I think most hard engineering problems are made up of a lot of smaller solutions and especially made up of the lessons learned attempting to implement small solutions, so I think it’s incorrect to think of something that’s useful but incomplete as being competitive to the true solution rather than actually being a part of the path to it.
I definitely agree with that. There has to be room to find traction. The concern is about things which specifically push the field toward “near-term” solutions, which slides too easily into not-solving-the-same-sorts-of-problems-at-all. I think a somewhat realistic outcome is that the field is taken over by standard machine learning research methodology of achieving high scores on test cases and benchmarks, to the exclusion of research like logical induction. This isn’t particularly realistic because logical induction is actually not far from the sorts of things done in theoretical machine learning. However, it points at the direction of my concern.