China’s Success Is Forcing a U.S. AI Rethink

Is China's Pragmatic AI Strategy Winning?

!Foreign Policy

Foreign Policy

Get analysis and alerts more quickly in the mobile app.

Install

Back to Foreign Policy Magazine home page

* Latest
* Regions
+ Asia & the Pacific
+ China
+ Middle East & Africa
+ Americas
+ Europe
* Newsletters
View All Newsletters

* FP Live

* Latest
* Trending:
* Iran war
* China's bet on AI

Search this website

* Preferences
* My FP Feed
* Saved Articles
* Newsletters
* Magazine Archive
* Subscription Settings
* FAQs
* Log Out

Sign In

Sign In

Partner with FP at UNGA81 Partner with FP at UNGA81

Subscribe SubscribePartner with FP at UNGA81 Partner with FP at UNGA81

Analysis:

China’s Success Is Forcing a U.S. AI Rethink

*
*
* Facebook
* Bluesky
* X
* LinkedIn
* WhatsApp
* Reddit

Save

1. Create an FP account to save articles to read later.
Sign Up
ALREADY AN FP SUBSCRIBER? LOGIN

PDF

1. **Downloadable PDFs** are a benefit of an FP subscription.
Subscribe Now
ALREADY AN FP SUBSCRIBER? LOGIN

Gift

*
*
* WhatsApp

1. Gifting articles is a subscriber benefit.
Subscribe Now
ALREADY AN FP SUBSCRIBER? LOGIN

2. This article is an Insider exclusive.
Contact us at [email protected] to learn about upgrade options, unlocking the ability to gift this article.

Analysis

China’s Success Is Forcing a U.S. AI Rethink

Silicon Valley obsessions may have led Washington down a dead end.

By **Robert A. Manning**, a distinguished fellow with the Strategic Foresight Hub at the Stimson Center, where he works on its strategic foresight and China programs, and **Giulia Neaher**, a research analyst at the Stimson Center’s Strategic Foresight Hub.

!Five people sit in folding chairs pushed up against a wall, all of them on their phones and several looking board. Text on the wall behind them says "AI for Real" with images of robotic arms and household appliances printed around it.

*Five people sit in folding chairs pushed up against a wall, all of them on their phones and several looking board. Text on the wall behind them says "AI for Real" with images of robotic arms and household appliances printed around it.*

People look at their mobile phones as they take a break in the AI section at the Chinese International Supply Chain Expo in Beijing on June 26. Kevin Frayer/Getty Images

1. Get audio access with any FP subscription.
Subscribe Now
ALREADY AN FP SUBSCRIBER? LOGIN

August 27, 2026, 2:35 PM

The United States has bet the farm on artificial intelligence across the board, from industry to finance to government policy. But the combination of a China shock and multiple cases of AIs gone rogue, set against the backdrop of a growing populist backlash, have forced a moment of truth that calls into question the foundational assumptions guiding U.S. development and regulation of AI.

The July release of Chinese start-up Moonshot’s Kimi K3 open-weight model, which proved nearly as capable as closed U.S. frontier models, stunned the American AI industry, which has largely ignored open models in favor of closed ones. Open-weight models provide users with free access to the “weights,” or the parameters that shape a model’s outputs, enabling easier customization and lower-cost access than closed models.

The United States has bet the farm on artificial intelligence across the board, from industry to finance to government policy. But the combination of a China shock and multiple cases of AIs gone rogue, set against the backdrop of a growing populist backlash, have forced a moment of truth that calls into question the foundational assumptions guiding U.S. development and regulation of AI.

The July release of Chinese start-up Moonshot’s Kimi K3 open-weight model, which proved nearly as capable as closed U.S. frontier models, stunned the American AI industry, which has largely ignored open models in favor of closed ones. Open-weight models provide users with free access to the “weights,” or the parameters that shape a model’s outputs, enabling easier customization and lower-cost access than closed models.

Until Kimi K3, many presumed that this ease of customization—and sharing of intellectual property in the form of open weights—meant sacrificing advanced AI capability. U.S. policymakers and developers prioritizing the pursuit of AI superintelligence were shocked when Kimi achieved both openness and frontier capability at once. Kimi K3’ s success raised the serious possibility that while the United States has been pursuing closed frontier capability, China has been running a different race—and winning.

So how did the United States go down what may be entirely the wrong path? Just what artificial general intelligence (AGI) would mean is widely disputed, but it’s a concept that’s used widely in American AI strategy. The most basic interpretation of AGI is that it means AI that matches or surpasses human intelligence—but this definition struggles to encompass the broad set of ideas that people hold when they talk about human intelligence itself.

Practically speaking, “AGI” is often used in the United States as a catch-all for the end goal of AI development, with little measurable or testable dimension to it. So, when and how did such an ambiguous, even eschatological, goal such as AGI become the endgame for American AI policy?

Much of the answer lies in Silicon Valley’s sci-fi-inspired, techno-utopian/techno-dystopian culture. An almost religious movement has arisen from the work of sci-fi authors such as Vernor Vinge and Ray Kurzweil, conceiving the idea of AI superintelligence merging with or superseding human control of the planet.

This belief in the future omnipotence of AI is evident in industry leaders’ approaches to Washington earlier in the 2020s, when tech CEOs such as Sam Altman begged Congress to regulate AI and control its risks, in a move that only served to reinforce policymakers’ idea that AI could be unprecedentedly powerful. After all, what kind of CEO would ask to be regulated, unless they thought their product was dangerous?

The assumptions behind the rationale for continuing to develop AI despite its potential danger were that its supposed omnipotence could be equally consequential for economic growth and the greater good. Any discussion of the danger of AI only served to reinforce the reason to develop it. However, this dual nature had an important implication—if another country secured advanced AI, it would have great power to do harm.

The idea of “recursive self-improvement” (RSI) in AI compounded this worry about foreign leadership in the technology. In an RSI scenario, superintelligent AI becomes able to modify itself, conducting autonomous research in which the AI continuously improves its own intelligence. Assuming that AGI comes with RSI capacity means that the first AI developer to reach AGI will forever be one step ahead of the competition, since the AGI will continuously improve itself. This idea may seem far-fetched, and it carries the weight of significant assumptions that are still hotly contested between AI technologists and national security experts, but it has taken root deeply in policy spaces.

The assumptions have been that if AGI is as powerful as figures such as Altman have claimed, then the first country to reach AGI will have a permanent lead in the technology. Accepting those premises means that there is a clear imperative for the United States to get there ahead of China and do so in a closed, secretive way that ensures that competitors stay behind. This assumption has driven American AI policy to prioritize competition with China above all else, anchoring U.S. policies such as export controls, deregulation and state AI law moratoriums, and proposed open-source AI bans.

But China’s success demonstrates that U.S. assumptions are flawed. The sanctions, export controls, and bans mostly incentivized China to seek end-runs around tech curbs and accelerate its efforts toward autonomy—and toward pursuing a pragmati