I have been confused for a long time why FB is not motivated enough to invest in world models, it IS the key to unblock their "metaverse" vision. And instead they let go Yann LeCun.
I have been confused for a long time why FB is not motivated enough to invest in world models, it IS the key to unblock their "metaverse" vision. And instead they let go Yann LeCun.
LeCun wasn't producing results. He was obstinate and insistent on his own theories and ideas which weren't, and possibly aren't, going anywhere. He refused to engage with LLMs and compete in the market that exists, and spent all his effort and energy on unproven ideas and research, which split the company's mission and competitiveness. They lost their place as one of the top 4 AI companies, and are now a full generation behind, in part due to the split efforts and lack of enthusiastic participation by all the Meta AI team. If you look at the chaos and churn at the highest levels across the industry, there's not a lot of room for mission creep by leadership, and LeCun thoroughly demonstrated he wasn't suited for the mission desired by Meta.
I think he's lucky he got out with his reputation relatively intact.
To be fair, this was his job description: Fundamental AI Research (FAIR) lab. Not AI products division. You can't expect marketable products from a fundamental AI research lab.
It's "Facebook Artificial Intelligence Research", not fundamental. So basically involves both fundamental and applied research.
[1]: https://engineering.fb.com/category/ai-research/
Ref: Yann lecun post on linkedin, 3years ago: FAIR now stand for "Fundamental AI Research"
I literally linked the official site and it currently says Facebook. I have known FAIR for many years and I have always know it as Facebook. Can you link any official source of changing the name.
I gotcha. https://www.linkedin.com/posts/yann-lecun_big-changes-for-ai..., https://ai.meta.com/blog/fair-10-year-anniversary-open-scien...
Were you there or just an attentive outsider?
Most serious researchers want to work on interesting problems like reinforcement learning or robotics or RNN or dozen other avant-garde subjects. None want to work on "boring" LLM technology, requiring significant engineering effort and huge dataset wrangling effort.
This is true - Ilya got an exit and is engaged in serious research, but research is by its nature unpredictable. Meta wanted a product and to compete in the AI market, and JEPA was incompatible with that. Now LeCun has a lab and resources to pursue his research, and Meta has refocused efforts on LLMs and the marketplace - it remains to be seen if they'll be able to regain their position. I hope they do - open models and relatively open research are important, and the more serious AI labs that do this, the more it incentivizes others to do the same, and keeps the ones that have committed to it honest.
Attentive outsider and acquaintance of a couple people who are or were employed there. Nothing I'm saying is particularly inside baseball, though, it's pretty well covered by all the blogs and podcasts.
What podcast?
Machine Learning Street Talk and Dwarkesh are excellent. Various discord communities, forums, and blogs downstream of the big podcasts, and following researchers on X keeps you in the loop on a lot of these things, and then you can watch for random interviews and presentations on youtube when you know who the interesting people and subjects are.
It's insane that you can argue this in a world where facebook continues to be state of the art (and it's not even close) on semantic segmentation. Those SAM models they produce deliver more value than a hypothetical competitive llama5 model coming out tomorrow.
I'm banning my wife from ever buying any Alexander Wang clothing, because his leadership is so poor in comparison that he's going to also devalue the name-collision fashion brand that he shares a name with. That's how bad his leadership is going to be in comparison to Yann. Scale AI was only successful for the same reason Langchain was. Easy to be a big fish in a pond with no other fishes.
In an industry of big bets, especially considering the company has poured resources and renamed itself to secure a place in the VR world... staking your reputation on everyone's LLMs having peaked and shifting focus to finding a new path to AI is a pretty interesting bet, no?
This sounds similar to the arc of Carpathy, who also managed to preserve his reputation despite sending Tesla down a FSD deadend and missing the initial LLM boat.
Since a hot take is as good as the next one: LLMs are by the day more and more clearly understood as a "local maximum" with flawed capabilities, limited efficiency, a $trillion + a large chunk of the USA's GDP wasted, nobody even turning a profit from that nor able to build something that can't be reproduced for free within 6 months.
When the right move (strategically, economically) is to not compete, the head of the AI division acknowledging the above and deciding to focus on the next breakthrough seems absolutely reasonable.
You really need to be obstinate in your convictions if you can dismiss LLMs at the time when everyone's job is being turned around by them. Everywhere I look, everyone I talk to, is using LLMs more and more to do their job and dramatically increase their productivity. It's one of the most successful technologies I've ever witnessed arriving on the market, and it's only just started- it's just three years old.
What are you seeing people do with it? To my eyes everyone is in the same amount of meetings lol.
For one, since last month, AI is writing about 95% of my code and that of my colleagues. I just describe what I want and how it should be implemented and the AI takes care of all the details, solves bugs, configuration issues, etc. Also I use it to research libraries, dig into documentation (and then write implementations based on that), discuss architectural alternatives, etc.
Non-developers I know use them to organise meetings, write emails, research companies, write down and summarise counselling sessions (not the clients, the counselor), write press reports, help with advertising campaigns management, review complex commercial insurance policies, fix translations... The list of uses is endless, really. And I'm only talking of work-related usage, personal usage goes of course well beyond this.
> You really need to be obstinate in your convictions if you can dismiss LLMs at the time when everyone's job is being turned around by them.
I'm factual. You are the one with the extraordinary claim that LLMs will find new substantial markets/go through transformative breakthrough.
> Everywhere I look, everyone I talk to, is using LLMs
And everywhere I look, I don't. It might be the case that you stand right in the middle of an LLMs niche. Never did I say that one doesn't exist or that LLMs are inadequate at parroting existing code.
> Non-developers I know use them […]
among those are:
- things that have nothing to do with LLMs/AI
- things that you should NOT use LLMs for the reason that they will give you confidently wrong and/or random answers (because it's not in their training data/cut-off window, it's non-public information, they don't have the computing abilities to produce meaningful results)
- things that are low-value/low-stakes for which people won't be willing to pay for when asked to
> The list of uses is endless
no, it is not
> And I'm only talking of work-related usage
and we will get to see rather sooner than later how much business actually value LLMs when the real costs will be finally passed on to them.
> things that have nothing to do with LLMs/AI
These are things that have to do with intelligence. Human or LLM doesn't matter.
> things that you should NOT use LLMs for / parroting existing code / not in their training data/cut-off window, it's non-public information, they don't have the computing abilities to produce meaningful results
Sorry, but I just get the picture that you have no clue of what you're talking about- though most probably you're just in denial. This is one on the most surprising things about the emergence of AI: the existence of a niche of people that is hell-bent on denying its existence.
> intelligence. Human or LLM doesn't matter.
Being enthusiastic about a technology isn't incompatible with objective scrutiny. Throwing-up an ill-defined "intelligence" in the air certainly doesn't help with that.
Where I stand is where measured and fact-driven (aka. scientists) people do, operating with the knowledge (derived from practical evidence¹) that LLMs have no inherent ability to reason, while making a convincing illusion of it as long as the training data contains the answer.
> Sorry, but I just get the picture that you have no clue of what you're talking about- though most probably you're just in denial.
This isn't a rebuttal. So, what is it? An insult? Surely that won't help make your case stronger.
You call me clueless, but at least I don't have to live with the same cognitive dissonances as you, just to cite a few:
- "LLMs are intelligent, but when given a trivially impossible task, they happily make stuff up instead of using their `intelligence` to tell you it's impossible"
- "LLMs are intelligent because they can solve complex highly-specific tasks from their training data alone, but when provided with the algorithm extending their reach to generic answers, they are incapable of using their `intelligence` and the supplemented knowledge to generate new answers"
¹: https://arstechnica.com/ai/2025/06/new-apple-study-challenge...
> This isn't a rebuttal.
I don't really think it's possible to convince you. Basically everyone I talk to is using LLMs for work, and in some cases- like mine- I know for a fact that they do produce enormous amounts of value- to the point that I would pay quite some money to keep using them if my company stopped paying for them.
Yes LLMs have well known limitations, but at they're still a brand new technology in its very early stages. ChatGPT appeared little more than three years ago, and in the meantime it went from barely useful autocomplete to writing autonomously whole features. There's already plenty of software that has been 100% coded by LLMs.
"Intelligence", "understanding", "reasoning".. nobody has clear definitions for these terms, but it's a fact that LLMs in many situations act as if they understood questions, problems and context, and provide excellent answers (better than the average human). The most obvious is when you ask an LLM to analyse some original artwork or poem (or some very recent online comic, why not?)- something that can't be in its training data- and they come up with perfectly relevant and insightful analyses and remarks. We don't have an algorithm for that, we don't even begin to understand how those questions can be answered in any "mechanical" sense, and yet it works. This is intelligence.
You know what this reminds me of? Language X comes out (e.g., Lisp or Haskell), and people try it, and it's this wonderful, magical experience, and something just "clicks", and they tell everyone how wonderful it is.
And other people try it - really sincerely try it - and they don't "get it". It doesn't work for them. And those who "get it" tell those who don't that they just need to really try it, and keep trying it until they get it. And some people never get it, and are told that they didn't try enough (and also it gets implied that they are stupid if they really can't get it).
But I think that at least part of it is in how peoples' brains work. People think in different ways. Some languages just work for some people, and really don't work very well for other people. If a language doesn't work for you, it doesn't mean either that it's a bad language or that you're stupid (or just haven't tried). It can just be a bad fit. And that's fine. Find a language that fits you better.
Well, I wonder if that applies to LLMs, and especially to LLMs doing coding. It's a tool. It has capabilities, and it has limitations. If it works for you, it can really work for you. And if it doesn't, then it doesn't, and that doesn't mean that it's a bad tool, or that you are stupid, or that you haven't tried. It can just be a bad fit for how you think or for what you're trying to do.
> You know what this reminds me of? Language X comes out (e.g., Lisp or Haskell), and people try it, and it's this wonderful, magical experience, and something just "clicks", and they tell everyone how wonderful it is.
I can relate to this. And I can understand that, depending on how and what you code, LLMs might have different value, or even none. Totally understand.
At the same time.. well, let's put it this way. I've been fascinated with programming and computers for decades, and "intelligence", whatever it is, for me has always been the holy grail of what computers can do. I've spent a stupid amount of time thinking about how intelligence works, how a computer program could unpack language, solve its ambiguities, understand the context and nuance, notice patterns that nobody told it were there, etc. Until ten years ago these problems were all essentially unsolved, despite more than half a century of attempts, large human curated efforts, funny chatbots that produced word salads with vague hints of meaning and infuriating ones that could pass for stupid teenagers for a couple of minutes provided they selected sufficiently vague answers from a small database... I've seen them all. In 1968's A Space Odyssey there's a computer that talks (even if "experts prefer to say that it mimics human intelligence") and in 2013's Her there's another one. In between, in terms of actual results, there's nothing. "Her" is as much science fiction as it is "2001", with the aggravating factor that in Her the AI is presented as a novel consumer product: absurd. As if anything like that were possible without a complete societal disruption.
All this to say: I can't for the life of me understand people who act blasé when they can just talk to a machine and the machine appears to understand what they mean, doesn't fall for trivial language ambiguities but will actually even make some meta-fun about it if you test them with some well known example; a machine that can read a never-seen-before comic strip, see what happens in it, read the shaky lettering and finally explain correctly where the humour lies. You can repeat to yourself a billion times "transformers something-something" but that doesn't change the fact that what you are seeing is intelligence, that's exactly what we always called intelligence- the ability to make sense of messy inputs, see patterns, see the meanings behind the surface, and communicate back in clear language. Ah, and this technology is only a few years old- little more than three if we count from ChatGPT. These are the first baby steps.
So it's not working for you right now? Fine. You don't see the step change, the value in general and in perspective? Then we have a problem.
Isn't it more like this: JEPA looks at the video, "a dog walks out of the door, the mailman comes, dog is happy" and the next frame would need to look like "mailman must move to mailbox, dog will run happily towards him", which then an image/video generator would need to render.
Genie looks at the video, "when this group of pixels looks like this and the user presses 'jump', I will render the group different in this way in the next frame."
Genie is an artist drawing a flipbook. To tell you what happens next, it must draw the page. If it doesn't draw it, the story doesn't exist.
JEPA is a novelist writing a summary. To tell you what happens next, it just writes "The car crashes." It doesn't need to describe what the twisted metal looks like to know the crash happened.
You are beyond correct. World models is what saves their Reality Labs investment. I would say if Reality Labs cannot productize World Models, then that entire project needs to be scrapped.
Is Project Genie a "world model" as defined by Yann LeCun? Doesn't "world model" mean that the model generates things from a theory of the world, rather than the colloquial meaning of generating 3d navigable scenes (using a temporal ViT or whatever)?
Failures are not publicly reported, in general. Do you we know what they have invested in?
Most people don't like putting on VR headsets, no matter what the content is. It just never broke out of the tech enthusiast niche.