I didn't say anything about low-value output. No one actually knows the value of any particular piece of mathematics within that deluge. Mathematicians don't have a magical ability to differentiate high-value mathematics from low-value merely by reading paper titles and abstracts.

The dirty secret in the mathematical world -- that has been going on for a long time already -- is that papers get attention based on the reputation of the authors, not on the rigour or validity of the proof. The big headline-grabbing papers are getting read by mathematicians because AI researchers have leveraged media exposure to bypass the reputation network, but media exposure doesn't scale.

When everyone is using LLMs to generate proofs, only reputable mathematicians will be able to get their work read. And herein lies the crux of the problem: an exponential takeoff in the volume of output from respected mathematicians will leave a critical shortage of readers.

it could end up being far easier to reasonably direct and evaluate the research direction and output of AI systems than human mathematicians

That's baseless speculation. All indications so far are that LLMs produce proofs far longer and far more complicated than humans are capable of, such that only machines can check the proofs for validity. Digesting them into a human-readable interpretation of the results is an open problem.