As of now, the evidence is real but still limited in scope: AI has materially advanced several previously open mathematical problems, and in a few cases has disproved long-standing conjectures or produced machine-checked proofs of open results.[1][2][3]
What the strongest evidence shows
1) AI has helped resolve some genuine open problems
A notable example is 
OpenAI’s report that an internal reasoning model produced a proof disproving the planar unit-distance conjecture as it had been commonly understood, yielding an improved lower-bound construction with exponent \(1.014\) rather than \(1+o(1)\).[2]
That is a substantive mathematical advance: it is not just solving competition-style exercises, but addressing a published research question.
Another important line of evidence comes from Google 
DeepMind’s formal-proof work. Their public reports describe systems that, using Lean-based verification, solved a subset of open Erdős problems and other research-level tasks, with results checked in a formal proof assistant.[1] This matters because formal verification greatly reduces the risk of “looks right but is wrong” AI output.
There are also reports of AI-assisted results in commutative algebra, where a system called Rethlas produced Lean-verified proofs for some open problems.[3]
2) AI has materially improved known bounds and constructions
Even when a system does not fully “solve” a problem, it can still produce mathematically meaningful progress. Examples include:
- new constructions in additive combinatorics from FunSearch,
- improved algorithms and bounds from AlphaTensor and AlphaEvolve,
- and multiple verified advances in Erdős and OEIS-style problems from DeepMind’s formal-search systems.[1]
These are best described as material advances, not complete general solutions.
3) The field is moving, but not to “AI has solved mathematics”
The current evidence does not show that AI has broadly solved mathematics, or even that it can routinely crack the hardest famous open problems. The public results are concentrated in areas where:
- the problem can be formalized cleanly,
- correctness can be checked mechanically,
- and the proof search space is amenable to exhaustive or semi-exhaustive exploration.[1][3]
DeepMind’s own classification suggests a cautious reading: its public reports emphasize lower-category results and do not amount to claims of “landmark breakthrough” across mathematics as a whole.[1]
Bottom line
The best-supported conclusion is:
AI has now crossed from benchmark performance into genuine research-grade mathematical contribution in selected areas, including some open-problem disproofs, verified proofs, and improved bounds/constructions—but it has not generally solved mathematics, and the strongest results are still narrow, formalization-friendly, and often human-checked.[1][2][3]
