I never said otherwise. Like physicists, we need both new theory (for new insights, ideas, models, etc.) and new experiments (for replication, performance, scalability, etc.)

Also, Moore's Law is running on fumes, yes, but there's quite a bit of R&D focused on coming up with hardware that massively scales up (e.g., by more efficiently parallelizing) the dense and sparse multiply-sum operations common to so many AI models. I think Sutton's point about models that leverage computation is spot-on.