Yann LeCun

“The shelf life of the current [LLM] paradigm is fairly short, probably three to five years. I think within five years, nobody in their right mind would use them anymore, at least not as the central component of AI systems.”

Yann LeCunResearcher

Chief AI Scientist, Meta (at time of statement); Turing Award laureate

Deadline is January 2030. As of 2026, LLMs remain the central component of essentially all deployed AI systems; LeCun left Meta in late 2025 and founded AMI Labs to pursue world-model architectures instead.

“Within three to five years, AMI plans to produce "fairly universal intelligent systems" that could be used for almost any task requiring intelligent machines, such as autonomous driving and robotics.”

Yann LeCunResearcher

Co-founder and executive chairman of AMI Labs; former Chief AI Scientist at Meta

Made the day AMI Labs announced a $1.03bn seed round at a $3.5bn pre-money valuation; the company has no product or revenue yet, and CEO Alexandre LeBrun said at least a year of pure research lies ahead.

“Before we get all those things to work together, and then on top of this, have systems that can learn hierarchical planning, hierarchical representations, systems that can be configured for a lot of different situations at hand, the way the human brain can, all of this is going to take at least a decade and probably much more... So it's not just around the corner.”

Yann LeCunResearcher

Chief AI Scientist at Meta; NYU professor; Turing Award laureate

A lower-bound claim: human-level AI arrives no sooner than ~2034. It is falsified only if a system with human-level, configurable, hierarchically-planning intelligence appears before then.

“I take an object, I put it on the table, and I push the table. It's completely obvious to you that the object will be pushed with the table... There is no text in the world, I believe, that explains this. And so if you train a machine as powerful as it could be — your GPT-5000 or whatever it is — it's never gonna learn about this.”

Yann LeCunResearcher

Chief AI Scientist, Meta; Turing Award laureate

Within roughly a year, text-only trained LLMs far short of a hypothetical 'GPT-5000' — GPT-4 and successors — answered this exact tabletop-physics scenario correctly and reliably, a falsification widely noted (e.g. by 80,000 Hours). LeCun has since shifted his argument to broader limits of autoregressive LLMs rather than this specific inference.