Lean makes the proofs verifiable, and sure to a degree understandable, but normal Lean proofs written by humans are already a challenge to understand without additional documentation, and, as with all code-generation, AI generated proofs are probably even harder to understand. There are many paths to arrive at a proof, the AI may take very messy routes that don't align with how humans create abstractions and assign meaning to break the process down into understandable steps.

In a sense all AI is understandable, nothing is hidden, you can see all the numbers inside a Transformer. Just being able to see the code might not necessarily help mathematicians too much in extracting deeper meaning and lessons from AI-generated proofs, at least not without significant additional work.

I understand that mathematicians are all about understanding why a theorem is true, not just checking that it is, and then using that understanding to push forward other research. Same as most scientists care more about understanding how the world works rather than developing methods to control and manipulate it.

But proven theorems are mathematical tools that can be used to make progress, regardless of your understanding of how they work. I think there is a path forward for maths as an engineering discipline, possibly with much faster progress, just as computer science expanded into software engineering. AI is just the next step of many, software libraries, interfaces and abstractions are also black-boxes in a sense that do stuff for you without knowing how.

A good engineer learns to build on the shoulders of giants, mastering the understanding of what tools do and how they behave, without necessarily needing to know how they are built from scratch. It might be less satisfying to the curious, but you can get a lot more done.