Nobody has solved the Riemann Hypothesis, but it has been highly fertile ground for new mathematics. Answering the question is of far less value than the question itself.

When is a problem in mathematics "solved?"

There's an old joke in math and computer science about a huge leap of logic in a proof either being flagged as "left as an exercise for the reader" or "trivially shown." The problem being that things are only trivial if you have all the context to know why they're trivial.

We can claim a problem is solved if there is one paper explaining the solution with airtight logic that is understood by five people on the planet. And that is the kind of solution that LLMs can kick out (by basically, to hand wave a lot of details that I will admit to not fully understanding, doing a search across the whole problem domain with vastly more capacity than most human beings to do the symbolic manipulation that makes up the mechanics of mathematics). But what does that really mean? To have something "proven," but only to the comprehension of five people on the planet?

3blue1brown is making the interesting argument that perhaps mathematics practice shifts from a frontier that AI might actually be better at exploring than humans, to the process of digesting the output of that exploration and making it sensible and comprehensible to the billions of mines that could take advantage of that knowledge... If it were shaped in a way that could stick in their brains.

That's a kind of creativity that AI is not likely to be good at in the near future, because AI doesn't really "understand people."

1. One view of the field treats Maths as directly solving real-world problems. Under that lens, yes, solving problems is directly advantageous for the field. Even so, it's not necessarily a good thing for the participants. Suppose the incentive structures require tackling hard, long problems, the field attracts people who enjoy that sort of thing, it's hard a-priori to tell a hard problem from an aggrandized brute-force proof search, etc. Even if directly producing proofs is the desired output for the field, a capricious machine occasionally solving 40% of the big problems the field is working on is still demoralizing. On a human level, if a society has organized around producing 100yr+ problems and bestowed on you the privilege of tackling a few of them having the last 40yrs of your work solved by a fancy calculator is never going to feel good. Sure, that means that in retrospect that was a problem we could have waited to devote human effort toward, but you couldn't possibly have known that at the time. You worked hard, made locally correct decisions, and later found out that the work was worthless. Beyond that, I haven't seen any model of "Maths 2.0" produced where that issue goes away. We still want people to devote their lives to hard problems. The machine will still advance and solve a moderate fraction of them at random, killing many of the field's life's work. What motivation do you have in that environment to pursue those challenges in the first place? Why not pursue adtech or fintech and make the world a worse place instead? Solving 40% of the problems isn't +40% throughput; it's -60% as the rest of the field leaves for greener pastures, -inf when nobody's around to construct the right problems in the future..

2. "Solving problems" isn't directly the point of most of the field. That's true both from the perspective of most problems requiring many intermediate results (solving one useful thing requires solving many less useful things) and from the perspective of building the machinery capable of solving problems being much more important than the solutions themselves (a properly presented proof leveraging clever, new techniques is infinitely more valuable than whichever thing it proved -- indeed, the Millennium Prize problems were chosen precisely because it was assumed they would create such techniques, and for the human-solved ones they did -- without new techniques, eventually the field caps out at whichever frontier of knowledge we're currently stuck at).

3. A third view of the field treats it more like art. A machine producing behind your back the painting you were envisioning, or at least a close enough simile to fool the masses, doesn't advance "art" in any meaningful sense. It's extremely demoralizing when flaky cast tools are passed off for proper forged variants (your work is better, and it's sad to see people choosing a wrench they'll later snap in half instead), and when at a creative level people are accepting mediocrity instead of something objectively better.