I often wonder about the kind of research done in computer science--how much is it influenced by the kinds of things that easily get you a Ph.D., versus the kinds of things that are useful but less apparently flashy.

A lot of the PL students at my school are extremely wary of doing any follow-up work on ideas that have been published before, even if the implementations of those things are obviously shoddy and don't really demonstrate that the idea works. There's a lot of novelty chasing, which is part of what's pushing people to include deep learning in their work, since it often allows them to claim novelty, even if their results aren't very good.