ω = the smallest exponent such that we can multiply n×n matrices in nω + o(1)
We know 2 ≤ ω ≤ 2.807. Where exactly? Nobody knows.
OpenAI, An Upper Bound of 9/4 for the Matrix Multiplication Exponent (released 6 Oct 2026; written by an internal model). Over ℂ. Main theorem checked in Lean; not peer reviewed yet.
Previous record ω < 2.371177: Dupont et al., Google DeepMind + Alman, Vassilevska Williams, Zhou (Aug 2026, with AlphaEvolve).
2. Integer multiplication
Remember Karatsuba's algorithm? nlog2 3 ≈ n1.585
Schönhage–Strassen (1971): O(n log n log log n) — and they conjectured n log n is optimal
Harvey–van der Hoeven (2019): O(n log n). Case closed?
Oct 2026, OpenAI: O(n (log n)1−κ) with κ = 2−182 ≈ 10−55
multitape Turing machine, deterministic, exact — the same model as the conjecture
10−55 → 10−3 in three days: hundreds of people + Codex / Claude improving OpenAI's construction (CrocSwap/integer-mult-bounds). Today: κ ≈ 1.5·10−3. All conditional on OpenAI's paper being correct.
Screenshot: Beyond n log n dashboard by Aurel Prosz, posted by @dysmemic, 9 Oct.
Bonus: All-Pairs Shortest Paths
Remember Floyd–Warshall?
Floyd–Warshall (1962): O(n3). For 60 years, nobody beat n3 by a polynomial factor (best: n3 / 2Ω(√log n), R. Williams 2014)
"APSP hypothesis": no O(n3−ε) algorithm exists. Many conditional lower bounds were built on it.
Oct 2026: O(n2.9995) — deterministic, directed, integer weights up to poly(n) (also real weights: O(n2.998) expected; and 3SUM in O(n1.9992))
"Claude, an AI model developed by Anthropic, discovered the algorithm" — one session, 16M output tokens, no human input