Part of me wonders (maybe naively) if with falling cost of AI inference and improvement of models means there is an oversupply of inference infrastructure. since we may reach a point where no one needs more and everything you could want can be done by the machine and adding more compute for most users is not any kind of benefit (like it was before LLMs there was an obvious ceiling where the majority of people did not need more compute).

I know Jevons paradox is a thing and I could see it going the other way as well but the first possibility seems to be ignored and I am not sure we can. Eventually demand for training might dry up as well if are models become largely very good and only a bit of fine tuning is required for most applications.