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Justifiable.
There are a lot more degrees of freedom in world models.
LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (grounded in spatiotemporal understanding, not just language patterns) would be attacking what I see as the actual bottleneck to AGI. Even if they succeed only partially, they may unlock the kind of generalization and creative spark that current LLMs structurally can't reach.
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world itself is the bottleneck, you can just put a vision-action LLM on a reinforcement learning loop in a robotic/simulated body.
The sum of human knowledge is more than enough to come up with innovative ideas and not every field is working directly with the physical world. Still I would say there's enough information in the written history to create virtual simulation of 3d world with all ohysical laws applying (to a certain degree because computation is limited).
What current LLMs lack is inner motivation to create something on their own without being prompted. To think in their free time (whatever that means for batch, on demand processing), to reflect and learn, eventually to self modify.
I have a simple brain, limited knowledge, limited attention span, limited context memory. Yet I create stuff based what I see, read online. Nothing special, sometimes more based on someone else's project, sometimes on my own ideas which I have no doubt aren't that unique among 8 billions of other people. Yet consulting with AI provides me with more ideas applicable to my current vision of what I want to achieve. Sure it's mostly based on generally known (not always known to me) good practices. But my thoughts are the same way, only more limited by what I have slowly learned so far in my life.
> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions.
This seems wrong to me on a few levels.
First, there is no way to "experience the world directly," all experience is indirect, and language is a very good way of describing the world. If language was a bad choice or limited in some fundamental way, LLMs wouldn't work as well as they do.
Second, novel ideas are often existing ideas remixed. It's hard/impossible to point to any single idea that sprung from nowhere.
Third, you can provide an LLM with real-world information and suddenly it's "interacting with the world". If I tell an LLM about the US war on Iran, I am in a very real sense plugging it into the real world, something that isn't part of its training data.
Finally, modern LLMs are multi-modal, meaning they have the ability to handle images/video. My understanding is that they use some kind of adapter to turn non-text data into data that the LLM can make sense of.
I'm gonna be a cynic and say this is money following money and Yann LeCun is an excellent salesman.
I 100% guarantee that he will not be holding the bag when this fails. Society will be protecting him.
On that proviso I have zero respect for this guy.
Agree. LLMs operate in the domain of language and symbols, but the universe contains much more than that. Humans also learn a great deal from direct phenomenological experience of the world, even without putting those experiences into words.
I remember a talk by Yann LeCun where he pointed out that in just the first couple of years of life, a human baby is exposed to orders of magnitude more sensory data (vision, sound, etc.) than what current LLMs are typically trained on. This seems like a major limitation of purely language-based models.
I have a pet peeve with the concept of "a genuinely novel discovery or invention", what do you imagine this to be? Can you point me towards a discovery or invention that was "genuinely novel", ever?
I don't think it makes sense conceptually unless you're literally referring to discovering new physical things like elements or something.
Humans are remixers of ideas. That's all we do all the time. Our thoughts and actions are dictated by our environment and memories; everything must necessarily be built up from pre-existing parts.
> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions.
No hate, but this is just your opinion.
The definition of "text" here is extremely broad – an SVG is text, but it's also an image format. It's not incomprehensible to imagine how an AI model trained on lots of SVG "text" might build internal models to help it "visualise" SVGs in the same way you might visualise objects in your mind when you read a description of them.
The human brain only has electrical signals for IO, yet we can learn and reason about the world just fine. I don't see why the same wouldn't be possible with textual IO.
Thank you for not saying "language", but "text".
It's true, but it's also true that text is very expressive.
Programming languages (huge, formalized expressiveness), math and other formal notation, SQL, HTML, SVG, JSON/YAML, CSV, domain specific encoding ie. for DNA/protein sequences, for music, verilog/VHDL for hardware, DOT/Graphviz/Mermaid, OBJ for 3D, Terraform/Nix, Dockerfiles, git diffs/patches, URLs etc etc.
The scope is very wide and covers enough to be called generic especially if you include multi modalities that are already being blended in (images, videos, sound).
I'm cheering for Yann, hope he's right and I really like his approach to openness (hope he'll carry it over to his new company).
At the same time current architectures do exist now and do work, by far exceeding his or anybody's else expectations and continue doing so. It may also be true they're here to stay for long on text and other supported modalities as cheaper to train.
> There are a lot more degrees of freedom in world models.
Perhaps for the current implementations this is true. But the reason the current versions keep failing is that world dynamics has multiple orders of magnitude fewer degrees of freedom than the models that are tasked to learn them. We waste so much compute learning to approximate the constraints that are inherent in the world, and LeCun has been pressing the point the past few years that the models he intends to design will obviate the excess degrees of freedom to stabilize training (and constrain inference to physically plausible states).
If my assumption is true then expect Max Tegmark to be intimately involved in this new direction.
I had lunch with Yann last August, about a week after Alex Wang became his "boss." I asked him how he felt about that, and at the time he told me he would give it a month or two and see how it goes, and then figure out if he should stay or find employment elsewhere. I told him he ought to just create his own company if he decides to leave Meta to chase his own dream, rather than work on the dream's of others.
That said, while I 100% agree with him that LLM's won't lead to human-like intelligence (I think AGI is now an overloaded term, but Yann uses it in its original definition), I'm not fully on board with his world model strategy as the path forward.
> But this is not an applied AI company.
There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything.
It could be a management issue, though, and I sincerely wish we will see more competition, but from what I quoted above, it does not seem like it.
Understanding world through videos (mentioned in the article), is just what video models have already done, and they are getting pretty good (see Seedance, Kling, Sora .. etc). So I'm not quite sure how what he proposed would work.
Yann LeCun seeks $5B+ valuation for world model startup AMI (Amilabs).
He has hired LeBrun to the helm as CEO.
AMI has also hired LeFunde as CFO and LeTune as head of post-training.
They’re also considering hiring LeMune as Head of Growth and LePrune to lead inference efficiency.
https://techcrunch.com/2025/12/19/yann-lecun-confirms-his-ne...
This couldn't have happened sooner, for 2 reasons.
1) the world has become a bit too focused on LLMs (although I agree that the benefits & new horizons that LLMs bring are real). We need research on other types of models to continue.
2) I almost wrote "Europe needs some aces". Although I'm European, my attitude is not at all that one of competition. This is not a card game. What Europe DOES need is an ATTRACTIVE WORKPLACE, so that talent that is useful for AI can also find a place to work here, not only overseas!
I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper question: what is the moral status of such systems? If they have experiences or agency, treating them purely as tools could start to look uncomfortably close to slavery.
Regardless of your opinion of Yann or his views on auto regressive models being "sufficient" for what most would describe as AGI or ASI, this is probably a good thing for Europe. We need more well capitalized labs that aren't US or China centric and while I do like Mistral, they just haven't been keeping up on the frontier of model performance and seem like they've sort of pivoted into being integration specialists and consultants for EU corporations. That's fine and they've got to make money, but fully ceding the research front is not a good way to keep the EU competitive.
So it is a startup? I expected it in fact from his reply to my concern. In my opinions, to explore the unknown, I think an institute like Mila, led by Yoshua Bengio, would have been more fitting. But Yann LeCun's career and his reply to my rant[1] speak for himself. I wonder how he is going to make money. Aside all my concerns, I wish him the best.
> You're absolutely right. Only large and profitable companies can afford to do actual research. All the historically impactful industry labs (AT&T Bell Labs, IBM Research, Xerox PARC, MSR, etc) were with companies that didn't have to worry about their survival. They stopped funding ambitious research when they started losing their dominant market position.
It's really inevitable isn't it, we are going from RAG to PAG, or physical augmented generation.
We already have PINN or physics-informed neural networks [1]. Soon we are going to have physical field computing by complex-valued network quantization or CVNN that has been recently proposed for more efficient physical AI [2].
[1] Physics-informed neural networks:
https://en.wikipedia.org/wiki/Physics-informed_neural_networ...
[2] Ultra-efficient physical field computing by complex-valued network quantization:
I feel like I'm the only one not getting the world models hype. We've been talking about them for decades now, and all of it is still theoretical. Meanwhile LLMs and text foundation models showed up, proved to be insanely effective, took over the industry, and people are still going "nah LLMs aren't it, world models will be the gold standard, just wait."
It's curious to me why we have no theory of intelligence. By which I mean an actual hard and verified theory, as in physics for gravity, electromagnetism, quantum mechanics.
Intelligence is simply not well-understood at a mathematical level. Like medieval engineers, we rely so heavily on experimentation in AI. We have no idea how far away from the human level we actually are. Or how far above the human level we can get. Or what, if anything, the limits of intelligence are.
Alternative free to read article: https://sifted.eu/articles/yann-lecun-ami-labs-meta-funding-...
Interesting that AMI is betting on video-first world models. A 4-year-old learns physics mostly through interaction, pushing, dropping, breaking things, not just watching. Vision helps but the feedback loop from acting in the world seems at least as important. Still, glad someone is putting $1B on a fundamentally different bet than "more text, bigger model."
Off topic, in case anyone wants to reject cookies, click the underlined "228" in the popup's:
> We, and our 228 partners use cookies
And then you'll see a "reject all" button. Can't make this up.
Looks like they'll be hiring on in Montreal in addition to Paris (and NYC and Signapore): https://jobs.ashbyhq.com/ami
I hope they grow that office like crazy. This would be really good for Canada. We have (or have had) the AI talent here (though maybe less so overall in Montreal than in Toronto/Waterloo and Vancouver and Edmonton).
And I hope Carney is promoting the crap out of this and making it worth their while to build that office out.
I don't really do Python or large scale learning etc, so don't see a path for myself to apply there but I hope this sparks some employment growth here in Canada. Smart choice to go with bilingual Montreal.
Seems like it's the second largest seed round anywhere after Thinking Machines Labs? https://news.crunchbase.com/venture/biggest-seed-round-ai-th...
That article is from June 2025 so may be out of date, and the definition of "seed round" is a bit fuzzy.
At least some of that money should definitely go towards improving his powerpoint slides on JEPA related work :)
Archive: https://archive.md/5eZWq
The startup is Advanced Machine Intelligence Labs: https://amilabs.xyz/
As someone in the tech twitter sphere this is yann and his ideas performing a suplex on LLM based companies. It is completely unfathomable to start an ai research company… Only sell off 20% and have 1 billion for screwing around for a few years.
Why world model? To emulate how we became sentient?
A "world" is just senses. In a way the context is one sense. A digital only world is still a world.
I think more success is in a model having high level needs and aspirations that are borne from lower level needs. Model architecture also needs to shift to multiple autonomous systems that interact, in the same ways our brains work - there's a lot under the surface inside our heads, it's not just "us" in there.
We only interact with our environment because of our low level needs, which are primarily: food, water. Secondary: mating. Tertiary: social/tribal credit (which can enable food, water and mating).
What use is it to understand the physical world if all investments are misallocated to the virtual world? Perhaps the AI will detect that there is a housing shortage and politicians will finally believe it because AI said so?
Or is it to accelerate Skynet?
Wasn't there some recent argument that world models won't achieve AGI either due to overlooking the normative framework, fundamental symmetries of the world purely from data and collapse in multi-step reasoning? JEPA is sacrificing fidelity for abstract representation yet how does that help in the real world where fidelity is the most important point? It's like relying on differential equations yet soon finding out they only cover minuscule amount of real world problems and almost all interesting problems are unsolvable by them.
If he's right (that LLMs cannot achieve AGI, but what he's working on can, and does), this would be huge for AI and humanity at large.
Hope it puts to bed the "Europe can't innovate" crowd too.
If, for even 1s, they get in a position which is threatening, in any way, Big Tech AI (mostly US based if not all), they will be raided by international finance to be dismantled and poached hardcore with some massive US "investment funds" (which looks more and more as "weaponized" international finance!!). Only china is very immune to international finance. Those funds have tens of thousands of billions of $, basically, in a world of money, there is near zero resistance.
Does anyone have a sense of how funding like this is typically allocated?
how much tends to go toward compute/training versus researchers, infrastructure, and general operations?
A fair amount of negative comments here, but Yann might very well be the person who brings the Bell Labs culture back to life. It’s been badly missing, and not just in Europe.
I think we will all now witness that LeCun is just an impostor with an unbelievably overblown ego.
I predict that he will burn through the investment in not time (most of it will probably trickle into regulator pockets) and he will not have anything to show for in the end.
I feel HN comments have been getting hijacked for a long time now by LLM agents. Always so early, very positive, and hard to spot. Some replaced em-dash with --, some replace them with a single dash, some remove them all together. I wonder how much time it is taking from @dang and other moderators helping to maintain this community.
Interesting perspective from LeCun. The debate between scaling LLMs versus building systems that understand the physical world seems like one of the big open questions in AI right now. It will be fascinating to see whether “world models” end up complementing LLMs or eventually replacing parts of them.
I wish him luck.
Recently all papers are about LLM, it brings up fatigue.
As GPT is almost reaching its limit, new architecture could bring out new discovery.
There's been a few very interesting JEPA publications from LeCun recently, particularly the leJEPA paper which claims to simplify a lot of training headaches for that class of models.
JEPAs also strike me as being a bit more akin to human intelligence, where for example, most children are very capable of locomotion and making basic drawings, but unable to make pixel level reconstructions of mental images (!!).
One thing I want to point out is that very LeCunn type techniques demonstrating label free training such as JEAs like DINO and JEPAs have been converging on performance of models that require large amounts of labeled data.
Alexandr Wang is a billionaire who made his wealth through a data labeling company and basically kicked LeCunn out.
Overall this will be good for AI and good for open source.
LeCun has been arguing for years that predicting text isn’t enough for real intelligence. This startup is basically his attempt to prove that world models — not bigger LLMs — are the path to AI that can reason about the physical world.
That's between 1 and 10 training runs on a large foundational model, depending on pricing discounts and how much they manage to optimize it. I priced this out last night on AWS, which is admittedly expensive, but models have also gotten larger.
He raises $1B, couldn't OAI, Google or Anthropic try similar approaches? Lack of funding isn't a problem those companies have. Why wouldn't they also spend $1B or 5 times that and outcompete (in theory)?
Selfless plug here... Some collaborators and I just released a first version of a benchmark we think highlights a critical gap in recent models in understanding causality in the real-world, beyond a physics focus.
Everyday environments are rich in tangible control interfaces (TCIs), like, light switches, appliance panels, and embedded GUIs, that are designed for humans and demand commonsense and physics reasoning, but also causal prediction and outcome verification in time and space (e.g., delayed heating, remote lights).
SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios (https://huggingface.co/papers/2511.17649)
Feedback, suggestions, and collaborators are very welcome!
I wonder how Carmack's AGI work is going. He's been quite for a while.
Yann LeCun said a number of things that are very dubious, like autoregressive LLMs are a dead end, LLMs do not have an internal world model, and this morning https://www.youtube.com/watch?v=AFi1TPiB058 (in french) that an IA cannot find a strategy to preserve itself against the will of its creator.
As a french, I wish him good luck anyway, I'm all for exploring different avenues of achieving AGI.
$1B at a $3.5B valuation. Seems problematic from a cap table perspective.
He couldn't achieve at least parity with LLMs during his days at Meta (and having at his disposal billions in resources most probably) but he'll succeed now? What is the pitch?