OpenAI Math Solutions: What Students Still Need to Learn

Legacy & Insights

LEGACY & INSIGHTS

OpenAI Published 370+ Math Solutions. Does Your Child Still Need Long Division?

The OpenAI math solutions split mathematicians. For parents, the question is simpler: does a student still need to learn the steps?

By Pamela F. Nichols·October 7, 2026·3 min read

OpenAI math solutions illustrated by a university student working through mathematics problems in a modern classroom — Illustration: KMOB1003 Global Media

The OpenAI math solutions published Tuesday landed in a field already arguing with itself. OpenAI released full or partial solutions to more than 370 outstanding problems. The company says an unreleased internal model produced them, taking about three hours of computing time per solution on average. (Fortune, New York Times)

Parents and teachers are likely to ask a simpler version. If a machine can crack problems that stumped professionals, does a fourth grader still need long division, or could a student just memorize the solutions? Nothing in the reporting says either shortcut works.

What the OpenAI Math Solutions Did and Did Not Settle

The release follows OpenAI’s announcement last month that it had solved the Navier-Stokes equations. Mathematicians split. Some see new ground to explore. Others call the approach an attack on mathematics as a human academic discipline.

Attribution is the open question. NYU’s Tristan Buckmaster told the New York Times it is unclear whether mathematicians using OpenAI’s tools helped steer the model, and he does not believe OpenAI did its due diligence on plagiarism. OpenAI denied a similar accusation over Navier-Stokes and says it is sharing computer-checkable versions of many proofs. (New York Times)

The Answer Was Never the Point

Terence Tao, the UCLA mathematician, has argued that reaching a solution builds understanding more than the solution does, Fortune reports. In a Tuesday post he describes how a breakthrough proof has traditionally brought talks, workshops, collaborations and eventually textbook chapters. He calls that Math 1.0. (Tao on Mastodon)

Tao sees this release as the end of that model, with problems now solved by prompters who cannot answer questions about the result. He wants a Math 2.0 that gives exposition and community building more weight than raw problem solving. (Fortune)

Dan Litt of the University of Toronto is more hopeful. He calls the results good for mathematics and says understanding them will take a great deal of human work. His warning concerns perception: if people decide AI has solved math, funders could pull back and young people could stay away. (Fortune)

WATCH / BACKGROUND ON OPENAI’S EARLIER ASTRA MATH RESULTS

Background video: a panel discussion of OpenAI’s August 1 release of ten results from an unreleased internal model it calls Astra, an earlier release than the 370-problem batch reported here. The views expressed are the panelists’ own.

Where the Reporting Stops and Our Reasoning Starts

Nobody quoted in the coverage addresses elementary school, and none of it says long division is obsolete. What follows is KMOB1003’s reasoning, not theirs.

Take long division. Calculators have handled it for decades, and the practice was never only about the quotient. A declaration published September 11, with 28 Fields Medalists named on it, Tao among them, says years of training build understanding and the ability to ask new questions, not only to produce answers. (Math and AI declaration) It concerns research, but the logic scales down: long division is practice at following a method and noticing when a step looks wrong.

Memorizing published solutions only works for questions someone has already answered. Tao’s complaint describes people who get a problem solved but cannot discuss the result. A student who copies an answer sits in that position on a smaller scale, and one follow-up question from a teacher shows it.

Litt’s worry about perception may reach younger children too. If a nine-year-old hears that AI has solved math, effort can start to look pointless. AI may help as a patient tutor, but the reporting offers no evidence on that for children.

Checking Is the Skill That Remains

OpenAI can now publish answers faster than people can read them. The independent advisory board OpenAI says it works with calls the release “the beginning, not the completion, of the process of human understanding.” (New York Times) In our view, the skill that grows in value is the human work Litt describes, extracting understanding from a solution. That means reading it, testing it and deciding whether it holds. A child who has worked the steps by hand has started building that skill. A child who has only seen answers has not.

The OpenAI math solutions did not settle whether students still need the steps. They made the reason easier to see.

Signal Breakdown

Confirmed / Documented

A public declaration titled “A Severe Misalignment of AI in Mathematics” was published September 11, 2026, and names 28 Fields Medalists, Terence Tao among them.
That declaration says solving problems is a proxy for conceptual understanding, and that years of training develop understanding and the ability to formulate new questions as well as final answers.
Tao posted on Mastodon on October 6, 2026, describing how a breakthrough proof traditionally generates talks, workshops, collaborations and eventually textbook material, and calling that “Math 1.0.”

Reported / Attributed

OpenAI says it published full or partial solutions to more than 370 outstanding math problems (the New York Times counts 377 results), using an unreleased internal model that averaged about three hours of computing time per solution.
OpenAI says it made progress on three other Millennium Prize problems without fully solving them.
OpenAI says it is sharing computer-checkable versions of many of the proofs and, for 10 problems, summaries of the model’s reasoning, compute estimates and attempt statistics.
Fortune reports that Tao calls the release the end of “Math 1.0” and wants a “Math 2.0” that weights exposition and community building more heavily.
The independent advisory board called the release the beginning, not the completion, of the process of human understanding, in a statement released Tuesday night, according to the New York Times.
Dan Litt of the University of Toronto calls the results good for mathematics and warns that a belief that AI has “solved math” could reduce funding and discourage young mathematicians.

Disputed

Whether OpenAI followed the independent advisory group’s publication recommendations. The group says it is up to the mathematical community to judge.
Whether mathematicians’ own work helped steer the model. Tristan Buckmaster says it is unclear and doubts OpenAI did its due diligence; OpenAI denied a similar accusation over its Navier-Stokes result.
Whether AI labs should test their models on unsolved research problems at all. The advisory board said on September 29 that it does not endorse the practice; OpenAI’s research lead says the proofs were a byproduct of testing internal models (New York Times).
Whether the release helps or harms mathematics as a human discipline.
Whether mathematical ability carries over to fields without objectively checkable answers, such as law or business strategy. Fortune calls this unclear.

Not Established

The available evidence does not establish:

that long division or multi-step problem solving is obsolete
that students can build mathematical skill by memorizing published solutions
that anyone quoted in the coverage was speaking about elementary or secondary education
that all of the more than 370 results have been independently checked
that AI tutoring improves children’s learning of mathematics
that OpenAI’s model copied anyone’s work