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Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, says today’s AI only looks intelligent.

It is very good at manipulating language.

But it still cannot understand the physical world, maintain real memory or truly plan.

The next generation of AI will need to understand the world, not just talk about it.

📁 Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, says today’s AI only looks intelligent.

It is very good at manipulating language.

But it still cannot understand the physical world, maintain real memory or truly plan.

The next generation of AI will need to… pic.twitter.com/tz8Hunssyq

— Jon Hernandez (@JonhernandezIA) March 12, 2026

According to LeCun, most modern AI—especially large language models—are excellent at statistical pattern matching in language. They generate convincing responses because they have learned vast correlations in text. However, this does not necessarily mean they possess true understanding of the world.

One key limitation is the lack of grounded knowledge of the physical environment. Humans learn through interaction with the real world—touching objects, observing cause and effect, and developing internal mental models. Current AI systems largely learn from text and images rather than real-world experience, which restricts their ability to reason about physical processes.

Another issue is persistent memory and planning. Many AI models operate mainly within a single prompt-response interaction. They do not maintain long-term internal memories or structured models of reality that allow them to plan complex sequences of actions over time.

To address this gap, LeCun advocates the development of World Models. These systems would learn how the world works—predicting how environments change and how actions lead to consequences.

Such architectures could support more advanced capabilities, including robotics, autonomous vehicles, and adaptive agents that can operate safely in complex environments.

In this vision, the next phase of AI evolution moves beyond language-based intelligence toward systems that preempt, predict, perceive, reason about, and interact with the physical world, bringing artificial intelligence closer to human-like understanding and decision-making.

Full Video:
https://www.youtube.com/watch?v=XnnnAx5lrx8?si=XGxD2EnkVSrl8UwT
"LLMs Are A Dead End": An Exclusive Interview With The Genius Father of AI | Yann LeCun

This is great,he is a legend

Imagine a future where intelligence is metered.

He left Meta late last year.

He and Nsukkaberg had a falling out.

He refused to accept unrealistic expectations that the AI industry was using to hype up AI and also being made to answer to a 29 year old Alexandr Wang that has no theoretical understanding about AI.

his argument was be weary of hype culture in the AI space.

remember web3 and crypto hype?
yeah, he said they shouldn't bring that nonsense to AI and allow it grow organically.

p.s: the AI industry is a bubble waiting to pop.

Na wa! So, AI, with all its capabilities and associated wonders is still at an infantile stage?
Well, as it is written, in the last days, knowledge shall increase.
I will really like to take advantage of this "knowledge".

I've always been saying this

We can never get to AGI with transformers (LLM)
because they don't exactly "reason" but only have a large "dictionary" of texts (the whole internet content {which includes human conversations}) so it only match words the words in its memory with statistics/probability. Similar to how a human who has an English dictionary "technically" knows the meaning of all English words.

To reach AGI, we need something other than transformer models.
So leave all the B.S Sam Altman (and other CEOs) says about AGI

Okay that's good for his advocacy

cool. that is more to come on internet of things in agriculture.

Agriculture is life

Everybody should understand that AGI is not happening soon, even if it will happen at all.

Vilgax:
He left Meta late last year.

He and Nsukkaberg had a falling out.

He refused to accept unrealistic expectations that the AI industry was using to hype up AI and also being made to answer to a 29 year old Alexandr Wang that has no theoretical understanding about AI.

his argument was be weary of hype culture in the AI space.

remember web3 and crypto hype?
yeah, he said they shouldn't bring that nonsense to AI and allow it grow organically.

p.s: the AI industry is a bubble waiting to pop.

Truer words. Meta has Alexander Wang now to push the 'super intelligence' models out.

Well, it's really cool. Knowledge shall increase according to the Bible. The only thing in this world is change. May this change add positively to all of our lives.

WorkTheTalk:

📁 Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, says today’s AI only looks intelligent.

It is very good at manipulating language.

But it still cannot understand the physical world, maintain real memory or truly plan.

The next generation of AI will need to… pic.twitter.com/tz8Hunssyq

— Jon Hernandez (@JonhernandezIA) March 12, 2026

According to LeCun, most modern AI—especially large language models—are excellent at statistical pattern matching in language. They generate convincing responses because they have learned vast correlations in text. However, this does not necessarily mean they possess true understanding of the world.

One key limitation is the lack of grounded knowledge of the physical environment. Humans learn through interaction with the real world—touching objects, observing cause and effect, and developing internal mental models. Current AI systems largely learn from text and images rather than real-world experience, which restricts their ability to reason about physical processes.

Another issue is persistent memory and planning. Many AI models operate mainly within a single prompt-response interaction. They do not maintain long-term internal memories or structured models of reality that allow them to plan complex sequences of actions over time.

To address this gap, LeCun advocates the development of World Models. These systems would learn how the world works—predicting how environments change and how actions lead to consequences.

Such architectures could support more advanced capabilities, including robotics, autonomous vehicles, and adaptive agents that can operate safely in complex environments.

In this vision, the next phase of AI evolution moves beyond language-based intelligence toward systems that preempt, predict, perceive, reason about, and interact with the physical world, bringing artificial intelligence closer to human-like understanding and decision-making.

Full Video:
https://www.youtube.com/watch?v=XnnnAx5lrx8?si=XGxD2EnkVSrl8UwT
"LLMs Are A Dead End": An Exclusive Interview With The Genius Father of AI | Yann LeCun

https://www.youtube.com/watch?v=XnnnAx5lrx8?si=XGxD2EnkVSrl8UwT

I laugh. You expect a child of 2-4 years to be able to understand everything. AI is currently a toddler, wait till 15-18 years 😂😂😂

Okay
That's an interesting perspective.
It's a matter of time

I really don’t like Al as much as I like it.

InifinteXR:
I've always been saying this

We can never get to AGI with transformers (LLM)
because they don't exactly "reason" but only have a large "dictionary" of texts (the whole internet content {which includes human conversations}) so it only match words the words in its memory with statistics/probability. Similar to how a human who has an English dictionary "technically" knows the meaning of all English words.

To reach AGI, we need something other than transformer models.
So leave all the B.S Sam Altman (and other CEOs) says about AGI

reasoning is subjective and only comes from our interacting with the real world, no baby is able to reach inside the womb. AI is still at that stage