7 ms·
LeCun calls Alex Wang inexperienced, predicts more Meta AI employee departure
- labrador 9mo ago"I'm sure there's a lot of people at Meta, including perhaps Alex, who would like me to not tell the world that LLMs basically are a dead end when it comes to superintelligence" - Yann LeCun I've been following Yann for years and in my opinion he's been consistently right. He's been saying something like this for a long time while Elon Musk and others breathlessly broadcast that scaling up would soon get us to AGI and beyond. Mark Zuckerberg bought in to Musk's idea. We'll see, but it's increasingly looking like LeCunn is right.
- aspenmartin 9mo agoMore like Yann had a long time to prove out his ideas and he did not deliver, meanwhile the industry passed Meta/Facebook by due to the sort of product-averse comfortable academic bubble that FAIR lived in. It wasn’t Zuckerberg getting swindled it was giving up on ever seeing Yann deliver anything other than LinkedIn posts and small scale tests. You do not want to bank on Yann for a big payoff. His ideas may or may not be right (joint predictive architectures, world modeling, etc), but you’d better not have him at the helm of something you expect to turn a profit on. Also almost everyone agrees the current architecture and paradigm, where you have a finite context (or a badly compressed one in Mamba / SSM), is not sufficient. That plus lots of other issues. That said scaling has delivered a LOT and it’s hard to argue against demonstrated progress.
- labrador 9mo agoAs I said in my cousin comment, it depends on how you define AGI and ASI. Claude Opus 4.5 tells me "[Yann LeCun] thinks the phrase AGI should be retired and replaced by "human-level AI." which supports my cousin comment
- CharlieDigital 9mo ago> ...but you’d better not have him at the helm of something you expect to turn a profit on I don't understand this distinction. Is anyone (besides NVDA) turning a profit on inference at this point?
- aspenmartin 9mo agoI don’t know I assume not but everyone has a product that could easily be profitable, it would just be dumb to do it because you will lose out to everyone else running at a loss to capture market share. I just mean the guy seems to have an aversion to business sensibility generally. I think he’s really in it for the love of the research. He’s of course rightly lauded for everything he’s done, he’s extremely brilliant, and in person (at a distance) very kind and reasonable (something that is very different than his LinkedIn personality which is basically a daily pissing contest). But I would not give him one cent of investment personally.
- throw310822 9mo ago> He's been saying something like this for a long time [...] it's increasingly looking like LeCunn is right. No? LLMs are getting smarter and smarter, only three years have passed since ChatGPT was released and we have models generating whole apps, competently working on complex features, solving math problems at a level only reached by a small percentage of the population, and much more. The progress is constant and the results are stunning. Really it makes me wonder in what sort of denial are those who think this has been proven to be a dead end.
- labrador 9mo agoIf you call that AGI as many do or ASI, then we are not talking about the same thing. I'm talking about conversing with AI and being unable to tell if it's human or not in kind of a Turing Plus test. Turing Plus 9 would be 90% of humans can't tell if it's human or not. We're at Turing Plus 1. I can easily tell Claude Opus 4..5 is a machine by the mistakes it made. It's dumb as a box of rocks. That's how I define AGI and beyond to ASI
- rvz 9mo agoThis goes for any experienced senior SWE individual with a sharp attention to detail can easily tell if an AI wrote a project or not. Right now the definition of AGI has been hijacked so much that it can mean absolutely anything.
- nutjob2 9mo agoNo one has even given a rigorous definition of AGI. A prime environment for snake oil salesmen like Altman and Musk.
- dragonwriter 9mo ago> No one has even given a rigorous definition of AGI. No one has even given a rigorous definition of the I, much less the G qualifier.
- rvz 9mo agoWe are due for much more optimizations and new deep learning architectures rather than throwing more compute + RAM + money + GPUs + data at the problem, which you can do only for so long until a bottleneck occurs. Given that we have seen research from DeepSeek and Google on optimizing parts of the lower layers of deep neural networks, it's clear that a new form of AI needs to be created and I agree that LeCun will be proven right. Instead of borrowing tens of trillions to scale to a false "AGI".
- skybrian 9mo agoIt's too soon to say anything like that is proven. Sure, AGI hasn't been reached yet. I suspect there's some new trick that's needed. But the work going into LLM's might be part of the eventual solution.
- catigula 9mo ago> but it's increasingly looking like LeCunn is right. This is an absolutely crazy statement vis-a-vis reality and the fact that it’s so upvoted is an indictment of the type of wishful thinking that has grown deep roots here.
- D-Machine 9mo agoIf you are paying attention to actual research, guarded benchmarks, and understand how benchmarks are being gamed, I would say there is plenty of evidence we are approaching a clear plateau / the march-of-nines thesis of Karpathy is basically correct long-term. Short-term it remains to be seen how much more we can do with the current tech.
- gbnwl 9mo agoCan you point me to some of the actual research you're talking about? I'd love to read.
- D-Machine 9mo agoYour best bet would be to look deeply into performance on ARC-AGI fully-private test set performances (e.g. https://arcprize.org/blog/arc-prize-2025-results-analysis https://arcprize.org/blog/arc-prize-2025-results-analysis), and think carefully about the discrepancies here, or, just to broadly read any academic research on classic benchmarks and note the plateaus on classic datasets. It is very clear when you look at academic papers actually targeting problems specific to reasoning / intelligence (e.g. rotation invariance in images, adversarial robustness) that all the big companies are doing is just fitting more data / spending more resources on human raters and other things to boost performance on (open) metrics, but that clear actual gains in genuine intelligence are being made only by milking what we know very well to be a limited approach. I.e. there are trivially-basic problems that cannot be solved by curve-fitting models, which makes it clear most current advances are indeed coming from curve(manifold) fitting. It just isn't clear how far we can exploit these current approaches and in what domains this kind of exploitation is more than good enough. EDIT: Are people unaware Google Scholar is a thing? It is trivial to find modern AI papers that can be read without requiring access to a research institution. And e.g. HuggingFace collects trending papers (https://huggingface.co/papers/trending https://huggingface.co/papers/trending), and etc.
- stevenhuang 9mo ago> I've been following Yann for years and in my opinion he's been consistently right Lol. This is the complete opposite of reality. You realize lecun is memed for all his failed assertions of what LLMs cannot do? Look it up. You clearly have not been following closely, at all.
- labrador 9mo agoI meant he's held the line against those warning us about superintelligent AI soon destroying us all
- stevenhuang 9mo agoSure and that is fair. Seldom are extreme viewpoints likely scenarios anyways, but my disagreement with him stems from his unwarranted confidence in his own abilities to predict the future when he's already wrong about LLMs. He has zero epistemic humility. We don't know the nature of intelligence. His difficulties in scaling up his research is a testament to this fact. This means we really have no theoretical basis upon which to rest the claim that superintelligence cannot in principle emerge from LLM adjacent architectures--how can we make such a statement, when we don't even know what such thing looks like? We could be staring at an imperative definition of superintelligence and not know it, nevermind that approximations to such a function could in principle be learned by LLMs (universal approximation theorem). It sounds exceedingly unlikely, but would you rather be comforted by false confidence or be told the honest truth of what our current understanding of the sciences can tell us?
- anonzzzies 9mo ago> LLMs basically are a dead end Not sure if anyone who works in the foundational model space and who doesn't directly depend on LLMs 'making it' for VC money would claim differently. It is rather obvious at this point, but some companies are too far in and not cash rich enough so they have to keep the LLM dream alive.
- D-Machine 9mo ago> Not sure if anyone who works in the foundational model space and who doesn't directly depend on LLMs 'making it' for VC money would claim differently This is the problem. The vast majority of people over-hyping LLMs don't even have the most basic understanding of how simple LLMs are at core (manifold-fitting the semantic space of the internet), and so can't understand why they are necessarily dead ends, theoretically. This really isn't debatable for anyone with a full understanding of the training + basic dynamics of what these models do. But, practically, it remains to be seen where the dead end with LLMs lies. I think we are clearly approaching plateaus in both academic research and in practice (people forget or are unaware how much benchmarks are being gamed as well), but, even small practical gains remain game-changers in this space, and much of the progress / tradeoffs we actually care about can't be measured accurately yet (e.g. rapid development vs. "technical debt" from fast but not-understood / weakly-reviewed LLM code). LLMs are IMO undebatably a theoretical dead end, and for that reason, a practical dead end too. But we haven't hit that practical dead end yet.
- refulgentis 9mo agoWhy are LLMs a theoretical dead-end? I understand the "manifold-fitting the semantic space of the internet", but I don't understand "why they are necessarily dead ends, theoretically." If I had to steelman a counterargument, I'd handwave about RL and environments creating something greater-than the semantic space of the internet, and then highlight the part you mention where we haven't reached a practical dead-end. Maybe link out to the Anthropic interp work on them planning-in-advance via poking at activations when working on a rhyming poem.
- 9mo ago
- fooker 9mo agoBeing early is often the same as being wrong. LLMs could be a dead end, but aren't anywhere close to saturating the technology yet.
- songodongo 9mo agoWell, he sure is confident in himself with quotes like “you certainly don’t tell a researcher like me what to do” and “I’m a visionary”. Best of luck.
- D-Machine 9mo agoHe's right that LLMs are a dead end, but yeah, those quotes were cringe as hell. Hubris.
- websiteapi 9mo agohuman beings are estimated to use roughly 50 to 100W when idle (up to maybe 1000-2000W when exerting ourselves physically), and I think it's fair to say we're generally intelligent. Something is fundamentally missing with LLMs w.r.t. intelligence per watt. assuming gpt4 is around human intelligence, that needs 2-4 H100s, so roughly the same and that doesn't include the rest of the computer. That being said, we're willing to brute force our way to a solution to some extent so maybe it doesn't matter, but I say the fact that we don't use that much energy is proof enough we haven't perfected the architecture yet.
- leoh 9mo agoNot for inference, right?
- websiteapi 9mo agocorrect - h100 can do like 100 tokens per second on a gpt4 like model, but you'd need to account for regular fine-tuning to accurately compare to a person, hence 4 or so. of course the whole comparison is inane since computers and humans are obviously so different ha...
- dangus 9mo agoI think the more important point to bring up is “you can hire a human for minimum wage.” The median monthly salary in Bangladesh is cheaper than the Cursor Ultra plan. And Cursor loses money. An experienced developer in India makes around $20k.
- LogicFailsMe 9mo agoAt 5 cents or less per kWH these days, 10 kW is 50 cents per hour, well below minimum wage. LLMs aren't AGI and I'm not convinced we're anywhere close to AGI, but they are useful. That the people deploying them have the same product instincts as Microsoft executives seems to be the core issue.
- nutjob2 9mo agoBrains use approximately 20W.
- htrp 9mo agoReminder that it's in the lecun's interests to talk up AMI and to explain why they're going to win when they didn't do so at FAIR.
- mbac32768 9mo agoYann joins Ilya, Karpathy, Sutton + Carmack when he says LLMs are a dead end, though. Karpathy is probably the most careful not to write off LLMs entirely but he seems pretty skeptical.
- alyxya 9mo agoI don't get the anti-LLM sentiment because plenty of trends continue to show steady progress with LLMs over time. Sure, you can poke at some dumb things LLMs do as evidence of some fundamental issue, but the frontier capabilities continue to amaze people. I suspect the anti-LLM sentiment comes from people who haven't given a serious chance at seeing all the things they're capable of for themselves. I used to be skeptical, but I've changed my mind quite a bit over the past year, and there are many others who've changed their stance towards LLMs as well.
- nutjob2 9mo agoPeople who think that "steady progress" will continue forever have no basis for their assumption. You have a ad-hominem attack and your own personal anecdote with, which are not an argument for LLMs.
- alyxya 9mo agoIt'll steadily continue the same way Moore's law has continued for a while. I don't think people question the general trend in Moore's law besides the point where it's nearing the limit of physics. It's a lot harder to claim LLMs don't work as a universal claim, whereas claiming something is possible for LLMs only needs some evidence.
- nutjob2 9mo agoYes, LLMs will continue to progress until they hit the limits of LLMs. The idea that LLMs will reach AGI is entirely speculative, not least because AGI is undefined and speculative.
- stevenhuang 9mo agoLecun has already been proven wrong countless times over the years regarding his predictions of what LLMs can or cannot do. While LLMs continue to improve, he has yet to produce anything of practical value from his research. The salt is palpable, and for this he's memed for a reason.
- 9mo ago
- djmips 9mo agoLeCun is agist - he said a 65 year old man is too old to be a CEO.
- carolynmichael 9mo ago[dead]
- carolynmichael 9mo ago[dead]