The bottleneck now is human understanding.

Ironically, that has been that way for most people since forever.

Comparing with the situation in programming, where code evaluation (obviously analogous to "understanding") is that bottleneck now, should get people the idea of being in the same boat.

I’d say it’s understanding in general. We currently have zero evidence the AI models can understand mathematical breakthroughs in a productive way. (When they intuit a new technology or theory from a mathematical construct, we’ll be along that way.)

What is that the bottleneck for, though? Certainly, it's tautologically the bottleneck for "humans understanding math"; but what about things like using math to drive discoveries in other fields, or for practical work in CS, or applications elsewhere? I fear that practically those aren't bottlenecked on human understanding.

Putting asside the stolen research -

It is my understand that there isnt a lot of innovation here. Rather the (broad) field of mathematics got mined for solutions that already existed within the corpus.

It implies "We didn't know we already knew how to solve these problems". How is that a good look for mathematics as a field?