Playing devil's advocate here, but in theory, you could claim that setting up harnesses, targets, verification and incentives for different tasks might be the learning that you are doing. I think that there can be a fair argument made that we are just moving the abstraction a layer up. The learning is then not in the specifics of the field knowledge, but knowing the hacks, monkey patches, incentives and goals that the models should perform.