What I told Congress about the US-China AI race

My opening remarks as an expert witness testifying before the U.S. House Select Committee on China

Last week, I had the honor of testifying as an expert witness before the U.S. House Select Committee on China. The topic for the hearing was US-China competition in AI. In my opening remarks, I tried to highlight a few basic points:

• “Winning” in AI means more than just having the best models.

• China is focused on adoption and diffusion, especially physical AI.

• US export controls have slowed down China’s AI development but have also spurred China’s semiconductor development across the entire supply chain.

• The US needs to make AI work for ordinary Americans, many of whom are worried about jobs and energy costs. Congress needs to play a role in addressing these issues.

My 5-min opening statement

Chairman, Ranking Member, Members of the Committee, thank you for inviting me to speak.

I’d like to frame my opening remarks around one big question:

What does it mean for America to win in AI?

We can all agree that AI is likely to be the most consequential technology of our lifetimes and that the United States must win the AI race with China.

But what does it actually mean for America to win in AI?

Let me offer three versions of what it means to win and describe how the U.S. stacks up against China.

1. Does winning mean developing the world’s most powerful AI models?

If yes, then the US has the decisive lead. American AI models are widely recognized as the best in the world on virtually any metric you can think of, from math and reasoning to coding and AI agents. American private companies continue to set the pace at the technological frontier, supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world.

China’s AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources. So instead, they’re prioritizing other goals. They’re focused on efficiency, building models that are cheaper to train and run. They’re focused on adoption, using an open-source strategy to win users around the world. And they’re focused on integration with other platforms and services.

On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything.

2. Does winning mean having the strongest AI stack?

America leads in several critical layers: frontier models, advanced semiconductors, and compute infrastructure.

But on energy, China is ahead. In the past four years alone, China built the equivalent of the entire US power grid. Energy is one of our biggest bottlenecks, and we must find ways to add capacity while keeping a lid on energy bills for local communities.

In general, China is pursuing a full-stack approach to accelerating their AI development, from chips and compute to models and applications. They’re focused on not just the best models, but on deployment and diffusion. They want to use AI to turbocharge a wide range of sectors, from manufacturing and research to education and healthcare as well as of course their military. They’re placing a special emphasis on physical applications of AI, including humanoid robots, autonomous vehicles, and industrial automation.

In particular, a large part of their industrial policy efforts are aimed at semiconductors, which they’ve kicked into high gear in the face of our export controls and other measures. China is now racing to build a resilient domestic semiconductor industry across the entire supply chain: photoresists, etching and deposition tools, chip design software, memory, packaging, and even advanced lithography machines.

Export controls are not a panacea, but they have bought us some time. The question is how we use this time effectively.

3. Does winning mean making sure that AI benefits the American economy and the American people?

This may be the most important definition of winning—and the one we focus on the least.

Technological leadership does not automatically translate into broad-based social and economic gains. Having the biggest and baddest models alone won’t cut it. The real test will be whether we can leverage AI to make our economy more productive, our workers better off, and our communities more vibrant.

This also means managing the disruptions caused by such a transformative and new technology. When many Americans hear about AI, they feel worried—worried about jobs, worried about energy costs, worried about their children’s future. We cannot just focus on the upsides while leaving these real concerns unaddressed.

So when I think about what it means for America to win in AI, I think about all three definitions. We need the best AI models. We need the strongest AI stack, which includes energy. And we need to make sure AI benefits ordinary Americans while addressing the disruptions it may cause. This is where Congress plays a crucial role—and where American democracy has the greatest edge over China.

Thank you.

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You can read my 11-page written testimony here.

You can watch the full hearing here, which includes opening remarks by the other two expert witnesses and a Q&A with House members:

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