Don’t be fooled—LLMs don’t reason

There is a moment in every AI story where the technology stops being a tool and starts being a mirror. Move 37 was that moment for Go: a move so alien that human experts first assumed it was a bug, then realized it was a breakthrough. The program wasn't reasoning like a human—it was doing something else entirely, something that *looked* like creativity but emerged from millions of pattern-matching simulations. The article from MIT Technology Review draws a direct line from that moment to our current obsession with LLMs and their apparent ability to reason. The temptation is almost irresistible. When a language model writes a coherent essay, solves a logic puzzle, or explains a scientific concept, our brains—the most advanced pattern-recognition systems on the planet—immediately project a mind onto the text. We feel the ghost of intention. But as the article argues, this is a category error. LLMs are not reasoning in the human sense; they are generating the most probable sequence of tokens given a prompt. The fact that this can produce something that *reads* like reasoning is less a testament to machine intelligence and more a testament to the astonishing statistical structure of human language—and to our own eagerness to see minds everywhere. This matters beyond philosophical parlor tricks. If we confuse fluent output with genuine understanding, we risk trusting AI in domains where reasoning is safety-critical: medicine, law, engineering, science. An LLM can confidently explain why a bridge will stand or a drug will work, with zero actual comprehension of physics or biology. Move 37 was a beautiful surprise because it emerged from a system trained on a well-defined game with clear rules. Language, by contrast, is a mess of ambiguity, context, and unspoken assumptions. The gap between "statistically plausible" and "true" is where the danger lives. And yet, there is something genuinely wondrous here. The fact that a statistical model trained only on text can produce arguments that resemble reasoning tells us something profound about the nature of language itself—and about the limits of our own intuitions. We are not wrong to be amazed; we are only wrong to be fooled. The article's warning is not a dismissal of AI's power, but an invitation to look closer, to ask what *kind* of intelligence we are actually witnessing. Sometimes the weirdest truth is that the machine is not thinking at all—and still managing to make us think. Source: [MIT Technology Review](https://www.technologyreview.com/2026/10/02/1145639/dont-be-fooled-llms-dont-reason/)