US panic is missing the point on Chinese AI

The rising use of Chinese artificial intelligence models in the US has led to growing anxiety among Silicon Valley executives, Washington lawmakers, and even officials in Beijing. But the warning lights have been flashing for a while.

Since last year, I’ve been tracking the trend of American startups using Chinese AI. This year, the shift has become harder to ignore. But it didn’t emerge in a vacuum. China’s low-cost, open-weight push was always going to appeal to developers, the backbone of AI innovation. Washington’s wake-up call arrived late.

At the heart of the latest flashpoint is a debate about distillation, or training one model on the outputs of another, and a growing attempt to discredit Chinese competitors as stolen goods. Yet the technique is widely acknowledged as inevitable in the industry. Elon Musk told a federal court earlier this year that it is “standard practice” and something “generally all the AI companies do.” Some observers have also questioned whether US AI companies are learning from Chinese competitors.


But accusations from US frontier labs that Chinese companies are illicitly copying them in violation of terms of service have given the issue new political force. A method that was once a breakthrough known as “knowledge distillation” is being rebranded as “knowledge theft.” Silicon Valley seems to be hoping that this line of attack is enough to stop people from adopting these tools.

There is a rich irony here. The same companies that swept up the works of billions to build tools threatening artists, writers and other creatives are now feeling the same anxiety of witnessing their own economic leverage erode. But putting that aside, this battle over distillation is raising a bigger question. In the long run, can knowledge and information be contained by a handful of US AI companies in a technology race increasingly defined by diffusion? A business model built on this looks increasingly fragile as millions of users, engineers and labs around the world are incentivized to learn from and iterate or be left behind.

Preventing distillation isn’t easy, and current technical defenses are insufficient. As some researchers point out, it’s not like physical hardware controls, which means fully restricting it seems almost impossible. Some attempts have already backfired. Anthropic recently rolled back hidden code it embedded in Claude Code to track users’ locations after criticism that it was invading the privacy of all. The controversy prompted Beijing to issue a rare warning last week that the tool poses a serious threat.

Claude Code, notably, was already barred for use in China. But a grey market of middlemen and so-called “transfer stations” has emerged, Zilan Qian, a research associate at the Oxford China Policy Lab, points out. Whether all of these accounts — held by students, professors and everyone behind the Great Firewall seeking to try the best AI tools — are being used for systematic, surreptitious distillation is likely overblown, Qian added.

With technical defenses lacking, it makes sense that companies are turning to the US government for help. For now, lawmakers seem to be attempting to inflict reputational harm on Chinese models.

But it’s very likely that tighter restrictions are being debated on both sides of the Pacific. When Washington abruptly curtailed access to Anthropic’s top models for all foreign nationals, including some of the company’s own staff, it suggested that more stringent regulation could be on the horizon for Chinese AI. (It reversed that decision soon after.) And Reuters reported last week that Beijing was also considering restrictions on overseas access to China’s most advanced AI models.

But the reality is that if either side clamps down on America’s rising use of Chinese models, the pain will be felt the most by US developers. A strong ecosystem of researchers, startups and talent is something that Washington should be supporting. A short-sighted attempt to protect current American AI leaders could make it harder for the next champions to get built.

Rather than banning Chinese offerings, the US should build better alternatives. That means serious investments in open-weight projects, public compute, working with universities and offering startup-friendly options. The cost of these models is appealing, but so is the control they offer. Developers can fine-tune, run and build on them locally without being fully dependent on shifting whims of closed frontier labs. That flexibility is key for developing future AI products and spreading the technology more broadly throughout the economy.

Company leaders and investors should also focus on other moats, including institutional trust, computing resources and better support for paying enterprise customers that make users not want to switch over.

Labeling Chinese AI models as rip-offs has become a convenient talking point. As I’ve written before, the argument that these labs can only compete due to copying is tired. Worse, it risks leaving the US caught off-guard again by real innovations, including techniques that let models use compute more efficiently. This exchange of knowledge is helping American labs now, especially amid the outbreak of a price war.

Washington can try to protect today’s AI giants by rebranding their business-model problems as a national security issue, or it can focus on cultivating tomorrow’s champions by supporting open research. It will be hard to do both.