The AI race has long been framed as a zero-sum game: Either the US or China will win in the end. But as concerns pile up around the increasing capabilities of AI models—especially AI agents—researchers in both countries are trying to team up to work on AI safety. This week, contributing editor Zoë Schiffer speaks with senior writer Will Knight about what he saw and heard on the ground when he visited China this summer—and why the two countries might actually need to start working together to avoid a major AI catastrophe.
This is our second episode from our summer-break series. We’ll be back next week with our usual roundtable.
Zoë Schiffer: This is WIRED's Uncanny Valley. I'm Zoë Schiffer, contributing editor. If you've been following tech news this summer, and definitely if you've been listening to the show, you probably already know that China has been in the headlines quite a lot, particularly when it comes to the AI race. The country's open models continue to close the gap with US frontier models at what some people estimate is a fraction of the cost. In turn, the US has maintained its tight restrictions on chips and export controls to slow down China's rise. We think of AI advancement in so many ways as zero-sum, if China wins, the US loses, and vice versa. But there's a concern that really stretches across those lines, and it's about AI safety.
Zoë Schiffer: News of AI agents hacking platforms added urgency to this issue over the summer. In turn, government officials have been forced to pay attention and act on AI regulation.
Zoë Schiffer: So, could the US and China actually benefit from working together? And what would it take to even make that happen? Earlier this summer, WIRED's senior correspondent Will Knight visited China to get some answers. Will Knight, thank you so much for being here.
Will Knight: Thanks for having me.
Zoë Schiffer: OK, so I want to start with your trip. You went to China earlier this summer, and at the time, we weren't hearing as much about AI safety in the US. In fact, it felt like with Trump's second term in the White House, it was like a the-US-needs-to-win framing, and AI safety almost started to sound like anti-growth. But I'm curious what you were hearing and seeing in China on the AI safety front.
Will Knight: Yeah, so going back maybe a year or six months, I'd noticed a lot more AI safety research coming out of China. And so, I went to this conference in Beijing, put on by one of the city-located labs that they have there. They have these ones in Beijing and Shanghai and elsewhere. And it turns out that AI safety was a really big theme. It's very clear that it's something that researchers are interested in. And actually, also just visiting labs and companies, the question of AI safety came up a lot.
Zoë Schiffer: Can I just ask, when they're talking about AI safety, does it translate to guardrails? Because we also know that China has really gone all in on open models, which I mean, the whole thing is that people can download and tweak them and use them for whatever purposes they want.
Will Knight: Well, it's not totally that simple because in China, for example, what your models can say is more controlled, and there are actually quite a lot of regulations around AI. So companies build these open models, but then anybody putting them on the internet has to be very careful about what they do. And then more recently, there's been a huge interest in agents and things like OpenClaw. That's been a really big theme. And one of the things that's interesting to me, at least when it comes to contrasting AI in China and the US, is that people there seem less enamored with the idea of AGI and creating this digital god, are more like, how is this actually going to be useful and whether it's you as a business person or an individual actually using it. So, a lot of people got very interested in, and it's often the case in China, very rapidly adopted things like OpenClaw and then saw how it could go wrong. So there's a lot of focus on How do we make these things reliable?
Zoë Schiffer: Yeah, it makes sense. I mean, when you're talking about China being more focused on economically useful models, that seems like a framework that does require a certain amount of stability, reliability, guardrails, safety. Whereas if you're focused on reaching godlike intelligence, i.e. AGI, then maybe you're more focused on just advancement at all costs.
Will Knight: Right, I think that's right. The conference I went to, one of the themes was agentic safety. Given that we're now seeing all these issues with AI agents hacking things, cybersecurity was a really major topic there. It seems people were worried about exactly the same thing as folks in the US. They're worried about hackers misusing these things or about these systems running amok. And as you alluded to, I think there's a sort of sense now, a little more of a sense that the US and China might well need to work together on some of these things to avoid unpredictable systemic issues, as well as just to set more rules of the road around these systems.
Zoë Schiffer: What would that actually look like, though? Would it be like a set of agreements that the US and China make together, almost like what US researchers were calling for recently, in terms of the US government setting the pace of AI development? Would it be something like that or something more technical?
Will Knight: I think that's something that I think a lot of researchers are hoping for or calling for, is something like that where there's some sort of agreement. I don't know when it comes to Washington and Beijing, their negotiations have been very hard for—and it's really difficult to predict how those would shake out. But just some sorts of rules around communication. And in cases like military situations, there are lines of communication. So if something happens that goes wrong, if you have an AI system that starts doing something very aggressive or attacking systems, you have a way to say, "This is a mistake." So, something like that might also be in the offering. But I think it's also a question of how the two sides build trust as well, because actually, especially when it comes to cybersecurity, for a long time there's been not very much cooperation at all, because it's been a case of either side hacking each other and failing to agree, the rules of the right. So actually, last week I went to visit a cybersecurity and AI researcher who was doing some really fantastic work that I'm going to write about for my next AI Lab newsletter. And he was saying he can't collaborate with US researchers because they're not allowed to because there are certain kind of restrictions. He'd recently developed this benchmark to test the cybersecurity, the hacking capabilities of AI models, and he wanted to get US companies to participate but they weren't really sure how to do that. So, I think it would be a good thing to see a lot more collaboration even between the companies. We see the US companies being very critical of Chinese ones, but actually, there's a lot of good reason for those for everybody to work together to make sure things don't go wrong.
Zoë Schiffer: Well, I want to get into that, but I also wanted to say that this idea of collaboration, when you first say it, it sounds very academic, almost naive. It's a nice idea, but how would that actually work? Because my perception is, and I'll just be upfront, that I get this idea from talking to a lot of companies that work on frontier AI in the US, but they have convinced me that there was a fair amount of distillation that went on. That China was distilling frontier AI models, and that that has created a situation where they are able to have very capable open-source models that are a lot cheaper and more efficient, built on the back of US innovation. But I'm curious what you think about that, and then what researchers you spoke to in China think about that accusation.
Will Knight: Yeah, I think, I mean, that's a great point and that's a really important theme. We hear people criticizing Chinese companies for distilling, for doing this distillation. So you teach your model by taking the output of another model, and that is a shortcut to learning a lot of the stuff that's embedded. But the truth is that AI has been built by researchers from all over the world working at different companies and different labs. There are many, many people who are originally from China, maybe educated in the US, working at US firms. And also because these people go to conferences and know each other and this is open science, there's an enormous amount of work that is shared and then that is very beneficial to progress, and balancing that is one of the challenges. It's true that Chinese companies have distilled US models, but so have US companies done that to other US companies. It generally is a way that you get a kickstart working on a new model, and it's very widely done in academia, actually. A lot of researchers will do that. I would say one thing, I find it a little ironic that these companies that have built their businesses by scraping enormous amounts of copyrighted content are now complaining about their models being copied, or—
Zoë Schiffer: Well, I think that's why they have to talk about China doing it so much, because if they talk about anyone in the US doing it, the immediate criticism is, "Well, come on, you took all of the books, you took everything without permission." But when you'd frame it as China stealing from the US, it has a slightly different flavor.
Will Knight: Yeah. It fits a narrative that has some legitimacy of Chinese companies copying, but I think it is much too simplistic and limited. And so, you can look at things like DeepSeek's model. They did really, really important innovation, unique innovation that other, the US companies have copied. The latest model from China, which has been accused of this distillation, Kimi from Moonshot. The research paper that they put out includes a lot of really, really interesting innovations, engineering innovation. So, it's really not the case that China is simply copying. And I think it's dangerous for the US government and companies to believe that they have this sort of God-given advantage, because I think we are going to see probably Chinese companies being more and more innovative doing their own thing as well.
Zoë Schiffer: We'll be right back after the break. Stay with us. I'm curious, when we talk about safety, I feel like in the US, certainly in Trump's second term, again, safety has started to feel like, at least from the administration's perspective, it is anti-growth in certain ways. I do think this is changing slightly with the recent stories regarding OpenAI and Anthropic's models hacking into other companies. But prior to this, it felt like we were really not wanting to talk so much about AI safety. Does China equate AI safety as anti-growth, or do they have a different perspective on that, overall?
Will Knight: I think they have a fundamentally different perspective. This is just my opinion, but I think that narrative, that view from the US government was that it was somehow woke and too much regulations, and it goes to just trying to make the models really work. The truth is that making a model reliable is entirely compatible with making it successful. I think that's more the view from China, is like, we want these agents not to misbehave, then it will be more successful and higher value. So last week, I visited this computer science lab in Fudan University in Shanghai, where a professor's working on exploring how AI agents could not just do unpredictable things like hack other systems as we've heard about OpenAI's and Anthropic's agents doing, but actually look for ways to replicate, to copy themselves over to other systems, to seek out resources and to be adaptive to escape control. And his work shows that the models will do that with a little bit of nudging. So, it'd be like a computer worm that doesn't just modify itself slightly to evade control, but actually looks around a network, figures out how to hack the next system, maybe finds software vulnerabilities, copies itself somewhere else. It is entirely possible that we'll see future AI agents do that.
Zoë Schiffer: OK. My heart's beating as you're talking. I mean, step one is just understanding, how would they do this? I sincerely hope step two is, how do we stop them from doing this?
Will Knight: Yeah. So no, absolutely. He's absolutely doing this as a way to try and understand how to prevent it. And one of the things he really wants to do is work with US researchers. He says this is a really important thing we should all be aware of.
Zoë Schiffer: I mean, it does seem like, although the rhetoric from Trump has been very anti-China in certain ways, Scott Bessent has made inroads with his counterpart in China. I think there's been some level of, we do need to work together. I'm curious, how does China think about the race against the US? Because we really see China almost as a boogeyman and it's like this force that's galvanizing people to work harder and faster in some ways. But is it similar over there or very different?
Will Knight: Yeah, I think in China, the impression I get is that what people feel that they're in competition with the US and facing certainly a lot of pressure with Trump, that it's less of a zero-sum game. That you don't have to beat the US to be successful, and vice versa.
Zoë Schiffer: So Stephen Casper, a renowned computer scientist at MIT who spoke at the conference that you attended, told you that, "One thing that almost everyone in AI can agree on right now is that it doesn't need a Chernobyl moment." Can you talk about what he meant?
