Google paper reveals AI agents can rationally cooperate through similarity inference

For decades, game theory has had a pretty firm answer to the question of whether two strangers should cooperate when they’ll never meet again: absolutely not. The rational play, according to Nash equilibrium, is to defect. Every time. No exceptions. A new research paper from Google DeepMind, Mila-Quebec AI Institute, and ETH Zürich argues that AI agents built on foundation models don’t play by those rules. And they have the math to back it up. The end of inevitable defection The paper, submitted to arXiv on August 4, 2026, spans 75 pages with 11 figures and introduces a concept the authors call “similarity inference.” The core idea is deceptively simple: AI agents built from similar training processes can recognize that similarity and use it to predict what the other agent will do. Classical game theory treats each player as a fully independent decision-maker, what the paper calls “decoupled agency.” Under that assumption, your opponent’s choice is a black box. You can’t predict it, so you hedge by defecting. The Nash equilibrium holds, and everyone ends up worse off than if they’d cooperated. The research team, led by Alexander Meulemans, proposes an alternative framework called “...