The Bitter Lesson

Sutton observed a repeating pattern across seven decades of AI research: researchers build systems that encode human expertise and intuition about a domain (like handcrafted chess strategies or linguistic grammar rules), these systems perform well initially, and then eventually get overtaken by much simpler, more general methods that just leverage more computation — search and learning — once enough compute becomes available. The 'bitter' part is that this keeps surprising and disappointing researchers who invested years building domain-specific expert knowledge into their systems, only to watch a more general, compute-hungry approach eventually surpass it.