>is that there isn't really a lot of unexplored ground
My take is, there isn't a lot of obvious unexplored ground. Where "obvious" includes many things fresh graduate student would come up thinking about some logical first steps. Possibly also many things what most professors would consider.
However, that does not say anything about unobvious unexplored ground. Also, many professors seem to think about in paper-sized increments. Narratives about blessings of incremental steps to expand the body of knowledge sound suspiciously self-serving to researches who publish incremental steps.
(And given the noise in ML experiments, are the incremental steps incremental or merely steps in random directions?) [1]
One way I have started to think about this: How many of the results one uses everyday are product of small increments? How small the increments were? Is another study really helpful to finding the next the such result? Naive case for anti-incrementalist science is naturally false, as every result is built on something, but I wonder if the prominent narrative is discounting the role of genuine novel insights and ideas to the detriment of science, as people do not try to obtain them. The question "how" is above my paygrade (FYI, I am no longer in academia), but I suspect one place where useful things often have came from people who manage to ask new but interesting and useful questions, which open up the much desired new unexplored territory. Which are good questions nobody in the field seems to ask?
[1] Wasn't there a paper some years back how most of the advances and tricks in neural net structures turned out not very important nor generalizable compared to more traditional models given more compute? Can't find the reference now.