The Wrong Architecture
Yann LeCun has spent years arguing that the entire AI industry is building on a flawed foundation. This week he raised $1.03 billion to prove it.
In this piece:
What everyone else is building and why it works
Why LeCun thinks the foundation is wrong
What world models actually are
The case for — why this could matter
The case against — why it might not
What the money actually means
What everyone else is building
The dominant architecture in AI right now is the transformer. It was introduced by Google researchers in 2017 in a paper called Attention Is All You Need. Almost everything you interact with in AI today — ChatGPT, Claude, Gemini, Copilot — is built on some version of this architecture.
The core idea is prediction. You train a model on vast amounts of text. The model learns to predict what word comes next. Do this at sufficient scale with sufficient data and something remarkable happens — the model appears to understand language, reason about problems, write code, summarise documents, answer questions.
The operative word is appears.
LeCun’s argument is that what these models are doing is not understanding. It is pattern matching at extraordinary scale. And pattern matching, however sophisticated, has a ceiling.
Why LeCun thinks the foundation is wrong
LeCun’s critique has three parts.
First, language is not the source of intelligence. It is the output of it. Humans develop an understanding of physical reality — causality, object permanence, gravity, the consequences of actions — before they develop language. A child understands that a ball dropped from a height will fall before they have words to describe it. LLMs have no equivalent grounding. They know the word “fall” in every context it has ever appeared in text. They do not know what falling is.
Second, the architecture cannot be made reliable. Hallucination is not a bug that can be patched. It is a structural consequence of training on language without grounding in reality. The model produces plausible text. Plausible and true are different things. In most consumer applications this is manageable. In healthcare, industrial automation, and any safety-critical environment, it is disqualifying.
Third, LLMs cannot plan. They can simulate planning in text. They cannot model the consequences of actions in the real world, reason about them, and select a course of action accordingly. This limits their usefulness as autonomous agents in physical environments.
What world models actually are
LeCun’s alternative is built around what he calls JEPA — Joint Embedding Predictive Architecture — proposed in 2022.
The core insight is this. Real-world data — from cameras, sensors, physical environments — is continuous, high-dimensional and noisy. Much of it is unpredictable. A generative model trained to predict exactly what happens next in a physical environment will fail, because much of what happens next cannot be predicted.
The solution is to stop trying to predict the unpredictable details and instead learn abstract representations. What matters is not the exact pixel configuration of a scene but the structure underlying it. A world model learns to represent that structure and make predictions at the level of abstraction, not the level of raw data.
Action-conditioned world models go further. They allow an AI system to model the consequences of its own actions — to ask, in effect, “if I do this, what happens?” — and to plan sequences of actions accordingly. This is closer to how human cognition actually works. We do not generate plausible descriptions of what we might do. We model outcomes and choose between them.
AMI’s goal is to build systems that understand the real world in this sense. The first disclosed application is healthcare, through a partnership with Nabla. The broader targets are industrial process control, automation, wearable devices and robotics.
The case for
If LeCun is right, the current stack has a structural ceiling it will never clear.
The applications that matter most — surgery, industrial automation, autonomous vehicles, anything where a wrong answer has physical consequences — require reliability that LLMs structurally cannot provide. Not because they are not powerful enough. Because they are not grounded enough.
World models, if they work, solve this at the architecture level. A system that models physical reality, reasons about consequences, and plans actions accordingly is not producing plausible outputs. It is modelling outcomes. The reliability comes from the foundation, not from adding safety layers on top of an unreliable one.
The investor list suggests some of the most sophisticated technology investors alive believe this is possible. Nvidia makes the chips that run both architectures. Toyota needs autonomous systems that work in physical environments. Bezos Expeditions, Eric Schmidt, Samsung — none of them missed the LLM wave. They funded it. They are still running it. And they wrote a cheque for the thing designed to replace it.
The case against
This has been five years away for a long time.
LeCun has been making this argument publicly since at least 2022. JEPA is a theoretical framework, not a shipped product. AMI’s own CEO Alexandre LeBrun is explicit about the timeline. This is fundamental research. It could take years before it becomes a commercial product. There is no revenue plan.
Meanwhile LLMs keep improving. Scaling laws have not hit a wall yet. The architecture LeCun says is wrong keeps getting less wrong with each generation.
There is also a track record question. LeCun spent years at Meta arguing this position while Meta built LLMs. The argument did not stop Meta. It did not stop OpenAI, Google or Anthropic. The research community has not converged on his view. AMI’s own CEO acknowledged that in six months every company will call itself a world model company — which is another way of saying the terminology will be captured before the technology is proven.
What the money actually means
$1.03 billion is not a research grant. It is the largest seed round in European history. The investors are not philanthropists. They expect a return.
But the investor list is also a hedge. The same people backing the current LLM stack are now backing its potential replacement. That is rational portfolio construction. It does not tell you who is right. It tells you the people with the most information are not certain enough to bet only one way.
LeCun left Meta to build this. He has the funding, the team and no constraints. The question he now has to answer is not whether the architecture is better. He has been making that argument for years and nobody stopped building the wrong thing.
The question is whether he can ship it before the wrong thing gets good enough.