First: marginal inference cost vs total business profitability. It’s very plausible (and increasingly likely) that OpenAI/Anthropic are profitable on a per-token marginal basis, especially given how cheap equivalent open-weight inference has become. Third-party providers are effectively price-discovering the floor for inference.
Second: model lifecycle economics. Training costs are lumpy, front-loaded, and hard to amortize cleanly. Even if inference margins are positive today, the question is whether those margins are sufficient to pay off the training run before the model is obsoleted by the next release. That’s a very different problem than “are they losing money per request”.
Both sides here can be right at the same time: inference can be profitable, while the overall model program is still underwater. Benchmarks and pricing debates don’t really settle that, because they ignore cadence and depreciation.
IMO the interesting question isn’t “are they subsidizing inference?” but “how long does a frontier model need to stay competitive for the economics to close?”
Remember "worse is better". The model doesn't have to be the best; it just has to be mostly good enough, and used by everyone -- i.e., where switching costs would be higher than any increase in quality. Enterprises would still be on Java if the operating costs of native containers weren't so much cheaper.
So it can make sense to be ok with losing money with each training generation initially, particularly when they are being driven by specific use-cases (like coding). To the extent they are specific, there will be more switching costs.
You'd need something like probabilistic programming language to model discretion.
You'd want some way to compare organizational forms -- minimally build vs buy, but preferably also control via monitoring+specification vs selection+incentive alignment.
You'd probably need the kind of sensors and telemetry that no one would like, to avoid drowning in book-keeping.
Overall, what would the benefit be?