[criemen]

But why? Training hardware maybe will appear, but everything that's running inference has real, paying customers on it, with current capabilities level. Even if the bubble bursts, why would that demand evaporate? People still would want to use chatgpt, claude code etc., even if they stop getting any better tomorrow.

[PeaceTed]

Yes, but how much do people want to pay to use this stuff? That is the real question.

It might be that more than $30 a month is too much and people stop using it. However, I suspect it would end up a fair bit higher than many would think. If you are a 'whale' (to use the gambling term) then there is a good chance that they could charge in the hundreds of dollars.

Maybe it become a wealth divide between those that can afford it and those who cannot. In equality yet again. Then the echos of Dune, the 'Butlerian jihad' and ‘Thou shalt not make a machine in the likeness of a man’s mind' will start to come up.

[stavros]

They're probably buying this equipment anticipating that the current demand will continue. If it stops, all the surplus will need to be sold.

[krior]

But maybe not for the price it actually takes to run these models. Afaik atm they are all subsidized.

[criemen]

I'd expect at least API pricing for all major players to be margin-positive. Positive ROI if you include model training cost? Maybe not, but in a hypothetical scenario where all capital for new model training goes away, the latest frontier model weights still continue to exist.

Subscription prices for claude code et. al? No idea.

[Kuinox]

No, that's a rumor lots of people have been taking at face value. If you do the math, inferrence is very lucrative. Here someone deployed a big model, the costs are $0.20/1M token https://lmsys.org/blog/2025-05-05-large-scale-ep/

[anigbrowl]

If some large companies implode local models will fill the gaps soon enough. I can see a crash in data center construction and operation once the economics get too far out of whack.

[lisbbb]

Not if the paying customers aren't sustainable. Many of us have seen this before--2001.