alt Hacker News> A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers
This gets repeated everywhere but I don't think it's true.
The company is unprofitable overall, but I don't see any reason to believe that their per-token inference costs are below the marginal cost of computing those tokens.
It is true that the company is unprofitable overall when you account for R&D spend, compensation, training, and everything else. This is a deliberate choice that every heavily funded startup should be making, otherwise you're wasting the investment money. That's precisely what the investment money is for.
However I don't think using their API and paying for tokens has negative value for the company. We can compare to models like DeepSeek where providers can charge a fraction of the price of OpenAI tokens and still be profitable. OpenAI's inference costs are going to be higher, but they're charging such a high premium that it's hard to believe they're losing money on each token sold. I think every token paid for moves them incrementally closer to profitability, not away from it.
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I can see a case for omitting R&D when talking about profitability, but training makes no sense. Training is what makes the model, omitting it is like omitting the cost of running the production facility of a car manufacturer. If AI companies stop training they will stop producing models, and they will run out of a products to sell.
The reports I remember show that they're profitable per-model, but overlap R&D so that the company is negative overall. And therefore will turn a massive profit if they stop making new models.