Source: EconLog
by Joy Buchanan
“Sutton draws from decades of AI history to argue that researchers have learned a ‘bitter’ truth. Researchers repeatedly assume that computers will make the next advance in intelligence by relying on specialized human expertise. Recent history shows that methods that scale with computation outperform those reliant on human expertise. For example, in computer chess, brute-force search on specialized hardware triumphed over knowledge-based approaches. Sutton warns that researchers resist learning this lesson because building in knowledge feels satisfying, but true breakthroughs come from computation’s relentless scaling. … The Bitter Lesson is less about any single algorithm than about intellectual humility: progress in AI has come from accepting that general-purpose learning, persistently scaled, outperforms our best attempts to hard-code intelligence.” (02/17/26)
https://www.econlib.org/econlog/learning-the-bitter-lesson-in-2026