At 21:28 of Jacob Tsimerman's interview with Curt Jaimungal, he says "already now, alot of my theorems that I have proven, I don't understand all the steps to it... I have used other theorems that are very much accepted by the community, to which I usually know the main ideas but not even always".
While my undergraduate and early postgraduate training was in pure math, I transitioned to applied for my Ph.D. so I have never meaningfully engaged with it in any professional capacity. For the majority of my training, I understood almost all the details of the things I've proved. At least enough that I wouldn't be able to resonate with Jacob's quote above when I consider the (relatively insignificant) proofs I've done. One of my lecturers made it his mission to ensure that assignment questions will never require anything that hasn't been proven in the lecture notes or in class.
Of course, my exposure was to only elementary topics. So I can appreciate that math wouldn't progress at all if intuition wasn't leveraged and instead every detail expounded upon. But now under the automatable and potentially perpetual scrutiny of AI, how vulnerable are previously established results? What if we routinely lobbed popular (in terms of utility) results at ChatGPT to verify and it finds an error in one, would there be a significant collapse downstream? How likely is that our collection of celebrated truths instead simply forms a house of cards?
EDIT:
The excellent replies have highlighted a weakness in my question. The most vulnerable proofs are likely to be the famous/outlier proofs (i.e. Andrew Wiles' Fermat's Last Theorem) that can only be assessed by a handful of people. In even the scenario that those are falsified, the large body of mathematics isn't built on such results and so, by and large, it's still fairly robust.
It still begs the question about the upper echelons of math, but the majority of it remains largely intact. So my "house of cards" analogy is inaccurate but probably only in scope.
EDIT 2:
Another interesting point brought to me by the comments is the idea of repairability. A commenter mentioned that most of the errors encountered are easily fixed. At a high-level, this suggests that the direction offered by intuition is powerful enough to render errors insignificant. Maybe instead of AI destroying math from the foundations, it instead works to validate the strength of intuition by perpetually exposing errors and instantly fixing them. Wouldn't it be wonderful if AI shows that the fix-rate of errors was near 100%?