Both can be true at the same time. Similar to the early days of the Internet, the dot-com bubble eventually popped, but the Internet (and dot-coms, for that matter) didn't go away.

What people are saying is that this mad race to throw cash at anything that has "AI" in it will eventually stop, and what will remain are the useful things.

No the tone is generally that some mythic AI winter is going to happen because of current valuations and that AI is simply the current crypto grift.

Yeah, I saw "AI winter" mentioned elsewhere in the thread...

IMO there is a real qualitative difference between AI and crypto in terms of the durable impact it's going to have on the world. Does that mean I've bought into the AI hype? Maybe. But I think the signs are there.

AI winters are a recurring phenomenon, not a myth, and, like the dotcom bust, involve a collapsing hype bubble, reductions in focussed speculative investment in the field, but the technologies that were big during the preceding hype cycle continuing to be important, and develop, though in the case of AI winters often they stop being thought of as AI and just get referred to with a name for the specific technology (often a different one than the main one they were known by in the hype cycle, e.g. “expert systems” from th blast hype cycle are largely “business rules engines” now.)

Googling, there seem to have been two AI winters, the first (late 1970s - early 1980s) when people first figured AI was overhyped, and the second (late 1980s - early 1990s) with the collapse of expert systems. I don't think we are about to get one now - more like AI spring leading to AGI summer.

If revenue doesn't catch up with the cost of developing and running these huge LLMs then the only way to avoid an AI winter is to find a way to make them way cheaper to develop.

Inference costs are largely break even and profitable. Now yes there is absolutely questions on the training side.

You could turn off the switch today though and most of your leading models could keep the lights on.

I keep seeing everyone guessing what the margin is on inference. You say that it's largely break even. We have this person in the thread claim 80% margin (https://news.ycombinator.com/item?id=45462442).

These numbers are complete make believe.

Why would they be make believe? I am generalizing across multiple companies and simplifying it as much as possible as we won’t know all the cost buckets but we do know at current costs the bare inference cost of keeping the machines running is being covered.