LeCun calls Alex Wang inexperienced, predicts more Meta AI employee departure

https://www.businessinsider.com/yann-lecun-alexandr-wang-criticism-inexperienced-meta-ai-future-2026-1

by _____k • 7 months ago

48 61 7 months ago

labrador5• 7 months 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.

aspenmartin3• 7 months ago

More 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.

labrador5• 7 months ago

As 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• 7 months 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?

throw3108222• 7 months 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.

labrador5• 7 months ago

If 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

nutjob26• 7 months ago

Nothing you say proves or indicates that progress will continue indefinitely.

Your argument says we should have flying cars by now, because they kept on getting better.

LeCun says LLMs do text processing so won't scale to AGI, just like a faster can never fly (controllably).

rvz2• 7 months ago

We 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".

labrador5• 7 months ago

This seems so obvious to me that scaling advocates seem to be exhibiting a form of magical thinking

skybrian• 7 months ago

It'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.

catigula2• 7 months 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-Machine10• 7 months ago

If 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.

stevenhuang3• 7 months 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.

labrador5• 7 months ago

I meant he's held the line against those warning us about superintelligent AI soon destroying us all

anonzzzies• 7 months 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-Machine10• 7 months 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• 7 months ago

Why 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.

tim333• 7 months ago

I'm not sure about the dead end thing because you may be able to add on to them?

In human terms LLMs seem similar to talking without thinking but we can also think as a separate activity to waffling on.

In AI research terms, DeepMind have done some interesting things with Mind Evolution and AlphaEvolve, the latter being the one that came up with a more efficient matrix multiplication algorithm.

(https://deepmind.google/research/publications/122391/

https://deepmind.google/blog/alphaevolve-a-gemini-powered-co...)

aspenmartin3• 7 months ago

I agree but I think to be fair it seems that there’s an open question of just how much more we can get from scaling / tricks. I would assume that there’s agreement that e.g. continual learning just won’t be solved without a radical departure from the current stack. But even with all of the baggage we have right now, if you believe extrapolations we have ~2 GPT4->5 sized leaps before everyone has to get out of the pool

fooker• 7 months ago

Being 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• 7 months ago

Well, 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-Machine10• 7 months ago

He's right that LLMs are a dead end, but yeah, those quotes were cringe as hell. Hubris.

websiteapi2• 7 months ago

human 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• 7 months ago

Not for inference, right?

websiteapi2• 7 months ago

correct - 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• 7 months ago

I 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• 7 months ago

At 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.

nutjob26• 7 months ago

Brains use approximately 20W.

boroboro4• 7 months ago

This being said in this setup of 2-4 h100 you’ll be able to generate with batch size of somewhere around 128 ie its 128 humans and not one. And just like that difference in efficiency isn’t that high anymore.

htrp• 7 months ago

Reminder 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• 7 months ago

Yann 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.

alyxya2• 7 months ago

I 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.

nutjob26• 7 months ago

People 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.

alyxya2• 7 months ago

It'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.

stevenhuang3• 7 months ago

Lecun 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.

D-Machine10• 7 months ago

Or, people who've actually trained and used models in domains where "stuff on the internet" is of no relevance to what you are actually doing realize the profound limitations to what these LLMs actually do. They are amazing, don't get me wrong, but not so amazing in many specific contexts.

djmips2• 7 months ago

LeCun is agist - he said a 65 year old man is too old to be a CEO.

LeCun calls Alex Wang inexperienced, predicts more Meta AI employee departure

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