Rich Sutton publishes “The Bitter Lesson”
General methods that scale with compute win
Confirmed
Importance: major (4 of 5)The takeaway
On March 13, 2019 reinforcement-learning pioneer Rich Sutton published the short essay “The Bitter Lesson”.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- Published March 13, 2019 on incompleteideas.net
- Core claim: ‘general methods that leverage computation are ultimately the most effective, and by a large margin’, driven by the falling cost of computation (a generalization of Moore’s law)
- Examples: computer chess and Go (search), speech recognition, computer vision
- Conclusion: build in ‘only the meta-methods that can find and capture this arbitrary complexity’, not our own discoveries
What happened
In about 1,100 words Sutton argued that researchers keep trying to build human knowledge into AI systems, which helps in the short term, but that approaches which scale with computation, such as search and learning, eventually win every time, which is ‘bitter’ for the researchers involved.
Why it matters
The essay is widely cited as the philosophical basis of the scaling era, from GPT-3 and the scaling-laws papers to today’s compute-heavy frontier training and the RSI debates of 2026, in which lab leaders such as Jakub Pachocki describe progress as driven mainly by compute.
Sources
1 source from 1 site. Numbers match the chips in the text.
1 source: 1 primary
Primary
- Rich Sutton: The Bitter Lessonincompleteideas.net, official
Changes
- Filed (primary source checked)