While I fundamentally agree with the basis of compute getting cheaper by the year, I think a missed consideration here is the fact that these models are also requiring exponentially more compute with each iteration to train, in a way that arguably has outscaled the advances in compute.

Whether a generalized and broadly usable model will be able to trained within some N multiple of our current compute availability allowing the price to come down with iterative compute advances is yet to be seen. With the current race to the top in terms of SOTA models and increasingly iteratively smaller improvements on previous generations, I have a feeling the scaling need for compute will outpace the improvements in our hardware architecture, and that's if Moore's law even holds as we start to reach the bounds of physics and not engineering.

However as it stands today, essentially none of these providers are profitable so it's really a question of whether that disconnect will come within their current runway or not and they'll be required to increase their price point to stay alive and/or raise more capital. It's pure conjecture either way.