It boils down to creativity. What we truly admire in mathematical achievements are the creative leaps when creating new theories (category theory, calculus), not mathematical prowess, which is a tool. Good problem solving is also highly regarded, but it's also admired for the creative ideas it takes while doing the proof. Computers are terrible at creativity, but insanely better than humans at raw computational power. We don't even have what can barely sounds like a mathematical model for creativity.

When people thought computers could not beat people at chess, it was because they assumed it was a task that required creativity, and could not be done with only raw computational power, fine-tuned heuristics, and good interpolation from known results (in most games, this move fails, so it's probably a bad move). It's basically the Chinese room Gedankenexperiment all over again: you don't need to understand Chinese to form valid Chinese sentences given that you have a good enough grasp of the syntactic game. Gowers said in a talk that at least 90% of a mathematican's work is routine, and could very well be automated. This is exactly the same thing, to make most proofs you apply some heuristics, a few classical theorems and techniques. There's no reason to think that this could not be fully automated for a wide range of problems, given enough time.

Now for mathematics, if humans were working at the low-level of axiomatic set theory, then they would have been out-competed by computers long ago. But the fact is that humans (at least, some) can work in inconsistent high-level systems, and fix the problems as they arise because they have a good intuition of what should work (take set theory, lambda-calculus, etc). Beating a human at go is the same kind of work that had been done on chess, so the misunderstanding was just on the true nature of what 'being good at chess/go/X' means, and in particular what it means computationally. We're getting a better idea on these thanks to the advances in proof theory, but that doesn't help with the question of creativity. Note that most 'creative work' can also be reduced to a short set of generic techniques (blending two ideas, changing a parameter, etc), but it doesn't really help getting to the next scientific revolution.

To come back at the issue at hand, the problem is not only finding proofs in mathematics; if you were to run an excellent algorithm that would give you tons of theorems, you would be well-embarassed in finding which ones are useful, and which ones are very convoluted tautologies with little content. Computers can be much better than humans at some mathematical tasks (like we know from a while that they are orders of magnitude better at arithmetic than humans), they are already much better at specific theorem-proving tasks, and the number of such tasks will continue to grow. This doesn't address the creative advantage humans have.