It may not matter. Think about why SOTA model companies are exploring chips. What do chips offer?
If SOTA models haven’t peaked, then the SOTA model companies would still be churning out better and better intelligence.
If SOTA models haven’t peaked, then the SOTA model companies would still be churning out better and better intelligence.
If companies working on ML-specific chips was evidence that large transformer models have fully saturated their potential, the field would have been done circa GPT-2.
Chips are another axis for improvements in training and inference. Orgs large enough to explore the space have been doing it for at least a decade now. This is just a silly line of reasoning based on the faulty assumption that somehow, looking for increases in efficiency in training/inference means teams have reached some theoretical limit in model capability.