Much of the discussion about AI and mathematics concerns what we might lose: understanding, creativity, professional identity, or employment. These concerns deserve attention. But I also see a possible opportunity to challenge a system in which opportunities to do mathematics remain deeply unequal.
Daniel Markovits’s The Meritocracy Trap, published in 2019, provides a useful starting point. Written only a few years before conversational generative AI became widely available, it describes a system whose mechanisms these new tools might give us an opportunity to change.
Markovits argues that affluent families invest heavily in their children’s education, enabling them to acquire the skills and credentials rewarded by elite employment. The resulting income then finances exceptional educational opportunities for the next generation. This cycle reproduces inequality through achievement itself. Even its apparent winners become trapped in relentless work and competition to maintain their position. He explains this argument in a Yale Insights interview.
What I find especially important is that the acquired skills can be real. Unequal access can produce genuine differences in accomplishment. Calling the final competition meritocratic does not resolve the inequality in how people became equipped to compete.
I see a related problem in mathematics, although academic careers do not map neatly onto the wealthy professions Markovits discusses.
Consider two graduate students. One joins an active group in a fashionable area, with a prominent adviser, frequent visitors, knowledgeable peers, and good funding. Someone nearby can suggest a promising problem, explain an unfamiliar technique, notice a connection, or introduce a future collaborator.
Another works on a respectable but less fashionable problem, with fewer resources and fewer people interested in the outcome. They may develop substantial understanding and produce thoughtful, technically sound doctoral work, yet emerge with a thinner publication record and fewer advocates. They then struggle for temporary positions while worrying about their family and future.
Neither outcome is predetermined. But when the resulting CVs are compared, how much of that difference in opportunity do we acknowledge? How much do we simply call “merit”?
This is where I see AI as a possible opening.
My thinking was partly inspired by Terry Tao’s discussion of the history of computation. In Machine assisted proofs (January 3, 2024), pp. 3–6, he describes human computers constructing mathematical tables and performing scientific calculations, before turning to modern computer algebra systems.
That history suggests a broader pattern. Printing and mass education widened access to recorded knowledge. Calculators and computers made powerful computational capabilities widely available. The Internet lowered barriers to finding specialized information and communicating ideas. AI may extend this process to intellectual assistance itself.
A paper can be freely available yet remain practically inaccessible to someone without the necessary background or someone to ask. AI can help unpack an argument, supply prerequisites, generate examples, and suggest connections. Its answers require checking, but access to that conversation is valuable.
There is a social dimension, too. Some people will recognize the experience of asking a question online and being dismissed for using the wrong terminology or failing to formulate it properly. Having somewhere to ask elementary questions repeatedly, without embarrassment, can change what one is willing to learn. So can having assistance available outside the limited time an adviser or colleague can offer.
AI will not supply research funding, job security, or the judgment of an excellent mentor. But it may make some forms of intellectual support less dependent on admission to a privileged academic environment. That is the possibility I want us to take seriously.
What AI could redistribute is the ability to turn curiosity into work that others can evaluate; whether that work receives fair recognition remains an institutional question.
It could also unsettle the measures through which mathematical careers are ranked. If producing certain kinds of publishable results becomes substantially easier, publication counts will become weaker evidence of intellectual depth. I would welcome the opportunity to reconsider “publish or perish,” including its tendency to reward visible output while overlooking patient teaching, explanation, verification, and work whose value is not immediately fashionable.
But weakened measures do not guarantee fairer institutions. Committees could respond by relying even more on pedigree and recommendations. Better-funded groups could use more powerful AI systems to increase their advantage. Departments could raise publication expectations until everyone is running faster merely to remain in place. That would reproduce the trap.
The opportunity, then, requires choices: broad access to useful tools, support for learning how to question their outputs, and evaluation that does not convert every gain in productivity into a higher threshold for employment. Greater mathematical capability should make room for more people to participate and for more sustainable working lives.
I am extending Markovits’s critique here, rather than attributing this proposal about AI to him. His analysis helps explain why changing the tools alone will not suffice. It also helps clarify what a worthwhile change could accomplish.
I hope for a mathematical culture in which the opportunity to develop understanding depends less on institutional pedigree, professional connections, and financial security—and in which people need not continually prove their worth by producing more papers.
AI offers no guarantee of that world. But it may give us an opportunity to build it.
AI-use disclosure: I developed this post through a conversation with ChatGPT, using it to explore and organize the argument and to draft and revise the prose.
Received 16 September 2026
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