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Bitter Lesson is about AI agents
The Race for AI Progress In 2019, Richard Sutton, wrote his groundbreaking essay titled ‘The Bitter Lesson’. Simply put, the essay concludes that systems which get better with higher compute beat the systems that do not. Or specifically in AI: raw computing power consistently wins over intricate human-designed solutions. I used to believe that clever orchestrations and sophisticated rules were the key to building better AI systems. That was a typical sofware dev mentality. You build a system, look for edgecases, cover them and you are good to go. Boy, was I wrong.
The system started handling edge cases we hadn’t even thought of, and more importantly, it discovered interaction patterns that emerged naturally from having the freedom to explore multiple paths. Take OpenAI’s Deep Research or Claude’s computer-use capabilities - they demonstrate how investing in compute-heavy post-training processes yields better results than intricate orchestration layers. Design systems that can effectively utilize increasing compute resources Build robust learning environments that scale Create architectures that can grow without requiring fundamental redesigns
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