# lecun-still-far-from-human-level-ai-llm-2026-09-25

## Veille

X post by **Yann LeCun** (founder and president of **AMI Labs**, professor at NYU, former Chief AI Scientist at Meta, Turing Award), September 25, 2026, ~110 words. He replies to **Noah Smith**, who deemed a video *« already falsified »*, in which LeCun calls the idea of a *« country of geniuses in a data center »* *« complete BS »*. (A) Four test questions: where is the domestic robot, the voiture autonome de **niveau 5**, the car that learns to drive *« in a few hours of practice like any 17 year old »*, the system that understands the real world and learns fast *« like a house cat »*? (B) A concession: AI will match humans in every domain, and the technology **already surpasses them on a growing number of tasks**. (C) Two caveats: *« We're still far from that »*, and it won't be built on **LLMs**, confined to a text-interface role. A second post points back to his September 20 formulation: autoregressive LLMs, *« in and of themselves »*, will not lead to human-level AI. Until now, the corpus held LeCun only as a reference cited by others ([[deanwball-open-weights-decelerationnistes-kimi-2026-07-17]]), not as a source; the text reads alongside the skepticism in [[luc-julia-ai-controversy-statements-media-2025-08-22]].

## Titre Article

Nope. It's still true. (post X en réponse à Noah Smith)

## Date

2026-09-25

## URL

https://x.com/ylecun/status/2103543949151658369

## Keywords

Yann LeCun, AMI Labs, NYU, Meta, Turing Award, Noah Smith, Noahpinion, human-level AI, human-level AI, AGI, limits of AI, LLM, autoregressive LLMs, text interface, understanding of the real world, world models, learning efficiency, learning to drive, autonomous vehicle, level 5, domestic robot, house cat, country of geniuses in a data center, surpassing humans on tasks, skepticism

## Authors

Yann LeCun — fondateur et président d'AMI Labs, professeur à NYU, ex-Chief AI Scientist de Meta, prix Turing. Post X.

## Ton

A short polemical reply, typical of LeCun on X: a dry opening (*« Nope. It's still true. »*), followed by an anaphora of four rhetorical questions (*« Where is your… »*) that shift the debate from measured performance to everyday capabilities anyone can check. The second half changes register: an explicit concession, typographically underlined (*« *** BUT *** »*), followed by two affirmatively toned numbered points, with no figure or reference. The argument does not rest on a benchmark but on examples of learning efficiency (the teenager, the cat), which have long recurred in his statements. The post defends a position already stated rather than introducing a new one; the second post (*« Also: »*) says so itself by pointing back to his September 20 formulation and his clarification on the word "autoregressive."

## Pense-betes

- **The balancing sentence to remember**: AI *« surpasses humans in an ever-increasing number of tasks »* and *« we're still far from »* human intelligence. Both hold at once. It's a useful point to make in a talk against the "AI will surpass humans" narrative: yes on tasks, no on general intelligence.
- **The driving example**: a 17-year-old learns to drive in a few hours of practice (in France, the B license requires a minimum of 20 hours of supervised driving). Years of development and millions of kilometers of data have not produced a voiture autonome de niveau 5 that could drive anywhere without restriction, including in scenes never seen before. The gap is about **learning efficiency**, not raw performance.
- **The house cat** as a benchmark: understanding the physical world and quickly learning a new skill, which no current system does, according to LeCun. He is aiming at out-of-distribution generalization, not linguistic competence.
- **The nuance on LLMs**: LeCun does not say they are useless, he says they will not be enough *« in and of themselves »* and that they will keep a text-interface role. The word "autoregressive" matters in his September 20 formulation.
- **What the post does not contain**: no timeline, no measurement, no description of the alternative architecture (the world models he advocates elsewhere, via AMI Labs, are not named here). To be cited as the position of a leading researcher, not as a demonstration.
- **Context of the debate**: the video that triggered the exchange ("no way in hell" of reaching AGI by scaling up LLMs; "country of geniuses in a data center" called *« complete BS »*) was circulating the same day; Noah Smith considered it already falsified. For the September 2026 sequence on the replacement narrative, see [[mhalla-what-if-the-future-does-not-need-you-2026-09-20]].

## RésuméDe400mots

On September 25, 2026, **Yann LeCun** replies on X to economist **Noah Smith**. Smith had commented on a video, widely shared the same day, in which LeCun stated that simply scaling up LLMs would not reach human-level intelligence, and that the idea of a "country of geniuses in a data center" was "complete BS." To Smith, this position seemed "already falsified."

LeCun holds his ground: "Nope. It's still true." He backs his reply with four questions. Where is your domestic robot? Where is your voiture autonome de niveau 5? Where is your robot car able to learn to drive in a few hours of practice, like any 17-year-old? Where is your AI system able to understand the real world and quickly learn new skills, like a house cat?

He adds a concession. There is "no doubt" that AI will eventually become as intelligent as humans in every domain. But he sets two caveats. First, "we're still far from that," even though AI and computing surpass humans on an ever-increasing number of tasks. Second, that intelligence will not rest on LLMs, even though they will have a role to play, for instance as a text interface.

In a second post ("Also:"), LeCun points back to a reply from September 20 addressed in part to Geoffrey Hinton: he recalled having said that "autoregressive LLMs, in and of themselves, will not lead to human-level AI," and that this statement remains "completely true."

The post contains no figures, no timeline, and no description of the alternative architecture. Its argument rests on examples of learning efficiency: a human or an animal picks up a new skill with little experience and transfers it to unfamiliar situations, which current systems do not do. The driving example sums up the gap: a few hours of practice are enough for a teenager, while unrestricted self-driving still has not been achieved.

The text separates two claims often conflated in public debate: AI surpasses humans on an increasing number of tasks, and AI remains far from general human intelligence. LeCun holds both at once.

## GrapheDeConnaissance

- Yann LeCun —dirige→ AMI Labs (ORGANISATION, 0.95)
- Yann LeCun —s_oppose_à→ Noah Smith (PERSONNE, 0.9)
- Noah Smith —affirme_que→ la position de LeCun sur les limites des LLM semble déjà démentie (AFFIRMATION, 0.85)
- Yann LeCun —affirme_que→ nous sommes encore loin d'une IA de niveau humain (AFFIRMATION, 0.95)
- Yann LeCun —affirme_que→ l'IA et l'informatique surpassent les humains dans un nombre croissant de tâches (AFFIRMATION, 0.9)
- Yann LeCun —prédit→ l'IA deviendra à terme aussi intelligente que les humains dans tous les domaines (AFFIRMATION, 0.9)
- Yann LeCun —prédit→ l'IA de niveau humain ne reposera pas sur les LLM, qui garderont un rôle d'interface textuelle (AFFIRMATION, 0.9)
- Yann LeCun —affirme_que→ les LLM autorégressifs, en eux-mêmes, ne mèneront pas à une IA de niveau humain (AFFIRMATION, 0.9)
- Yann LeCun —s_oppose_à→ country of geniuses in a data center (CONCEPT, 0.85)
- Yann LeCun —affirme_que→ aucune voiture ne sait apprendre à conduire en quelques heures de pratique comme un adolescent de 17 ans (AFFIRMATION, 0.9)
- Yann LeCun —affirme_que→ aucun système d'IA ne comprend le monde réel ni n'apprend vite de nouvelles compétences comme un chat domestique (AFFIRMATION, 0.9)
- voiture autonome de niveau 5 —fait_partie_de→ limites de l'IA (CONCEPT, 0.75)
- efficacité d'apprentissage —s_applique_à→ IA de niveau humain (CONCEPT, 0.8)
- LLM —permet→ interface textuelle vers des systèmes d'IA (AFFIRMATION, 0.8)
- Yann LeCun —référence→ Geoffrey Hinton (PERSONNE, 0.75)

---
Canonical: https://www.thekb.eu/en/fiches/lecun-still-far-from-human-level-ai-llm-2026-09-25/