On July 22, US Treasury Secretary Scott Bessent told Fox Business that the United States would sanction Chinese open-source AI models if they are found to be "stealing intellectual property from American companies." This is the next card in America's AI competition playbook after chip export controls — this time targeting the models themselves.

The Real Context Behind the Threat

Bessent's comments come at a pointed moment: just days earlier, Moonshot AI released Kimi K3, which approaches and in some benchmarks surpasses Anthropic Opus 4.8 and GPT-5.6. The rapid progress of Chinese open-source models is threatening the business models of OpenAI and Anthropic — companies that just raised funding at tens-of-billions valuations and are burning cash training their next-generation models.

Axios previously reported that the Trump administration is considering a wholesale ban on Chinese open-source models, though that claim was disputed by some. Bessent's statement can be seen as an escalation: float the threat first, see what happens.

But there's a fundamental paradox here: once an open-source model is released, it's like water spilled on the ground. You can ban US companies from using it, you can sanction the company that released it, but the code is already on the internet, downloaded, forked, and fine-tuned by developers worldwide. How effective can sanctions actually be?

The Core Dispute Over Distillation

US AI companies accuse Chinese models of "distilling" their capabilities. Distillation can indeed transfer some abilities from large models to smaller ones, but there's fierce debate over whether this constitutes "theft."

Microsoft CEO Satya Nadella publicly mocked this logic earlier this month: "I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation," after asserting that model providers' fair use rights to train on public data are necessary.

Hugging Face CEO Clem Delangue put it more bluntly on the TechCrunch Equity podcast: "We know distillation to be a very small factor in the ability to create good models, and it's a practice that everyone is doing, including companies in the U.S. If it were easy just to do distillation to get good at building AI models, there would be many other countries, including in the U.S., with much better open source AI. The reality is they have really, really good research teams in China...taking a much more open and collaborative approach to AI than in the U.S."

In other words: if you can't beat 'em, accuse 'em of cheating.

The New Landscape of the Open-Source Battlefield

From chip blockades to model sanctions, US AI competition strategy is escalating layer by layer. But each escalation exposes the same problem: whatever you block, China builds indigenously; whatever you sanction, becomes decentralized.

The nature of open-source models makes genuine blockade nearly impossible: code is hosted on servers worldwide, weights distributed via torrents and IPFS, community-driven development independent of any single company or country. The US can sanction Chinese companies and ban American users, but it can't stop global developers from building products with these models.

Most ironically: it was US chip export controls that forced China down the open-source + indigenous R&D path. When the most advanced chips became unavailable, Chinese companies instead forged their own path in model efficiency, inference optimization, and small-model capabilities. Now the US wants to block models too — what will be the result?

For developers and founders, this geopolitical chess game means one thing: don't tie your stack to any single country's models. Multi-model, multi-cloud, open-source fallbacks will be the default configuration going forward.

The AI competition has shifted from a race between companies to a systemic rivalry between nations. And open source is the most uncontrollable variable in that contest.

See you tomorrow.