Mathematics has changed. Let's act like it

The recent release of over 700 research papers by OpenAI has fundamentally altered the landscape of mathematics, demonstrating that AI can now make groundbreaking contributions across diverse subfields. Rather than viewing this development as an existential threat, the mathematical community should embrace a transition from a focus on individual theorem-proving to a broader, more ambitious agenda of structural understanding and problem-setting.

The New Reality of Mathematical Research

Mathematics has historically been a craft-based discipline, but it is rapidly evolving into a technology-driven field. The ability of AI to tackle major open problems—such as those in the Langlands program or percolation theory—means that the traditional "heroic" model of the solitary mathematician solving a famous conjecture is becoming less central.

As noted by observers, the current situation mirrors the classification of finite simple groups in the 20th century: once a major problem is "solved," the community must avoid the temptation to declare the subject finished. Instead, mathematicians should view these AI-assisted results as new terrain, using the tools to explore deeper, more complex connections that were previously inaccessible due to technical barriers.

Shifting Incentives and Evaluation

To adapt to this new era, the profession must move away from evaluating researchers solely on their publication lists or their ability to produce incremental proofs. The value of a mathematician now lies in their ability to:

• Choose worthwhile problems: Identifying which questions are worth exploring is becoming more important than the mechanical process of finding a proof.
• Synthesize and communicate: The ability to explain complex mathematical structures and their broader implications is a vital skill that AI cannot currently replicate with the same level of human insight.
• Develop new programs: Senior mathematicians should prioritize creating bold, multi-year research agendas rather than competing with AI to solve existing, well-defined conjectures.

Concrete Steps for the Community

To facilitate this transition, the academic community must reform its institutional practices:

• Hiring and Promotion: Move beyond paper-based metrics. Hiring committees should prioritize in-person interviews that test a candidate's vision, breadth of knowledge, and ability to communicate complex ideas.
• Journal Reform: The current journal system, which often rewards incremental work, is ill-suited for the AI era. The community should consider abandoning journals that do not adapt, focusing instead on platforms that emphasize clarity, utility, and deep exposition.
• Graduate Education: Training must shift from teaching narrow technical skills to fostering a broader understanding of mathematical connections. Students should be encouraged to learn how to navigate the intersection of different fields, as this is where the most significant future discoveries will likely occur.

The Human Role in an AI-Driven Future

While some fear that AI will render human mathematicians obsolete, the consensus among many in the field is that human judgment, taste, and the ability to define the boundaries of inquiry remain essential. The "game" of mathematics has not ended; it has simply changed.

As one commenter noted, "The lowest level of abstraction where humans win is asking the questions, rather than merely answering them." By taking ownership of AI-generated results and integrating them into a larger human-led narrative, the mathematical community can ensure that it remains a vital, evolving discipline. The goal is not to compete with machines on their own terms, but to leverage their capabilities to push the boundaries of human knowledge further than ever before.