Who’s Afraid of Chinese Models?
- ID
- 6148
- Status
- summarized
- Published
- 21 Jul 2026, 1:09 AM
- Fetched
- 21 Jul 2026, 9:46 PM
- Provider
- Simon Willison
- Category
- developer-ai
- Original URL
- https://simonwillison.net/2026/Jul/20/afraid-of-chinese-models/
- Source URL
- https://simonwillison.net/atom/everything/
Summary
- Score
- 7.5
- Created
- 21 Jul 2026, 9:46 PM
- Tags
- Audience
- developersai_ml_learnersai_agent_users
What happened
Simon Willison highlights Ben Thompson's proposal that the US should explicitly legalize training data collection as fair use while barring terms of service that forbid distillation, arguing this would both indemnify labs and fuel open innovation. The post also notes Alibaba's release of Qwen 3.8 Max as open weights—a 2.4T parameter model—possibly influenced by Xi Jinping's recent call for open source collaboration, reversing their earlier decision to withhold it.
Why it matters
For Malaysian builders, the open-weight release of a frontier-class 2.4T parameter model like Qwen 3.8 Max means more options for local deployment, fine-tuning, and cost control without API lock-in. The broader policy debate on distillation and training data fair use could shape which models remain accessible and how freely developers can build on top of them.
Discussion angle
What does open-weight access to a 2.4T parameter model practically mean for Malaysian teams—can you actually self-host something this large, or is the value mainly in fine-tuning derivatives and using hosted endpoints?