI — either its products or its frontier labs or its models — are ahead. It doesn't strike me as completely inconceivable that that might change. So Hayden, you spend a lot of time talking to these companies, talking to the people who make the products. We're in a weird economic moment with all of these AI companies. Like, in theory, if all of a sudden the next DeepSeek comes out and it is by obvious measures the best AI product anyone makes anywhere, what happens?
That's a great question, and I think that the fact that we have no idea is what's fueling a lot of these fears. I've seen a ton of OpenAI employees leave the company and write huge manifestos about their fears about what will happen if China overtakes us. No one's really been super specific about what that actually looks like besides, you know, potentially just data privacy issues and just giving China more power on the world stage.
Lauren, call me crazy. This sounds like TikTok. Like, we just did this with TikTok. You have been on the show talking about this with TikTok, right? This idea that if we let China get ahead, it will gain this economic and sort of cultural soft power that will make it take over the world in some unknowable but problematic way. Like, are we just doing this again with AI?
