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US AI labs' efforts to prevent foreign rivals from distilling their models face accusations of hypocrisy. Critics point out that these labs train their own models on vast amounts of public data without permission. This "pot calling the kettle black" dynamic complicates legal and ethical arguments against industrial-scale distillation.

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Accusations that Chinese labs cheat by copying US models are misleading. The practice, known as distillation, is common across the industry (including by Elon Musk's xAI) and academia. Now, with Chinese labs dominating open source, American startups are increasingly building on top of Chinese models.

As more of the public internet and code repositories are generated by LLMs, any new model trained on this public data is, in effect, being 'distilled' from other models. This complicates accusations of direct distillation and blurs the line for what constitutes original training data.

Leading AI labs, despite intense competition, are collaborating through the Frontier Model Forum to detect and prevent Chinese firms from creating imitation models. This rare alliance is driven by the shared existential threat that 'adversarial distillation' poses to their business models and to U.S. national security.

There is a profound hypocrisy in the AI industry's stance on intellectual property. Companies that built their foundational models by scraping the entire internet are now seeking regulatory protection to prevent others from distilling or learning from their models—mirroring how the music industry fought Napster after profiting from an open ecosystem.

As more of the internet and code repositories are generated by leading AI models, any new model trained on this public data inadvertently "distills" the knowledge and quirks of those proprietary systems. This blurs the line between original training and outright copying.

Despite intense domestic rivalry, top US AI labs like OpenAI, Anthropic, and Google are collaborating to detect "adversarial distillation"—where Chinese firms copy their models. This rare cooperation shows the shared commercial and national security threat from foreign competitors outweighs their direct competition.

In his trial against OpenAI, Elon Musk admitted under oath that using one AI model to train another—a practice known as distillation—is something 'all the companies do.' This confirms that a legally and ethically gray practice is widespread across the industry.

A new battle line in AI is emerging around model distillation. US officials are framing "covert industrial distillation," like Moonshot AI's alleged activities, as unacceptable IP theft. This is distinct from legitimate distillation used to create smaller, efficient open-source models, setting the stage for future regulation and trade disputes.

While foreign AI companies allegedly distill US models to accelerate progress, American counterparts like Meta refrain from the practice. The significant legal and reputational risks in the US create an uneven playing field, effectively handicapping domestic players who cannot leverage this powerful, albeit controversial, technique for model development.

The US accuses China of "distillation"—querying American AI models millions of times to reverse-engineer their logic and capabilities. This marks a shift from commercial competition to industrial-scale intellectual property theft, escalating the geopolitical conflict beyond government rhetoric.