Don’t be fooled—LLMs don’t reason
The author argues that modern Large Language Models (LLMs) lack the genuine reasoning capabilities that allowed systems like AlphaGo to make creative, non-intuitive moves. The piece suggests that current AI relies on pattern matching rather than the deep strategic calculation required for true problem-solving.
Why it matters
It challenges the prevailing narrative that current AI is capable of human-like reasoning, which is critical for evaluating AI's reliability in high-stakes fields like science and medicine.
Ten years after AlphaGo’s match against Go champion Lee Sedol, today’s AI still isn’t tapping into the machinery that made that win possible.
On an afternoon in Seoul in March 2016, I watched a program I helped build put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a programming glitch. It wasn’t. AlphaGo won the game, ultimately triumphing 4-1 over Lee Sedol, one of the greatest professional Go players of all time. “I thought AlphaGo was based on probability calculation and that it was merely a machine,” Lee said afterwards . “But when I saw this move, I changed my mind. Surely, AlphaGo is creative.”
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