From Posting to Policy: Saif Khan on DUV and the AI Balance

Jordan Schneider (0:00)

Saif Khan makes his ChinaTalk debut.

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 in his time at CSET, then went into the NSC and Commerce in the early 20, into 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 excited to announce the launch of the Center for Technology and Statecraft, his own think tank which is going to produce, well, I can personally confirm, is already producing some excellent, grounded, technical, forward-looking work which will certainly shape many an AI policy discussion in the years to come. Saif Khan, at long last, welcome to ChinaTalk.

Saif Khan (1:11)

Thank you. As you know, very, very long-time listener and finally excited to be on the show.

Jordan Schneider (1:18)

So Saif, why does the world need another think tank?

Saif Khan (1:21)

Yeah, that's a good question.

Yeah, what is the appetite right now for evidence-based policy? Well, there is, right now, I would say surprisingly a gap in the AI policy think tank ecosystem that we're trying to fill. Now, one thing is this is in the backdrop of a world where right now you have industry gobbling up lots of talent, foundations gobbling up lots of talent.

I think there is a lot of space to do lots of really impactful research and have a policy impact at think tanks in DC. So I would encourage people not to give up on that. But I would say we have our own special spin on what's important. One thing is, I go back to the days when I was at Georgetown CSET. And those were the days before anybody really cared about AI policy or even semiconductor policy and we had all of the time in the world to think about forward-looking research topics, topics that would be real important in the years and have policy ideas on the shelf for when the policy window opens. Now you look today, obviously, every single day is coming with a new piece of news on AI, in policy, on chips and the like. And I think there's actually less space these days for forward-looking research. And so I think we are trying to fill a little bit of a gap in the ecosystem to just kind of put our heads down and try to come up with some new ideas that will be important on the one- to two- to three-year time frame. And I do think we have a really strong team to do that, that has experience with that and skill at that. And then the other piece that I think is we're trying to be a central home for is, I do think some of the most productive policy research that we've had in the last few years has really been, if you're understanding the AI supply chain, whether it's chip making tools, to tokens, to robots and the like, understanding what is happening in the physical world is actually a thing that grounds research in a way that makes it, I think, much more impactful. And we talk a little bit about this in the launch essay, which you can see in our website where really understanding physically what's happening in the real world of AI, it helps you identify patterns that other people don't see. You can also predict how the world is going to unfold. I spent a lot of time doing semiconductor policy research back at CSET. And there's a predictability to understanding supply chains. And if you move a policy lever, how the world will unfold. And also, if you are really looking at something that is empirically grounded, there's almost like a fractal-like element, right? Like you can dig into the details and you'll always learn more. It's time that's never wasted. You're not researching something where it's angels dancing on the head of a pit. It's like real-world things. And so I think we're trying to be a hub of that kind of work.

And I think that that kind of work is still relatively underserved by people who kind of have the technical expertise to do it. So and all that said, the forward-looking piece, regardless of how you feel about the policy window right now, there are going to be points in time where it matters.

Jordan Schneider (4:34)

So it's interesting thinking about the sort of evolution of this field and what gaps remain. Because I remember having conversations with you when you were in commerce being like, you know, you're like, Jordan, what's up with this China AI stuff? I mean, there's three people staring at it. And this was in 2023 This is no longer the case, which is something where I feel like relief almost in a certain way where we've gone from four people whose full-time job it is and to serve the English language-speaking policy community, looking at China AI to, I don't know, 30, 50 more. And the government has also clued in in a way which was not the case in the pre-deep-seek days.