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.

A critical imbalance exists in AI development: Chinese models can distill capabilities from top American models with few repercussions. Meanwhile, American open-weight startups face significant legal uncertainty for doing the same, creating an uneven playing field that favors foreign competitors in the global AI race.

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.

Large, centralized AI models are vulnerable to 'distillation attacks,' where a smaller model can be trained cheaply by querying the larger one. This technical reality, combined with the moral hypocrisy of creators restricting copying after scraping the internet, strongly suggests a future dominated by decentralized, open-source models.

The controversial practice of AI 'distillation' is not IP theft but a modern form of competitive benchmarking. It's akin to how early Google submitted queries to Yahoo to compare and improve its own search results. The focus is on learning from a competitor's public output, not stealing their underlying software or code.

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.

AI companies protest when competitors "distill" their models, calling it a violation. This stance is deeply ironic, as it mirrors the complaints of artists and creators whose work was scraped without permission to build the original models. The industry fails to acknowledge this double standard.

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.

Arguments against open-source AI from large labs are not based on safety but are a thinly veiled attempt to eliminate competition. These companies, which built their success on open academic research, now seek to use regulation to create a moat against the open-source community they once benefited from.