Well, let's not forget that text models are not the only models! Video models are much slower and need comparatively more resources, and all they can do even at that size is generate videos a few seconds long. Clearly a ton more work is going to go into those, and demand for them will probably increase as more creative tools get authored using them as a central part of the workflow. Low-res local rendering for preview might be a thing, but the lion's share of the work for high-res, near-realtime rendering is going to be done on huge clusters for a long time yet.

I think there’s still an open question around are the ultra-large next-gen models worth it? For those of us without early access to Mythos, it’s hard to verify whether it’s been held back from the public due to actually being “too dangerously powerful to release yet” as implied or because the gains aren’t outpacing the costs.

I think GPT 4.5 showed that there is indeed a practical limit we're close too. That was supposedly a high-trillions of parameter model that was deprecated almost immediately because it was slow, insanely expensive, and had questionable benefits over the smaller models. Though apparently the new Mythos and whatever GPT Spud is (if it wasn't 5.5) are back up in the high trillions.

It’s still diminishing returns yes? It isn’t Moore’s Law