> 1. US frontier lab unit economics are better

That's not generally true, since there is generally still much reliance on NVIDIA. The true low cost providers are Google with their TPU and vertically optimized stack, and Amazon with Trainium. However, Google does not have their own frontier model, and Anthropic (who are partially served by Amazon) are also paying a premium for extra NVIDIA-based capacity from SpaceX, maybe soon from Meta too.

I don't know how the economics of domestic Chinese Huawei-based clouds (no NVIDIA) compares to the west, but since serving cost is mostly hardware depreciation and to a lesser extent electricity, they are not necessarily at a disadvantage (Ascend 950 costs roughly 50% of an NVIDIA H100), and more to the point it is irrelevant when considering US commercial use that is more likely to be using Chinese open weights models from US providers served on NVIDIA based hardware.

I think the real significance of Chinese frontier models being open weight is that it takes development cost amortization out of the US-based serving cost, while the US AI labs can't afford to do this. The US labs therefore need to reduce development spending to remain price competitive. The Chinese companies are of course still making money from the Chinese market, whether by selling API access or by other business models such as Ziphu making 75% of it's total revenue by selling services to Chinese customers who are running their models on-prem due to the Chinese apparently being very concerned about data privacy.