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Yann LeCun says basing agentic systems on the current LLM paradigm is a recipe for disaster Intelligent behavior requires a world model to predict the consequences of actions, which LLMs do not have "the basic architecture is not there"

Haider.

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61,211 views • 6 months ago •via X (Twitter)

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Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Yann LeCun argues that large language models (LLMs) cannot reach human-level or superintelligence just by scaling. He says the current LLM paradigm is hitting its limits. Many researchers are now exploring “agentic systems,” but building them on top of LLMs alone is flawed. LLMs can't plan actions well because they don’t truly understand or predict consequences. To get intelligent behavior, we need something fundamentally different.

Wes Roth

71,832 views • 6 months ago

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the consequences of their actions before taking them "they just act, and whatever happens next is someone else's problem" Without that, it's not intelligence

Haider.

332,353 views • 2 months ago

“An AI that can act before it understands is not reliable infrastructure.” Yann LeCun, Meta’s Chief AI Scientist and one of the fathers of modern deep learning, says current LLMs are intrinsically unsafe. Not because they are evil, but because they hallucinate, lack common sense, and cannot reliably predict the consequences of agentic actions.

“An AI that can act before it understands is not reliable infrastructure.” Yann LeCun, Meta’s Chief AI Scientist and one of the fathers of modern deep learning, says current LLMs are intrinsically unsafe. Not because they are evil, but because they hallucinate, lack common sense, and cannot reliably predict the consequences of agentic actions.

Jon Hernandez

10,497 views • 2 months ago

Yann LeCun (Yann LeCun) just revealed why he left Meta, Why are LLMs an extremely narrow field, and how "World model" is the way to build really meaningful agentic future. 🎯 Beautiful and simple in 1.5 mints. "I can’t imagine we can build agentic systems without those systems having the ability to predict, in advance, what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions, and that’s what allows us to plan. So what is a world model? Given the state of an environment, a system you want to control at time t, and given an action or intervention you imagine taking, can you predict the state of the world (or the system) at time t + 1? If you can, that’s a world model. You don’t do this at a pixel level, if it’s video. You do this in an abstract representation space, and that’s a crucial key insight." --- From 'AI House Davos" YT channel (full link in comment)

Yann LeCun (Yann LeCun) just revealed why he left Meta, Why are LLMs an extremely narrow field, and how "World model" is the way to build really meaningful agentic future. 🎯 Beautiful and simple in 1.5 mints. "I can’t imagine we can build agentic systems without those systems having the ability to predict, in advance, what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions, and that’s what allows us to plan. So what is a world model? Given the state of an environment, a system you want to control at time t, and given an action or intervention you imagine taking, can you predict the state of the world (or the system) at time t + 1? If you can, that’s a world model. You don’t do this at a pixel level, if it’s video. You do this in an abstract representation space, and that’s a crucial key insight." --- From 'AI House Davos" YT channel (full link in comment)

Rohan Paul

383,490 views • 6 months ago

Yann Lecun says within the next 3-5 years we will see the emergence of a new paradigm of AI architectures that don't have the limitations of current systems, leading to a revolution in AI capabilities

Yann Lecun says within the next 3-5 years we will see the emergence of a new paradigm of AI architectures that don't have the limitations of current systems, leading to a revolution in AI capabilities

Tsarathustra

171,009 views • 1 year ago

Yann LeCun (AMI Labs Founder): "The AI industry is completely LLM-pilled. Everybody is working on the same thing. They're all digging the same trench." LeCun explains why no lab dares break from the pack: "They are stealing each other's engineers. So they can't afford to do something different because if they start going on a tangent, they're going to fall behind the other guys. And so they're all doing the same thing." This groupthink is exactly what drove him out of Meta. "Meta also became LLM-pilled with sort of recent reshuffling. And it's fine, a strategic decision that maybe makes sense for them. It's just not what I'm interested in." For Yann LeCun, the problem runs deeper than strategy. LLMs are missing something essential about how intelligence actually works: "I cannot imagine that we can build agentic systems without those systems having an ability to predict in advance what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions and that's what allows us to plan." His broader critique is that the industry has mistaken fluency for intelligence. Language turned out to be the easy part. The hard part is the physical world. It's why we still don't have domestic robots or level-five self-driving cars, even though today's systems can pass the bar exam and write code.

Yann LeCun (AMI Labs Founder): "The AI industry is completely LLM-pilled. Everybody is working on the same thing. They're all digging the same trench." LeCun explains why no lab dares break from the pack: "They are stealing each other's engineers. So they can't afford to do something different because if they start going on a tangent, they're going to fall behind the other guys. And so they're all doing the same thing." This groupthink is exactly what drove him out of Meta. "Meta also became LLM-pilled with sort of recent reshuffling. And it's fine, a strategic decision that maybe makes sense for them. It's just not what I'm interested in." For Yann LeCun, the problem runs deeper than strategy. LLMs are missing something essential about how intelligence actually works: "I cannot imagine that we can build agentic systems without those systems having an ability to predict in advance what the consequences of their actions are going to be. The way we act in the world is that we can predict the consequences of our actions and that's what allows us to plan." His broader critique is that the industry has mistaken fluency for intelligence. Language turned out to be the easy part. The hard part is the physical world. It's why we still don't have domestic robots or level-five self-driving cars, even though today's systems can pass the bar exam and write code.

Big Brain AI

288,696 views • 3 months ago

Yann LeCun says LLMs are intrinsically unsafe because they cannot be made fully reliable They can still hallucinate or take agentic actions without predicting the consequences properly "coding works because you can verify the output, but real-world tasks are harder to verify"

Yann LeCun says LLMs are intrinsically unsafe because they cannot be made fully reliable They can still hallucinate or take agentic actions without predicting the consequences properly "coding works because you can verify the output, but real-world tasks are harder to verify"

Haider.

33,332 views • 2 months ago

Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.

Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.

Humanoids daily

1,443,650 views • 9 months ago

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

Yann LeCun says language isn’t intelligence. Predicting text doesn’t mean understanding reality. The real world is messy, physical, and causal and today’s LLMs barely touch that. The next leap is Physical AI: world models, cause and effect, real planning. Do you think LLMs can evolve into this, or do we need a completely new architecture?

VraserX e/acc

76,194 views • 6 months ago

New interview of Yann LeCun "In the end, if you’re interested in building systems that have the intelligence of, let’s say, a cat, let alone humans, you need common sense. You need the ability to predict the consequences of your actions. You need the ability to plan. You need the ability to reason. And you’re not going to get this with VLA, VLM, or LLM or any generative architectures." --- From 'AI House Davos" YT channel (full link in comment)

New interview of Yann LeCun "In the end, if you’re interested in building systems that have the intelligence of, let’s say, a cat, let alone humans, you need common sense. You need the ability to predict the consequences of your actions. You need the ability to plan. You need the ability to reason. And you’re not going to get this with VLA, VLM, or LLM or any generative architectures." --- From 'AI House Davos" YT channel (full link in comment)

Rohan Paul

52,715 views • 6 months ago

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

Yann LeCun says the real world is far more complex than the world of language LLMs can accumulate knowledge, but they fail with high-dimensional, continuous, noisy sensory data "the next revolution is physical AI" Systems that can truly plan, reason, and understand the physical environment

Haider.

68,894 views • 6 months ago

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,165 views • 17 days ago

AMI Labs Executive Chairman Yann LeCun says the future of AI depends on systems that can predict what happens next, not just absorb more data and compute. Watch the full conversation with Tom Mackenzie at the VivaTech X Bloomberg Awards:

AMI Labs Executive Chairman Yann LeCun says the future of AI depends on systems that can predict what happens next, not just absorb more data and compute. Watch the full conversation with Tom Mackenzie at the VivaTech X Bloomberg Awards:

Bloomberg

13,096 views • 1 month ago

Yann LeCun: I'm not interested in LLMs anymore - they're the past. The future is in four more interesting areas: machines that understand the physical world, persistent memory, reasoning, and planning.

Yann LeCun: I'm not interested in LLMs anymore - they're the past. The future is in four more interesting areas: machines that understand the physical world, persistent memory, reasoning, and planning.

Victor

525,746 views • 1 year ago

📁 Yann LeCun, Chief AI Scientist at Meta, says many people misunderstand what a world model really is. He explains that it is not about reproducing the world in full detail like a visual simulator. He warns that generating video may look impressive, but it does not guarantee the system has learned the underlying dynamics of reality.

📁 Yann LeCun, Chief AI Scientist at Meta, says many people misunderstand what a world model really is. He explains that it is not about reproducing the world in full detail like a visual simulator. He warns that generating video may look impressive, but it does not guarantee the system has learned the underlying dynamics of reality.

Jon Hernandez

36,093 views • 7 months ago

Richard Sutton says that the current dominance of LLMs is a "momentary fixation" the real breakthroughs will come from scaling computation, not building AI systems based on how humans think they work. LLMs will not be the leading edge of AI for more than another decade, perhaps even half a decade.

Richard Sutton says that the current dominance of LLMs is a "momentary fixation" the real breakthroughs will come from scaling computation, not building AI systems based on how humans think they work. LLMs will not be the leading edge of AI for more than another decade, perhaps even half a decade.

Haider.

137,573 views • 1 year ago

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."

Chubby♨️

103,967 views • 9 months ago

Yann LeCun (Yann LeCun ) explains why LLMs are so limited in terms of real-world intelligence. Says the biggest LLM is trained on about 30 trillion words, which is roughly 10 to the power 14 bytes of text. That sounds huge, but a 4 year old who has been awake about 16,000 hours has also taken in about 10 to the power 14 bytes through the eyes alone. So a small child has already seen as much raw data as the largest LLM has read. But the child’s data is visual, continuous, noisy, and tied to actions: gravity, objects falling, hands grabbing, people moving, cause and effect. From this, the child builds an internal “world model” and intuitive physics, and can learn new tasks like loading a dishwasher from a handful of demonstrations. LLMs only see disconnected text and are trained just to predict the next token. So they get very good at symbol patterns, exams, and code, but they lack grounded physical understanding, real common sense, and efficient learning from a few messy real-world experiences. --- From 'Pioneer Works' YT channel (link in comment)

Yann LeCun (Yann LeCun ) explains why LLMs are so limited in terms of real-world intelligence. Says the biggest LLM is trained on about 30 trillion words, which is roughly 10 to the power 14 bytes of text. That sounds huge, but a 4 year old who has been awake about 16,000 hours has also taken in about 10 to the power 14 bytes through the eyes alone. So a small child has already seen as much raw data as the largest LLM has read. But the child’s data is visual, continuous, noisy, and tied to actions: gravity, objects falling, hands grabbing, people moving, cause and effect. From this, the child builds an internal “world model” and intuitive physics, and can learn new tasks like loading a dishwasher from a handful of demonstrations. LLMs only see disconnected text and are trained just to predict the next token. So they get very good at symbol patterns, exams, and code, but they lack grounded physical understanding, real common sense, and efficient learning from a few messy real-world experiences. --- From 'Pioneer Works' YT channel (link in comment)

Rohan Paul

639,977 views • 5 months ago

General Intuition CEO Pim de Witte (Pim de Witte) says: "Most of AI is just glorified data science." And... "Yann LeCun is underrated." His reasoning? "Good models mostly are downstream from good data & simple things." General Intuition is taking a different approach than traditional LLMs: "We train models that can predict lots of different actions, where text is just a subset." When I asked why he thinks Yann LeCun is underrated..

General Intuition CEO Pim de Witte (Pim de Witte) says: "Most of AI is just glorified data science." And... "Yann LeCun is underrated." His reasoning? "Good models mostly are downstream from good data & simple things." General Intuition is taking a different approach than traditional LLMs: "We train models that can predict lots of different actions, where text is just a subset." When I asked why he thinks Yann LeCun is underrated..

Molly O’Shea

31,992 views • 27 days ago

What happens in a world post-foundation model? "Foundation models are not going away, they will remain a core part of future systems. The question is not whether they get replaced, but what gets built on top of them. Future AI systems will likely combine foundation models with additional components to create more complete systems." Demis Hassabis Love to hear your thoughts Yann LeCun François Chollet Jürgen Schmidhuber Aran Komatsuzaki

What happens in a world post-foundation model? "Foundation models are not going away, they will remain a core part of future systems. The question is not whether they get replaced, but what gets built on top of them. Future AI systems will likely combine foundation models with additional components to create more complete systems." Demis Hassabis Love to hear your thoughts Yann LeCun François Chollet Jürgen Schmidhuber Aran Komatsuzaki

Harry Stebbings

13,648 views • 3 months ago