Comment by ben_w

The rumour mill (justified, given cloud cost of running big open models with similar performance scores) says that these companies make money on inference, but lose it all on training.

So: when the money runs out and the bubble pops, we'll still get cheap existing models, what we lose is the race for new models.

We'd probably even keep free models: I forget where I saw it, but back in the early days someone noticed that models were so cheap that you could generate a decent sized blog post about any topic for about the same as the expected revenue from putting a few adverts on it and having it viewed *exactly once*.

That said, when (/if) these businesses stop chasing new models, it can make sense to burn the weights of the best at that date into a fixed (and analog, given how well they work with only a few bits of precision) circuit, making them more efficient. Not my field, so I'm not sure exactly how much more efficient analog can be; one or two orders of magnitude from what I've heard, but don't hold me to that, not my field.