[ACCount37]

Model training does NOT dominate the model costs.

The rate of inference compute to training compute is ~10:1, for popular frontier models. Models are routinely overtrained past the Chinchilla optimum now because it makes an immense amount of economic sense to do so.

Worse the more niche and unused your models get, but when this "making a loss" fuckery pops up, it's usually about the big guys like Anthropic, OpenAI, GDM and maybe xAI and Meta. Of which only the latter can be accused of not selling enough inference to offset the training runs.

The real money sinks are: R&D and infrastructure buildouts.