Sequoia Capital announces its partnership with David Silver and his new AI research lab, Ineffable Intelligence, based in London. The lab's mission is to build a superlearner — a system that discovers all knowledge purely through its own experience using reinforcement learning, without any pre-training on human data. The vision is a system that could rediscover and surpass humanity's greatest intellectual achievements, from the laws of physics to new branches of mathematics, by learning entirely from the consequences of its own actions.

Alfred Lin frames this as a deliberately contrarian bet against the dominant LLM paradigm, which relies on training over vast amounts of human-generated internet data. Silver's track record lends credibility to the approach: he was the lead researcher behind DeepMind's Alpha series (AlphaGo, AlphaGo Zero, AlphaZero, AlphaStar, AlphaProof), where removing human pre-training and relying on self-play produced decisively superhuman performance in Go. The key insight is that AlphaGo Zero, trained without any human data, dramatically outperformed its predecessor — suggesting that human knowledge can be a ceiling rather than a foundation. Sequoia is co-leading Ineffable's first funding round, positioning it as one of the most ambitious scientific bets in the current AI landscape.

Prime Intellect announces Prime Agent, a self-improving Reinforcement Learning Model (RLM) harness built for coding tasks and long-running autonomous operations. The system emphasizes both token efficiency and expressiveness through several key design choices: programmatic tool calling, treating context as a variable, multi-agent messaging, and a self-modifiable harness state.

These architectural decisions suggest Prime Agent is designed to adapt and optimize its own behavior over time, distinguishing it from static agent frameworks. The self-modifiable harness state is particularly notable, as it implies the agent can restructure how it operates during execution rather than being locked into predefined workflows.

Richard Sutton, widely regarded as the father of reinforcement learning, has launched a new lab with an ambitious goal: building a trillion-parameter agent that learns in real time while consuming only 20 watts of power. This represents a dramatic challenge in efficiency and scale, aiming to create powerful AI systems that operate within extreme energy constraints.

The post references Sutton's influential 2019 essay, The Bitter Lesson, which argued that general methods leveraging computation consistently outperform approaches that try to encode human domain knowledge. This new lab appears to be a direct extension of that philosophy — pushing toward massively scaled, computation-driven agents while simultaneously targeting radical energy efficiency, a combination that could reshape how AI systems are designed and deployed.