To be honest, making sense of finances of fully public companies is often hard, because in practice, accounting is hard. How you account for depreciacion, cost, investment, fixed vs marginal costs is in practice fluid, companies have an incentive to make it look attractive, while also optimising for tax and shifting revenue around to narrowly beat analyst recommendations.

Here's a concrete example. Does some random AI company make operating profit on inference? I.e. if you only kept marginal costs, would you make a profit?

Well, depends what you account as your costs. If you're using hand-me-down hardware from previous generation's training, how much do you charge yourself internally for it? Maybe you show less, so investors take solace in profitable inference, even if you're losing money overall. How exactly are you accounting for electricity costs between training and inference? Is your army of SREs mostly servicing training new models (R&D expenditure) or inference (operating cost)?

This even has a name, and is called the "big bath" approach. If investors expect one part of your business to be a fiscal black hole, just shove all your costs there. They are accepting of it, and you make the rest of the business look better.

I'm not accusing AI companies of cooking the books, rather I'm trying to highlight you could see all the cash flows and still not know how much money is made or lost where.

Well it probably doesn't help that Dario is going around on podcasts saying things like "frontier labs need $1T of revenue or they will go bankrupt" lol.