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In this case, John is going off on this inane tangent because of his prior experience with hardware and video games instead of challenging himself to solve the actual hard and open problems.
I’m going to predict how this plays out for the inevitable screenshot in one to two years. John picks some existing RL algo and optimizes it to run in real time on real hardware. While he’s doing this the field moves on to better and new algorithms and architectures. John finally achieves his goal and posts a vid of some (now ancient) RL algo playing some Atari game in real time. Everyone says “neat” and moves on. John gets to feel validated yet all his work is completely useless.
John's document covers why he's doing what he's doing:
> Fundamentally, I believe in the importance of learning from a stream of interactive experience, as humans and animals do, which is quite different from the throw-everything-in-a-blender approach of pretraining an LLM. The blender approach can still be world-changingly valuable, but there are plenty of people advancing the state of the art there.
He thinks interacting with the real world and learning as you go isn't getting enough attention and might take us farther than the LLM approach. So he's applying these ideas to a subject that he's an expert in. You don't seem to find this approach interesting but John does (and I do too, for the record).
Everybody dismissing him might be right. Those keeping score know that Carmack's batting average isn't one thousand. But those people also know Carmack has the resources to work on pretty much whatever he wants to work on. I'm happy he's still working hard at something and sharing his work.
To me this reads as "this is very far-fetched but he's got the money so golly for him"