Are OpenAI and Anthropic losing money on inference?

https://www.axios.com/2025/08/15/sam-altman-gpt5-launch-chat... quotes Sam Altman saying:

> Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company.

Which is like saying, “If all we did is charge people money and didn’t have any COGS, we’d be a very profitable company.” That’s a truism of every business and therefore basically meaningless.

The Amodei quote in my other reply explains why this is wrong. The point is not to compare the training of the current model to inference on the current model. The thing that makes them lose so much money is that they are training the next model while making back their training cost on the current model. So it's not COGS at all.

So is OpenAI capable of not making a new model at some point? They've been training the next model continuously as long as they've existed AFAIK.

Our software house spends a lot on R&D sure, but we're still incredibly profitable all the same. If OpenAI is in a position where they effectively have to stop iterating the product to be profitable, I wouldn't call that a very good place to be when you're on the verge of having several hundred billion in debt.

There’s still untapped value in deeper integrations. They might hit a jackpot of exponentially increasing value from network effects caused by tight integration with e.g. disjoint business processes.

We know that businesses with tight network effects can grow to about 2 trillion in valuation.

How would that look with at least 3 US companies, probably 2 Chinese ones and at least 1 European company developing state of the art LLMs?