I’ve always had a soft spot for science and engineering—the kind of curiosity that used to keep me up at night in high school, tinkering with circuits and reading about physics long before I understood the math. My mother thought I’d grow up to be an engineer, and for a while, so did I—until college calculus chewed me up and spit me out. But the fascination never left. These days, I chase those same questions through words instead of equations. So when someone like Yann LeCun starts talking about machines that can actually understand the world, not just predict words, it hits that old nerve—the wonder that got me into all this in the first place.
LeCun just walked off Meta’s deck, and Silicon Valley’s been buzzing ever since. If you’ve followed AI for more than five minutes, you know the name. If not—he’s the guy who gave machines the gift of sight. Back in the 1980s, when most of Silicon Valley was still fiddling with floppy disks, LeCun built convolutional neural networks—the same tech that now drives everything from facial recognition to self-driving cars. He’s a Turing Award winner, a genuine heavyweight, and until this week, Meta’s Chief AI Scientist.
Now he’s out.
And not to retire quietly with a stack of academic papers and a French countryside view. No—LeCun’s starting his own company. If the recent exodus of AI royalty from OpenAI is any precedent—the godfather of AI—LeCun’s next act will probably be worth a billion dollars before the first press release finishes printing.
The reason for the split tells you a lot about where AI’s heading—or at least, where LeCun thinks it should be heading. While the rest of the world keeps trying to make smarter parrots, LeCun wants to raise something that can actually think.
He doesn’t buy into the gospel of Large Language Models (LLMs)—the ChatGPT-style systems that predict the next word with statistical grace and the emotional depth of a thesaurus. His argument is simple: scaling up these text-predicting machines will never get us true intelligence. You can feed them the entire internet, he says, but they’ll still be blind.
Instead, LeCun’s betting on “world models.” The idea is almost charming in its simplicity—build AI that learns the way humans and animals do: by observing, experimenting, and forming a mental model of how the world actually works. Not just reading about gravity, but understanding why the apple falls.
His approach centers on something called JEPA—Joint Embedding Predictive Architecture—a mouthful of a name for a concept that could redefine the field. JEPA teaches AI to build internal simulations of the world, letting it predict how objects move, collide, or change over time. Think of it this way: instead of memorizing recipes, the machine learns to cook. It knows what heat does to oil, what pressure does to dough. It understands cause and effect.
That kind of intelligence—rooted in the physics and patterns of the real world—might be what separates the next generation of AI from the clever toys we’ve built so far.
LeCun’s been an outspoken critic of the AI panic brigade, too. He thinks fears of runaway superintelligence are premature, maybe even lazy. The real challenge, in his view, isn’t stopping AI from taking over—it’s making it smart enough to actually understand what it’s doing. He’s not trying to build God. He’s trying to build common sense.
Of course, Meta’s not exactly hurting for talent, but losing LeCun is a public relations gut punch. He’s been one of the few voices inside Big Tech who could speak both to the math and the morality of machine learning without sounding like a marketing deck. Zuckerberg’s AI empire just lost its philosopher-engineer—the one guy who could make the code sound human.
LeCun’s move could mark a turning point for the field. The age of word prediction might be giving way to an era of world prediction. The next frontier isn’t a chatbot that aces your emails—it’s a machine that understands why the coffee cup falls off the desk when you bump it.
And if LeCun’s right, the future of artificial intelligence won’t be built on words at all. It’ll be built on understanding.