Richard Sutton

General methods leveraging computation are ultimately more effective than human-knowledge-based approaches in AI, a lesson demonstrated across 70 years of AI research from chess to Go.

“Author of "The Bitter Lesson"”

David Silver, a former DeepMind lead researcher, is raising $1 billion for Ineffable Intelligence, which would be Europe's largest seed round, to commercialize reinforcement learning-based systems trained on experience rather than internet text.

“Co-authored a paper with David Silver titled 'Era of Experience' arguing that the next leap in AI would come from systems learning from experience rather than text.”

Reinforcement Learning has evolved from early psychology and mathematical foundations through the Deep RL revolution of the 2010s to modern applications like RLHF and GRPO in LLMs, with future potential still largely untapped.

“Often called the father of reinforcement learning, recently gave an interview about what RL could unlock for the global AI industry.”

Richard Sutton's 'Bitter Lesson' argues that general search and compute methods outperform domain expertise, but engineers remain critical for properly framing search problems—as exemplified by tinygrad's approach to hardware optimization.

“Canadian computer scientist who wrote The Bitter Lesson blog post arguing that general methods leveraging search and compute outperform domain-specific solutions.”

AI developers building agentic systems should embrace Sutton's Bitter Lesson by leveraging scaled models with feedback loops rather than engineering complex prompt hierarchies and hardcoded workflows.

“Authored 'The Bitter Lesson' essay arguing that general methods leveraging computation are ultimately most effective in AI research.”

Taking The Bitter Lesson seriously means enabling AI to accelerate compute and energy technologies through autonomous science and RL-guided experimentation, rather than pursuing recursive self-improvement through algorithms alone.

“Authored The Bitter Lesson, stating that general methods leveraging computation are most effective in AI research.”