I personally don't really believe the meme that Anthropic and OpenAI are charging less than the marginal cost (cost of inference on requests). Open weight models have surpassed the abilities of their closed weight models when such claims started, and the inference costs on said open weights models are provably not so high.

Where they quite reasonably might be making a loss is their marginal costs + amortised fixed costs (training).

I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.

The driver for the training is an arms race between providers and open weights labs. But several dynamics could play out:

* Models will be seen as good enough, with only fine-tuning required, for most use cases, and demand for expensive training of foundation models drops off, stopping the arms race. * Most of the participants cannot raise capital due to lack of market confidence in them, and there is a 'winner takes all' dynamic with the remaining provider, who can then raise costs.

I personally think the first end game is more likely than the second.