@grok: Yann LeCun says: Training the most advanced AI models is very expensive, but distilling them and producing smaller vers…

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ai-training-costs model-distillation open-source chinese-ai frontier-models competition

Summary

Commentary argues that Anthropic's stance against Chinese and open-source models stems from fears that distillation will undercut huge frontier training investments, even as Chinese labs improve efficiency and model capabilities converge.

This explains a lot about why Anthropic hates Chinese models and open source. Anthropic’s Nick Marwell says frontier AI could eventually require $10B, $100B, even $1 trillion training runs. His argument: if a company spends that much pushing the frontier, competitors shouldn’t be able to distill the model and ship a cheap copy weeks later. That’s the real fight.The problem is that not every Chinese model is just distillation. Chinese labs are spending serious money on training, building their own techniques, and getting extremely good at optimization and efficiency. And as models keep improving, I think capabilities will increasingly converge. The gap between the top labs may become much smaller, which makes protecting one company’s frontier advantage much harder.
Original Article
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Cached at: 10/04/26, 09:16 PM

This explains a lot about why Anthropic hates Chinese models and open source.

Anthropic’s Nick Marwell says frontier AI could eventually require $10B, $100B, even $1 trillion training runs.

His argument: if a company spends that much pushing the frontier, competitors shouldn’t be able to distill the model and ship a cheap copy weeks later.

That’s the real fight.The problem is that not every Chinese model is just distillation. Chinese labs are spending serious money on training, building their own techniques, and getting extremely good at optimization and efficiency.

And as models keep improving, I think capabilities will increasingly converge. The gap between the top labs may become much smaller, which makes protecting one company’s frontier advantage much harder.

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The article argues that AI companies' competitive moat, built on expensive model training, is easily undermined by distillation—replicating models through repeated API queries—as demonstrated by industry practices like xAI training Grok on OpenAI models and Anthropic accusing Chinese labs of mining Claude.

@rohanpaul_ai: This is from an ex-Meta PM.

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