World Models Explained
World Models explained
While the tech industry is heavily capitalizing on LLMs (Large Language Models), a major challenge is emerging at the very top of Meta at the end of 2025. Yann LeCun, Turing Award laureate and leading figure in AI (notably convolutional models), diagnoses a structural limitation of LLMs: they master sequences of words, and that is what gives them their understanding of the world.
Have LLMs now hit a technological glass ceiling?
To move beyond this limit, Yann LeCun’s vision seems to steer AI research toward a new paradigm: World Models.
What are the stakes of this architectural break? How does this approach fundamentally differ from solutions like ChatGPT/Llama/Claude/etc.?
An analysis of a vision that favors cognitive relevance over statistical eloquence.
What is a World Model?
The bold architect versus the efficient parrot
To understand the “World Model,” you first need to understand the current limits of LLMs.
An LLM is an outstanding probabilistic engine. It strings words together like logical beads, computing the most statistically likely continuation. It will write “the glass falls” out of linguistic habit, without possessing the slightest internal notion of gravity or fragility. It masters grammatical logic. An LLM’s understanding of the world comes from text.
The World Model aims to do the opposite: it simulates the physical world before layering grammar and syntax on top. It breaks the shell and looks inside to see what is happening. It does not play with words; it simulates reality.
As you’ve probably guessed, World Models are trained on pixels (photos and videos).
When “common sense” means plain good sense
The promise of World Models can be summed up in two words—the Holy Grail of AI: Common Sense.
- Understanding rather than parroting: where an LLM hallucinates with confidence, a World Model reasons with caution. It has an “intuitive physics.” It knows what is plausible and what is impossible.
- Learning efficiency: a child does not need to read three billion pages to understand that fire burns. They observe. World Models promise to learn quickly, with far less data, by observing the world (video) rather than reading text.
- Real planning: to act, you must anticipate. The promise of an AI capable of planning complex actions in the real world (robotics, logistics) by anticipating the consequences of its choices—something a text generator struggles to do without errors.
The World Model bet: moving slowly but surely!
You could say it’s an arrow of common sense that cannot miss its target: evolution produced brains capable of navigating the world long before the invention of language. With infant swimming babies know when not to breathe underwater; that’s why they can be tossed into the deep end without fear. Even if they know very little language, they know how to “predict” when to breathe or hold their breath.
Behind this prose lies a body of scientific literature that lays out the hypotheses behind the success of World Models: true snipers when it comes to finding the right learning manual in an inexhaustible library of knowledge.
This technological break promises radical frugality (but only once the model is in place). Far from the energy sinkholes of supervised learning as we practice it today, these models learn through simple observation, transforming fragments of data into robust knowledge without recurring human intervention. The potential is that of a model that no longer needs to see everything billions of times to be effective, but instead learns to navigate the waters of the unknown, improving its skills day after day with compass and helm.
World Models come with a set up challenge
Yet the road is paved with uncertainty. The dream of World Models collides with the roughness of reality.
- Infinite detail: the world is infinitely richer and noisier than text. Predicting the fall of a dead leaf is mathematically chaotic. The model risks drowning in insignificant details (like the movement of every blade of grass) instead of grasping what matters.
- Difficult abstraction: learning to ignore is as hard as learning to know. The algorithm must succeed in extracting high-level concepts from moving pixels, without human help. It is a colossal mathematical challenge.
- Computational power: simulating a representation of the world, even an abstract one, could require pharaonic amounts of energy, making these models—at least for now—economically non-viable compared with already more advanced LLMs. It is especially the first building blocks of World Models that will be expensive.
- Successfully using / retraining this world model so that it can “speak,” and thus perform LLM tasks. This step is currently very theoretical and relatively long-term.
And surely many other limitations we do not yet know…
Our take on World Models
LLMs dazzled us with their eloquence; World Models aim to convince us with their perception.
Betting on World Models is, in a way, betting on a paradigm shift in AI.
We love the boldness of taking on this challenge, and we can’t wait to see which mathematical tactics will be deployed on this topic in the future!
For the techies and science buffs among you who want to dig into related sources:
- LeCun Y. A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27. https://openreview.net/pdf?id=BZ5a1r-kVsf
- Assran M, Duval Q, Misra I, Bojanowski P, Vincent P, Rabbat M, et al. Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture [Internet]. arXiv; 2023 [cited Nov 19, 2025]. Available at: http://arxiv.org/abs/2301.08243
- Yang H, Huang D, Wen B, Wu J, Yao H, Jiang Y, et al. Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders [Internet]. arXiv; 2022 [cited Nov 19, 2025]. Available at: http://arxiv.org/abs/2210.04154
