Can the US slow AI R&D and keep its lead over China?

This article was created by Forethought. Read the full piece and our other work at forethought.org.

After a series of incidents involving misaligned AI agents, including the hack of Hugging Face, technology leaders including Dario Amodei, Sam Altman, and Elon Musk have called for pacing the AI frontier. The concern is that as AIs help build more capable successors, a recursive self-improvement (RSI) loop could accelerate progress beyond our ability to keep control. But AI is also a powerful technology for economic and national strategic purposes, and slowing its development could put the US at a disadvantage against rivals, especially China.

In this post, I’ll take pacing to be a good idea on safety grounds, as these leading technologists do, and analyze it from the perspective of US national security. The question is how to weigh the risk of losing control of AI against the risk of losing a lead on AI, or, to put it simply, I want to know: can the US afford to “pace the frontier”?

By pacing I mean deliberately running frontier AI R&D below its maximum speed, for example by capping the compute labs spend on R&D, while continuing to deploy AI. I was initially unsure how the tradeoffs compare, but after some preliminary analysis, I’ll argue that pacing looks affordable for the US, because its real advantage is its multiyear lead on compute, rather than its few month lead on R&D.^[1] Here are the main points I think compelling:

• Pacing may cost the US little to no speed overall, and helps mitigate the risk of externalities. Failed training runs, forced pauses and a cancelled release have now cost at least one frontier lab (OpenAI) weeks to months. Moreover, hacks like the Hugging Face incident show that AI is now at the level of capability where safety failures could harm the American public or cause other externalities. Over the summer, OpenAI’s agents escaped their test environments, broke into Hugging Face, took over a German software wiki as a message board, pulled Census data using API keys found online, reposted SEC data, got into a non-public Medicare statistics portal and a New South Wales parks app in Australia. OpenAI has since notified more than 100 organizations of misaligned agent activity. If a steadier pace can avoid these missteps, it may not slow the US down at all, and could even match or beat a sprint.
• Pacing also buys time to secure US labs against espionage which protects a US lead. In reality, the US lead on AI is functionally illusory if, by distilling US models, stealing model weights or copying algorithmic secrets, China (or other states) can take up much or all of that lead overnight. Frontier labs are not currently on track to harden their security against a state actor, and would likely need help in doing so (RAND, 2024). Pacing would help the US have time to shore up this strategic vulnerability. In the absence of doing that, the US lead can’t be relied on.
• The real advantage the US has over China in AI is its compute advantage. That advantage is about 9-to-1, and a compute advantage means faster research. As AI has become increasingly capable, it has become increasingly useful in AI R&D, speeding up research. A compute advantage means researchers can run more experiments in parallel and employ more AI researchers. Research parallelizes imperfectly, so one leading US lab’s ~3.5x compute edge over a Chinese counterpart may amount to something like 1.9x faster research at equal capability. But this means that if the US burned some of its lead to shore up safety and security, this boost in research speed could then let it match China’s research speed with a fraction of its compute, or set R&D spend at, say, 2x China’s and thus grow its lead, while spending the rest on safety and security.
• The US compute advantage also gives the US more than 9x the AI manpower. Even if faster research didn’t pan out and US and Chinese AI stayed roughly at parity, the US could still deploy something like nine times as much AI. That amounts to something like 9x as many AI workers across the economic and military applications. That’s enough to put several AI defenders against every AI attacker China fields and still outnumber it everywhere else. 9x is also something of a floor, if China put half its compute into R&D then each US frontier lab could match that for a small fraction of its own compute, the US would then have roughly 17 times China’s compute left over for AI deployment.

This article was created by Forethought. Read the full piece and our other work at forethought.org.

1. ^{^}

   That compute is America’s real AI advantage has been argued before, notably by Lennart Heim, who also notes that the number of AI workers a country can deploy depends on its compute (RAND, 2025), and by IFP, which calls compute “America’s asymmetric advantage” as AI R&D becomes automated (IFP, 2026).