Across AI research, broader, more general models consistently outperform narrow, task‑specific ones. Anthropic embraces this by betting on future, larger models rather than fine‑tuning current versions.
Fei-Fei Li revisits Richard Sutton's "Bitter Lesson" that simple methods with massive data win, discusses its relevance to vision, and explains why robotics cannot rely on the same shortcut alone.