Imo the thinking is that whenever humans have tried to pre-process or feature-engineer a solution or tried to find clever priors in the past, massive self-supervised-learning enabled, coarsely architected, data-crunching NNs got better results in the end. So, many researchers / industry data scientists may just be disinclined to put effort into something that is doomed to be irrelevant in a few years. (And, of course, with every abstraction you will lose some information that may bear more importance than initially thought)