I don't see a reason this dynamic couldn't continue with zero dedicated effort toward "pure math" as such. Fundamentally, the function of math in practical application is producing deterministic(or stochastic, but obviously not in the LLM sense) models that, given initial observations/conditions, are capable of predicting some aspect of the future with reasonable accuracy. In the same way that human society does, a machine would grab the physics rung before the theory rung every time, but why does that matter? If progress is stuck, the machine turns the gauge away from "exploit" and toward "explore" and put together a bag of new tools it can sequentially try on the real-world problem.
I still don't see a role for humans in this process. They might direct the practical/physical aspect(if AI turns out to be less superhuman there) but they'd likely turn the hard conceptual problems over to the machine and never look inside the box - no human alive could understand even the smallest part of what's going on in there in less than a thousand lifetimes anyway.
> If progress is stuck, the machine turns the gauge away from "exploit" and toward "explore"
It sounds like you've answered your own question of:
> why "pure mathematics" should persist as a field of human or machine activity.
And you're really just quibbling about scheduling.
Whether humans will be involved or not is an entirely different debate.