Chess grandmasters find new ways to win by making less optimal moves after AI pushed classical chess toward perfect play, breathing new life into the game
Artificial intelligence drove chess toward perfect play, leading to more draws at top tournaments. Now grandmasters are winning by making less optimal moves.
Context & Ripple Effects
AI-assisted preparation has long reshaped elite chess: coverage of supercomputers’ influence on top-level play framed computer guidance as a defining competitive force, while neural engines were also linked to new forms of creativity and psychological play.
This report marks a further adaptation. As highly accurate play produces more draws, grandmasters are treating engine-optimal moves less as an instruction manual than as a baseline to depart from when a decisive result matters.
First-order effects
- Grandmasters can deliberately choose positions that are less objectively optimal but harder for a human opponent to navigate, creating more winning chances in classical games.
- The immediate competitive premium shifts toward judgment about imbalance, deception and practical pressure—not simply reproducing the engine’s top line.
Second-order effects
- Opponents must prepare for a wider range of intentionally unbalanced choices, making engine-backed opening preparation alone less sufficient as a defensive tool.
- Tournament spectators and organizers stand to benefit if fewer top-level games resolve as draws, because decisive games create clearer competitive stakes.
Third-order effects
- If this approach endures, elite chess may settle into a human-versus-human layer above engine evaluation: computers define the reference point, while players differentiate themselves through practical choices.
- The pattern illustrates a broader AI-era adjustment in expert domains: when optimization becomes widely available, advantage can move toward knowing when not to optimize for the model’s preferred objective.
The trend: As AI makes baseline technical performance more uniform, elite practitioners are increasingly seeking advantage through human judgment, uncertainty and incentive-aware deviations from the optimum.