At a recent mathematics workshop at the University of California, Los Angeles (UCLA), the community’s leading figures gathered amid growing unease over the rapid advancements in artificial intelligence (AI) and its impact on the field. Terence Tao, a distinguished mathematician and professor at UCLA, described the current atmosphere as a state of emergency, highlighting the disruptive effects AI has had on mathematical research and careers.
The concerns escalated following OpenAI’s announcement on October 6 of a purported solution to the Navier-Stokes problem, one of the seven Millennium Prize problems that have challenged mathematicians for decades. Alongside this, OpenAI published over 350 research findings across various mathematical domains, including algebra, number theory, and topology. While these achievements demonstrate AI’s potential, they have unsettled many in the academic community, particularly early-career mathematicians who have seen years of work suddenly made redundant.
Dr. Tao, who has been a tenured professor for nearly three decades and is widely regarded for his profound contributions to pure mathematics, expressed mixed feelings. He acknowledged using AI tools since 2023 for tasks such as running calculations and literature searches and initially viewed AI as a means to augment human research. However, he now feels alienated by OpenAI’s rapid and large-scale releases, which he said seem driven more by self-evaluation needs than by a shared vision to empower science. Tao criticized this approach for undermining the traditional exploratory nature of mathematics, writing that the disclosure of complete solutions can eliminate the intellectual journey that motivates the field.
The frustration extends beyond individual careers to the foundational culture of mathematics. Unlike applied mathematics, which often welcomes AI’s ability to solve practical problems in engineering or finance, pure mathematics values the process of discovery as much as the results, offering its practitioners moments of deep insight and creativity. As Tao and others have noted, these breakthroughs are often not just about completing proofs, but about the personal and communal experience of grappling with complex ideas.
The financial costs and accessibility of AI-generated research have also raised concerns. During the workshop, participants questioned the disproportionately high expenses associated with AI’s large-scale computations, with estimates for the Navier-Stokes solution ranging from $6 million to as much as $22 million. This has sparked worries about uneven access to AI resources, especially among mathematicians in developing countries, potentially exacerbating existing inequalities in global scientific participation.
In response to these challenges, Dr. Tao has assumed an informal leadership role, organizing discussions, providing guidance, and amplifying the voices of those unsettled by the AI surge. His efforts have included coordinating a statement signed by multiple Fields Medal recipients condemning certain AI practices as detrimental to the field. Despite his advocacy, Tao describes the situation as “soul-searching at gunpoint,” reflecting the urgency and uncertainty mathematicians face.
Others in the field emphasize the need to reevaluate the role of human mathematicians as AI capabilities evolve. Tatiana Toro, director at the Simons Laufer Mathematical Sciences Institute, called for a reconsideration of mathematics’ origins and purpose, while Andrea Bertozzi, a UCLA professor, highlighted the traditional experience of mathematical proof as an essential educational and intellectual practice now challenged by automated solutions.
Amid this climate, graduate students like Ben Goldman, a Ph.D. candidate advised by Tao, confront an unclear future. They must navigate a profession in which foundational milestones once achieved through painstaking human effort are increasingly accessible to machines, challenging established notions of merit, creativity, and progress.
OpenAI has stated its commitment to collaborating with the mathematics community and responding to feedback as it advances AI research. Nonetheless, voices like Tao’s underscore the complex tensions at the intersection of AI innovation and mathematical tradition, as the discipline seeks to integrate new technologies while preserving its core values.