“Dario Amodei's "Big Blob of Compute Hypothesis" from 2017, which is similar to Rich Sutton's "Bitter Lesson," states that seven factors are crucial for AI progress: raw compute, data quantity, data quality and distribution, training duration, a scalable objective function (like pre-training or RL), and numerical stability for compute flow.”