This is a reasonably well-examined take of the situation.
On the technical side, one of the additional things I've had on my mind is the potential that these mega models are in fact hiding a ton of inefficiency.
The approach of simply shoving higher dimensionality and more parameters into largely tweaks to the current models has delivered results, but it feels like "mainframe" era of computing to me.
Throwing reams of annotated human content and forcing the machine to globally draw associations from it feels clumsy. Just as people are able to learn structured knowledge via rule-systems that are successively elaborated with extensions and situational contradictions, I feel like there's probably a much more compact representational model that can be reached by adapting the current technical foundations (transformers, attention, etc.) to work well with generated examples from rule-systems, that then gets used as a base layer to augment the "high level" models that process unstructured data.
The risk for the behemoth datacenter might be similar to the risk in the early computing era of building compute centers right before the PC revolution took off.
If it turns out that there exists some more compact and efficient representation for this intelligence (which IMHO is likely given that we are still in the first generation of this technology), the datacenters may end up decaying mausoleums of old tech that has no relevance to a distributed intelligence future.
That's the big technical unknown unknown for me. How much efficiency juice is there left to squeeze, and what does that mean for a distributed landscape vs a centralized datacenter based landscape.