Absolutely not. Computers used to be extremely centralized and the decentralization revolution powered a ton of progress in both software development and hardware development.
You can run many AI applications locally today that would have required a massive investment in hardware not all that long ago. It's just that the bleeding edge is still in that territory. One major optimization avenue is the improvement of the models themselves, they are large because they have large numbers of parameters, but the bulk of those parameters has little to no effect on the model output and there is active research on 'model compression', which has the potential to be able to extract the working bits from a model while discarding the non-working bits without affecting the output and realize massive gains in efficiency (both in power consumption as well as for running the model).
Have a look at the kind of progress that happened in the chess world with the initial huge ML powered engines that are beaten by the kind of program that you can run on your phone nowadays.
https://en.wikipedia.org/wiki/Stockfish_(chess)
I fully expect something similar to happen to language models.
