Yann LeCun, an iconic figure in tech and considered one of the founding fathers of modern artificial intelligence, continues to shape the vision of AI with a perspective that is both ambitious and critical.
Known for his pioneering work on convolutional neural networks, which revolutionized image recognition, Yann LeCun is now skeptical of the hype surrounding large language models (LLMs).
According to him, the future of AI does not lie in the continuous improvement of these text-based models, but in a new generation of systems: “world models.”
This article, written by our team at Yiaho, explores this bold vision and what it implies for the future of artificial intelligence.
The limits of large language models
LLMs, such as those powering chatbots or virtual assistants, have marked an important milestone in the development of AI.
Trained on massive text corpora, they excel at content generation, translation, and answering complex questions.
However, for Yann LeCun, these models have a fundamental limitation: they are confined to language processing, a field that, while impressive, remains insufficient for achieving truly advanced artificial intelligence.
According to Yann LeCun, relying solely on text data to train an AI is like giving it a partial view of the world. Language, however rich, does not capture the complexity of human interactions or the dynamics of the physical world. LLMs are improving, but they are no longer disruptive for the researcher. For him, the true technological breakthrough lies in the ability of AI to understand, anticipate, and interact with the real world, beyond words.
Read more on this topic: ChatGPT 5: Close to AGI?
“World models”: AI that understands reality
But what is meant by “world models” in AI? For Yann LeCun, these are systems capable of building an internal representation of the physical and social world, and using this representation to reason, plan, and predict. Unlike LLMs, which are limited to manipulating text data, these models would integrate multimodal information: images, sounds, sensations, movements, and even abstract concepts like human intentions.
In other words, an AI equipped with a world model could not only understand a sentence like “the cat jumps on the table,” but also anticipate the cat’s trajectory, the consequences of its jump, or even the motivations behind its behavior.
To illustrate, imagine an AI assisting a surgeon
An LLM could provide text-based information on a medical procedure, but a world model could analyze images of the operation in real time, anticipate potential complications, and suggest adjustments based on the physical situation.
This ability to “see” and “understand” the world as a whole is, according to Yann LeCun, the key to achieving a higher level of artificial intelligence.
Why will world models be essential in AI?
Yann LeCun emphasizes a crucial point: without the ability to anticipate events in the physical world, AI will never reach the level of “super-intelligence” often mentioned in futuristic discourse. An AI that only understands text can generate plausible answers, but it remains disconnected from concrete realities.
For example, it could describe the laws of gravity, but without a world model, it would be unable to predict that a dropped object will fall to the ground or anticipate the consequences of a collision.
World models, on the other hand, would allow AI to reason causally and plan its actions while taking into account real-world dynamics. This paves the way for revolutionary applications: autonomous cars capable of predicting unpredictable pedestrian behavior, domestic robots that anticipate user needs, or systems capable of assisting scientists in modeling complex phenomena like climate change.
World Models in AI: How to get there?
Yann LeCun’s vision is appealing, but it raises colossal technical challenges. Building world models requires collecting and integrating extremely diverse data, ranging from video streams to physical sensors and social interactions.
Furthermore, these systems must learn to reason abstractly, generalize from limited examples, and manage the uncertainty inherent in the real world. While LLMs have benefited from the massive availability of text on the internet, the data needed to train world models is much more complex to obtain and structure. Another challenge lies in the need to make these models ethical and safe.
An AI capable of predicting and influencing the physical world could have a significant impact, both positive and negative. Yann LeCun, aware of these issues, calls for responsible research and rigorous governance to oversee the development of these technologies.
See also: ChatGPT: Why and how the French are using it in 2025? The 10 most common uses
A vision for the future
By turning away from LLMs to focus on world models, Yann LeCun is charting an ambitious path for AI.
It is no longer just about creating machines that speak or write like humans, but systems that understand and interact with the world as we do, or even better. This transition marks a turning point in AI research, moving from a language-centered approach to a holistic vision that encompasses reality in all its complexity.
For AI enthusiasts, the prospect of world models is both exciting and intimidating. It promises spectacular advances but requires a collective effort to overcome technical and ethical obstacles. As Yann LeCun points out, the AI of tomorrow will not be judged on its ability to imitate humans, but on its ability to understand and anticipate the world around them.
World models could well be the next revolution in artificial intelligence, a revolution that will bring us closer to truly intelligent AI.
To see the full interview with the French researcher, here is the video available here:
