Isn't it just that he left way before gpt-5, then? At that point a sufficiently naive person could have believed that scaling was going to lead to AGI, but that sort of optimism died after he was already an outsider.
What if it turns out that the more you scale the more your LLM resembles a lobotomized human. It looks like it goes really well in the beginning, but you are just never going to get to Einstein. How does that affect everything?
What if it turned out that those AI companies were maybe having a whole bunch of humans solving the problems that are currently just below the 50% reliability threshold they set, and do fine tuning with those solutions. That will make their models perform better on the benchmark, but it's just training for the test... will the constant gap be a good approximation then?