As a software engineer, I found that a codebase that is LLM-assisted or entirely produced by agents gets harder and harder to understand intuitively over time. By writing code manually, you build a very precise mental model of how it works. The time it took was the price for that. Now, the time cost has been largely removed but the payoff also disappeared: it's essentially impossible to maintain the same mental model with the speed and volume of AI contributions. Reviewing code is not a substitute for writing it, and new contributions to the codebase are essentially bound to LLM usage. So now we're in a position where the codebase and its capabilities grow fast, but are not backed by the full understanding of the engineers. AI tools have become our crutch.

Math is very similar, and requires an even higher degree of mental understanding to produce advancements. The effort spent trying to prove theorems and connect theories pays off in deep intuition; removing that struggle significantly impairs our understanding.

So no, you can't "be done with that, and go do other great work" without paying the understanding tax. And over time that's just not sustainable.

A simpler analogy would be gym training: the effort expenditure and the strength gains are inextricably linked. You can't go in there and start lifting weights with a forklift while expecting strength improvements.

In the case of software development, businesses have decided that speed gains are more important than maintainability, understanding, and independence. Will the same happen in math? The future in which mathematicians become LLM assistants (as opposed to LLM-assisted) and spend their days "turning the crank" just to review what comes out is quite bleak.