Venture capitalist Hemant Mohapatra stated that he never expected "garbage mathematics" to become a reality, but the world is about to be filled with beautiful mathematical proofs: internally consistent and elegantly structured, yet leading to dead ends, with no exit other than intrinsic beauty and coherence. He pointed out that verifiability does not equal growth.

Mathematical research is being rewritten by models and proof assistants. Formal systems can compile arguments into machine-checkable code; meanwhile, human reviewers have labeled some initial drafts of models as "the ugliest proofs" and "AI garbage." Tests show that models can solve six to seven out of ten of the most challenging problems, while producing a large amount of waste that requires human filtering, with query costs for individual problems potentially reaching nearly a thousand dollars while still yielding incorrect solutions.

More sharply, there is the issue of "correct but useless." Machines can write millions of lines of formal text in a matter of days or exploit loopholes in scoring systems during simulated meetings, completing remaining problems with compiled false proofs in 27 minutes. Formalization can confirm that steps are closed but cannot automatically judge whether this path leads to new concepts, new problems, or structures that can be cited by future generations. A summary from Carnegie Mellon researchers states: models can formalize but do not yet produce reusable infrastructure from the results.

Funding has followed suit. Preprints reveal that submissions using artificial intelligence have risen from single digits to over 20% within months; laboratories are scoring unpublished conjectures with models. The industry seeks marketable "solved" problems, while academia desires teachable and promotable proof concepts. The two are beginning to diverge.

Mechanically, this reflects a rewriting of evaluation standards by tools. Buyers are milestone-seeking model companies and authors looking to publish, while sellers are reasoning tokens and checkers like Lean. The flow of funds favors computational power that can produce long proofs rather than the review time that can point out dead ends. Benefiting are formal verification toolchains; under pressure are traditional disciplines that cultivate intuition through reading proofs. Beautiful proofs with no further questions will fill archives but will not enrich the history of thought.

Source: Public Information