The relentless arms race among AI hyperscalers to amass more and more computing power will eventually hit a wall, according to Gary Marcus.
That’s because the enormous amounts of capital expenditures have failed to clean up errors their large language models produce while also removing any technical moats that might give them a competitive edge.
