> They probably are strongly motivated by spending quite a bit of their time on innovation I would rephrase it as spending time on research. Which might or mig

> They probably are strongly motivated by spending quite a bit of their time on innovation I would rephrase it as spending time on research. Which might or might not be innovative. I know for a fact that there is a plethora of applied ML papers where people simply toy around with the sample set and publish their results. There is nothing innovative about it. Although it requires some knowledge in feature analysis and some other in related algorithms and in basic statistics but that's nothing that cannot be picked up by taking a few months of courses in applied machine learning. So really, in fact the only case where that extra knowledge is justified is your case #1. And even then I am not convinced that these PhDs actually have ever done anything innovative. There is just too much of a chance that they simply iterated on existing research ideas in their field and they can only continue to do so. Which is again not very innovative I think.