Sure, sure. And I'll shamelessly hold it up here. Ta-da. Okay. So, you know, I was really, really thrilled to be asked to participate in this book by Hal Brands. He's the Henry Kissinger Distinguished Professor of Global Affairs at Johns Hopkins School of Advanced International Studies. Whew, it's a mouthful. You know, a lot of the book focuses on the intersection between AI and national security, military, and defense. My chapter takes a slightly different tack. I'm looking at where AI and economics meet. So specifically, I was tasked with trying to determine which countries are most likely to be the economic beneficiaries of AI. And I'll tell you up front, Sebastian, no one knows. It's just these things, as you and I both know, are changing so rapidly that to make a prediction like that would really be a fool's errand. But I still think you can look at all the inputs that are going into which economy can get economic benefits out of AI and create a framework that at least you can track and debate.

Right, right. And so let's get right into it because I think you have an interesting position on one of the questions which will be front of mind for a lot of our listeners, which is, you know, do we think the U.S. will retain its current leadership in producing the AI?

Yeah. And I mean, there's another stat in your book, which I think comes originally from an article in The Economist, which estimates that around 37% of the world's top AI researchers now work in Chinese organizations, compared to 32% for US ones. So China is ahead by quite a lot, by five percentage points. And then more provocatively, the article suggests that if the shift in favor of China that's been going on over the past decade were to persist by 2028, Top Chinese-based researchers could outnumber American-based researchers by two to one, two to one, double the number, right? And of course, maybe the trend doesn't continue. But this is a projection that says in 2028, China will be that far ahead. So, you know, that kind of talent advantage for China could be a pretty tough obstacle for the US to overcome if it does come to pass.

Right. And we could get into lots of conversations about skilled immigration and, you know, are we getting the right people in the U.S. to looking ahead to maintain a lead? But there's so much to cover here. So let's move beyond talent and look at capital and infrastructure. Two other obvious important inputs. You know, we are seeing exactly how capital-intensive AI development is. I mean, hundreds of billions. I think we're now breaking into the trillions of dollars a year being spent into this. And that's just in the United States. You know, the U.S. clearly has the world's deepest, most flexible, broadest capital markets. And that's a huge advantage over other countries, including China. That said, you don't want to ignore what China's government is up to. So maybe we can rely on the private sector. They can rely more on the public sector, relatively speaking. Their latest five-year plan from the government suggests they're just going to keep doubling down on their AI focus, which is going to include a big budget line for those companies in China.

I mean, I think on the CapEx side, I mean, the way I've looked at the numbers at least suggests to me that the US does retain a pretty significant lead. So I think it's like $700, $800 billion of private companies spending in the US this year on AI CapEx. China's just over $150 billion. So you've got more than a 4X, maybe even a 5X delta there. And if you add in the government spending, I've read that to be $60 billion a year. So it's moving the needle a bit, but not massively. But what I would say is that even with that gap in CapEx, China may be way more efficient in what it gets out of the CapEx because of this phenomenon called distillation, where the US trains a new frontier model. That's very, very expensive. You have to hire lots of expert PhDs in materials science or whatever it is you're trying to train your model on to make it really good at material science problems. And you're paying those PhDs quite a lot of money, and it's a big, cumbersome project to get all that together. And then as soon as the model is out, the Chinese just show up and query the American model and effectively get an AI version of all those material science PhDs. And so they can train way, way, way cheaper than the US can because of this sort of reverse engineering of the frontier research in the US. So even though the CapEx gap appears to me to be really quite big still, the kind of when you adjust it for the efficiency of the expenditure, maybe the US lead is not quite so assured.