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May 28, 20261
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AI Systems Solve Multiple Longstanding Mathematical Problems, Raising Questions About Future of Human Mathematics
In May 2026, AI systems including OpenAI's internal models and DeepMind's AlphaProof Nexus solved multiple longstanding mathematical problems, including Paul Erdös's Unit Distance Problem from 1946 and nine additional Erdös conjectures. The developments have prompted mathematicians to consider whether AI will fundamentally change the role of human researchers in mathematics.

Quick Facts
Who
Scott Aaronson
What
AI model solved Erdös's Unit Distance Problem
When
May 28, 2026
Where
UT Austin
- AI model solved Erdös's Unit Distance Problem
- AI refuted Erdös's conjecture about unit distance pairs
- DeepMind's AlphaProof Nexus settled nine Erdös problems
- GPT5.5Pro solved problem about electrical flows on graphs
- Human mathematician Will Sawin improved AI's construction
In a striking series of developments in May 2026, artificial intelligence systems have solved several longstanding mathematical problems that have remained open for decades, prompting reflection on the future role of human mathematicians. An internal OpenAI model recently solved Paul Erdös's Unit Distance Problem from 1946, a central open problem in discrete geometry. Using algebraic number theory, the AI refuted Erdös's conjecture by constructing a set with n^(1+ε) unit-distance pairs, where ε is approximately 10^-38. Shortly after the AI breakthrough, human mathematician Will Sawin improved upon the construction to achieve approximately n^1.014 pairs, though the best-known upper bound remains n^(4/3), an improvement over Erdös's original n^(3/2).
The solution was notable for its efficiency: Scott Aaronson reports that the process appeared to be accomplished in a single attempt, with student Lijie Chen simply presenting the problem to GPT, which then produced a several-page mathematical argument that experts subsequently verified as correct. Shortly thereafter, DeepMind's AlphaProof Nexus system, which includes involvement from UT Austin colleague Swarat Chaudhuri, announced the settlement of nine additional Erdös problems, primarily in additive combinatorics, with proofs fully formalized in the Lean proof assistant. Most recently, an AI system called GPT5.5Pro solved a longstanding open problem in computer science concerning electrical flows on graphs.
These developments have prompted significant reflection within the mathematical community about the implications for human researchers. Aaronson notes that graduate students expressed concern about the future relevance of human mathematicians and scientists in a landscape where AI systems can solve fundamental problems. However, Aaronson cautions that selection bias may play a role—AI systems are likely failing to solve hundreds of other problems without announcement—and notes that human mathematicians had focused their efforts on proving Erdös correct rather than seeking counterexamples, a different research strategy. The AI's success may also reflect that the required expertise in algebraic number theory is uncommon among discrete geometers.
Despite these remarkable successes, uncertainty remains about the trajectory of AI mathematical capability. OpenAI accompanied its breakthrough with commentary from renowned mathematicians including Timothy Gowers and Noga Alon, reflecting on the AI's approach and methods visible through examination of its chain-of-thought reasoning. Aaronson expresses ambivalence about whether AI systems will soon solve major unsolved problems like P versus NP, noting that the role of human mathematicians could potentially reduce to selecting interesting questions and understanding AI-generated solutions, or whether systems will encounter fundamental limitations. He concludes that this question will be resolved empirically in the near term.
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Why This Matters
These breakthroughs directly challenge the traditional role of human mathematicians by demonstrating that AI can independently discover novel proofs to longstanding open problems. For researchers and students, this raises urgent questions about career trajectory and the future value of mathematical expertise. However, the development also reveals important nuances: AI may succeed where humans focused on different strategies, and the translation of AI solutions into human understanding remains crucial. Understanding these shifts helps readers anticipate shifts in scientific labor markets and educational priorities.
Timeline & Sources
Jan 1, 1946
WirePaul Erdös formulates Unit Distance Problem
May 28, 2026
WireOpenAI internal model solves Erdös's Unit Distance Problem
May 28, 2026
WireGPT5.5Pro solves electrical flows on graphs problem
May 28, 2026
WireScott Aaronson publishes dispatch about AI mathematical breakthroughs