Well, if I were a mathematician, what I'd be noodling about with right now is a way to model the number of failed attempts that AI companies must be making for every success they report.

I guess you don't have to be a mathematician to do that sort of calculation, but I'm just proposing it as a way to lift mathematicians' spirits a bit.

Also pay attention to the fact that every time a new model is released there's a slew of new results and then they dry out for a while, which suggests a "throw stuff at the wall and keep what sticks" approach that's incompatible with a kind of system that can just magickally solve all maths right now.

I'm saying that because I get the feeling that mathematicians don't have a good model for the true capabilities of those systems and that can lead to an overreaction, like "woe is me, all of mathematics will be solved and my entire discipline will be rendered obsolete". Coming from an AI background I don't think that's right. I think because mathematicians are not AI researchers they simply don't have a very clear idea of what's going on with those systems. And tbf even many AI researchers (the ones who don't enjoy the benefits of a long tradition that goes back to the 1950's and basically only joined the field in the last 10 years or so) don't understand those systems very well either.

Bottom line: don't panic.

Or, not yet :0)