For the best AI researchers, creating better AI systems takes only time and compute. If we can create systems with the ability to do anything at the level of the best human, as Jacob believes is possible, then these systems can do AI research at the level of the best human, and creating better AI systems is mostly a matter of compute. (AI systems are easy to copy, so time is irrelevant to the extent that the research can be parallelized.) In this scenario, the paradigm-changing breakthroughs can come from the AI system itself.

The AI system would be bottlenecked by data in the sense that it will have to run experiments, but it's not clear that it needs a new paradigm to resolve this bottleneck. It just has to propose an experiment and interpret its results. So it writes code, and the experimental outcome gets fed back into the model as any other normal input. As an AI researcher, I'd like to believe that this is not going to happen anytime soon, but I'm not sure we're far.