Thore Graepel, who helped build Google DeepMind’s AlphaGo, has launched Metis Reasoning in London. The company’s founding claim is that large language models cannot be trusted to reason. Its founders want to apply the lessons of AlphaGo to general reasoning instead.
The company set out its thesis in a white paper titled “The Age of Reasoning”. Graepel left Google DeepMind this summer, Tech.eu reported.
His co-founders are Piers Ingram, a technology and biotech entrepreneur who has raised more than $350m, and Michael Warren, a robotics expert who worked at Boeing. The fourth is David Berman, head of AI at Cambridge Consultants. He also advises the UK government and NATO on AI. Tech.eu puts the team at 12 people. The white paper lists offices in London, Singapore and Los Angeles.
What Metis says is wrong with LLMs
The paper lists three problems. LLMs hallucinate, and nobody can audit their reasoning. Users also cannot inspect the knowledge behind an answer, because it sits inside the model’s weights.
“There exists, however, a significant set of problems that no amount of scaling can solve,” the white paper says.
It argues that a model’s step-by-step “chain of thought” often does not reflect how it reached its answer. Ingram put the business case more bluntly.
“When you think about higher-stakes cases in engineering or science, or medical or legal reasoning, the arguments that the models come up with basically can’t be trusted,” Ingram told Tech.eu.
The AlphaGo lesson
The paper opens with Move 37, which AlphaGo played against Lee Sedol in 2016. Experts first took it for a blunder. AlphaGo’s own policy network had rated the chance of a human expert playing it at about one in 10,000. The founders argue the move came from search and evaluation, which they call reasoning, rather than from prediction alone.
Metis wants AI to keep an explicit record of what it knows and how certain it is. A separate judgment system would accept a new claim only when the evidence supports it. The system would then pick each next step by how much it cuts uncertainty. The paper models this on philosopher Jaakko Hintikka’s idea of reasoning as a game between an inquirer and nature.
As in AlphaZero’s self-play, the system would then learn better strategies from its own recorded inquiries. Metis says this would mean fewer hallucinations and reasoning that anyone can check step by step. Users could also update or remove knowledge without retraining. Smaller models could handle many steps, which it says could cut costs and put reasoning on devices and robots.
Ingram told Tech.eu the design is “much much more compute-efficient” than frontier models.
“We are very much not going in this direction of ever larger models and ever larger training budgets,” he said.
No model yet
The white paper describes an approach, not a product, and it publishes no test results. Ingram said Metis had achieved “encouraging” results on frontier reasoning problems at a fraction of the cost, without giving figures. Metis has not said when it will release its first model.
Ingram would not comment on reports that Metis is raising tens of millions of dollars. He said Metis had raised money “more than adequately to date”.
Metis joins a cluster of new London AI labs led by DeepMind alumni and other big names. They include Ineffable Intelligence, founded by Graepel’s former AlphaGo colleague David Silver, and Richard Socher’s Recursive Superintelligence. Ingram called Metis’s approach “really a very different, almost philosophical approach”.