The label game is a trap
While much of the AI industry scrambles to brand its latest breakthroughs as “AGI” or “superintelligence,” Alexandre LeBrun, CEO of Yann LeCun’s world model startup AMI Labs, is deliberately sitting that race out. In an interview with TechCrunch, LeBrun made it clear: his company doesn’t use either term. Not now. Not ever.
“We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence,” he said. “Next time we’ll switch to something else.” He isn’t impressed by the new buzzword either. “There’s no good definition. What is superintelligence? I don’t know. It’s not a very useful word.”
That’s a pointed stance from a founder whose startup just raised over a billion dollars. But LeBrun isn’t chasing hype. He’s chasing something harder to define: a machine that actually understands the physical world.
World models vs. LLMs: a different kind of intelligence
LeBrun spoke with TechCrunch last week in Seoul, where he was attending the International Conference on Machine Learning. His mission there wasn’t just academic. He was scouting industrial partners — robotics firms, manufacturers, electronics companies — for AMI Labs, which is still pre-product. The startup is building what’s called a world model: an AI system that incorporates physics to predict and interact with the real world, rather than just generating text or images.
“An LLM predicts the next word or text,” LeBrun explained. “A world model predicts the next state.” Nudge a glass off the table, and you already know it will tip and spill. That intuitive understanding of cause and effect is what a world model is meant to capture.
He isn’t claiming world models are superior to LLMs. They’re “complementary, not replaceable,” he said. Drawing a parallel to how the human brain separates language and reasoning, he argued that LLMs will remain the most efficient tools for processing language, while world models will provide the context and real-world understanding that LLMs lack.
Robots are still dumb — and dangerous
One area where world models could make a real difference is robotics. Right now, LeBrun says, robots are “completely static,” running fixed routines with no awareness of their surroundings. “AI remains really dumb in the physical world,” he told TechCrunch.
Even basic context-awareness would be a leap forward. LeBrun pointed to a widely shared incident where a dancing robot at a public event approached and kicked a child. “The hardware is very advanced; progress in hardware in the last few months is incredible, but there’s no brain,” he said. A world model could have helped that robot understand it was about to harm someone and stop.
“Robots are not safe right now,” LeBrun added bluntly. “There’s no solution for that today.”
Why Korea? Hardware, speed, and a billion-dollar bet
LeBrun’s interest in Asia isn’t accidental. A world model, he argues, can’t be built inside a lab. “We need access to the real world,” he said, and that means partnering with companies that actually build things. South Korea has advanced robotics, semiconductor, and manufacturing industries — exactly the sectors where physical AI could have the biggest impact.
But the second reason is speed. “Korea was the fastest adopter of the internet 25 years ago,” LeBrun noted. He sees the same pattern now with AI. The government has thrown significant money behind the technology, including a June plan to mobilize roughly $880 billion for chips, AI data centers, and physical AI. “They should coexist,” said JP Lee, CEO of SBVA and one of AMI’s Asian backers, referring to the need to fund both LLMs and physical AI.
Lee has been pushing AMI to establish a presence in Korea from the start. “I’ve been telling Alex and the team to come to Korea,” he told TechCrunch. Local developers, he argued, are quick to adopt and adapt new tools — a pattern that has already produced homegrown internet players like Naver and Kakao.
Healthcare, factories, and the 1% problem
LeBrun’s previous company was Nabla, an AI health startup. That background colors his view of where LLMs fall short. He likens today’s AI systems to a doctor trained only on textbooks, with no residency or real-world experience. “LLMs cover only 1% of healthcare,” he said. The rest depends on hands-on practice and physical intuition — exactly what a world model aims to provide.
For now, AMI Labs has nothing to sell. The startup, co-founded by Turing Award winner Yann LeCun after he left Meta, raised $1.03 billion in March at a $3.5 billion pre-money valuation. There’s no product yet, and LeBrun won’t commit to a timeline. “We’ll make a surprise when we’re ready,” he said.
That might sound evasive. But for a CEO who refuses to play the naming game, it’s consistent. He’s not selling a label. He’s selling something that doesn’t exist yet — and he’d rather get it right than call it superintelligence.
