Large language models don't actually understand things; they convincingly imitate understanding, and this pretense works fine until it suddenly and completely breaks down.
Howard resolves the online debate over whether LLMs 'understand' by saying both sides are right: models cosplay understanding convincingly, which is functionally fine until the pretense collapses. ✦ AI generated
Jeremy Howard · Machine Learning Street Talk · 2026-03-03 · original ↗
starts at this moment · 31:08
There's often this these arguments online between people who are like, "LMs don't understand anything. They're just pretending to understand." And then other people are like, "Don't be ridiculous. Look what this LLM just did for me." Right? And the funny thing is they're both right. LLM's cosplay understanding things. They pretend to understand things.
verbatim transcript · starts at 31:08
31:08you start delegating cognitive tasks to language models, you actually have this weird paradoxical effect that you erode the knowledge inside the organization. >> Well, that's true and that's terrifying. There's often this these arguments online >> between people who are like, "LMs don't understand anything. They're just pretending to understand." >> And then other people are like, "Don't be ridiculous. Look what this LLM just did for me." Right? And the funny thing
31:34is they're both right. LLM's cosplay understanding things. They pretend to understand things. And this was the interesting thing about the early kind of uh work with like uh cognitive science work with like Daniel Dennett. Um that's basically what the Chinese room experiment is, right? Is you've got a guy in a room who can't speak Chinese at all, but he sure looks like he does because you can feed in
32:00questions and he gives you back answers, but all he's actually doing is looking up things in a huge array of books or machines or whatever. The difference between pretending to be intelligent and actually being intelligent is entirely unimportant as long as you're in the region in which the pretense is actually effective, you know. So, so it's actually fine for a great many tasks that LLMs only pretend to be intelligent
32:28because for all intents and purposes, it it it just doesn't matter until you get to the point where it can't pretend anymore. And then you realize like oh my god this thing's so stupid. >> I'm a fan of so by the way. So you know he said that um you know understanding is causally reducible but ontologically irreducible and he was saying there was a phenomenal component to understanding
32:50but you don't even need to go there. Like the interesting thing about knowledge being protein is this idea that the you know it's basically this canon idea the world is a complex place. None of us understand it. It's like the blind men and the elephant. We all have different perspectives. It's very complex thing. And so we we all we all do this kind of modeling. But the
33:08interesting thing is that the language models sometimes they seem to understand and they understand because the supervisor places them in a frame. So inside that frame, so when you have that perspective of the elephants, they're actually surprisingly coherent, but we discount the supervisor placing the models in that frame. >> Yeah. Yeah. So that so C cell versus Dennit or is it versus cell and Dennut was what everybody was talking about