Demonstrating that Rich Sutton was never really on the 'LLM bus' in the first place. Note the remarkable absence from the essay of language models & large language models from that essay despite BERT and GPT-2 and 'unreasonable effectiveness of data' etc. He only briefly mentions speech recognition. (Note also Sutton's general absence from LLM research, the Edmund Plan or switch from DeepMind to Keen Technologies as DeepMind was forced into LLM-centric research, and his published research since 2019's emphasis on small models and trying to fix their pathologies like catastrophic forgetting.)

> The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

You could easily seem most LLM work as a dead end because it is about 'building knowledge into your agents' (eg. by paying data labelers billions of dollars total to supplement your scrapes), and not about 'search' (still a major open problem for LLMs - o1-style serial reasoning traces are obviously inadequate) or 'learning' (LLMs depend so heavily on the knowledge already encoded in so much data for them).