Jeremy Cohen

AMI Labs founder Yann LeCun on why LLMs are fooling us the same way AI has for decades: He argues that every generation of AI scientists has made the same mistake: confusing task performance with real intelligence. LeCun's core challenge to the current hype: "We're fooled into thinking those machines are intelligent because they can manipulate language. And we're used to the fact that people who can manipulate language very well are implicitly smart." He's clear that LLMs are useful, but being a useful tool and being intelligent are two very different things. The real insight is the historical pattern he's lived through. Since the 1950s, wave after wave of AI researchers have claimed their breakthrough was the path to human-level intelligence. Marvin Minsky. Herbert Simon. Frank Rosenblatt — who invented the perceptron, the first learning machine, in the 1950s — all predicted machines as smart as humans within a decade. "They were all wrong." LeCun has personally witnessed three of these cycles of hype and disappointment. And his verdict on the current one is blunt: "This generation with LLMs is also wrong. It's just another example of being fooled." The pattern: A new technique emerges → machines get good at specific tasks → we assume general intelligence The question worth asking: are we impressed by these tools because they're intelligent, or because they sound like they are?

It’s still common belief that the loss landscapes of neural networks are too non-convex for averaging of model weights to work. Meanwhile, the empirical evidence is overwhelming. It works! There is a whole tutorial on it this year. Adapt your beliefs or fall behind.