Demis Hassabis thinks human brains plan moves differently from AlphaGo because brains are not built for Monte Carlo tree search.

The claim is hedged ('I think'). It holds that human planning in games such as Go differs from AlphaGo's because the brain is not built to do Monte Carlo tree search. The premise that AlphaGo uses MCTS is checkable from DeepMind's papers. TRUE: AlphaGo uses MCTS, and cognitive-science or neuroscience research broadly supports that human planning does not implement MCTS-style large-scale simulated rollouts or search, relying instead on far more limited, pattern-based or heuristic search. MOSTLY TRUE: the difference is supported, but the research shows that humans do perform some limited tree-search-like lookahead, so the contrast is one of degree. MIXED: the evidence is genuinely split on whether human planning resembles MCTS. MOSTLY FALSE: research has found substantial MCTS-like mechanisms in human planning, or the question is unresolved and the claim overstates it as settled. FALSE: AlphaGo does not use MCTS, or the evidence shows that human brains do implement MCTS-like planning in the relevant sense.

Set before the research began.

1. E1 Mastering the game of Go with deep neural networks and tree search - Nature Nature · 27 January 2016 Own record

   “We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks.”

2. E2 [PDF] THE GAME OF CHESS - Carnegie Mellon University Elsevier Science Publishers (Handbook of Game Theory, Vol. 1; authors H. A. Simon and J. Schaeffer, Carnegie-Mellon and University of Alberta) · 1992

   “we have known that a grandmaster, even in a difficult position, carries out a very modest amount of search of the game tree, probably seldom more than 100 branches”

3. E3 Expertise increases planning depth in human gameplay - Nature Nature · 31 May 2023

   “We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search.”

4. E4 Neural Signatures of Tree Search - Cognitive Computational ... Cognitive Computational Neuroscience (CCN) 2025 conference · 26 September 2025

   “A leading theoretical framework proposes that human planning follows an algorithm akin to tree search.”

The AlphaGo paper confirms the premise: its search algorithm combines Monte Carlo simulation with value and policy networks (E1). Independent research finds that human experts search the game tree only a little (E2), and that a cognitive model built on heuristic search captures human play (E3). Both sources also show that humans do some limited tree-search-like lookahead, and E4 notes that a leading framework treats human planning as akin to tree search. The difference from MCTS is therefore supported, but it is one of degree, which matches the "mostly true" criterion.