The Case for DUV Controls

Saif Khan makes his ChinaTalk debut. He is perhaps my favorite example of posting-to-policy that I’ve seen over the course of my time following Washington. Saif wrote some truly excellent deep dives into AI and semiconductors during his time at CSET, then went into the NSC and Commerce in the early-to-mid 2020s, across both the Biden and Trump II administrations, to help manifest the export control regulatory regime we have today. He is now out of government and launching the Center for Technology and Statecraft , an IFP -affiliated think tank, which is already producing excellent, grounded, technical, forward-looking work that will certainly shape many an AI policy discussion in the years to come. We discuss: Why Saif started a new think tank, and the value of deep, forward-looking policy research in an AI world moving at breakneck speed; CTS’s landmark DUV report, which projects that stopping tool exports today could leave China producing roughly 100x fewer AI chips than the US by the mid-2030s, making DUV controls the US’s most consequential lever over the AI balance; How rigorous research actually translates into government policy, and why think tank experience is valuable before working in government; Robotics as the next frontier of US-China AI competition, and the security vs. diffusion mindsets shaping AI policy; How AI is changing policy research itself, the challenge of bringing AI into government, and what AI-driven economic and social change could mean for humans. Plus, why everyone should read Lu Xun, why AI might socialize like insects, and AI-nannying. Listen now on your favorite podcast app ! Why Another Think Tank? Jordan Schneider: Saif Khan, at long last, welcome to ChinaTalk. Why does the world need another think tank? What is the appetite right now for evidence-based policy? Saif Khan: There is, surprisingly, a gap in the AI policy think tank ecosystem that we’re trying to fill. Industry and foundations are gobbling up lots of talent, but there’s still a lot of space to do impactful research at think tanks in DC. I would encourage people not to give up on that. We have our own special spin on what’s important. Back in my days at Georgetown CSET, before anybody really cared about AI policy or even semiconductor policy, we had all the time in the world to think about topics that would be really important in the coming years and to have policy ideas on the shelf for when the policy window opened. Today, every single day brings a new piece of news on AI, on policy, on chips so there’s less space these days for forward-looking research. We’re trying to fill a bit of a gap in the ecosystem by putting our heads down and coming up with new ideas that will matter on a one-to-three-year timeframe. The other thing we’re trying to be a central home for: some of the most productive policy research of the last few years has come from understanding the AI supply chain — from chipmaking tools to tokens to robots. Understanding what is happening in the physical world grounds research in a way that makes it much more impactful. We talk about this in the launch essay on our website. Understanding physically what’s happening in the real world of AI helps you identify patterns that other people don’t see, and you can also predict how the world is going to unfold. I spent a lot of time doing semiconductor policy research at CSET, and there’s a predictability to understanding supply chains: if you move a policy lever, you can see how the world will unfold. If you’re looking at something empirically grounded, there’s also almost a fractal-like element. You can dig into the details, and you’ll always learn more. It’s time that’s never wasted. You’re not researching angels dancing on the head of a pin — these are real-world things. We’re trying to be a hub of that kind of work, which is still relatively underserved by people with the technical expertise to do it. And regardless of how you feel about the policy window right now, there are going to be points in time when the forward-looking piece matters. Jordan Schneider: It’s interesting to think about the evolution of this field and what gaps remain. I remember conversations with you when you were at Commerce, when you’d ask, “Jordan, what’s up with this China AI stuff?” and I’d say, “I mean, there are three people staring at it.” That was 2023. That is no longer the case, which I feel almost relieved about. We’ve gone from four people whose full-time job it is to serve the English-speaking policy community looking at China and AI to, I don’t know, thirty or fifty. The government has also clued in, in a way that was not the case in the pre-DeepSeek days. But I see your point that now there’s all this noise. The two investing styles of doing policy work, let’s call them, are having longer horizons and looking into physical stuff. The thing I’ve always been scared of about investing six months of my life into writing a hundred-page report is that when it comes to AI in particular, six months is an eternity. Six months is three decades. I’d love to hear you expand on why, if you follow the supply chain, you can have higher confidence that whatever you’re investing time, energy, and talent into deeply understanding is going to be relevant in the medium-term. Saif Khan: Although a lot more analysts are looking at China and AI, basic questions about the US-China AI balance aren’t being addressed. A few examples of things we’ll be looking into: people talk a lot about the US having a model capability lead of something like eight months. It’s really not well understood why this is the case. To what extent is it that we are training larger models, versus what is the quantitative level of capability uplift that Chinese companies are getting from distillation or reinforcement learning? What happens to China if US capabilities progress speeds up or slows down? Modeling what the future of the world looks like is fairly difficult and not well understood at all. That’s one piece of the equation we’ll be addressing. You see this when a version of GLM 5.3 Flash comes out and it’s announced that they’re producing tens of trillions of tokens, and the reaction is, “Okay, export controls are no longer working.” Why is that right or not right? A lot of these systematic explanations of the basics of US-China AI competition are not well known. Another couple of areas are barely addressed at all. One is robotics and its intersection with AI. One of our researchers, Amelia Michael, is spending all of her time on this topic, and I can count on one hand the number of people in DC who are deeply thinking about it, let alone from the China side. There’s an enormous amount of white space that we could fill if we do good research. The other piece: there are a lot of arguments that now is the most consequential time to move policy — that we’re going to have fully automated AI R&D in maybe a year, and all the key policy decisions have to be made by then. I don’t know if I fully agree with that. A lot of the big decisions we’re going to make will span the next five to ten years, in my opinion, and there will be dividends to doing that research ahead of time and shaping how things go. Something I take from my experience working previously as a fellow at a think tank and then going into government: the hope is that people develop their expertise at a think tank and then bring it with them when they go into government, so they have the worldview and the tools to implement policy more effectively. I have really seen that people who spend time doing that before a government policymaking tenure have the opportunity to be more effective in their jobs. Jordan Schneider: Why? Saif Khan: Especially at the National Security Council, where I spent a couple of years, you have day-to-day emergencies where you have to respond to what is happening right now. It’s easy to get caught up in your entire job just responding to the day-to-day, as opposed to