Since Richard Sutton’s influential essay in 2019, the concept of the bitter lesson has guided much of modern progress in AI. This concept espouses general methods that leverage computation and learn with scale, as free of human-engineered priors as possible. Our interpretation is that, fundamentally, intelligence is about learning from raw data: autonomously finding patterns, building abstractions, and developing meaning from messy low-level inputs, ideally end-to-end and as data-efficiently as possible. As AI advances into complex domains such as science, robotics, and biology – where data is scarce and often difficult for humans to understand, let alone engineer for – internalizing this philosophy to develop more general learning algorithms is more important than ever.