Introduction

  • TL;DR: On October 27, 2025, Yann LeCun, Meta’s Chief AI Scientist, intensified his long-standing critique by predicting that Large Language Models (LLMs) will become “useless within five years.” This forecast mandates an accelerated shift toward World Models, which are AI systems that learn the structure and dynamics of the physical world from video and interaction, not just text. Meta AI’s lead alternative is the Joint Embedding Predictive Architecture (JEPA), with the latest iteration, V-JEPA2, released in June 2025, marking a pivotal moment in the race for Autonomous AI.
  • While LLMs dominate the current AI landscape, this article analyzes the push by deep learning pioneers like LeCun to move beyond the limitations of text-based models. LeCun’s arguments, rooted in his March 24, 2023, presentation, emphasize that true human-level intelligence (AGI) requires capabilities LLMs structurally lack: robust reasoning, long-term planning, and physical world understanding.

1. LeCun’s 2025 Warning: The End of LLM Dominance

LeCun’s recent comments in Seoul, South Korea, served as a powerful declaration that the AI community must focus its energy on solving problems that lie outside the LLM paradigm.

1.1. The Five-Year Expiration Date for AR-LLMs

Speaking at the AI Frontier International Symposium 2025 on October 27, 2025, LeCun stated bluntly: “Large language models (LLMs) will become useless within five years. If you are interested in advancing AI to the human level, you should study what LLMs cannot do” (Source: CHOSUNBIZ, 2025-10-27).

His critique is centered on the inability of current architectures to handle essential intelligent behaviors:

  1. Understanding the physical world.
  2. Having persistent memory or world state.
  3. Being capable of reasoning and complex planning (Source: World Economic Forum, 2025-01-24).

This echoes his earlier assertion (2023-03-24) that the auto-regressive nature of LLMs leads to an exponential decay of correctness in long answers, making them inherently unreliable and uncontrollable for complex, real-world tasks.

Why it matters: This 2025 prediction is not merely theoretical; it reflects the real-world performance gaps of LLMs in applications requiring sequential actions, physical constraints, and long-horizon decision-making. It validates the growing consensus that the future of AI lies in systems that can simulate outcomes, not just recall patterns (Source: Medium, 2025-10-12).

2. The Necessary Shift to World Models

The concept of a World Model—an internal computational representation of reality’s dynamics—is gaining intense momentum in late 2025, championed by LeCun and other AI leaders as the “next leap beyond LLMs.”

2.1. World Model: The Core of Common Sense

A World Model enables an AI to predict the consequences of its actions, which LeCun argues is the essence of common sense and intelligence. Unlike LLMs, which learn implicitly from language, World Models are designed to learn explicitly from observation, video, and interaction.

The Role of World Models (Late 2025)

FeatureLLM ParadigmWorld Model Paradigm
Learning GoalNext-token predictionPrediction of world states and dynamics
FoundationStatistical patterns from textLearned model of physics and causality
Key OutputCoherent text, codeAction consequence prediction, Hierarchical Planning
Data FocusText corpus (e.g., Common Crawl)Interactive video, sensory data, simulation

Why it matters: As research moves toward Autonomous Agents and robotics, reliable action necessitates more than next-token prediction. World Models provide the required scaffolding for agents to imagine, plan, and decide safely by enforcing physical and temporal consistency over multiple steps (Source: Medium, 2025-10-12).

3. Meta AI’s Response: The Advancement of V-JEPA2

LeCun’s JEPA architecture is Meta AI’s primary answer to the challenge of building scalable, efficient World Models.

3.1. JEPA’s Non-Generative Approach

The Joint Embedding Predictive Architecture (JEPA) is fundamentally a non-generative model. Instead of predicting every detail of a future state (like pixels or exact values, which are prone to stochastic errors), JEPA learns to predict an abstract representation of the future state from the current state’s representation.

This approach, which avoids the pitfalls of Generative Models and uses non-contrastive self-supervised learning (such as VICReg), is highly efficient. It allows the model to ignore irrelevant or unpredictable high-frequency details, focusing only on the core, actionable information required for planning.

3.2. V-JEPA2: Advancing Multimodal Learning

In June 2025, Meta AI publicly released V-JEPA2, an evolution of the JEPA architecture designed for video data. V-JEPA2 learns about the world by watching massive amounts of unlabeled video—a core form of multimodal learning.

LeCun emphasized that JEPA, particularly through its video-learning extensions like V-JEPA2, represents the type of multimodal world model that will take over from chat-focused AI within years (Source: CHOSUNBIZ, 2025-10-27). This focus ensures the resulting AI is grounded in physical reality, a crucial step toward building controllable and safe systems.

Why it matters: The release of V-JEPA2 in 2025 confirms the industry shift toward architectures that prioritize sensory grounding and efficient abstract prediction. It positions Meta AI, under LeCun’s guidance, as a leader in defining the post-LLM era of Hierarchical Autonomous AI.

Conclusion

Key takeaways in 3–5 bullet points.

  • LLM Obsolescence: Yann LeCun declared LLMs will be ‘useless within five years’ (2025-10-27), citing their inability to perform complex reasoning and physical planning.
  • The World Model Imperative: The path to AGI requires World Models capable of simulating the consequences of actions and learning the dynamics of the environment.
  • JEPA as the Vehicle: Meta AI is implementing this vision through the non-generative JEPA architecture, which predicts abstract representations of future states.
  • Latest Innovation: The V-JEPA2 model, released in June 2025, demonstrates the current focus on leveraging large-scale video and multimodal data to build robust, physical-world understanding.

Summary

  • LeCun’s October 2025 keynote warned that LLMs are structurally limited and will fail to achieve true human-level intelligence.
  • The next paradigm is the World Model, which provides an explicit understanding of physical dynamics and causality.
  • V-JEPA2, Meta’s multimodal World Model, is a key focus, utilizing non-generative JEPA principles for efficient, abstract prediction.

#YannLeCun #JEPA #WorldModel #VJEPA2 #AutonomousAI #LLMLimitations #AGI #MetaAI #2025AI

References