Ex-DeepMind researcher: LLMs don't reason—future AI needs AlphaGo-style deliberation

MIT Tech Review AI · rss · 2026-10-02

Writing in MIT Tech Review, a researcher who recently left Google DeepMind argues that today's LLMs don't truly reason, using AlphaGo's famous move 37 as the lens.

Core argument

Move 37 wasn't machine intuition: AlphaGo's policy network gave it 1-in-10,000 odds of an expert playing it; what chose it was explicit tree search weighing thousands of future branches—Kahneman's System 2 deliberation, whereas next-token prediction is pure System 1.
Chain of thought yields real gains in math and coding, but the intermediate steps are still produced by the same next-token process—no separate reasoning mechanism.

Three reasons LLM output isn't reasoning

No explicit, persistent, inspectable epistemic state (hypotheses, confidence, open questions).
Knowledge and reasoning are inseparably woven into the same weights.
Research shows models often fabricate reasoning chains after the fact.

Proposed direction

The author calls for AlphaGo-style architecture: a system that maintains an explicit epistemic state (what's settled, doubted, ruled out, open) and reasons via moves that reduce uncertainty, with an independent component accepting only evidence-backed belief updates—a "scientific method on steroids." Scale alone won't make System 1 trustworthy; only such reasoning systems can deliver novel insights in drug discovery, materials, climate, and diagnosis.

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Ex-DeepMind researcher: LLMs don't reason—future AI needs AlphaGo-style deliberation

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