So far, it's really only the Chinese labs (and FAIR or whatever Meta's project is called now) that are doing this. Oh yeah, and Google's Gemma.

At the moment, this is all massively distorted by the prestige and investment money flowing into the space. None of the labs have to charge the real cost of inference let alone the marginal cost of training because they are instead lighting investment money on fire to cover that.

One imagines (though I have not investigated in detail) that there's a degree of national prestige work going on too. The Chinese labs are trying to show that they can build better and more efficient models and are releasing open to undercut the US labs.

https://openrouter.ai/deepseek/deepseek-v4-pro/providers

Deepseek v4 Pro is much cheaper when provided by Deepseek itself, likely as a combination of the loss leader strategy you mention and the desire to have more data flow through their pipeline for training. However, the same open weights model, provided by other providers, is somewhere in the $2-3/1M output-tokens range. Compare Opus 4.7 at $25/1M output-tokens.

Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong. We've seen some of this from Qwen at least - their latest model is closed only. I hope there's always someone willing to make this bet and release better and better open models.

This is true for traditional SaaS too, but the number of concurrent users that could be served by one machine and the cost of the hardware were both at least an order of magnitude better.

Dunno how trustworthy this source is, but it says ~35 MWh/person in China and 77 MWh/person in USA.

https://ourworldindata.org/grapher/per-capita-energy-use

https://cryptonews.com/news/china-doubles-down-on-crypto-ban...