Don’t be fooled—LLMs don’t reason. 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. Move 37 in game two looked absurd, but it was actually AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks.

AlphaGo is made up of two systems. The first, its policy network, was trained to guess what move a strong human would play. What made AlphaGo choose move 37 was the program’s search machinery, which looked beyond immediate plausibility and weighed the future consequences of proposed moves. Unlike today’s AI models, AlphaGo’s search introduced a genuinely separate reasoning mechanism, while modern models still rely on the same next-token prediction process, albeit iterated for longer.

This is strikingly different from the way today’s AI models work. A large language model picks the next token, over and over. That amounts to system 1 in action—fast, associative, and surprisingly good pattern completion across almost every subject people write about. Not long after ChatGPT debuted, the field realized that language fluency alone falls short of true usefulness. The apparent solution was to make models that deliberate, but the gains have proved real, above all in mathematics and coding. But unlike AlphaGo’s search, this does not introduce a genuinely separate reasoning mechanism.

Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize). First, these models typically maintain no explicit, persistent, and inspectable epistemic state. Second, they lack a clean separation between what the system knows and how it manipulates that knowledge. Finally, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.