Comment by mediaman

The (quite excellent) article discusses several of your points. If you haven't read it, I recommend it.

  - Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not.

  - Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

  - The highest tier Chinese models are not more economical than US frontier models. Try GLM 5.2 and see how much it costs to do real work. I did, and it was more expensive than GPT 5.6.

  - This is because US labs are leading on cost efficacy of inference ($/task)

  - Training will decline as a percentage of costs as inference expands compute share due to agentic workloads. A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.

  - With increasing inference as % of total compute, if labs create efficient models -- which they can, because they can create highly optimized models amortized over very high inference loads -- they can be low cost producers, and be competitive at $/task rates

OpenAI really shows the way here. Their cost per task is less than half that of Anthropic because of more efficient tokenization and less verbosity. OpenAI is both cheaper and better than Chinese models for frontier work.