Key Takeaways

• Lacking Core Traits: Meta’s AI Chief, Yann LeCun, says current AI lacks core traits of intelligence: physical understanding, memory, reasoning, and planning.

• True Cognitive Behavior: Modern LLMs function mainly as advanced pattern recognizers and don’t exhibit true cognitive behavior.

• RAG & V-JEPA: Meta is experimenting with new systems, including RAG and V-JEPA, to bridge the gap in AI cognition.

• Underwhelming Rollout: Llama 4’s rollout and reception have been underwhelming, especially compared to models like GPT-4o and Gemini 2.5.

Modern AI Isn’t There Yet

On May 26th, LeCun highlighted four core pillars of intelligent behavior that modern AI has yet to master:

“There are four essential characteristics of intelligent behavior that every animal, or relatively smart animal, can do, and certainly humans.”

Yann LeCun’s ‘Bombshell’ Revelation About LLMs

Source: X (@PatOnTheLevel)

These traits include:

• Understanding the physical world
• Persistent memory
• Reasoning
• Hierarchical planning

According to LeCun, these abilities form the foundation of real intelligence, traits that even the most advanced AI models, including OpenAI’s GPT-4o or Google’s Gemini 2.5 Pro, currently lack.

The Limits of Today’s LLMs

AI Models Are Pattern Recognizers, Not Thinkers

LeCun argues that most LLMs are still glorified pattern recognition systems. While they excel at mimicking language and performing certain cognitive tasks, they do not genuinely understand the world around them.

He noted:

“Incorporating these capabilities would require a shift in how AI models are trained.”

Current AI development trends, he said, are leaning heavily into bolting on new capabilities to legacy architectures rather than innovating at the foundational level. This reactive approach may be hindering real progress in AGI.

Meta’s RAG & V-JEPA

Retrieval-Augmented Generation (RAG)

In a bid to push past the limitations of conventional LLMs, Meta has begun experimenting with Retrieval-Augmented Generation (RAG). This method enhances AI outputs by pulling from external knowledge bases, potentially allowing for more accurate, context-aware responses.

V-JEPA: A Different Kind of Learning

Earlier this year, Meta released V-JEPA, a non-generative model that learns by predicting missing or masked portions of video frames. Unlike generative models that rely on statistical pattern completion, V-JEPA is designed to simulate real-world reasoning, giving the AI a more grounded understanding of time and causality.

AI Competition Heats Up

The latest iteration of Meta’s language model, Llama 4, reportedly failed to capture any noteworthy attention. While technically solid, it lacks the reasoning capabilities of newer offerings from rivals like:

• OpenAI’s GPT-4o: A multimodal model designed for complex problem-solving
• Google’s Gemini 2.5 Pro: Focuses on seamless multimodal integration
• Anthropic’s Claude 4 Sonnet: A model trained specifically for advanced reasoning

The lukewarm response to Llama 4 has also raised concerns about Meta’s ability to keep pace with faster-moving competitors.

Mark Zuckerberg Discussing Meta’s AI Capabilities

Source: Business Insider

Further complicating matters, The Wall Street Journal reported on May 15th that Meta is also delaying the full rollout of its flagship AI system, Llama 4 “Behemoth.”

A Shift Toward True Intelligence?

LeCun’s vision for a more human-like AI, one that understands, reasons, remembers, and plans, is certainly different to today’s text-driven models.

While Meta’s experiments with RAG, V-JEPA, and world-based learning are promising, the company faces stiff competition and internal upheaval.

Still, if Meta can navigate these challenges, it may yet redefine what intelligence means in the age of artificial minds.

FAQ

What are the four characteristics of intelligence mentioned by Yann LeCun?

LeCun identifies the following as essential traits of intelligent behavior:

• Understanding the physical world

• Having persistent memory

• Reasoning ability
• Planning complex actions hierarchically

What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that improves AI output by referencing external databases and documents, helping the model generate more accurate and informed responses beyond its training data.

How is V-JEPA different from other AI models?

V-JEPA is a non-generative video model that learns by predicting missing parts in a video sequence. This helps it understand time-based patterns and cause-effect relationships, unlike traditional LLMs.