> "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 a flatly false statement.

It may not be false but may be a "category error" [0]. Reserved GPU pricing & bulk inference pricing is 3x to 6x cheaper than "API rates", but renting your own GPU cluster (in this crunch) to run a 600b+ open weights is going to be "more expensive than GPT 5.6".

Even then, it remains to be seen if Huawei will pull their weight (and match up to Nvidia) as spectacularly as their fellow Chinese AI Labs have. If so, the WAICO alliance is ready to go all-in.

[0] Ben, and probably other "influencers" in this space, may be prone (knowingly or unknowingly) to favour points that make their conclusion for them (https://en.wikipedia.org/wiki/Motivated_reasoning).

Fair. Too strong a statement.

But much like Ben's point that commoditization is a relatively novel concept to many in tech, it's not the consumer AI applications at risk of commoditization. They have distribution there.

It's the literally millions of engineers who are updating codebases with tools replacing workers partially or wholly. It's the supply-side where there's compression, and no need for distribution.

I would argue, given the enormity of the existing SaaS stack and how it integrates with the human machinery of personnel, that's where volume is. And that is clearly cheaper and a home run.

Commoditizing a ~$100B AI consumer market is no small feat. Commoditizing 20% of the $500B SaaS market, to say nothing of the underlying systems in the who-knows-how-many trillions "Big Tech" market (you're obligated to say that like the Kool Aid man), is shocking.