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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... show more

Wes Roth

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71,824 次观看 • 5 个月前 •via X (Twitter)

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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"

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.

61,211 次观看 • 5 个月前

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.

331,815 次观看 • 2 个月前

Yann LeCun says LLMs are not a bubble in value or investment; they will power many useful apps and justify big infra The bubble is believing LLMs alone will reach human-level intelligence Progress needs breakthroughs, not just more data/compute "we're missing something big"

Yann LeCun says LLMs are not a bubble in value or investment; they will power many useful apps and justify big infra The bubble is believing LLMs alone will reach human-level intelligence Progress needs breakthroughs, not just more data/compute "we're missing something big"

Haider.

321,516 次观看 • 8 个月前

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Yann LeCun says we're fooled by LLMs because they manipulate language well, and we associate that with intelligence But language fluency doesn't mean underlying intelligence Every generation since the 1950s claimed its technique was the ticket to human-level AI All were wrong. "this generation with LLMs is also wrong"

Haider.

625,568 次观看 • 7 个月前

“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

10,481 次观看 • 1 个月前

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

📁 Yann LeCun says that scaling models will not get us to human intelligence. He explains that the industry remains obsessed with making LLMs bigger, but that this path is fundamentally broken. It does not matter how many parameters we add or how many clusters we build, because the models only imitate language patterns. Human intelligence does not emerge from size, it emerges from understanding the world.

Jon Hernandez

156,594 次观看 • 7 个月前

AMI Labs founder Yann LeCun on why LLMs are fooling us the same way AI has for decades: He argues that every generation of AI scientists has made the same mistake: confusing task performance with real intelligence. LeCun's core challenge to the current hype: "We're fooled into thinking those machines are intelligent because they can manipulate language. And we're used to the fact that people who can manipulate language very well are implicitly smart." He's clear that LLMs are useful, but being a useful tool and being intelligent are two very different things. The real insight is the historical pattern he's lived through. Since the 1950s, wave after wave of AI researchers have claimed their breakthrough was the path to human-level intelligence. Marvin Minsky. Herbert Simon. Frank Rosenblatt — who invented the perceptron, the first learning machine, in the 1950s — all predicted machines as smart as humans within a decade. "They were all wrong." LeCun has personally witnessed three of these cycles of hype and disappointment. And his verdict on the current one is blunt: "This generation with LLMs is also wrong. It's just another example of being fooled." The pattern: A new technique emerges → machines get good at specific tasks → we assume general intelligence The question worth asking: are we impressed by these tools because they're intelligent, or because they sound like they are?

AMI Labs founder Yann LeCun on why LLMs are fooling us the same way AI has for decades: He argues that every generation of AI scientists has made the same mistake: confusing task performance with real intelligence. LeCun's core challenge to the current hype: "We're fooled into thinking those machines are intelligent because they can manipulate language. And we're used to the fact that people who can manipulate language very well are implicitly smart." He's clear that LLMs are useful, but being a useful tool and being intelligent are two very different things. The real insight is the historical pattern he's lived through. Since the 1950s, wave after wave of AI researchers have claimed their breakthrough was the path to human-level intelligence. Marvin Minsky. Herbert Simon. Frank Rosenblatt — who invented the perceptron, the first learning machine, in the 1950s — all predicted machines as smart as humans within a decade. "They were all wrong." LeCun has personally witnessed three of these cycles of hype and disappointment. And his verdict on the current one is blunt: "This generation with LLMs is also wrong. It's just another example of being fooled." The pattern: A new technique emerges → machines get good at specific tasks → we assume general intelligence The question worth asking: are we impressed by these tools because they're intelligent, or because they sound like they are?

Big Brain AI

126,482 次观看 • 5 个月前

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

Milk Road AI

130,649 次观看 • 3 个月前

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

288,411 次观看 • 2 个月前

Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Wes Roth

38,833 次观看 • 5 个月前

📁 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 understand the world, not just talk about it.

Jon Hernandez

22,529 次观看 • 4 个月前

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

383,162 次观看 • 5 个月前

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

Jon Hernandez

63,723 次观看 • 6 个月前

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Karl Mehta

34,676 次观看 • 13 天前

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"

Haider.

17,743 次观看 • 4 个月前

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 次观看 • 9 个月前

A 4-year-old child has seen 50x more information than the biggest LLMs. Yann LeCun is the Chief AI Scientist at Meta. He recently spoke on “The Expanding Universe of Generative Models” panel at the World Economic Forum in Davos. Yann highlighted the idea that a 4-year-old child is way smarter than current cutting-edge large language models (LLMs). “Think about what a child sees through vision. Put a number on how much information a 4-year-old child has seen during their life. It’s 20 Mbps going through the optical nerve for 16,000 wake hours in the first 4 years of life. 3,600 seconds per hour is 10^15 bytes. This is 50x more information than the biggest LLMs we have. A 4-year-old child is way smarter than these models having acquired an enormous amount of knowledge about how the world works.” The real constraint right now is the ability of LLMs to think. Today, LLMs are only capable of System 1 thinking. System 1 vs System 2 thinking was popularised in the book 'Thinking, Fast and Slow' by Daniel Kahneman. System 1 tasks involve quick, instinctive, automatic responses. LLMs struggle with discontinuous tasks that require a creative leap in progress as they imitate human responses. It's hard to go above human response accuracy if LLMs are only trained on humans. Models are building the track in front of them with each word being generated. What could it mean to give language models System 2 thinking? This remains a future development I'm excited about.

A 4-year-old child has seen 50x more information than the biggest LLMs. Yann LeCun is the Chief AI Scientist at Meta. He recently spoke on “The Expanding Universe of Generative Models” panel at the World Economic Forum in Davos. Yann highlighted the idea that a 4-year-old child is way smarter than current cutting-edge large language models (LLMs). “Think about what a child sees through vision. Put a number on how much information a 4-year-old child has seen during their life. It’s 20 Mbps going through the optical nerve for 16,000 wake hours in the first 4 years of life. 3,600 seconds per hour is 10^15 bytes. This is 50x more information than the biggest LLMs we have. A 4-year-old child is way smarter than these models having acquired an enormous amount of knowledge about how the world works.” The real constraint right now is the ability of LLMs to think. Today, LLMs are only capable of System 1 thinking. System 1 vs System 2 thinking was popularised in the book 'Thinking, Fast and Slow' by Daniel Kahneman. System 1 tasks involve quick, instinctive, automatic responses. LLMs struggle with discontinuous tasks that require a creative leap in progress as they imitate human responses. It's hard to go above human response accuracy if LLMs are only trained on humans. Models are building the track in front of them with each word being generated. What could it mean to give language models System 2 thinking? This remains a future development I'm excited about.

Alex Banks

22,958 次观看 • 2 年前

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Rohan Paul

104,460 次观看 • 7 个月前

The man who built Meta’s entire AI research operation just made a $1 billion bet that everything OpenAI and Google are building is a dead end. Yann LeCun helped design the deep learning systems that made today’s chatbots possible. He spent years inside Big Tech, then walked away and called current AI “very stupid.” Because he says it only plays with words. LeCun says today’s models don’t truly understand the physical world, don’t keep real long‑term memory, and can’t genuinely reason or plan. They just predict the next token and sound smart while doing it. So he left to build something completely different. His new startup, AMI Labs, is trying to build world models, an AI that learns how reality itself works. Not just text in, text out but systems that watch video, interact with environments, and build an internal map of cause and effect. LeCun believes that once these systems can predict the future and care about consequences, they’ll even show emotion like states such as fear, excitement, anticipation. That’s why the stakes are so high. If he’s wrong, the LLM giants keep scaling and win by brute force. If he’s right, today’s AI leaders are racing down a side road, while the real path to machine intelligence is being built somewhere else.

The man who built Meta’s entire AI research operation just made a $1 billion bet that everything OpenAI and Google are building is a dead end. Yann LeCun helped design the deep learning systems that made today’s chatbots possible. He spent years inside Big Tech, then walked away and called current AI “very stupid.” Because he says it only plays with words. LeCun says today’s models don’t truly understand the physical world, don’t keep real long‑term memory, and can’t genuinely reason or plan. They just predict the next token and sound smart while doing it. So he left to build something completely different. His new startup, AMI Labs, is trying to build world models, an AI that learns how reality itself works. Not just text in, text out but systems that watch video, interact with environments, and build an internal map of cause and effect. LeCun believes that once these systems can predict the future and care about consequences, they’ll even show emotion like states such as fear, excitement, anticipation. That’s why the stakes are so high. If he’s wrong, the LLM giants keep scaling and win by brute force. If he’s right, today’s AI leaders are racing down a side road, while the real path to machine intelligence is being built somewhere else.

Milk Road AI

77,675 次观看 • 4 个月前