Secondly that the frontier model providers will not necessarily want to buy these up as the aforementioned lower demand for training (or costly and memory intensive training) and cost of inference goes down (and possibly requests as the models get better or at least plateau).
Further TPU's and if model weights do eventually stabilize then ASIC's might make more sense.
And lastly as models improve and make less mistakes the requirement trying things in parallel traditional compute may also go down. I think their is a quantity intercept (The demand for compute if it cost nothing would be a finite number and may go down rather then up with advancements in cheaper inference and accuracy) for compute both AI inference and traditional compute.
That leaves the real buyers who are also starting to run low on capital.
Finally, demand doesn't need to go to zero for there to be a price correction. Just dipping from insane demand to elevated levels would lead to a significant price drop.
I think there are two sides that want a particular outcome one want gpu's to get cheaper (like I admit I do), others want there investment to work out (both might want to huff copium at some point, who is huffing here is not easy to tell).
IMO its copium to think that chips which have 10x more demand than supply are suddenly going to be easy to get as "AI is eating the world" like software did over the past decades.