I've been thinking a lot about Move 37 lately, that AlphaGo play in 2016 that looked like a mistake until it turned out to be genius. This article uses that moment as a jumping off point to make a case I find genuinely useful as someone straddling web dev and AI work: LLMs produce fluent, confident reasoning-shaped text, but that is not the same thing as reasoning the way AlphaGo's search and evaluation process was.
The distinction matters in practice, not just philosophically. When I'm using an LLM to help debug code or think through a system design, it's easy to mistake a well written explanation for a verified one. The article's point is that these models are trained to produce plausible next tokens, not to run the kind of structured, verifiable search that something like AlphaGo did. They can still be enormously useful, but the output looks far more deliberate than the process actually is.
My takeaway: the fluency is the trap. A clean, step by step explanation from an LLM feels like evidence of careful thought, but it's worth treating it the way you'd treat a confident colleague's hot take, useful as a starting point, not a final answer.
https://www.technologyreview.com/2026/10/02/1145639/dont-be-fooled-llms-dont-reason/ #AI #MachineLearning