Who is betting on what in AI, and against whom.
Learn from experience
Intelligence is online reinforcement learning from a reward stream. Static datasets and the train-then-deploy split are a detour, and current deep learning cannot learn continually.
In plain words
The other camps train once, then freeze. This one says that is the mistake. Intelligence is an agent learning continually from a stream of experience and reward, building its own features and sub-goals as it goes, with no line between training and deployment.
Sutton's Bitter Lesson and the Alberta Plan are the founding documents; the 2025 essay 'Welcome to the Era of Experience' by Silver and Sutton is the manifesto. The engineering problem is that current deep networks lose plasticity and forget: a network trained on one thing for long enough stops being able to learn the next thing.
Until 2026 the camp was deliberately tiny. Then David Silver left DeepMind, raised $1.1B for Ineffable Intelligence, and Sutton left Keen to found Oak Lab with a target of a trillion-parameter agent learning in real time on 20 watts. Prime Intellect sells the tooling; Adaption Labs sells models that learn on the fly. Public results are still at toy scale.
Unfamiliar terms are in the glossary.
Where it stands
The camp doubled in size in 2026. Silver left DeepMind in January and raised $1.1B by April; Sutton and Javed left Keen in July to found Oak Lab in Toronto. Public results are toy-scale so far. The plasticity and catastrophic-forgetting problems are named but not solved. Sutton gives one-in-four odds of human-level AI by 2030.
What would prove them right. An agent that keeps improving after deployment in a non-stationary environment without forgetting, at a scale where frozen transformers plateau.
The disagreements, both sides cited
Can today's deep learning learn continually, or does it need new primitives?
Is the LLM camp already doing this?
What to watch
- An agent that keeps improving after deployment in a non-stationary environment without forgetting, at a scale where frozen transformers plateau.
- Oak Lab's first results against the 1T-parameters-at-20W target; Sutton gives one-in-four odds of human-level AI by 2030. AI pioneer Richard Sutton founds new research lab BetaKit 2026-07-14 · Turing Award winner Richard Sutton talks path to human-like AI BetaKit 2025-03-05
- Ineffable's first system: $1.1B buys a real test of whether 'no human data' can work beyond games. DeepMind's David Silver just raised $1.1B to build an AI that learns without human data TechCrunch 2026-04-27
People (19)
Researchers 17
Founders 2
Researchers have held a research or faculty role; scientist-founders count. Founders, executives and investors are listed separately so nobody mistakes a boardroom for a lab. Where someone argues for a different camp than the one they work in, it says so.
Where it is studied
Universities and institutes with people in this camp, from the roles recorded here. Not a ranking, and not complete: a place is listed when someone in the atlas works there.
- Brown University George Konidaris, Michael Littman
- Stanford University Emma Brunskill, Benjamin Van Roy
- ETH Zurich Jonas Hübotter
- Imperial College London Antoine Cully
- MIT Pulkit Agrawal
- McGill University Doina Precup
- Mila Doina Precup
- UCL Tim Rocktäschel
- University of Alberta Martha White
- University of British Columbia Jeff Clune
- University of Massachusetts Amherst Andrew Barto
- University of Michigan Satinder Singh
- University of Pisa Davide Bacciu
- University of Texas at Austin Roberto Martín-Martín
- University of Warsaw Piotr Miłoś
Also publishing here (15)
Senior authors in this camp's core literature, found through the alphaXiv research index or an institutional profile, and listed on the strength of one paper each. Being on this list means they publish in the field, not that they have taken a side. The full list is on the People page.
- David Abel Google DeepMind paper
- Pulkit Agrawal MIT paper
- Davide Bacciu University of Pisa paper
- André Barreto Google DeepMind paper
- Antoine Cully Imperial College London paper
- Hado van Hasselt Google DeepMind paper
- Jonas Hübotter ETH Zurich paper
- George Konidaris Brown University paper
- Joel Lehman Lila Sciences paper
- Roberto Martín-Martín University of Texas at Austin paper
- Piotr Miłoś University of Warsaw paper
- Razvan Pascanu Google DeepMind paper
- Tim Rocktäschel UCL and Recursive paper
- Benjamin Van Roy Stanford University and Google DeepMind paper
- Satinder Singh Google DeepMind and University of Michigan paper
Labs and companies (8)
NASDAQ:GOOGLAlphaZero lineage and the 'Era of Experience' essay. RL head David Silver left in January 2026 to found Ineffable Intelligence.
Also active here: Lila Sciences (Open-endedness research under Kenneth Stanley.); Physical Intelligence (Levine's wing is philosophically closer to the experience camp.).
Signature ideas
The Bitter Lesson
Sutton's 2019 essay: methods that use more computation (search and learning) always beat methods that encode human knowledge. Every camp quotes it; this one takes it literally.
Alberta Plan (12 steps)
A 2022 roadmap from Sutton, Bowling and Pilarski for building an agent that learns continually, from prediction of a single signal up to planning with learned options.
OaK architecture
Options and Knowledge: an agent that invents its own sub-goals (options) and learns models of them, all online. The namesake of Oak Lab.
Continual backprop / plasticity
Deep networks lose the ability to learn new things the longer they train. A 2024 Nature paper measured it and proposed reinitialising unused units.
Era of Experience
Silver and Sutton's 2025 essay: the age of human data is ending; the next leap comes from agents generating their own experience.
Reward is enough
The 2021 claim that a single scalar reward, maximised in a rich environment, is sufficient to produce every ability we call intelligence.
Big-world hypothesis
The world is far larger than any agent, so no agent can be trained to completion; it has to keep approximating and keep learning.
Read in this order
- Reinforcement Learning: An Introduction (2nd ed.) 2018
Sutton and Barto. Read chapters 1, 3 and 6 before anything else.
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm 2017
AlphaZero. Superhuman play from self-generated experience alone.
- Reward is Enough 2021
Silver, Singh, Precup and Sutton: the hypothesis that reward maximization is sufficient for intelligence.
- The Alberta Plan for AI Research 2022
A twelve-step program for continual, online agents. The camp's roadmap.
- Loss of plasticity in deep continual learning 2024
Why standard deep networks stop learning when trained forever, and one fix.
- Welcome to the Era of Experience 2025
Silver and Sutton's case that human data has run out and agents must generate their own.
How money reaches this camp
| Lab | Access | Note |
|---|---|---|
| Google DeepMind | NASDAQ:GOOGL | Alphabet (GOOGL) keeps the AlphaZero lineage, minus Silver. |
| Ineffable Intelligence | private | $5.1B; Sequoia and Lightspeed. |
| Prime Intellect | private | $1B; the picks-and-shovels play for RL environments. |
| Adaption Labs | private | $50M seed. |
| Oak Lab | private | Funding undisclosed. |
| Keen Technologies | private | $20M in 2022; Carmack-funded. |
Not investment advice. Private valuations are what the last round implied; "reported" means the press, not the company, gave the figure. Full table on the Capital page.