I'm probably saying something obvious here, but it seems like there's this pre-existing binary going on ("AI will drive amazing advances and change everything!" "You are wrong and a utopian / grifter!") that takes up a lot of oxygen, and it really distracts from the broader question of "given the current state of AI and its current trajectory, how can it be fruitfully used to advance research, and to what's the best way to harness it?"
This is the sort of thing I mean, I guess, by way of close parallel in a pre-AI context. For a while now, I've been doing a lot of private math research. Whether or not I've wasted my time, one thing I've found utterly invaluable has been the OEIS.org website, where you can just enter sequence of numbers and then search for it to see what contexts it shows up in. It's basically a search engine for numerical sequences. And the reason it has been invaluable is that I will often encounter some sequence of integers, I'll be exploring it, and then when I search for it on OEIS, I'll discover that that sequence shows up in much different mathematical contexts. And that will give me an opening to 1) learn some new things and recontextualize what I'm already exploring and 2) give me raw material to ask new questions. Likewise, Wolfram Mathematica has been a godsend. And it's for similar reasons - if I encounter some strange or tricky or complicated integral or infinite sum, it is frequently handy to just toss it into Mathematica, apply some combination of parameter constraints and Expands and FullSimplify's, and see if whatever it is I'm exploring connects, surprisingly, to some unexpected closed form or special function. And, once again, 1) I've learned a ton this way and gotten survey exposure to other fields of math I know much less well, and 2) it's been really helpful in iteratively helping me ask new, pointed questions. Neither OEIS nor Mathematica can just take my hard problems and solve them for me. A lot of this process has been about me identifying and evolving what sorts of problems I even find compelling in the first place. But these resources have been invaluable in helping me broaden what questions I can productively ask, and it's through something more like a high powered, extremely broad, extremely fast search. There's a way that my engagement with these tools has made me a lot smarter and a lot broader-minded, and it's changed the kinds of questions I can productively ask. To make a shaky analogy, books represent a deeply important frozen search of different fields of knowledge, and these tools represent a different style of search, reorganizing knowledge around whatever my current questions are - and acting in a very complementary fashion to books, too, as a way to direct me to books and articles once I have enough context.
Although I haven't spent nearly as much time with it, what I've just described about these other tools certainly is similar to what I've found with AI so far, only AI promises to deliver even more so. As a tool for focused search and reorganization of survey knowledge about an astonishingly broad range of knowledge, it's incredible. I guess I'm trying to name a "broad" rather than "deep" stance here, concerning the obvious benefits I'm finding with AI in the context of certain kinds of research. Or maybe I'm pushing on what I've seen called, over in the land of chess and chess AI, a centaur model - a human still driving, but deeply integrating the AI at all steps of that process.
I've spent a lot of my career as a programmer and game designer working closely with research professors in R1 university settings (in both education and computer science), and I've particularly worked in contexts that required researchers to engage in interdisciplinary work. And they're all smart people (of course), but the silofication of various academic disciplines and specialties is obviously real and pragmatically unavoidable, and it clearly casts a long shadow on what kind of research gets done. No one can know everything, and no one can really even know too much of anything out of their own specialties within their own disciplines - there's simply too much to know. There are a lot of contexts where "deep" is emphasized over "broad" for good reasons. But I think the potential for researchers to cheaply and quickly and silently ask questions outside of their own specializations, to get fast survey level understandings of domains outside of their own expertise, is potentially a huge deal for the kinds of questions they can productively ask.
But, insofar as any of this is true, it's a very different way of harnessing of AI than just taking AI and trying to see if it will produce new solutions to existing, hard, well-defined problems. But who knows, maybe I'm wrong in all of this.