---
title: "Who's Afraid of Chinese AI Models? US Should Legalize Distillation and Fair Use"
source: "Simon Willison 博客（Atom）"
source_tier: "t2"
url: "https://simonwillison.net/2026/Jul/20/afraid-of-chinese-models/#atom-everything"
permalink: "https://feed.astralor.com/items/itm_51f9e12e580038334f1e0f52"
published: "2026-07-20T17:09:19.000Z"
score: 68
channel: "ai"
tags: ["AI Distillation","Alibaba","Ben Thompson","Qwen 3.8 Max","Fair Use","Open Source Policy"]
---

# Who's Afraid of Chinese AI Models? US Should Legalize Distillation and Fair Use

## AI commentary

> What struck me about this piece is how it directly calls out the hypocrisy of US labs suing for copyright infringement while trying to restrict distillation. For readers following US-China AI competition, open source ecosystems, and AI copyright law, Thompson's proposal is worth pondering—though politically daunting, the direction may be correct.

## At a glance

Ben Thompson proposes a bold policy: the US should pass a law making training data collection fair use and barring terms of service that forbid distillation, to help US open models compete with Chinese counterparts. He argues distillation is nearly impossible to stop, so the US should adopt a new copyright policy that indemnifies labs and lets their learnings fuel further innovation. Meanwhile, Alibaba's decision to release Qwen 3.8 Max as open weights may have been influenced by Xi Jinping's speech encouraging open source and collaboration.

## Key points

- US should legislate training data as fair use and bar ToS restrictions on distillation.
- Distillation is effectively just querying the API and almost impossible to ban.
- Alibaba open-sourcing Qwen 3.8 Max may be influenced by Xi Jinping's speech.

## Full text

Who’s Afraid of Chinese Models? Interesting proposal from Ben Thompson that both addresses the hypocrisy of labs outlawing distillation against their models despite training on unlicensed data, and could help US open models compete more effectively with their Chinese counterparts: The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else. Ben also theorizes that Alibaba's decision to release Qwen 3.8 Max as open weights - a reversal from their decision not to release Qwen 3.7 Max in May - may have been influenced by a recent speech by Xi Jinping, who said: We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing. Via John Gruber Tags: ai, generative-ai, llms, training-data, qwen, ai-ethics, ai-in-china

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