My research (automated theorem proving with RL) sits partway between "good old-fashioned AI" and modern deep learning, and GEB struck me as amazingly prescient, with lots of lessons for modern AI research.

There's a growing sense among many ML researchers that there's something fundamentally missing in the "NNs + lots of data + lots of compute" picture. GPT-3 knows that 2+2=4 and that 3+4=7, but it doesn't know that 2+2+3=7. These heart of these kinds of problems indeed seems to be the sense of abstraction / problem reimagining / "stepping outside the game" that Hofstadter spent so much time talking about.

Chess (accidentally) turned out to be easy enough for a narrow algorithm to work well. But I'd be surprised if other problems don't require an intelligence general enough to say "I'm bored, let's do something else," and I don't believe current algorithms can get there at any scale.