The equivalence relationship between efficient AI and universal sequence prediction has been known for decades, so it would be surprising if AI algorithms were poor at sequence prediction. Of course, optimal universal sequence prediction is profoundly intractable and memory hard, which has implications for limits of AI efficiency and scalability.

There used to be a small hobbyist subculture on the Internet in the late 1990s that designed highly efficient approximate universal sequence predictor algorithms for the challenge of it. Now that AI is a thing, I've often wondered if there were some lost insights there on maximally efficient representations of learning systems on real computers. Most of those people would be deep into retirement by now.