For now I think recent successes of AI for mathematics should be understood as a complement to, rather than a substitute for, human mathematical labor. This is because AI, at present, is most productive working horizontally, whereas humans work vertically.
By this I mean that the highest quality AI mathematics thus far has been obtained by feeding entire problem lists into a model or scaffold and picking out the few high-quality successes. It is very hard to predict in advance where these successes occur. On the other hand, humans typically pick a few questions and try to understand them deeply--and historically, when they do so, they make progress!
I think this points to increasing value of problem lists, and also suggests that "solved an open problem" is an increasingly useless proxy for what we care about in mathematics. There are a lot of problems that have sat open for a long time because the right person didn't happen to look at them, and many others that are open because they benchmark our failure to fundamentally understand some basic object. I've solved old open problems that I think had the former flavor rather than the latter. I think my best work, however, is not about solving long-open problems, but rather inventing a new ones that help to understand something we care about, and making progress on that.