Discussion
Yann LeCun’s AI start-up raises more than $1bn in Europe’s largest seed round
abmmgb: Not based on true valuation unless h-index has become a valuation metric lolAcademics don’t always make great entrepeneurs
general1465: Here you can see why it is so hard to compete as European startup with US startups - abysmal access to money. Investment of 1B USD in Europe is glorified as largest seed ever, but in USA it is another Tuesday.
compounding_it: Europeans have free healthcare and retirement. They consider putting their money with long term benefits not just become CEO on Tuesday and declare bankruptcy on Wednesday.
ExpertAdvisor01: Free healthcare and retirement ?
ExpertAdvisor01: It is an universal system but definitely not free . In Germany you pay on average 17.5% of your salary for healthcare insurance and 18.6% for retirement . However contribution caps exists . 70k for healthcare and 100k for retirement .
A_D_E_P_T: 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.
andy12_: 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.
A_D_E_P_T: You could have continual learning on text and still be stuck in the same "remixing baseline human communications" trap. It's a nasty one, very hard to avoid, possibly even structurally unavoidable.As for the "just put a vision LLM in a robot body" suggestion: People are trying this (e.g. Physical Intelligence) and it looks like it's extraordinarily hard! The results so far suggest that bolting perception and embodiment onto a language-model core doesn't produce any kind of causal understanding. The architecture behind the integration of sensory streams, persistent object representations, and modeling time and causality is critically important... and that's where world models come in.
energy123: I don't understand why online learning is that necessary. If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know, that's still a very useful AGI. A hippocampus is a nice upgrade to that, but not something where it's super obviously on the critical path.
zelphirkalt: > 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.Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.
mentalgear: Adds up : We are seeing a clear exodus of both capital and talent from the US - with the current US administration’s shift toward cronyism - and the EU stands as the most compelling alternative with a uniform market of 500 million people and the last major federation truly committed to the rule of law.
drstewart: "Exodus of capital" as if OpenAI didn't just raise 115b
general1465: It is not free, we just pay taxes.
energy123: why LLMs (transformers trained on multimodal token sequences, potentially containing spatiotemporal information) can't be a world model?
ZeroCool2u: 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.
oceansky: A startup getting 1B net worth is so rare that such companies are called unicorns.As the other commenter pointed out, this is 1B seed.
ArnoVW: actually, they raised $1.03 billion at a $3.5 billion valuation.
MrBuddyCasino: „free“
insydian: 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.
insydian: I liken this to watching a godzilla esque movie. Just grab some popcorn and enjoy the ride.
ben_w: > Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation.While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions.* I think. Also, I'm saying "mammal" because of an absence of evidence (to my *totally amateur* skill level) not evidence of absence.
andsoitis: Where does that training data come from?
zelphirkalt: I guess the sheer amount and also variety of information you would need to pre-encode to get an Einstein at 40 is huge. Every day stream of high resolution video feed and actions and consequences and thoughts and ideas he has had until the age of 40 of every single moment. That includes social interactions, like a conversation and mimic of the other person in combination with what was said and background knowledge about the other person. Even a single conversation's data is a huge amount of data.But one might say that the brain is not lossless ... True, good point. But in what way is it lossy? Can that be simulated well enough to learn an Einstein? What gives events significance is very subjective.
ForHackernews: https://medium.com/state-of-the-art-technology/world-models-...> One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”
10xDev: Whether it is text or an image, it is just bits for a computer. A token can represent anything.
A_D_E_P_T: Sure, but don't conflate the representation format with the structure of what's being represented.Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!)A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing itself, that which generates those descriptions.You can tokenize an image just as easily as a sentence, sure, but a pile of images and text won't give you a relation between the system and the world. A world model, in theory, can. I mean, we ought to be sufficient proof of this, in a sense...
firecall: It’s worth noting how our human relationship or understanding of our world model changed as our tools to inspect and describe our world advanced.So when we think about capturing any underlying structure of reality itself, we are constrained by the tools at hand.The capability of the tool forms the description which grants the level of understanding.
fs111: https://archive.is/20260310070651/https://www.ft.com/content...
bsenftner: There will be no "unlocking of AGI" until we develop a new science capable of artificial comprehension. Comprehension is the cornucopia that produces everything we are, given raw stimulus an entire communicating Universe is generated with a plethora of highly advanceds predator/prey characters in an infinitely complex dynamic, and human science and technology have no lead how to artificially make sense of that in a simultaneous unifying whole. That's comprehension.
chilmers: Ironically, your comment is practically incomprehensible.
andy12_: That's true. Though could that hippocampus-less Einstein be able to keep making novel complex discoveries from that point forward? Seems difficult. He would rapidly reach the limits of his short term memory (the same way current models rapidly reach the limits of their context windows).
Brajeshwar: There seem to be other news articles mentioning that they are setting up in Singapore as their base. https://www.straitstimes.com/business/ai-godfather-raises-1-...
re-thc: > they are setting up in Singapore as their baseEurope in general has been tightening up their rules / taxes / laws around startups / companies especially tech and remote.It's been less friendly. these days.
Signez: Yann Le Cun litteraly said this morning on the radio in France that it is headquarted in Paris and will pay taxes in France. Go figure…
kvgr: There will be no corporate taxes for a long time, so alls good.
aerhardt: This would be very awkward for the myriad of LinkedIn users who have been posting this as proof of the turning of the tides in the technological vassalage of Europe.
npn: [delayed]
Oras: > 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.
Unearned5161: 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.
copperx: [delayed]
Signez: Hm, Singapour looks more like "one of their base"; they will have offices in Paris, Montréal, Singapour and New York (according to both this article and the interview Yann Le Cun did this morning on France Inter, the most listened radio in France).Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.
10xDev: The fact that models aren't continually updating seems more like a feature. I want to know the model is exactly the same as it was the last time I used it. Any new information it needs can be stored in its context window or stored in a file to read the next it needs to access it.
A_D_E_P_T: Suno is transformer-based; in a way it's a heavily modified LLM.You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible.The same goes for LLMs and invention generally, which is why they've made no important scientific discoveries.You can learn a lot by playing with Suno.
stingraycharles: That's a Singaporian newspaper, though; not sure if it's objectively their main base, or just one of them
margorczynski: 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?
energy123: But they don't only operate on language? They operate on token sequences, which can be images, coordinates, time, language, etc.
mkl: 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.
_giorgio_: Thinking Machines looks half-dead already.The giant seed round proves investors were willing to fund Mira Murati, not that the company had built anything durable.Within months, it had already lost cofounder Andrew Tulloch to Meta, then cofounders Barret Zoph and Luke Metz plus researcher Sam Schoenholz to OpenAI; WIRED also reported that at least three other researchers left. At that point, citing it as evidence of real competitive momentum feels weak.
kergonath: > The fact that models aren't continually updating seems more like a feature.I think this is true to some extent: we like our tools to be predictable. But we’ve already made one jump by going from deterministic programs to stochastic models. I am sure the moment a self-evolutive AI shows up that clears the "useful enough" threshold we’ll make that jump as well.
fnands: Probably just a satellite office.Might be to be close to some of Yann's collaborators like Xavier Bresson at NUS
sylware: 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.
boccaff: llama models pushed the envelope for a while, and having them "open-weight" allowed a lot of tinkering. I would say that most of fine tuned evolved from work on top of llama models.
0x3f: Novel things can be incremental. I don't think LLMs can do that either, at least I've never seen one do it.
oefrha: Llama wasn’t Yann LeCun’s work and he was openly critical of LLMs, so it’s not very relevant in this context.Source: himself https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.
ttoinou: No he said something like “well yes, only for the parts of profits made in France”
whiplash451: The term LLM is confusing your point because VLMs belong to the same bin according to Yann.Using the term autoregressive models instead might help.
secondary_op: That being sad, Yann LeCun's twitter reposts are below average IQ.
goldenarm: Do you have a recent example ?
jeltz: It could possibly be useful but I don't see why it would be AGI.
giancarlostoro: I didn't really know who he was, so I went and found his wikipedia, which is written like either he wrote it himself to stroke his ego, or someone who likes him wrote it to stroke his ego:> He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before leaving to work on his own startup company.That entire sentence before the remarks about him service at Meta could have been axed, its weird to me when people compare themselves to someone else who is well known. It's the most Kanye West thing you can do. Mind you the more I read about him, the more I discovered he is in fact egotistical. Good luck having a serious engineering team with someone who is egotistical.
lairv: https://cims.nyu.edu/dynamic/news/1441/This is just the official name of a chair at NYU. I'm not even sure Jacob T. Schwartz is more well known than Yann LeCun
stephencanon: Yann is definitely more well-known outside of academia. Inside academia, it's going to depend a lot on your specific background and how old you are.
staticman2: > If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know (meaning no online learning), that's still a very useful AGI.I like how people are accepting this dubious assertion that Einstein would be "useful" if you surgically removed his hippocampus and engaging with this.It also calls this Einstein an AGI rather than a disabled human???
az226: Was just a grift
whiplash451: You lost me at “uniform”…
az226: 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...
bonesss: Genuinely novel discovery or invention?Einstein’s theory of relativity springs to mind, which is deeply counter-intuitive and relies on the interaction of forces unknowable to our basic Newtonian senses.There’s an argument that it’s all turtles (someone told him about universes, he read about gravity, etc), but there are novel maths and novel types of math that arise around and for such theories which would indicate an objective positive expansion of understanding and concept volume.
myth_drannon: This could have been 1000 seed rounds. We are creating technological deserts by going all-in on AI and star personalities.
torginus: Most folks get paid a lot more in a corporate job than tinkering at home - using the 'follow the money' logic it would make sense they would produce their most inspired works as 9-5 full stack engineers.But often passion and freedom to explore are often more important than resources
vit05: Why didn't they just call it LeLabs?
adamors: I was thinking the same, are all people he hires LeSomething like those working at Bolson Construction having -son as a suffix.
imjonse: At least some of that money should definitely go towards improving his powerpoint slides on JEPA related work :)
Imustaskforhelp: This is a singaporean news article from a singporean company[0] (Had to look it up)As such, They are more likely to talk about singapore news and exaggerate the claims.Singapore isn't the Key location. From what I am seeing online, France is the major location.Singapore is just one of the more satellite like offices. They have many offices around the world it seems.[0]: https://www.sgpbusiness.com/company/Sph-Media-Limited
andrepd: Bolson-ass hiring policy.
jnd-cz: 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.
saxwick: It’s 4.7B actually, he confirmed it here https://x.com/ylecun/status/2031331124450931058?s=46
ardawen: That seems to be the valuation, not how much they raised afaik.
mmaunder: 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.
htrp: impressive that the round was 100% oversubscribed but to be expected when it's the prof that trained a good chunk of the current AI founders.
