Well in the first years of AI no, it wasn't because nobody was using it.
But at some point if you want to make money you have to provide a service to users, ideally hundreds of millions of users.
So you can think of training as CI+TEST_ENV and inference as the cost of running your PROD deployments.
Generally in traditional IT infra PROD >> CI+TEST_ENV (10-100 to 1)
The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time.
>The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time.
I think you're making assumptions here that don't necessarily have to be universally true for all successful models. Even without getting into particularly pathological cases, some models can be successful and profitable while only having a few customers. If you build a model that is very valuable to investment banks, to professional basketball teams, or some other much more limited group than consumers writ large, you might get paid handsomely for a limited amount of inference but still spend a lot on training.
if there is so much value for a small group, it is likely those are not simple inferences but of the new expensive kind with very long CoT chains and reasoning. So not cheap and it is exactly this trend towards inference time compute that make inference > training from a total resources needed pov.
So you can think of training as CI+TEST_ENV and inference as the cost of running your PROD deployments.
Generally in traditional IT infra PROD >> CI+TEST_ENV (10-100 to 1)
The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time.