Think of it like a trip.
When you're learning/internalizing existing knowledge, you tread a defined path skipping unnecessary stops and diversion and heading instead directly to the destination. The destination, not the journey, is the point.
When you're discovering new knowledge in the form of proving/disproving, there's no defined path to follow, so there can be quite a bit of backtracking and diversions and detours into other areas you weren't planning on going initially and while there might be some well-defined paths to follow, you might have to cut your own path through uncharted desert or jungle.
All those diversions and detours make the journey just as if not more important than the destination because they create new destinations of their own for future travel that would not have been spotted or noticed for a while longer (if ever) otherwise.
So to bring it back to math, transitioning from having to develop proofs for theorems to validating/learning extant proofs completely changes the scope of future discovery. Everyone will tread the exact same path to a proof/theorem and then stand in the same place to look for future destinations and then use AI to build them a path to that new destination and then everyone will follow the same path to the new destination and repeat the process, so the chances of finding something curious along the way plummets. And something curious could be an entirely new field of math, or maybe just a new problem that isn't relevant to the intended destination but gives a view of many new never before seen destinations for future trips.
When you're learning/internalizing existing knowledge, you tread a defined path skipping unnecessary stops and diversion and heading instead directly to the destination. The destination, not the journey, is the point.
When you're discovering new knowledge in the form of proving/disproving, there's no defined path to follow, so there can be quite a bit of backtracking and diversions and detours into other areas you weren't planning on going initially and while there might be some well-defined paths to follow, you might have to cut your own path through uncharted desert or jungle.
All those diversions and detours make the journey just as if not more important than the destination because they create new destinations of their own for future travel that would not have been spotted or noticed for a while longer (if ever) otherwise.
So to bring it back to math, transitioning from having to develop proofs for theorems to validating/learning extant proofs completely changes the scope of future discovery. Everyone will tread the exact same path to a proof/theorem and then stand in the same place to look for future destinations and then use AI to build them a path to that new destination and then everyone will follow the same path to the new destination and repeat the process, so the chances of finding something curious along the way plummets. And something curious could be an entirely new field of math, or maybe just a new problem that isn't relevant to the intended destination but gives a view of many new never before seen destinations for future trips.