The US is subsidizing in exactly the same way through the US Chip Act (as well as state level tax subsidies):
> The act includes $39 billion in subsidies for chip manufacturing on U.S. soil along with 25% investment tax credits for costs of manufacturing equipment, and $13 billion for semiconductor research and workforce training
https://en.wikipedia.org/wiki/CHIPS_and_Science_Act
> Presumably, they'll incorporate DeepSeek's MLA* architecture to get all the benefits for next year's releases (if not this year's upcoming releases) which will bring down their costs.
You can be sure the frontier labs all have similar approaches, but they just don't talk about them. That's why eg Google Flash (the old versions!) were do cheap.
I mean Google published MTP a month or so ago and it has sped up Qwen models by 1.7 times.
If that is what they still publish you get an idea of what they aren't.
They're subsidizing this in many ways - Huawei chips, new DDR5 memory fabs, etc.
Ultimately, DeepSeek's architecture is significantly more cost effective than anything from Google, OpenAI, or Anthropic.
Presumably, they'll incorporate DeepSeek's MLA* architecture to get all the benefits for next year's releases (if not this year's upcoming releases) which will bring down their costs...
They need to actually make money, though, so that might still not give them enough room to make enough money.
Ultimately, hardware depreciation is like 80% of total spending. So power is not as big of a deal in cost. The bigger problem is if you can get the power at all, not how expensive it is.
If you want to bring down inference costs, using less hardware is far more effective than getting cheaper electricity.
Google is in a sweet spot, because they aren't paying 80% margins to nVidia for hardware. So they're probably paying half as much deprecation as everyone else is (or maybe 1/4th for inference - which is now the biggest percentage overall).