For one, by synthesizing the results of multiple papers and suggesting novel experiments. If one paper sets constraints X for some system, and another paper sets constraints Y where Y!=X for a system that's similar but slightly different, then that's fertile ground for an experiment that can extract the more general underlying principles. This has already happened for domain-specific AI in fact, but the idea here is that it will become routine with general AI systems, as is happening now with math.