Innovation in U.S.-China AI race flows both ways - Rest of World

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By Paul Triolo

22 July 2026

Ideas

The U.S. wants to contain China’s AI. Silicon Valley keeps using it

By Paul Triolo

Ideas

Arguments, opinions and essays from a global perspective.

When Anthropic’s chief national security officer and former Biden administration export control architect Tarun Chhabra recently highlighted model distillation as an emerging national security concern, one detail stood out. Chhabra identified Chinese AI developer Zhipu among the companies that have allegedly distilled capabilities from frontier American models. The warning fits neatly into Washington’s narrative: Chinese firms are racing to close the AI gap by leveraging innovations pioneered by Silicon Valley.

But only hours after Chhabra’s comments, that narrative became considerably more complicated. Mira Murati’s Thinking Machines — which raised $2 billion on the strength of her reputation as OpenAI’s former chief technology officer — revealed that its first foundation model was built in part using Chinese models. Inkling’s architecture drew on DeepSeek-V3, and its post-training process incorporated synthetic data generated by Moonshot AI’s Kimi K2.5. This is how the open-weight/source model world works: One company builds on top of another’s innovation.

The juxtaposition is striking. One of America’s leading frontier companies is warning that Chinese laboratories are learning from U.S. models at precisely the moment one of Silicon Valley’s newest flagship ventures openly acknowledges learning from Chinese ones. That apparent contradiction says far more about the state of frontier AI than either announcement.

American firms … are more willing to incorporate advances from China’s rapidly improving open-weight ecosystem."

The word “distillation” has rapidly become one of the most politically charged terms in AI and now in geopolitics, driven by accusations from leading U.S. closed-source model developers that Chinese companies are able to stay close to the frontier by leveraging this practice. Increasingly, it is presented almost interchangeably with intellectual property theft. But distillation is neither new nor particularly exotic. It has been part of machine learning for more than a decade. A larger “teacher” model generates outputs that are then used to train a smaller “student” model capable of reproducing much of the original model’s behavior at a fraction of the computational cost.

Today, however, the technique has expanded well beyond simply compressing large models. Synthetic data generated by one model routinely becomes training material for another. Frontier laboratories increasingly rely on teacher models to improve reasoning, coding, multilingual performance, and post-training alignment. In many cases, the most valuable training data is no longer collected from humans at all — it is generated by other AI systems. This practice is not confined to China.

Indeed, almost every leading laboratory now employs some version of teacher-student training. OpenAI, Anthropic, Google DeepMind, Meta, Alibaba, Tencent, Moonshot, DeepSeek, and Zhipu all publish research describing synthetic data generation, reasoning distillation or related techniques. The competitive question now is not whether laboratories use distillation, but whose models become the teachers.

As frontier models become more capable, the value of their outputs has increased dramatically. If billions of dollars invested in training can be even partially replicated through extensive querying of commercial APIs, the incentive to extract those capabilities naturally grows. That is why OpenAI and Anthropic have steadily tightened API protections, implemented behavioral monitoring, limited automated querying, and invested in techniques designed to detect large-scale output harvesting.

The recent headlines demonstrate how difficult it has become to draw clean technological boundaries. While American firms understandably seek to prevent unauthorized extraction of their frontier capabilities, they are themselves more willing to incorporate advances from China’s rapidly improving open-weight ecosystem. Thinking Machines acknowledged incorporating Chinese models into its own development pipeline.

This should not be surprising. Chinese open-weight models have become extraordinarily competitive over the past year. DeepSeek, Moonshot, Alibaba’s Qwen family, and Tencent’s Hunyuan models are now regularly benchmarked alongside Claude, GPT, and Gemini by researchers. For engineers building new systems, as opposed to government regulators, the nationality of a model increasingly matters less than its performance, architecture, and licensing terms.

That reality presents an uncomfortable challenge for policymakers. Much of the current debate surrounding export controls and frontier AI governance continues to assume that innovation flows largely in one...