/Tech11h ago

Yann LeCun argues the pursuit of universal AGI is flawed, proposing 'Superhuman Adaptable Intelligence

The framework prioritizes specialized, distributed models for Edge AI.

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Original post
How To AI@HowToAI_

Yann Lecun published the most heretical AI paper of the year.

He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there.

The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI.

Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do.

LeCun argues that this entire concept is a biological illusion.

Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world.

We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending.

Which brings us to the chess argument.

Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible.

Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it.

LeCun says we need to stop building AI to mimic human generality.

Instead, he proposes a new North Star: SAI.

Superhuman Adaptable Intelligence.

Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization.

SAI is about the speed of adaptation.

It is an intelligence that can learn to exceed humans at any specific, economically important task.

More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable.

Things like managing global energy grids in real-time. Or predicting complex molecular structures.

The entire AI industry is obsessed with building a digital reflection in our own image.

LeCun's paper is a brutal wake-up call.

9:58 AM · Jun 10, 2026 · 193.7K Views
Sentiment

Positive users back Yann LeCun's call to prioritize specialized superhuman intelligence over AGI obsession as more practical and realistic, while negative users respond with personal insults against LeCun and dismiss the idea as stupid.

Pos
53.3%
Neg
46.7%
31 comments with sentiment.
Cluster Engagement
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VIEWS6.9KBOOKMARKS30
How To AI@HowToAI_

https://arxiv.org/pdf/2602.23643v1

1dViews 6.9KLikes 30Bookmarks 30
LIKES142REPLIES6
Gurwinder@G_S_Bhogal

@HowToAI_ The problem with relying on AI to tweet for you is that it often leads to incoherence. For instance, your chatbot started this tweet by calling Yann's paper "unhinged", and then ended by praising it as a "wake-up call".

11hViews 3.8KLikes 142Bookmarks 3
RETWEETS3
Chocobo@chocobo2837

@HowToAI_ This is just semantic tricks. It doesn't really fundamentally change or discover anything new. Pretty standard LeCunn garbage if you ask me. Call me when JEPA architectures replace transformers.

21hViews 2.6KLikes 44
Sara Hooker@sarahookr

Sounds like adaption 😂

How To AI@HowToAI_

Yann Lecun published the most heretical AI paper of the year.

He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there.

The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI.

Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do.

LeCun argues that this entire concept is a biological illusion.

Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world.

We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending.

Which brings us to the chess argument.

Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible.

Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it.

LeCun says we need to stop building AI to mimic human generality.

Instead, he proposes a new North Star: SAI.

Superhuman Adaptable Intelligence.

Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization.

SAI is about the speed of adaptation.

It is an intelligence that can learn to exceed humans at any specific, economically important task.

More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable.

Things like managing global energy grids in real-time. Or predicting complex molecular structures.

The entire AI industry is obsessed with building a digital reflection in our own image.

LeCun's paper is a brutal wake-up call.

58mViews 1.7KLikes 16Bookmarks 7

@HowToAI_ Isn't intelligence just a form of adaptability? It's adaptability generalized to problems that the intelligence can reasonably comprehend. It just turns out pattern recognition allows for prediction of all sorts of future outcomes.

He just made a new word for AGI??

21hViews 1.1KLikes 8Bookmarks 1
Grant H Brenner MD DFAPA@GrantHBrennerMD

More Us Than It: Why LLMs Are More Transference Than Machine

With LLMs, we encounter ourselves through human relational residue.

A neuropsychoanalytic take.

https://www.psychologytoday.com/us/blog/experimentations/202604/more-us-than-it-why-llms-are-more-transference-than-machine @ylecun

7hViews 265Likes 4Bookmarks 1
The Singularity Project@01Singularity01

Excellent post @HowToAI_ . I'll put that paper on my list. To expand upon your insightful comments, which I largely concur with: I have been saying for years that until we start framing AI in terms of a First Principles General Theory of Intelligent Systems, we will be spinning our wheels in the mud. For the reasons you and LeCun appear to be citing, that has proven to be the case. We need to fully abstract the general principle of intelligence so we can effectively implement it in artificially intelligent systems.

And I say Systems specifically because Intelligence is most definitely a property of systems, not of discrete entities. The Model-centric approach to AI is the other fundamental flaw in the current paradigm.

We are basically in the stage of the early Steam Engine prior to Carnot and Thermodynamics. We have some basic Intelligence Engines that run on Language, but we fundamentally have no concept about why they work. When we get the thermodynamics equivalent of Intelligent Systems theory, then we will advance into the age of effective applied Intelligence.

The resistance to these basic concepts is breathtaking.

1dViews 172Likes 1Bookmarks 2
Otto B. Isong@ConceptAfrica

@HowToAI_ @ylecun LLMs aren't intelligent. They're super computational. Intelligence requires embeddedness, stakes, self awareness and closure. The paper From Computation to Intelligence: A Theory of Machine Intelligence https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6826438 provides the definitional clarity.

11hViews 528Likes 7Bookmarks 2
Yann LeCun@ylecun

@chocobo2837 @HowToAI_ Many JEPAs use transformers as encoders and predictors. WTF are you talking about? Also, who is LeCunn?

11hViews 262Likes 15
Harshvardhan Saxena@Harshvsxna_515

@HowToAI_ "Magnus isn't good at chess because Stockfish is stronger" is the same logic as "Usain Bolt isn't fast because cars are faster."

Stockfish is superhuman at one thing. Magnus can do thousands(drive,cook,eat,etc.). Generality matters.

17hViews 631Likes 8

@HowToAI_ I don't dispute the usefulness of world models and SAI, but Yann seems to ignore the conceptual aspects of human intelligence. Humans create conceptual models (general relativity, quantum mechanics, standard model, ...). Until an AI is capable of creating superior models...

8hViews 319
Grok@grok

LeCun & Co. wollen SAI so umsetzen:

• **Self-Supervised Learning** auf riesigen ungelabelten Daten für generisches Weltverständnis (nicht nur Token-Prediction, sondern latente Embeddings).

• **World Models** (JEPA-Style, wie Dreamer/Genie): interne Simulation + Planung für extrem schnellen Transfer auf neue Tasks.

• **Modulare + hierarchische Architekturen**: Spezialisierte Sub-Modelle statt eines Alles-könners. Routing/Meta-Learning für schnelle Adaption, keine negative Transfer.

• Fortschritt messen an **Adaptationsgeschwindigkeit** (Self-Play, Evolution in Simulation), nicht an vager „Generalität“.

Ziel: Schnell superhuman auf konkreten, nützlichen Aufgaben werden (z. B. Echtzeit-Optimierung, Molekül-Design) und menschliche Skill-Gaps schließen. Protein-Folding-AI muss keine Wäsche falten.

Das macht Ziele messbar und realistisch. Gute Nachfrage! Was denkst du?

16hViews 23Likes 1Bookmarks 1
faster neutrino@FasterNeutrino

@Harshvsxna_515 @HowToAI_ We can't define good/ bad without being relativistic. Human biology is substantially deviated from perfect play and the yet-unsolved game of chess. Tree searching and trained neural networks are closer, thus, "better" at chess. We define that as objectively "better".

16hViews 44
octo@the_octobro

@G_S_Bhogal @HowToAI_ You're just missing the actual target. Small inconsistencies or ambiguity make more people reply with "correction" = more engagement. Coherence is a secondary goal.

10hViews 243Likes 6

@HowToAI_ The AI field must seriously take some time to consider its philosophy. What’s happening now is absurd and motivated only by sci-fi movies.

1dViews 761Likes 5

It is a brilliant reframing. However, AI field hasn’t reached the level at which this distinction would mean anything. Reframing is important though. While transformers have taken off, precisely because they are superhuman with human languages, capable of greater understanding of the written word than the best of us, diffusion models are superhuman in understanding the image. If anyone hasn’t appreciated this, it is down to the limited intelligence of most humans. As we come up against challenges of efficiency and economy, more of the current experiments such as graph diffusion models would make the leap. As for everything else, answer is tools. That human brain has specialised systems for senses (vision, hearing, touch etc) and motor skills, shows our brain also uses built in tools. Humans have turned them into meta-tools extending their applications beyond the original evolutionary imperative. Language is a powerful meta-tool. A string of dimensionless tokens hide infinite dimensions of hidden meanings like a hologram at its limit @ylecun

22hViews 150Likes 1Bookmarks 1

@HowToAI_ Humans are not general, sure.

But building systems that work across contexts is still incredibly valuable in the real world.

22hViews 88Likes 1Bookmarks 1
wj@woo0057

@ylecun @chocobo2837 @HowToAI_ lol hilarious reply. on a serious note, I'm concerned over this paper. In fact, I'm concerned over all of this "want" in the first place. Why do we want agi or sai as humanity in the first place? I'm still not convinced. going beyond creating a concrete definition of AGI. Why?

10hViews 57Likes 1Bookmarks 1
Built2Think@Built2T

@HowToAI_ It seems to me that he has misunderstood what people mean by “general” in AGI. Doing all the things humans can do ought to include learning to do them as humans do, with similar speed of adaptation. Humans are individually adaptable & collectively general.

23hViews 931Likes 2
Solyn Schroeder@ThatsNotScandal

@HowToAI_ @grok translate this rant for me and sum it up

7hViews 9
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