It is well known that at least part of Washington’s reluctance to regulate artificial intelligence comes from wanting to make sure the United States stays ahead of China. But what exactly are the two countries racing for? How do we define “winning”? What would victory actually entail?

On the latest episode of FP Live, I spoke with Matt Sheehan, a senior fellow at the Carnegie Endowment for International Peace, where he co-directs its China AI Initiative. Sheehan lived and worked in China for six years and has helped host unofficial U.S.-China dialogues on AI. He is also the author of The Transpacific Experiment: How China and California Collaborate and Compete for Our Future.

It is well known that at least part of Washington’s reluctance to regulate artificial intelligence comes from wanting to make sure the United States stays ahead of China. But what exactly are the two countries racing for? How do we define “winning”? What would victory actually entail?

On the latest episode of FP Live, I spoke with Matt Sheehan, a senior fellow at the Carnegie Endowment for International Peace, where he co-directs its China AI Initiative. Sheehan lived and worked in China for six years and has helped host unofficial U.S.-China dialogues on AI. He is also the author of The Transpacific Experiment: How China and California Collaborate and Compete for Our Future.

The full conversation is posted on the video box atop this page, as well as on the FP Live pages on Apple, Spotify, and YouTube. What follows here is a lightly edited and condensed transcript, exclusive to FP Insiders.

Ravi Agrawal: I want to start with the very basics here. If the United States is said to be ahead of China on AI, by how much and on what metrics?

Matt Sheehan: It’s useful to define what we mean by “ahead” or “winning.” The simplest way to do that is to distinguish who can build the most powerful and capable models—what we call frontier capabilities—versus broader applications of AI, such as how you are diffusing AI into your economy to enhance economic growth, drug discovery, or scientific research.

The place where the United States is clearly ahead, or at least has a defined lead, would be in the frontier capabilities. That lead isn’t huge. Most people would estimate it being a lead of six to nine months, which essentially means that if U.S. companies like Anthropic or OpenAI come out with a model today, Chinese models will probably match that capability about six to nine months from now. There are a lot of open questions about why China is so close. Are they using illicit methods of copying the U.S. models? Are they getting close due to the strength of their own research? Those are all open questions.

When we look at the diffusion of AI into the economy, we don’t know who is in the lead just yet. This type of thing takes a while to show up in the data, and that data is very diffuse—there’s no centralized database. We’re not seeing AI showing up in, for example, enhanced growth statistics in China. In the United States, it might be showing up in those ways, but mostly in terms of how much computing power or data centers are being built. We have hunches about what the United States and China are respectively good at when it comes to diffusing a technology, but at this point we don’t have a clear picture of how that’s going to play out.

RA: Fascinating. Now, when we think of a race, it’s important to also think about a finish line. I’m curious how the United States defines winning in this race. I use that word because it comes up a lot. [U.S. President Donald] Trump has said that winning is a must. Treasury Secretary Scott Bessent has warned that if China wins, “nothing else would matter.” So, explain to us, what is the race, and what does winning really mean, at least from the American perspective?

MS: I’ll center this on the leading U.S. labs and companies—OpenAI, Anthropic, maybe Google DeepMind. For a long time, these companies have had a pretty clear narrative of where AI is going. Capabilities will keep getting better and more general, until we get to a point of artificial general intelligence (AGI)—a model that can do everything a human can—or artificial superintelligence (ASI), which is a model that can outperform humans at essentially every task. One key moment in this theoretical chain—or, increasingly, real........

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