Reimagining Intelligence: How Yann LeCun Proposes a New Path Beyond LLMs

Artificial intelligence is at a turning point. At this year’s AI Action Summit in Paris, Yann LeCun—often dubbed one of the “godfathers of deep learning”—challenged the prevalent notion that bigger Large Language Models (LLMs) alone will deliver human-level intelligence. Instead, he proposed a holistic approach based on world models, energy-based architectures, and hierarchical planning. In a dynamic talk entitled “The Shape of AI to Come!”, LeCun outlined why the brute-force scaling of LLMs is insufficient and how the next generation of AI can truly “understand” the world, rather than merely predict text tokens.

Below is an in-depth look at his vision, including its implications for complex domains such as healthcare and biology.

1. Moving Beyond Token Prediction

LeCun drew a clear line between current LLMs (like GPT-style transformers) and genuine machine intelligence. LLMs learn by predicting the next token—word or sub-word—based on vast amounts of text. This method has yielded impressive capabilities in language tasks. However, the speaker underscored a fundamental limitation:

• Exponential Divergence: Each predicted token can lead the model astray, producing spurious information or “hallucinations” that compound over lengthy outputs.
• Lack of True Understanding: While LLMs can summarize documents and generate coherent text, they lack a causal grasp of the physical or conceptual world.

In short, LLMs excel at regurgitation and pattern matching, but they don’t possess an internal representation of reality, such as a sense of how objects move or how diseases progress.

2. World Models: A Vision of Structured Knowledge

Instead of fixating on bigger LLMs, LeCun advocated for “world models”—systems that capture the underlying structure and dynamics of environments. These models enable:

• Inference and Reasoning: By knowing how the world behaves (through physics, causality, and context), AI systems can perform long-horizon planning without brute-forcing every possibility.
• Planning with Objectives: Rather than simply predicting the next token or frame, a world model projects the outcome of sequences of actions—vital for robotics, healthcare, and any domain requiring strategic decision-making.

LeCun’s assertion is that world models will allow AI to move from “memorizing and predicting” to “understanding and doing.”

3. Key Insights for Healthcare and Biology

A major highlight of LeCun’s talk was the transformative potential of such world-model-based AI in complex fields like healthcare, biology, and pharmaceutical research. His perspective dovetailed with five critical insights:

1. Prioritize Key Insights Over Exhaustive Generation
   • In medicine, capturing every molecular interaction or biological variable is impossible. AI must focus on critical biomarkers—where Joint Embedding Predictive Architectures (JEPA) shine by foreseeing crucial relationships instead of drowning in irrelevant details.
2. Replace Probability Overload with Efficient Scoring
   • Traditional AI often calculates unwieldy probabilities for every possible outcome, but energy-based models track how “normal” or anomalous a scenario is. In healthcare, that means quickly identifying abnormal results, saving enormous computational effort.
3. Move Beyond Contrastive Learning to More Direct Approaches
   • While contrastive learning is ubiquitous, it can be data-hungry and cumbersome. Direct or regularized methods—like the ones LeCun’s team explores—can unearth valuable patterns with less data, a crucial edge in medical research where data can be scarce.
4. Shift from Trial-and-Error to Model-Driven Discovery
   • Biological research often involves massive trial-and-error experimentation. AI-driven world models can predict biological behaviors (e.g., protein folding, drug efficacy), ensuring real-world lab work only focuses on unexpected outcomes. This paves the way for faster, more cost-effective breakthroughs.
5. LLMs Alone Won’t Achieve Human-Level Intelligence
   • While useful for summarizing texts or automating paperwork, large language models do not internalize causal structures. True comprehension (e.g., understanding the why of a disease mechanism) arises from a deeper, model-based learning approach that goes well beyond scaling transformers.

4. The JEPA Approach: A Practical Blueprint

LeCun’s own framework, the Joint Embedding Predictive Architecture (JEPA), embodies his philosophy of discarding brute-force generative models in favor of learning directly in representation space. Instead of predicting every pixel in a future video frame or every token in a sentence, JEPA learns a more abstract, “critical” representation of reality—keeping only what matters for accurate prediction and planning.

Why JEPA Matters

• Efficient Representation: JEPA eliminates noise and extraneous details that are impossible to predict (e.g., the exact texture of a never-before-seen surface), focusing only on the core information needed to anticipate outcomes.
• Scalability: By not wasting effort on improbable or irrelevant details, these architectures can better handle high-dimensional problems like video understanding or multi-step medical interventions.
• Versatility: From self-driving cars to hospital robots, any domain where AI must foresee the results of actions stands to benefit from a JEPA-like system.

5. Energy-Based Models and Hierarchical Planning

LeCun introduced energy-based models as a way to transcend the limitations of simple token or pixel prediction. In these models, an “energy function” assesses how compatible a proposed explanation (or action) is with given observations. Instead of passively mapping inputs to outputs, the AI searches for an optimal set of actions or states with minimal “energy”—akin to how humans deliberate and plan before acting.

He highlighted a long-term objective: hierarchical planning. Much like a human who divides a complex goal (say, traveling from New York to Paris) into incremental steps, AI needs multiple layers of abstraction. Each level in the hierarchy handles progressively simpler actions, culminating in robust, real-world problem-solving.

6. Rethinking Reinforcement Learning (RL)

While RL has been a cornerstone of certain AI feats (like AlphaGo), LeCun described it as too data-inefficient for real-world tasks, especially where high sample efficiency is crucial (e.g., robotics, clinical trials). World models offer a more direct path:

• Plan First, Act Later: Instead of random exploration, a world-model-based agent can mentally simulate actions and only execute the ones most likely to achieve the goal.
• Guardrails and Constraints: Built-in objectives and constraints ensure safety and reliability—vital for domains like healthcare and autonomous driving, where errors can be life-threatening.

7. Toward Open-Source, Collaborative AI

Perhaps one of the most forward-looking points was LeCun’s call for open-source AI platforms. He warned of a scenario in which a few large technology companies or geopolitical powers dominate the creation of foundational models, leaving the rest of the world to rely on “black box” solutions with limited transparency. A collaborative, open-source approach, he argued, would:

• Ensure Cultural and Linguistic Inclusivity: Training data can and should cover diverse languages, cultures, and contexts.
• Democratize Access: By sharing the technical underpinnings, small labs, startups, and universities can adapt foundational models for specialized or local use.
• Accelerate Innovation: Transparency fosters faster improvements, leveraging a global community of researchers rather than siloed corporate labs.

8. The Future of AI: Not Just Bigger, But Smarter

LeCun’s central thesis—“LLMs alone are not enough”—resonated throughout the summit. The ultimate promise of AI lies in understanding the world deeply enough to model it, reason about it, and plan effective actions. In healthcare, this shift could revolutionize how we predict and treat diseases. In robotics, it may finally bring about safe, reliable domestic assistants. In every domain, the marriage of model-based reasoning, abstract representation, and constrained planning charts a path toward AI systems that are not only powerful but also genuinely aligned with human needs.

“Blindly scaling up large language models,” LeCun emphasized, “won’t magically yield human-level intelligence. What we need is a true comprehension of reality—built on robust world models, hierarchical planning, and a more direct, energy-efficient learning paradigm.”

Conclusion

Yann LeCun’s talk at the 2025 AI Action Summit was a clarion call for reimagining AI research and deployment. Far from dismissing the utility of LLMs, he emphasized their limitations and the urgent need for AI that grasps causality and physical context. His JEPA framework and energy-based models represent significant steps in that direction, with profound implications for healthcare, biology, robotics, and beyond.

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