Post-training focus bypasses Chinese compute constraints
Chinese AI labs are overcoming chip and capital shortages by focusing on post-training optimization and smart distillation of Western frontier models.
The argument
The guest argued that Chinese labs work backward from US frontier breakthroughs, utilizing Western models as teachers for data labeling and waiting out exclusivity windows to buy datasets at a fraction of the cost.
The thesis, stress-tested
✓ What validates it
- ✓Chinese models maintaining a narrow 6-to-9 month lag behind US frontier models at lower R&D costs
- ✓Successful deployment of highly specialized domestic agent and coding applications
▸ Risks discussed
- ▸Dependence on US frontier models to set the initial direction
- ▸Potential tightening of data exclusivity or distillation restrictions
Hear it yourself
"stack. Aside from Deepsea, can you kind of describe the differences or what China is trying to do on the actual frontier side? Because there are there are some. I I think if you really have it have to look at the ecosystem, we can kinda put aside the big tech for now. But looking at the maybe the foremost relevant startup labs, DeepSeek, Moonshot, who had Kimi, Ziay, who has GLM, and then MiniMax, they are still probably the most committed to frontier research. However, because the constraint we mentioned that"
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