In May 2026, OpenAI released a new math result that sent shock waves throughout the world of mathematical research. A major unsolved problem called the “unit distance conjecture” had just been resolved by generative AI.
Since then, there’s been a steady drumbeat of new results that either partially or completely leverage artificial intelligence to solve research-level mathematics problems. However, most new math results published in any given month are still generated by humans.
So where is this going? How good, and how quickly, will AI capabilities grow? Will most mathematical research be predominantly artificial intelligence?
Or, as some mathematicians suggest, will AI combine with human ingenuity and other computer tools to create a golden age of mathematics?
A list of unsolvable problems
One of the most prolific mathematicians of the 20th century was Hungarian Paul Erdős, known for the breadth of his collaborations with mathematicians across many sub-fields.
Throughout his career, he proposed hundreds of unsolved problems that today are known as Erdős problems. This list is a tempting place to start for any AI company wishing to show it can solve problems in mathematics.
Over the decades, human researchers have steadily resolved many of the Erdős problems, but a large number remain unsolved. One was the unit distance problem from the field of geometric graph theory.
Humans on the shoulders of AI
This wasn’t the first mathematical problem to be solved by AI — it wasn’t even the first Erdős problem to be solved by AI — but it was the most significant. The unit distance problem is a prominent one that many researchers have attempted to solve since 1946, when it was proposed.
It was particularly noteworthy that, having read the proof of the unit distance problem by AI, it was human researchers who managed to adapt the central technique of the proof to solve — only a week later — another significant conjecture called the “sum-product conjecture.”
Much as the AI result rested on the shoulders of many mathematicians before it, humans were able to climb one step higher due to advances by AI.
A combination of technologies
Computer-based tools have been helping humans with mathematics for as long as there have been computers. These tools have become increasingly sophisticated and might run on the world’s largest supercomputers, modeling climate change or pandemics.
Even within pure mathematics, preliminary research shows that proof of some theoretical results can grow to petabytes in size — a petabyte is equal to one million gigabytes.
Computational tools aren’t the only way computers are aiding humans. How can you be convinced about the validity of an argument too large or technical to be easily verified even by experts? Using a computer language created for proof-verification, mathematicians can make extremely precise versions of every component of an argument. Then the proof-verification system checks that every step logically follows from the underlying axioms, or starting points, of the proof.
In a recent pre-print paper, mathematicians combined each of these technologies — computational tools, proof verification and artificial intelligence — together with their own human ingenuity to achieve a new result in a field of math called Ramsey theory.
After exhaustive and rather clever computational searches, AI was able to conjecture a general pattern for a particular phenomenon, prove that the pattern did indeed hold and help the authors convert the argument for proof verification.
This combination of technologies led them to believe that we are now entering a “golden age” of mathematics.
Million-dollar math problems
Today the capabilities of AI are mixed. Some mathematical fields, notably graph theory, have been particularly amenable to AI-based proofs, but others seem quite resistant.
For every math problem AI does solve, there are huge numbers of problems that AI has failed to solve when prompted. For the most famous unsolved problems — such as the six remaining Millennium Prize problems where the Clay Mathematics Institute will pay you a million dollars if you can solve one of them — the problems seem as completely out of reach for AI as they do for us humans.
As to the future, we don’t know how far and how fast AI’s capabilities will grow. If progress is substantial, what will mathematical research look like for humans in five or 10 years? These early examples where humans built on AI results or leveraged AI along with existing tools to achieve new mathematical heights could give a glimpse of one possible future.
On the flip side, students’ anxiety about spending years of their lives honing their mathematical skills is understandable. There are natural fears about AI replacing humans in research mathematics. Thankfully, we’re not close to that yet.
Trefor Bazett is an associate teaching professor of mathematics, University of Victoria.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Facts Only
* OpenAI released a new math result in May 2026 resolving the unit distance conjecture.
* New results frequently leverage artificial intelligence to solve or assist in research-level mathematics problems.
* Most new math results published in any given month are still generated by humans.
* Paul Erdős proposed hundreds of unsolved problems known as Erdős problems.
* The unit distance problem is one of the Erdős problems from geometric graph theory.
* Human researchers adapted a proof technique derived from an AI solution to solve the sum-product conjecture.
* Computer tools assist mathematics, including modeling complex systems like climate change.
* Proofs of some theoretical results can reach petabyte in size.
* Mathematicians use computer language for proof verification, allowing checking against axioms.
* A recent paper combined computational tools, proof verification, and AI to achieve a result in Ramsey theory.
* AI was used to conjecture a general pattern, prove it held, and aid in the argument for proof verification.
Executive Summary
A new result in mathematics, the resolution of the unit distance conjecture by generative AI in May 2026, has prompted discussion about the future role of artificial intelligence in mathematical research. This event signals a trend where AI is increasingly used to solve or assist in solving complex problems, although most published results are still generated by humans. The text explores the trajectory of AI capabilities in mathematics, asking whether AI will become predominantly central to mathematical research or if it will function as a tool augmenting human ingenuity and other computational methods.
The piece uses Paul Erdős's list of unsolved problems as a framework to discuss the potential for AI in mathematics. It highlights an example where AI's result on the unit distance problem allowed human researchers to adapt techniques to solve another conjecture, the sum-product conjecture. Furthermore, it details how computer tools are being used alongside proof verification systems and AI to tackle extremely large mathematical proofs, demonstrating a combination of technologies leveraged by human insight.
The current state is characterized by mixed capabilities; AI excels in some areas like graph theory but struggles with famously unsolved problems like the Millennium Prize problems. The trajectory remains uncertain regarding the speed and extent of future growth, balancing optimism about a potential "golden age" of mathematics against understandable human apprehension about replacement.
Full Take
The narrative traces an evolution from human-led discovery to a potential synergy between human ingenuity and artificial intelligence in mathematics. The core tension lies in balancing the apparent power of AI in generating specific results against the role of human adaptation, critical verification, and conceptual leaps necessary for deep mathematical progress. The reference to Erdős problems frames the unsolved landscape as both a measure of mathematical difficulty and a target for technological advancement, suggesting that complexity itself remains a barrier, irrespective of computational power.
The progression where AI yields a solution that then triggers further human-led development highlights a potential pattern where AI functions less as a replacement and more as an accelerant or catalyst—a step-function mechanism for abstract pattern recognition that humans then refine through deductive rigor. The discussion about petabyte proofs and automated verification points toward a future where the bottleneck shifts from raw calculation to organizing, verifying, and conceptualizing extremely complex mathematical structures.
The underlying implication is a question of agency: if AI can solve specific problems, what defines human mathematical genius moving forward? The anxiety expressed by students regarding skill acquisition juxtaposed with the possibility of an AI-augmented "golden age" suggests a need to define whether human value lies in the initial insight or the subsequent synthesis. Future analysis must address who benefits from this potential convergence and how societal structures will adapt to a landscape where mathematical discovery is heavily mediated by algorithmic capability.
Bridge Questions: If AI systems can generate novel proofs, what new forms of human ingenuity are required to guide these systems toward fundamentally new mathematical axioms rather than optimizing existing ones? How should the value system for mathematical research shift when computational execution becomes increasingly automated? What specific mechanisms must be developed to ensure that collaborative AI-human work leads to genuine conceptual understanding rather than mere surface-level pattern matching?