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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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71,792 views • 4 months ago •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"

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“LLMs are intrinsically unsafe.” Yann LeCun argues that models that can’t predict the consequences of their actions can’t be made reliable.

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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

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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"

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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"

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625,361 views • 6 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"

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33,279 views • 26 days ago

"we will not get to human-level AI by just scaling up LLMs... it's not going to happen" Meta AI chief scientist, Yann LeCun: scaling up LLMs alone will not lead to human-level AI ...what we’ll have instead are systems with huge memory and retrieval abilities, but still not able to invent solutions to new problems

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123,610 views • 1 year ago

📁 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.

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154,597 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

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66,616 views • 4 months ago

Computer scientist Judea Pearl: There are mathematical limits to LLMs that cannot be crossed by scaling alone LLMs don't discover world models from raw data; they merely summarize the interpretations humans have already written down "this path is not the way to get AGI"

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622,372 views • 4 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?

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76,154 views • 4 months ago

Understanding LLMs by Building One 👉 We use large language models every day, but what actually happens inside them?

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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

129,818 views • 2 months ago

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

126,392 views • 4 months 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

288,001 views • 1 month ago

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 views • 4 months ago

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Haider.

346,762 views • 29 days ago

Listen to Yann LeCun, he makes great points! ◻️ Current AI systems like ChatGPT are intrinsically unsafe. ◻️ If LLMs could be scaled to human-level intelligence, they'd remain unsafe. ◻️ He has a potential solution for safe AI, but we don't know it works.

Listen to Yann LeCun, he makes great points! ◻️ Current AI systems like ChatGPT are intrinsically unsafe. ◻️ If LLMs could be scaled to human-level intelligence, they'd remain unsafe. ◻️ He has a potential solution for safe AI, but we don't know it works.

Liron Shapira

22,339 views • 2 years ago