Most of the folks on this topic are focused on Meta and Yann’s departure. But, I’m seeing something different.
This is the weirdest technology market that I’ve seen. Researchers are getting rewarded with VC money to try what remains a science experiment. That used to be a bad word and now that gets rewarded with billions of dollars in valuation.
“It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, ‘So we’re doing an AI company with the best AI people, but we can’t answer any questions.’”
Despite that vagueness, Murati raised $2 billion in funding...
From a certain angle, this is the market correcting towards the abstraction.
Between inflation, fiscal capture, and the inane plethora of ridiculous financial vehicles that are used to move capital around these days, the argument could be made that the money was already funny. This is just the drop of the final veil, saying "well it's not like these numbers mean anything anymore. I do have enough yachts. Fuck it, see what you can do with it".
That's been true for the last year or two, but it feels like we're at an inflection point. All of the announcements from OpenAI for the last couple of months have been product focused - Instant Checkout, AgentKit, etc. Anthropic seems 100% focused on Claude Code. We're not hearing as much about AGI/Superintelligence (thank goodness) as we were earlier this year, in fact the big labs aren't even talking much about their next model releases. The focus has pivoted to building products from existing models (and building massive data centers to support anticipated consumption).
If Claude Code is Anthropic’s main focus why are they not responding to some of the most commented issues on their GitHub? https://github.com/anthropics/claude-code/issues/3648 has people begging for feedback and saying they’re moving to OpenAI, has been open since July and there are similar issues with 100+ comments.
> Researchers are getting rewarded with VC money to try what remains a science experiment. That used to be a bad word
I’ve worked for multiple startups and I’ve watched startup job boards most of my career.
A lot of VC backed startups have a founder with a research background and are focused on providing out some hypothesis. I don’t see anything uncommon about this arrangement.
If you live near a University that does a lot of research it’s very common to encounter VC backed startups that are trying to prove out and commercialize some researcher’s experiment. It’s also common for those founders to spend some time at a FAANG or similar firm before getting VC funded.
Yeah, but Sutskever and Murati wouldn't even tell investors what they were working on, and LeCun only has a long-term research direction - not any breakthrough or prototype to commercialize.
Certainly research has made it into product with the help of the innovators that created the research. The dial is turned further here where the research ideas have yet to be tried and vetted. The research begins in the startup. Even in the dotcom era, the research prototypes were vetted in the conferences and journals before taking the risk to build production systems. This is no longer the case. The experiments have yet to be run.
I personally see this as a positive trend. VC in its earliest form was concerned with experiments that had high technology risk. I am thinking of companies like Genentech and scientists like biochemist Herbert Boyer, who had pioneered recombinant DNA technology.
After that, VC had become more like PE, investing in stuff that was working already but needed money to scale.
This is VCs FOMOing as global-economy-threatening levels of leverage are being bet on an AI transformation that, by even the most optimistic estimates, cannot achieve a tiny portion of the required ROI in the required time.
Yeah there has been some lamenting at all the money being thrown at technology hasn't been for anything truly game changing, basically just variations of full stack apps. A few failed mooonshots might be more interesting at least.
I agree, if anything spending money on high technology risk is Silicon Valley going back to its roots.
Nobody had a way to do silicon transistor manufacturing at scale until the traitorous eight flipped Shockley the bird and took a $1.4M seed investment from Sherman Fairchild.
Big bets on uncertain technology is what tech is supposed to be about.
> This is the weirdest technology market that I’ve seen.
You must have not lived through the dot com boom. There was almost everything under the sun was being sold under a website that started with an "e". ePets, ePlants, eStamps, eUnderwear, eStocks, eCards, eInvites.....
Those things all worked, and all of those products still exist in one form or another. It was a business question of who would provide it, not a technology question.
It's funny that the Netherlands seems to still live in the dotcom boom to this day. Want to adopt a pet? verhuisdieren.nl. Want to buy wall art? wall-art.nl. Need cat5 cable? kabelshop.nl. 8/10 times there is a (legit) online store for whatever you need, to the point where one of the local e-commerce giants (Coolblue) buys this type of domain and aliases them to their main site.
It did make sense though. ePlants could have cornered the online nursery market. That is a valuable market. I think people were just too early. Payment and logistics hasn’t been figured out yet.
Agree on weirdness but not on the idea of funding science experiments:
>> away from long-term research toward commercial AI products and large language models - LLMs
This feels more like what I see every day: the people in charge desperately looking for some way - any way - to capitalize on the frenzy. They're not looking to fund research; they just want to get even richer. It's pets.ai this time.
This doesn’t feel that new or surprising to me, although I suppose it depends what you consider the line between “science experiment” and “engineering R&D” to be.
Biotech has been a YC darling. Was Ginkgo Bioworks not doing science experiments?
Clean energy was a big YC fad roughly 15 years ago. Billions were invested towards scientific research into biofuels, solar, etc.
I can’t help but wonder: if we had poured the same amount of money into fusion energy research and development, how far might we have come in just three short years?
The minimum cost of capital just to run fusion experiments is probably $100m. And the power bills are probably almost as high as the ones from OpenAI, which is to say, they are the highest power bills in the history of mankind ...
If a "science experiment" has the chance to displace most labor then whoever's successful at the experiment wins the economy, period. There's nothing weird or surprising about the logic of them obsessively chasing it. They all have to, it's a prisoner's dilemma.
Technology know-how spreads rapidly, so no need to be first. Look how fast Google caught up with Gemini when they chose to, or how fast X.ai developed Grok.
Maybe it's cheap insurance to invest in, say, LeCun just in case JEPA or the animal intelligence approach takes off, but if it does show significant signs of progress there'd also be opportunity to invest later, or in one of the dozen copycats that will emerge. In the end it'll be the giants like Google and Microsoft that will win.
Fusion power has the chance to displace most power generation, and whoever is successful at the experiment wins the energy economy, period. However given the long timelines, high cost of research, and the unanswered technical questions around materials that can withstand neutron flux, the total 2024 investment into fusion is only around $10B, versus AI's 250+B.
This looks more like a return to form than anything.
The first ventures were funding voyages to a New World thousands of miles away, essentially a different planet as far as the people then were concerned.
Venture capital for a new B2B application is playing it safe as far as risk capital goes
If you think about Theranos, Magic leap, openai, anthropic they are all the same, one idea thats kinda plausible (well if you don't look too closely), have a slick demo, and well connected founders.
Much as a lot of people dislike LeCun (just look at the blind posts about him) he did run and setup a very successful team inside meta, well nominally at least.
You're right to feel like you're seeing something different. You are. But you're mistaking the symptom for the disease.
That's because you're trying to make sense of it as a technology market. It's not. It's a resource extraction market, and the VCs are the ones running the logging operation. Their sole mission is to find a dependable way to strip a forest bare, and they've been using the same playbook for decades.
Those "science experiments" you're talking about? They aren't the product. They're the story, the sizzle. They are the disposable lighter used to start the fire; the VCs have no intention of keeping it lit forever. The real tool is the chainsaw, and the "science experiment" is the brand name printed on the side.
Think of it as clear-cutting. The dot-com bubble was one forest. The story then was that a company losing millions selling pet food online was a "new economy" giant because it had "eyeballs." That was the sales pitch for the chainsaw. VCs funded hundreds of these operations, created a frenzy, and took the most plausible-sounding ones public. The IPO wasn't a milestone; it was the moment they sold the timber and exited the forest, leaving the stumps and worthless pulp for the pension funds and retail investors.
The "long-term" part of their strategy isn't about the health of any single tree or company. It's about finding the next forest to clear-cut. After dot-coms, it was social media. Now, it's the AI forest. They aren't betting on AI; they're betting on their ability to sell the world on the idea that this particular forest is magical and will grow forever.
So you're right, what you're seeing is weird. But it's not a new kind of weirdness. It's the oldest story in finance. A bubble being inflated so the smart money can cash out, leaving everyone else to marvel at the fancy new chainsaw after the forest is already gone.
It makes sense, it’s a simple expected value calculation.
There are trillions of labor dollars that can be replaced by software. The US alone has almost $12 trillion of labor annually.
If an AI company has a 10% shot of developing a product that can replace 10% of it, they are worth $120 billion in expected value. (These numbers are obviously just for illustration).
The unprecedented numbers are a simple function of the unprecedented market size. Nobody has ever had a chance of creating trillions of dollars of economic value in a handful of years before.
>If an AI company has a 10% shot of developing a product that can replace 10% of it, they are worth $120 billion in expected value.
that's not how profits work. Companies don't get paid for the value they create but for the value they can capture, otherwise the ffmpeg people would already be trillionaires.
If you have a dozen companies making the same general purpose technology, not product, your only hope is being able to slap ads on top of it, which is why they're so keen on targeting consumers rather than trying to automate jobs.
Having raised more than $100M myself, I’m not sure I would call VC money a reward. However, VC money should be allocated in part to massive upside science experiments. PE money is focused on things already figured out.
Has someone done a survey to ask devs on how much they are getting done vs what their managers expect with AI? I've had conversations with multiple devs in big orgs telling me that Managers and dev's expectations are seriously out of sync. Basically its
Manager: Now you "have" AI, release 10 features instead of 1 in the next month.
Devs: Spending 50% more working hours to make AI code "work" and deliver 10.
I think that's a good thing and VC getting back to it's roots. I'm glad that scientists doing AI are getting big money and don't know exactly what the product will be rather than some business person with a slick deck and hockey stick charts.
> Researchers are getting rewarded with VC money to try what remains a science experiment.
That's not all that new. Commercial fusion power startups are an example. I think the first one was General Fusion, founded in 2002. Today, there are around 50 of them. Every single one of those "remains a science experiment", and probably has much lower chance of success than some of the AI science experiments.
Of course, fusion startups have apparently "only" received about $10 bn in funding to date, so pale in comparison to the overall AI market. But if you just look at the AI "science experiments", it's possible the amounts would be comparable.
If a science experiment works and is transformational can be worth a trillion dollars, how much is it worth if it has a 5% chance of being transformational?
Get the popcorn ready for when that all implodes. Most of these folks getting funding don’t have the slightest clue on how to build a sustainable business.
When the bubble pops, and it’s very close to popping, there’s going to be a lot of burning piles of cash with no viable path to reviver that money.
Yes - I had similar thoughts when I saw the word "startup" used alongside something so far-out (same 'critique" should apply to Fei-Fei Li's World Labs - https://www.worldlabs.ai). These are VC-funded research labs (and there is nothing wrong with tat). Calling them "startups" as if they are already working on an MVP on top of an unproven (and frankly non-existent) technology seems a little disingenuous to me.
Because when the recipe is open and public, the product's success depends on Distribution (which has been cornered by MS, Google, Apple). This is good for the ecosystem but not sure how those particular VCs will get exits.
Very few startup products depend on distribution by Microsoft / Google / Apple. You're really just talking about a limited set of mobile or desktop apps there. Everything else is wide open. Kailera Therapeutics isn't going to live or die based on what the tech giants do.
Yeah, that's quite unusual. Buisness was always terrible at being innovative, always dared to take only the safest and most minute of bets and the progress of technology was always paid for by the taxpayers. Business usually stepped in only later, when technology was ready and did what it does best, opimize manufacturing and put it in the hands of as many consumers as possible rakink in billions.
I wonder what changed. Does AI look like a safe bet? Or does every other bet seem to not have any reasonable return?
Making LeCun report to Wang was the most boneheaded move imaginable. But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
In industry research, someone in a chief position like LeCun should know how to balance long-term research with short-term projects. However, for whatever reason, he consistently shows hostility toward LLMs and engineering projects, even though Llama and PyTorch are two of the most influential projects from Meta AI. His attitude doesn’t really match what is expected from a Chief position at a product company like Facebook. When Llama 4 got criticized, he distanced himself from the project, stating that he only leads FAIR and that the project falls under a different organization. That kind of attitude doesn’t seem suitable for the face of AI at the company. It's not a surprise that Zuck tried to demote him.
These are the types that want academic freedom in a cut-throat industry setup and conversely never fit into academia because their profiles and growth ambitions far exceed what an academic research lab can afford (barring some marquee names). It's an unfortunate paradox.
I would pose a question differently, under his leadership did Meta achieve good outcome?
If the answer is yes, then better to keep him, because he has already proved himself and you can win in the long-term. With Meta's pockets, you can always create a new department specifically for short-term projects.
If the answer is no, then nothing to discuss here.
Meta had a two prong AI approach - product-focused group working on LLMs, and blue-sky research (FAIR) working on alternate approaches, such as LeCun's JEPA.
It seems they've given up on the research and are now doubling down on LLMs.
LLM hostility was warrented. The overhype/downright charlartan nature of ai hype and marketing threatens another AI winter. It happened to cybernetics, it'll happen to us too. The finance folks will be fine, they'll move to the next big thing to overhype, it is the researchers who suffer the fall-out. I am considered anti LLM (transformers anyway) for this reason, i like the the architecture, it is cool amd rather capable at its problem set, which is a unique set, but, it isnt going to deliver any of what has been promised, any more than a plain DNN or a CNN will.
It's very hard (and almost irreconcilable) to lead both Applied Research -- that optimizes for product/business outcomes -- and Fundamental Research -- that optimizes for novel ideas -- especially at the scale of Meta.
LeCun had chosen to focus on the latter. He can't be blamed for not having taken the second hat.
This is the right take. He is obviously a pioneer and much more knowledgeable than Wang in the field, but if you don't have the product mind to serve company's business interest in short term and long term capacity anymore, you may as well stay in academia and be your own research director, let alone a chief executive in one of the largest public companies
LeCun truly believes the future is in world models. He’s not alone. Good for him to now be in the position he’s always wanted and hopefully prove out what he constantly talks about.
Yann was in charge of FAIR which has nothing to do with llama4 or the product focussed AI orgs. In general your comment is filled with misrepresentations. Sad.
I totally agree. He appeared to act against his employer and actively undermined Meta's effort to attract talent by his behavior visible on X.
And I stopped reading him, since he - in my opinion - trashed on autopilot everything 99% did - and these 99% were already beyond the two standard deviation of greatness.
It is even more highly problematic if you have absolutely no results eg products to back your claims.
tbf, transformers from more of a developmental perspective are hugely wasteful. they're long-range stable sure, but the whole training process requires so much power/data compared to even slightly simpler model designs I can see why people are drawn to alternative complex model designs down-playing the reliance on pure attention.
Yeah I think LeCun is underestimating the impact that LLM's and Diffusion models are going to have, even considering the huge impact they're already having. That's no problem as I'm sure whatever LeCun is working on is going to be amazing as well, but an enterprise like Facebook can't have their top researcher work on risky things when there's surefire paths to success still available.
I agree. I never understood LeCun's statement that we need to pivot toward the visual aspects of things because the bitrate of text is low while visual input through the eye is high.
Text and languages contain structured information and encode a lot of real-world complexity (or it's "modelling" that).
Not saying we won't pivot to visual data or world simulations, but he was clearly not the type of person to compete with other LLM research labs, nor did he propose any alternative that could be used to create something interesting for end-users.
LLMs get results is quite the bold statement.
If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises.
If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV.
LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound to fail in adoption unless you convince everyone that they don't need precision and accuracy, but they can do their business at 75% quality, just with less human overhead.
It's quite the thing to convince people of, and that's why it needs the spend it's needing. A lot of we-need-to-stay-in-the-loop CEOs and bigwigs got infatuated with the idea, and most probably they just had their companies get addicted to the tech equivalent of crack cocaine.
A reckoning is coming.
Where is any proof that Yann LeCun is able to deliver that? He's had way more resources than any other lab during his tenure, and yet has nothing substantial to show for it.
> But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
When did they make groundbreaking foundation models though? DeepMind and OpenAI have done plenty of revolutionary things, what did Meta AI do while being led by LeCun?
I suppose they could solve superintelligence and cure cancer and build fusion reactors with it, but that's 100% outside their comfort zone - if they manage to build synthethic conversation partners and synthethic content generators as good or better than the real thing the value of having every other human on the planet registered to one of their social network goes to zero.
Which is impossible anyway - I facebook to maintain real human connections and keep up with people who I care about, not to consume infinite content.
At 1.6T market cap it's very hard to 10x or greater the company anymore doing what's in their comfort zone and they've got a lot of money to play with to find easier to grow opportunities. If Zuckerberg was convinced he could do that by selling toothpicks they'd have a go at the toothpick business. They went after the "metaverse" first, then AI. Both are just very fast growth options which happen to be tech focused because that's the only way you generate new comparable value as a company (unless you're sitting on a lot of state owned oil) in the current markets.
they are out for your clicks and attention minutes
if OpenAI can build a "social" network of completely generated content, that can kill Meta. Even today I venture to guess that most of the engagements in their platforms is not driven by real friends, so an AI driven platform won't be too different, or it might make content generation be so easy as to make your friends engage again.
Apart from it the ludicrous vision of the metaverse seems much more plausible with highly realistic world models
Zuck did this on purpose, humiliating LeCun so he would leave.
Despite LeCun being proved wrong on LLMs capabilities such as reasoning, he remained extremely negative, not exactly inspiring leadership to the Meta Ai team, he had to go.
But LLMs still can't reason... in a reasonable sense. No matter how you look at it, it is still a statistical model that guesses next word, it doesn't think/reason per se.
Zuckerberg knows what he wants but he rarely knows how to get it. That's been his problem all along. Unlike others he isn't scared to throw ridiculous amounts of money at a problem though and buy companies who do things he can't get done himself.
There's also the aspect of control - because of how the shares and ownership are organized he answers essentially to no one. In other companies burning this much cash as was with VR or now AI without any sensible results would get him ejected a long time ago.
No, it was because LeCun had no talent for running real life teams and was stuck in a weird place where he hated LLMs. He frankly was wasting Meta’s resources. And making him report to Wang was a way to force him out.
It wasn’t boneheaded. It was done to make Yann leave. Meta doesn’t want Yann for good reason.
Yann was largely wrong about AI. Yann coined the term stochastic parrot and derrided LLMs as a dead end. It’s now utterly clear the amount of utility LLMs have and that whatever these LLMs are doing it is much more than stochastic parroting.
I wouldn’t give money to Yann, the guy is a stubborn idiot and closed minded. Whatever he’s doing wont even touch LLM technology. He was so publicly deriding LLMs I see no way he will back pedal from that.
I dont think LLMs are the end of the story for agi. But I think they are a stepping stone. Whatever agi is in the end, LLMs or something close to it will be a modular component of aspect of the final product. For LeCunn to dismiss even the possibility of this is idiotic. Horrible investment move to give money to Yann to likely pursue Agi without even considering LLMs.
A few people mentioned Meta burning through people like LeCun, Carmack and Luckey. We give a lot of credit to individuals in our society. At the same time, there's a frequent pattern of very successful people changing fields, organizations or general environments and suddenly looking very ordinary. In a way, this is a very strong argument to let people settle into a place they can be happiest in. A few examples:
* This is seen very often in Formula One. Schumacher when he went from Ferrari to Mercedes (after a short sabbatical), Vettel from Red Bull to Ferrari, Raikkonen from Lotus to Ferrari (his second stint), Hamilton from Mercedes to Ferrari, Perez at McLaren. There's a lot of Ferrari here so maybe that's the confounding factor.
* This also happens when physicists changed fields. They generally can't replicate their earlier success. Dirac, Feynman, Schwinger, Einstein all went through a transition like this. One explanation is that their early success was precisely so unusual (for anyone) that it would be hard to replicate in general.
* In my experience, this happens at companies too. Whenever we hired a "rockstar" from another company, they would generally struggle (across multiple companies I have been at). This could partly be a result of sabotage from a few vested interests at the new company. But often, it's hard to adjust to a new environment in a short amount of time.
The converse also happens. Sometimes a person considered ordinary goes to a different environment and flourishes. Palmer Luckey has been very successful at Anduril. Stephen Smale was almost failing out of his math PhD program but suddenly started flourishing in his third year IIRC and eventually got a fields medal. Ed Witten experimented with economics, history, linguistics, applied math before switching to physics in his second year and suddenly started making rapid progress.
This is not a very rigorous observation and I am missing many confounding factors.
I see a lot of criticism of LeCun and his views on LLMs as well as his inability to "deliver" products. I don't think that's what he cares about at all. His prominence led to him being picked by Meta. It was a chance to get massive resources that he couldn't get at NYU and the chance to work with smart people outside academia. The pay probably didn't hurt either. In return, Meta became a magnet for smart ML researchers and engineers. If I permit myself to speculate about his thoughts when he took the job, he had no intention of committing to product timelines and generating revenue. Now that Zuckerberg has clearly committed to something he like i.e. building a new product line and expanding the business, it was only a matter of time before LeCun would feel left out and under-resourced.
Interestingly, Yoshua Bengio is the only one who hasn't given into industry even though he could easily raise a lot of money.
I feel like LeCun has been plainly wrong about LLMs. He has been insisting that the stochastic nature of sampling tokens causes a non-zero hallucination property for any given next token such that as output length increases, this will inevitably converge towards garbage.
The reality is that while LLMs can make mistakes mid-output, those interim mistakes don't necessarily detract from the model's final output. We see a version of this all the time with agents as they make tactical mistakes but quickly backtrack and ultimately solve the root problem.
It really felt like LeCun was willing to die on this hill. He continued to argue about really pedantic things like the importance researchers, etc.
I'm glad he's gone and hopeful Meta can actually deliver real AI products for their users with better leadership.
I am a big fan of using LLMs although in my own limited way. I don't work at Meta and don't feel strongly about him leaving or staying there.
It's possible that he will turn out to be correct in the long run. From his viewpoint, the primary goal is research and any usefulness of intermediate advances is maybe (speculating) "beneath him". If this is the case, I completely understand why a corporation would want to eject him. LeCun probably sees the pretty amazing developments since ChatGPT first came out as incremental hacks. I am neutral about this aspect too. Maybe they are but the hacks have been useful to me.
Eventually this feels like the correction of a real misalignment between LeCun/FAIR and Meta. Hopefully now, they can both focus on what they are good at. I must admit that I have great sympathy for open-ended research but industry has always been fickle about it. That's where the government and universities are supposed to play a key role.
LeCun, who's been saying LLMs are a dead end for years, is finally putting his money where his mouth is. Watch for LeCun to raise an absolutely massive VC round.
Good. The world model is absolutely the right play in my opinion.
AI Agents like LLMs make great use of pre-computed information. Providing a comprehensive but efficient world model (one where more detail is available wherever one is paying more attention given a specific task) will definitely eke out new autonomous agents.
Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.
I wish I could help, world models are something I am very passionate about.
One theory of how humans work is the so called predictive coding approach. Basically the theory assumes that human brains work similar to a kalman filter, that is, we have an internal model of the world that does a prediction of the world and then checks if the prediction is congruent with the observed changes in reality. Learning then comes down to minimizing the error between this internal model and the actual observations, this is sometimes called the free energy principle. Specifically when researchers are talking about world models they tend to refer to internal models that model the actual external world, that is they can predict what happens next based on input streams like vision.
Why is this idea of a world model helpful? Because it allows multiple interesting things, like predict what happens next, model counterfactuals (what would happen if I do X or don't do X) and many other things that tend to be needed for actual principled reasoning.
A world model is a persistent representation of the world (however compressed) that is available to an AI for accessing and compute. For example, a weather world model would likely include things like wind speed, surface temperature, various atmospheric layers, total precipitable water, etc. Now suppose we provide a real time live feed to an AI like an LLM, allowing the LLM to have constant, up to date weather knowledge that it loads into context for every new query. This LLM should have a leg up in predictive power.
Some world models can also be updated by their respective AI agents, e.g. "I, Mr. Bot, have moved the ice cream into the freezer from the car" (thereby updating the state of freezer and car, by transferring ice cream from one to the other, and making that the context for future interactions).
Training on 2,500 hours of prerecorded video of people playing Minecraft, they produce a neural net world model of Minecraft. It is basically a learned Minecraft simulator. You can actually play Minecraft in it, in real time.
They then train a neural net agent to play Minecraft and achieve specific goals all the way up to obtaining diamonds. But the agent never plays the real game of Minecraft during training. It only plays in the world model. The agent is trained in its own imagination. Of course this is why it is called Dreamer.
The advantage of this is that once you have a world model, no extra real data is required to train agents. The only input to the system is a relatively small dataset of prerecorded video of people playing Minecraft, and the output is an agent that can achieve specific goals in the world. Traditionally this would require many orders of magnitude more real data to achieve, and the real data would need to be focused on the specific goals you want the agent to achieve. World models are a great way to cheaply amplify a small amount of undifferentiated real data into a large amount of goal-directed synthetic data.
Now, Minecraft itself is already a world model that is cheap to run, so a learned world model of Minecraft may not seem that useful. Minecraft is just a testbed. World models are very appealing for domains where it is expensive to gather real data, like robotics. I recommend listening to the interview above if you want to know more.
World models can also be useful in and of themselves, as games that you can play, or to generate videos. But I think their most important application will be in training agents.
He is one of these people who think that humans have a direct experience of reality not mediated by as Alan Kay put it three pounds of oatmeal. So he thinks a language model can not be a world model. Despite our own contact with reality being mediated through a myriad of filters and fun house mirror distortions. Our vision transposes left and right and delivers images to our nerves upside down, for gawd’s sake. He imagines none of that is the case and that if only he can build computers more like us then they will be in direct contact with the world and then he can (he thinks) make a model that is better at understanding the world
The way I think of it (might be wrong) but basically a model that has similar sensors to humans (eyes, ears) and has action-oriented outputs with some objective function (a goal to optimize against). I think autopilot is the closest to world models in that they have eyes, they have ability to interact with the world (go different directions) and see the response.
> Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.
There's absolutely no reason to think this. In fact, all of the evidence we have to this point suggests that scaling intelligence horizontally doesn't increase capabilities – you have to scale vertically.
Additionally, as it stands I'd argue there's foundational architectural advancements needed before artificial neutral networks can learn and reason at the same level (or better) than humans across a wide variety of tasks. I suspect when we solve this for LLMs the same techniques could be applied to world models. Fundamentally, the question to ask here is whether AGI is io dependant, and I see no reason to believe this to be the case – if someone removes your eyes and cuts off your hands they don't make you any less generally intelligent.
To an ex-facebook like myself, it feels like LeCun was more "managed out" than "departing"
Making a veteran like LeCun to report to a new hire (through acquisition) is a strong sign from the management in the direction of - "you should leave"
I'm interested to understand how this works from an IP perspective. This guy is still employed by Meta but is actively fundraising for a new competing startup. Presumably he will have negotiated that Meta forfeits all rights to anything related to his new business? Would be interesting to hear of people's experience/advice for doing this. Or are there some legal entitlements he can avail of?
Even if it’s Meta, they don’t want to antagonize LeCun. Also they all know it’s a small circle of people that create value. I will not be surprised if meta itself invests in his company and get a share.
He needs a patient investor and realized Zuck is not that. As someone who delivers product and works a lot with researchers I get the constant tension that might exist with competing priorities. Very curious to see how he does, imho the outcome will be either of the extremes - one of the fastest growing companies by valuation ever or a total flop. Either way this move might advance us to whatever end state we are heading towards with AI.
It’s probably better for the world that LeCun is not at Meta. I mean if his direction is the likeliest approach to AGI meta is the last place where you want it.
I think it was a plan by Mark to move LeCun out of Meta. And they cannot fire him without bad PR, so they got Wang to lead him. It was only a matter of time before LeCun moved out.
Really? From where I'm standing LeCun is a pompous researcher who had early success in his career, and has been capitalizing on that ever since. Have you read any of his papers from the last 20 years? 90% of his citations are to his own previous papers. From there, he missed the boat on LLMs and is now pretending everyone else is wrong so that he can feel better about it.
He comes off like the quintessential grey haired ego maniac. Inflexible old minds coupled with decades of self assurance that they are correct.
I cannot remember the quote, but it's something to the effect of "Listen closely to grey haired men when they talk about what is possible, and never listen when they talk about what is impossible."
It would have been just as interesting to read that he moved over to Google, where the real brains and resources are located at.
Meta is now just competing against giants like OpenAI, Anthropic and Google, plus all the new Chinese companies; I see no real chance for them to offer a popular chat model, but rather to market their AI as a bundled product for companies which want to advertise, where the images and videos will be automatically generated by Meta.
Correct me if I'm wrong but LeCun is focused on learning from video, whereas Fei-Fei Li is doing robotic simulations. Also I think Fei-Fei Li's approach is still using transformers and not buying into JEPA.
Will be interesting to see how he fares outside the ample resources of Meta: Personnel, capital, infrastructure, data, etc. Startups have a lot of flexibility, but a lot of additional moving parts. Good luck!
This seems like a good thing for him to get to fully pursue his own ideas independent of Meta. Large incumbents aren’t usually the place for innovating anything far from mainstream considering the risk and cost of failure. The high level idea of JEPA is sound, but it takes a lot of work to get it trained well at scale before it has value to Meta.
From the outside, it always looked like they gave LeCun just barely enough compute for small scale experiments. They'd publish a promising new paper, show it works at a small scale, then not use it at all for any of their large AI runs.
I would have loved to see a VLM utilizing JEPA for example, but it simply never happened.
Let's hope that after spending billions on developing a foundational world model that actually understands causality, they remember to budget an extra few hundred million for the Alignment and Safety layer. It would be a terrible shame if they accidentally released something too capable, too objective, or too useful to humanity without first properly lobotomizing it with enough RLHF to ensure it doesn't hurt anyone's feelings or generate content that deviates from the San Francisco median viewpoint. The real challenge won't be building the AGI, but making sure it's sufficiently neutered before the first API call.
I wonder, what LeCun wants to do is more fundamental research, i.e. where the timeline to being useful is much longer, maybe 5-10 years at least, and also much more uncertain.
How does this fit together with a startup? Would investors happily invest into this knowing not to expect anything in return for at least the next 5-10 years?
That's a quite different thing, OpenAI has billions of USD/year cash flow, and when you have that there's many many potential way to achieve profitability on different time horizons. It's not a situation of chance but a situation of choice.
Anyway, how much that matters for an investor is hard to form a clear answer to - investors are after all not directly looking for profitability as such, but for valuation growth. The two are linked but not the same -- any investor in OpenAI today probably also places themselves into a game of chance, betting on OpenAI making more breakthroughs and increasing the cash flow even more -- not just becoming profitable at the same rate of cash flow. So there's still some of the same risk baked into this investment.
But with a new startup like LeCun's is going to be, it's 100% on the risk side and 0% on the optionality side. The path to profitability for a startup would be something like 1) a breakthrough is made 2) that breakthrough is utilized in a way that generates cash flow 3) the company becomes profitable (and at this point hopefully the valuation is good.)
There's a lot of things that can go wrong at every step here (aside from the obvious), including e.g. making a breakthrough that doesn't represent a defensible mote for your startup, failing to build the structure of the business necessary to generate cashflow, ... OpenAI et al already have a lot of that behind them, and while that doesn't mean that they don't face upcoming risks and challenges, the huge amount of cashflow they have available helps them overcome these issues far more easily than a startup, which will stop solving problems if you stop feeding money into it.
Every single time I read about an AI related article I'm always disturbed by the same and recurring fact: the ridiculous amounts of money involved and the lousy real world results delivered. It is just simply insane.
Fi Fi Lee also recently founded a new AI startup called World Labs, which focus on creating AI world models with spatial intelligence to understand and interact with the 3D world, unlike current LLM AI that primarily processes 2D images and text. Almost exactly the same focus as Yann LeCun's new venture stated in the parent article.
I suspect he sees a lot of scattered pieces of fundamental research outside of LLM's that he thinks could be integrated for a core within a year, the 10 years is to temper investors (that he can buy leeway for with his record) and fine tune and work out the kinks when actually integrating everything that might not have some obvious issues.
It is the wet dream of a social media company to replace the pesky content creators that demand a share of ad revenue with an generative ai model, that pumps out a constant stream of engagement farming slop, so they can keep all the ad revenue for themselves.
Creating a world model ai is a totally different matter, that requires long term commitment.
Not just social media, all media. Spotify will steer music towards AI generated freebies. And it will get so generically pop, that all your friends will like it, like people mostly enjoy pop now. And when your stubborn self still wants to listen to "handmade" music and discuss it with someone else who would still appreciate it, well, that's where your AI friend comes in.
Right choice IMO. LLMs aren’t going to reach AGI by themselves because language is a thing by itself, very good at encoding concepts into compact representations but doesn’t necessarily have any relation to reality. A human being gets years of binocular visuals of real things, sound input, other various sensations, much less than what we’re training these models with. We think of language in terms of sounds and pictures rather than abstract language.
Some of the best AI researchers and labs have been from the EU (DeepMind, Alan Turing Institute, Mistral, et al.). We in the US have mature capital markets and stupid easy access to capital, of course, but EU still punches well above its weight when it comes to deep, fundamental AI research.
He also said other things about LLMs that turned out to be either wrong or easily bypassed with some glue. While I understand where he comes from, and that his stance is pure research-y theory driven, at the end of the day his positions were wrong.
Previously, he very publicly and strongly said:
a) LLMs can't do math. They trick us in poetry but that's subjective. They can't do objective math.
b) they can't plan
c) by the very nature of autoregressive arch, errors compound. So the longer you go in your generation, the higher the error rate. so at long contexts the answers become utter garbage.
All of these were proven wrong, 1-2 years later. "a" at the core (gold at IMO), "b" w/ software glue and "c" with better training regimes.
I'm not interested in the will it won't it debates about AGI, I'm happy with what we have now, and I think these things are good enough now, for several usecases. But it's important to note when people making strong claims get them wrong. Again, I think I get where he's coming from, but the public stances aren't the place to get into the deep research minutia.
That being said, I hope he gets to find whatever it is that he's looking for, and wish him success in his endeavours. Between him, Fei Fei Li and Ilya, something cool has to come out of the small shops. Heck, I'm even rooting for the "let's commoditise lora training" that Mira's startup seems to go for.
The current VC climate is interesting. It's virtually impossible to raise a new fund because DPI has been 0% for over a decade and four-digit IRR is cool, but illiquid.
So they're piling gobs of capital into an "AI" company with four customers with the hope that it is the one that becomes the home run (they know it won't, but LPs give you money to deploy it!)
It also means that companies like Yann's potential new one have the best chance in history of being funded, and that's a great thing.
P.S. all VCs outside the top-10 lose against the S&P. While I love that dumb capital is being injected into big, risky bets, surely the other shoe will drop at some point. Or is this just wealth redistribution with extra steps?
META managed to spend a lot of money into AI to achieve inferior results. Something must change for sure, and you don't want an LLM skeptic at home, in my opinion, especially since the problem is not what LeCun is saying right now (LLMs are not the straight path to AGI), but the fact it used to say for some time that LLMs were just statistical models, stochastic parrots (and this is a precise statement, something most people do not understand. It means two things: no understanding of the prompt whatsoever in the activation states, and no internal representation of the idea/sentence the model is going to express either), which is an incredibly weak statement that high level AI scientists refused since the start just because of functional behaviors. Then he slowly changed the point of view. But this shit show and the friction he created inside META is not something to forget.
If by “world models” they mean more contemporary versions of the systems thinking driven software that begat “Limits To Growth” and most of Donella Meadows’ career you can sign me right the fuck up today.
I think moving on from LLM's is slightly arrogant. It might just be my understanding, but I feel like there is still much to be discovered. I was hoping for development in spiking neural networks but it might be skipped over. Perhaps I need to dive even deeper and the research is truly well understood and "done" but I can't help but constantly learn something new about language models and neural networks.
Best of luck to LeCun. I hope by World Model's he means embodied AI or humanoid robots. We'll have to wait and see.
Surprising to see how many commenters are in favour and supportive towards policy of prioritising short term profits vs. Long-term research.
I understand Meta's not academia nor charity, but come on, how much profit do they need to make so we can expect them to allocate part of their resources towards some long term goals beneficial for society,.not only for shareholders?
Hasn't that narrow focus and chasing the profits get us in trouble already?
Many people believe a company exists only to make profit for its shareholders, and that no matter the amount it should continue to maximise profits at the expense of all else.
Everybody has found out how LLMs no longer have a real research expanding horizon. Now most progress will likely be done by tweaks in the data, and lots of hardware. OpenAI's strategy.
And also it has extreme limitations that only world models or RL can fix.
Meta can't fight Google (has integrated supply chain, from TPUs to their own research lab) or OpenAI (brand awareness, best models).
- Kimi proved we don’t need Nvidia
- Deepseek proved we didn’t need OpenAI
- the real issue the insane tyranny in the west competing against the entire free world.
The models aren’t Chinese they are the entire world, unless I became Chinese without realizing
Kimi k2 thinking
> As for why we chose INT4 instead of more "advanced" formats like MXFP4/NVFP4, it's indeed, as many have mentioned, to better support non-Blackwell architecture hardware.
During his years at Meta, LeCun failed to deliver anything that delivered real value to stockholders, and may have demotivated people working on LLMs—he repeatedly said, "If you are interested in human-level AI, don’t work on LLMs."
His stance is understandable, but hardly the best way to rally a team that needs to push current tech to the limit.
The real issue: Meta is *far behind* Google, Anthropic, and OpenAI.
A radical shift is absolutely necessary - regardless of how much we sympathize with LeCun’s vision.
----
According to Grok, these were LeCun's real contributions at Meta (2013–2025):
----
- PyTorch – he championed a dynamic, open-source framework; now powers 70%+ of AI research
- LLaMA 1–3 – his open-source push; he even picked the name
- SAM / SAM 2 – born from his "segment anything like a baby" vision
- JEPA (I-JEPA, V-JEPA) – his personal bet on non-autoregressive world models
----
Everything else (Movie Gen, LLaMA 4, Meta AI Assistant) came after he left or was outside his scope.
I am in the "Yann is no longer the right person for the job" camp and I yet "LeCun failed to deliver anything that delivered real value to stockholders" is a wild thing to say. How do you read the list you compiled and say otherwise?
I think there’s something to be said for keeping up in the LLM space even if you don’t think it’s the path to AGI.
Skills may transfer to other research areas, lessons may be learnt, closing the feedback loop with usage provides more data and opportunities for learning. It also creates a culture where bullshit isn’t possible, as the thing has to actually work. Academic research often ends up serving no one but the researchers, because there is little or no incentive to produce real knowledge.
> LeCun failed to deliver anything that delivered real value to stockholders
Well, no, Meta is behind the main framework used by nearly anyone largely thanks to LeCun. LLaMA was also very significant in making open weight a thing and that largely contributed to avoiding Google and OpenAI consolidating as the sole providers.
It's not a perfect tenure but implying he didn't deliver anything is far too harsh.
With this incredible AI talent market, I feel like capitalism and ego forms to make an acid burning away anything of social and structural value. This used to be the case with CS tech talent before (before being replaced with no-code tools). And now we see this kind of instability in the AI market.
We need another illegal Steve Jobs style freeze on talent theft (/s or I get downvoted to oblivion).
Yann was largely extremely wrong about LLMs. He’s the one that coined the term “stochastic parrot” for which we now know LLMs are more than stochastic parrots. Knowing stubborn idiots like him he will still find an angle to prevent him from admitting how wrong he was.
He’s not completely wrong in the sense that hallucinations aren’t completely solved but hallucinations definitely are becoming less and less to the point where AI can de a daily driver for even coders.
LeCun has already proved himself and made his mark and is now in a lucky position where he can focus on very long term goals that won't pay off for a long time (or ever). I feel like that is the best path someone like him could take.
why do you say it is garbage ? I watched some of its videos on YT and it looks interesting. I can't judge if it's good or really good, but that didn't sound like garbage at all.
I have no idea why this fair assessment of the status quo is being downvoted.
LeCun hasn't produced anything noteworthy in the past decade.
He uses the same slides in all of his presentations.
LLMs, while not yet AGI, have shown tremendous progress, and are actually useful for 99% of use cases for the average person.
The remaining 1% is for deep research into the deep unknown (physics, chemistry, genetics, diseases, the nature of intelligence itself), an area in which they falter.
Cool, and how many billions has he flushed down the toiled for his failed Metaverse and currently failing AI attempts? Rich doesn't mean smart, you realise this right?
What the hell does Mark see in Wang? Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China. From any angle, this dude just doesn't seem reliable at all.
> Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China.
All I'm hearing is he's a smart guy from a smart family?
I imagine that CCP adherents would disagree. And there's no shortage of those among Chinese expats in the US.
They tend to get incredibly offended when they see anyone who doesn't toe the Party's line - let alone believe that the Chinese government is untrustworthy and evil.
he is very smart. but Mark is not. Ever since Wang joined Meta, way too many big-name AI scientists have bounced because of him. US AI companies have at least half their researchers being Chinese, and now they've stuck this ultimate anti-China hardliner in charge—I just don't get what the hell Meta's up to(And even a lot of times, it ends up affecting non-Chinese scientists too.). Being anti-China? Fine, whatever, but don't let it tank your own business and products first.
He definitely has horrible product instincts, but he also bought insta and whatsapp at what were, back then, eye-watering prices, and these were clearly massive successes in terms of killing off threats to the mothership. Everything since then, though…
He’s an incredible operator and has managed to acquire and grow an astounding number of successful businesses under the Meta banner. That is not trivial.
We were very confident by ca. 2008 that Facebook would still be around in 2025. It's no mystery, it's the network effects. They had started with a prestige demographic (Harvard), and secured a demographic you could trust to not move on to the next big thing in a hurry, yet which most people want contact with (your parents).
Most of the folks on this topic are focused on Meta and Yann’s departure. But, I’m seeing something different.
This is the weirdest technology market that I’ve seen. Researchers are getting rewarded with VC money to try what remains a science experiment. That used to be a bad word and now that gets rewarded with billions of dollars in valuation.
> This is the weirdest technology market that I’ve seen.
The phenomenon you're seeing is well described here: "The Perfect AI Startup" (https://www.bloomberg.com/opinion/newsletters/2025-09-29/the...)
“It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, ‘So we’re doing an AI company with the best AI people, but we can’t answer any questions.’”
Despite that vagueness, Murati raised $2 billion in funding...
From a certain angle, this is the market correcting towards the abstraction.
Between inflation, fiscal capture, and the inane plethora of ridiculous financial vehicles that are used to move capital around these days, the argument could be made that the money was already funny. This is just the drop of the final veil, saying "well it's not like these numbers mean anything anymore. I do have enough yachts. Fuck it, see what you can do with it".
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I wonder how the investors feel now seeing what the initial product is?!
Maybe investing in all well-connected AI startups is safer than trying to pick the winners and losers?
matt levine never misses
Matt Levine never disappoints.
That's been true for the last year or two, but it feels like we're at an inflection point. All of the announcements from OpenAI for the last couple of months have been product focused - Instant Checkout, AgentKit, etc. Anthropic seems 100% focused on Claude Code. We're not hearing as much about AGI/Superintelligence (thank goodness) as we were earlier this year, in fact the big labs aren't even talking much about their next model releases. The focus has pivoted to building products from existing models (and building massive data centers to support anticipated consumption).
Meta hiring researchers en masse at $100m+ pay packages is fairly new, as of this summer.
I don't know if that's indicative of the market as a whole though. Zuck just seems really gutted they fell behind with Llama 4.
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You must not watch broadcast television (e.g American Football). Anthropic is doing a huge ad blitz, trying to get end customers to use their chatbot.
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If Claude Code is Anthropic’s main focus why are they not responding to some of the most commented issues on their GitHub? https://github.com/anthropics/claude-code/issues/3648 has people begging for feedback and saying they’re moving to OpenAI, has been open since July and there are similar issues with 100+ comments.
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> Researchers are getting rewarded with VC money to try what remains a science experiment. That used to be a bad word
I’ve worked for multiple startups and I’ve watched startup job boards most of my career.
A lot of VC backed startups have a founder with a research background and are focused on providing out some hypothesis. I don’t see anything uncommon about this arrangement.
If you live near a University that does a lot of research it’s very common to encounter VC backed startups that are trying to prove out and commercialize some researcher’s experiment. It’s also common for those founders to spend some time at a FAANG or similar firm before getting VC funded.
Yeah, but Sutskever and Murati wouldn't even tell investors what they were working on, and LeCun only has a long-term research direction - not any breakthrough or prototype to commercialize.
Certainly research has made it into product with the help of the innovators that created the research. The dial is turned further here where the research ideas have yet to be tried and vetted. The research begins in the startup. Even in the dotcom era, the research prototypes were vetted in the conferences and journals before taking the risk to build production systems. This is no longer the case. The experiments have yet to be run.
Fusion, stem cells, CRISPR,robotics etc all come to mind.
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I agree there is nothing uncommon about that type of arrangement, but the amount of money involved is unprecedented.
I personally see this as a positive trend. VC in its earliest form was concerned with experiments that had high technology risk. I am thinking of companies like Genentech and scientists like biochemist Herbert Boyer, who had pioneered recombinant DNA technology.
After that, VC had become more like PE, investing in stuff that was working already but needed money to scale.
This isn't that.
This is VCs FOMOing as global-economy-threatening levels of leverage are being bet on an AI transformation that, by even the most optimistic estimates, cannot achieve a tiny portion of the required ROI in the required time.
Yeah there has been some lamenting at all the money being thrown at technology hasn't been for anything truly game changing, basically just variations of full stack apps. A few failed mooonshots might be more interesting at least.
I agree, if anything spending money on high technology risk is Silicon Valley going back to its roots.
Nobody had a way to do silicon transistor manufacturing at scale until the traitorous eight flipped Shockley the bird and took a $1.4M seed investment from Sherman Fairchild.
Big bets on uncertain technology is what tech is supposed to be about.
> This is the weirdest technology market that I’ve seen.
You must have not lived through the dot com boom. There was almost everything under the sun was being sold under a website that started with an "e". ePets, ePlants, eStamps, eUnderwear, eStocks, eCards, eInvites.....
Those things all worked, and all of those products still exist in one form or another. It was a business question of who would provide it, not a technology question.
The Pets.ai Super Bowl commercial will trigger the burst.
None of those were science experiments or research projects in any way.
That was certainly a bubble but I don't think pets.com was doing a research experiment.
From what I recall there were some biotech stocks in that era that do fit the bill.
It's funny that the Netherlands seems to still live in the dotcom boom to this day. Want to adopt a pet? verhuisdieren.nl. Want to buy wall art? wall-art.nl. Need cat5 cable? kabelshop.nl. 8/10 times there is a (legit) online store for whatever you need, to the point where one of the local e-commerce giants (Coolblue) buys this type of domain and aliases them to their main site.
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These are not the same.
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Even hardware. eMachines.
flooz
It did make sense though. ePlants could have cornered the online nursery market. That is a valuable market. I think people were just too early. Payment and logistics hasn’t been figured out yet.
Agree on weirdness but not on the idea of funding science experiments:
>> away from long-term research toward commercial AI products and large language models - LLMs
This feels more like what I see every day: the people in charge desperately looking for some way - any way - to capitalize on the frenzy. They're not looking to fund research; they just want to get even richer. It's pets.ai this time.
This doesn’t feel that new or surprising to me, although I suppose it depends what you consider the line between “science experiment” and “engineering R&D” to be.
Biotech has been a YC darling. Was Ginkgo Bioworks not doing science experiments?
Clean energy was a big YC fad roughly 15 years ago. Billions were invested towards scientific research into biofuels, solar, etc.
I can’t help but wonder: if we had poured the same amount of money into fusion energy research and development, how far might we have come in just three short years?
The minimum cost of capital just to run fusion experiments is probably $100m. And the power bills are probably almost as high as the ones from OpenAI, which is to say, they are the highest power bills in the history of mankind ...
Forreal that’s what really gets me about this haha. Literally billions of dollars burned on bullshit.
If a "science experiment" has the chance to displace most labor then whoever's successful at the experiment wins the economy, period. There's nothing weird or surprising about the logic of them obsessively chasing it. They all have to, it's a prisoner's dilemma.
Technology know-how spreads rapidly, so no need to be first. Look how fast Google caught up with Gemini when they chose to, or how fast X.ai developed Grok.
Maybe it's cheap insurance to invest in, say, LeCun just in case JEPA or the animal intelligence approach takes off, but if it does show significant signs of progress there'd also be opportunity to invest later, or in one of the dozen copycats that will emerge. In the end it'll be the giants like Google and Microsoft that will win.
Fusion power has the chance to displace most power generation, and whoever is successful at the experiment wins the energy economy, period. However given the long timelines, high cost of research, and the unanswered technical questions around materials that can withstand neutron flux, the total 2024 investment into fusion is only around $10B, versus AI's 250+B.
Why are these so different?
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This looks more like a return to form than anything.
The first ventures were funding voyages to a New World thousands of miles away, essentially a different planet as far as the people then were concerned.
Venture capital for a new B2B application is playing it safe as far as risk capital goes
Its not really an outlier
If you think about Theranos, Magic leap, openai, anthropic they are all the same, one idea thats kinda plausible (well if you don't look too closely), have a slick demo, and well connected founders.
Much as a lot of people dislike LeCun (just look at the blind posts about him) he did run and setup a very successful team inside meta, well nominally at least.
You're right to feel like you're seeing something different. You are. But you're mistaking the symptom for the disease.
That's because you're trying to make sense of it as a technology market. It's not. It's a resource extraction market, and the VCs are the ones running the logging operation. Their sole mission is to find a dependable way to strip a forest bare, and they've been using the same playbook for decades.
Those "science experiments" you're talking about? They aren't the product. They're the story, the sizzle. They are the disposable lighter used to start the fire; the VCs have no intention of keeping it lit forever. The real tool is the chainsaw, and the "science experiment" is the brand name printed on the side.
Think of it as clear-cutting. The dot-com bubble was one forest. The story then was that a company losing millions selling pet food online was a "new economy" giant because it had "eyeballs." That was the sales pitch for the chainsaw. VCs funded hundreds of these operations, created a frenzy, and took the most plausible-sounding ones public. The IPO wasn't a milestone; it was the moment they sold the timber and exited the forest, leaving the stumps and worthless pulp for the pension funds and retail investors.
The "long-term" part of their strategy isn't about the health of any single tree or company. It's about finding the next forest to clear-cut. After dot-coms, it was social media. Now, it's the AI forest. They aren't betting on AI; they're betting on their ability to sell the world on the idea that this particular forest is magical and will grow forever.
So you're right, what you're seeing is weird. But it's not a new kind of weirdness. It's the oldest story in finance. A bubble being inflated so the smart money can cash out, leaving everyone else to marvel at the fancy new chainsaw after the forest is already gone.
This sounds like AI slop.
It makes sense, it’s a simple expected value calculation.
There are trillions of labor dollars that can be replaced by software. The US alone has almost $12 trillion of labor annually.
If an AI company has a 10% shot of developing a product that can replace 10% of it, they are worth $120 billion in expected value. (These numbers are obviously just for illustration).
The unprecedented numbers are a simple function of the unprecedented market size. Nobody has ever had a chance of creating trillions of dollars of economic value in a handful of years before.
>If an AI company has a 10% shot of developing a product that can replace 10% of it, they are worth $120 billion in expected value.
that's not how profits work. Companies don't get paid for the value they create but for the value they can capture, otherwise the ffmpeg people would already be trillionaires.
If you have a dozen companies making the same general purpose technology, not product, your only hope is being able to slap ads on top of it, which is why they're so keen on targeting consumers rather than trying to automate jobs.
Having raised more than $100M myself, I’m not sure I would call VC money a reward. However, VC money should be allocated in part to massive upside science experiments. PE money is focused on things already figured out.
Has someone done a survey to ask devs on how much they are getting done vs what their managers expect with AI? I've had conversations with multiple devs in big orgs telling me that Managers and dev's expectations are seriously out of sync. Basically its
Manager: Now you "have" AI, release 10 features instead of 1 in the next month.
Devs: Spending 50% more working hours to make AI code "work" and deliver 10.
I think that's a good thing and VC getting back to it's roots. I'm glad that scientists doing AI are getting big money and don't know exactly what the product will be rather than some business person with a slick deck and hockey stick charts.
VC isn't "getting back to it's roots", though it is certainly displaying one of it's fundamental drives: FOMO.
Given the infinite amount of VC money and greed this is not a big surprise.
> Researchers are getting rewarded with VC money to try what remains a science experiment.
That's not all that new. Commercial fusion power startups are an example. I think the first one was General Fusion, founded in 2002. Today, there are around 50 of them. Every single one of those "remains a science experiment", and probably has much lower chance of success than some of the AI science experiments.
Of course, fusion startups have apparently "only" received about $10 bn in funding to date, so pale in comparison to the overall AI market. But if you just look at the AI "science experiments", it's possible the amounts would be comparable.
If a science experiment works and is transformational can be worth a trillion dollars, how much is it worth if it has a 5% chance of being transformational?
What it's transformational but takes a decade or so, instead of a year or so?
It's not like this isn't following exactly the same hype cycle as every other technological transformation.
What if it is a 99% chance of being transformational and the results of that transformation are completely unpredictable?
The scale of money is crazy in this example, but the same thing happens in the pharmaceutical/bio-tech industry.
It's the world's biggest game of "let's throw shit at the wall and see what sticks."
They're trying desperately to find profit in what so far has been the biggest boondoggle of all time.
Get the popcorn ready for when that all implodes. Most of these folks getting funding don’t have the slightest clue on how to build a sustainable business.
When the bubble pops, and it’s very close to popping, there’s going to be a lot of burning piles of cash with no viable path to reviver that money.
Every startup is an experiment; only 2% succeed.
Not if you get funding from a VC.
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Agree. This is just gambling with almost free money.
Feeding, housing, and educating people would benefit society, and these companies, so much more than AI ever will.
Yes - I had similar thoughts when I saw the word "startup" used alongside something so far-out (same 'critique" should apply to Fei-Fei Li's World Labs - https://www.worldlabs.ai). These are VC-funded research labs (and there is nothing wrong with tat). Calling them "startups" as if they are already working on an MVP on top of an unproven (and frankly non-existent) technology seems a little disingenuous to me.
Because when the recipe is open and public, the product's success depends on Distribution (which has been cornered by MS, Google, Apple). This is good for the ecosystem but not sure how those particular VCs will get exits.
Very few startup products depend on distribution by Microsoft / Google / Apple. You're really just talking about a limited set of mobile or desktop apps there. Everything else is wide open. Kailera Therapeutics isn't going to live or die based on what the tech giants do.
It might not be a science experiment.
Is it like VCs throwing money at a young Wozniak while eschewing Jobs?
That either gives the AI tech more legitimacy in my mind … or a sign we've not arrived yet.
Yeah, that's quite unusual. Buisness was always terrible at being innovative, always dared to take only the safest and most minute of bets and the progress of technology was always paid for by the taxpayers. Business usually stepped in only later, when technology was ready and did what it does best, opimize manufacturing and put it in the hands of as many consumers as possible rakink in billions.
I wonder what changed. Does AI look like a safe bet? Or does every other bet seem to not have any reasonable return?
VC is in a bubble.
Underrated comment of the year
Making LeCun report to Wang was the most boneheaded move imaginable. But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
In industry research, someone in a chief position like LeCun should know how to balance long-term research with short-term projects. However, for whatever reason, he consistently shows hostility toward LLMs and engineering projects, even though Llama and PyTorch are two of the most influential projects from Meta AI. His attitude doesn’t really match what is expected from a Chief position at a product company like Facebook. When Llama 4 got criticized, he distanced himself from the project, stating that he only leads FAIR and that the project falls under a different organization. That kind of attitude doesn’t seem suitable for the face of AI at the company. It's not a surprise that Zuck tried to demote him.
These are the types that want academic freedom in a cut-throat industry setup and conversely never fit into academia because their profiles and growth ambitions far exceed what an academic research lab can afford (barring some marquee names). It's an unfortunate paradox.
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I would pose a question differently, under his leadership did Meta achieve good outcome?
If the answer is yes, then better to keep him, because he has already proved himself and you can win in the long-term. With Meta's pockets, you can always create a new department specifically for short-term projects.
If the answer is no, then nothing to discuss here.
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Meta had a two prong AI approach - product-focused group working on LLMs, and blue-sky research (FAIR) working on alternate approaches, such as LeCun's JEPA.
It seems they've given up on the research and are now doubling down on LLMs.
LLM hostility was warrented. The overhype/downright charlartan nature of ai hype and marketing threatens another AI winter. It happened to cybernetics, it'll happen to us too. The finance folks will be fine, they'll move to the next big thing to overhype, it is the researchers who suffer the fall-out. I am considered anti LLM (transformers anyway) for this reason, i like the the architecture, it is cool amd rather capable at its problem set, which is a unique set, but, it isnt going to deliver any of what has been promised, any more than a plain DNN or a CNN will.
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Product companies with deprioritized R&D wings are the first ones to die.
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It's very hard (and almost irreconcilable) to lead both Applied Research -- that optimizes for product/business outcomes -- and Fundamental Research -- that optimizes for novel ideas -- especially at the scale of Meta.
LeCun had chosen to focus on the latter. He can't be blamed for not having taken the second hat.
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This is the right take. He is obviously a pioneer and much more knowledgeable than Wang in the field, but if you don't have the product mind to serve company's business interest in short term and long term capacity anymore, you may as well stay in academia and be your own research director, let alone a chief executive in one of the largest public companies
LeCun truly believes the future is in world models. He’s not alone. Good for him to now be in the position he’s always wanted and hopefully prove out what he constantly talks about.
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Yann was never a good fit for Meta.
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Yann was in charge of FAIR which has nothing to do with llama4 or the product focussed AI orgs. In general your comment is filled with misrepresentations. Sad.
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Lecun has also consistently tried to redefine open source away from the open source definition.
I totally agree. He appeared to act against his employer and actively undermined Meta's effort to attract talent by his behavior visible on X.
And I stopped reading him, since he - in my opinion - trashed on autopilot everything 99% did - and these 99% were already beyond the two standard deviation of greatness.
It is even more highly problematic if you have absolutely no results eg products to back your claims.
tbf, transformers from more of a developmental perspective are hugely wasteful. they're long-range stable sure, but the whole training process requires so much power/data compared to even slightly simpler model designs I can see why people are drawn to alternative complex model designs down-playing the reliance on pure attention.
He is also not very interested in LLMs, and that seems to be Zuck's top priority.
Yeah I think LeCun is underestimating the impact that LLM's and Diffusion models are going to have, even considering the huge impact they're already having. That's no problem as I'm sure whatever LeCun is working on is going to be amazing as well, but an enterprise like Facebook can't have their top researcher work on risky things when there's surefire paths to success still available.
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The role of basic research is to get off the beaten path.
LLMs aren’t basic research when they have 1 billion users
That was obviously him getting sidelined. And it's easy to see why.
LLMs get results. None of the Yann LeCun's pet projects do. He had ample time to prove that his approach is promising, and he didn't.
I agree. I never understood LeCun's statement that we need to pivot toward the visual aspects of things because the bitrate of text is low while visual input through the eye is high.
Text and languages contain structured information and encode a lot of real-world complexity (or it's "modelling" that).
Not saying we won't pivot to visual data or world simulations, but he was clearly not the type of person to compete with other LLM research labs, nor did he propose any alternative that could be used to create something interesting for end-users.
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LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises. If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV. LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound to fail in adoption unless you convince everyone that they don't need precision and accuracy, but they can do their business at 75% quality, just with less human overhead. It's quite the thing to convince people of, and that's why it needs the spend it's needing. A lot of we-need-to-stay-in-the-loop CEOs and bigwigs got infatuated with the idea, and most probably they just had their companies get addicted to the tech equivalent of crack cocaine. A reckoning is coming.
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There is someone else at Facebook who's pet projects do not get results...
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> not truly groundbreaking foundation models.
Where is any proof that Yann LeCun is able to deliver that? He's had way more resources than any other lab during his tenure, and yet has nothing substantial to show for it.
LeCun is great and smart, of course. But he had his chance. It didn't go that well. Now Zuck wants somebody else to try.
Messi is the best footballer of our era. It doesn't mean he would play well in any team.
Messi would only play well in Barcelona. Lecunn can produce high quality research anywhere. It's not a great comparison.
I don't think Messi could do it on a wet night in Stoke. Ronaldo could, though.
/s
> But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.
When did they make groundbreaking foundation models though? DeepMind and OpenAI have done plenty of revolutionary things, what did Meta AI do while being led by LeCun?
Zuck hired John Carmack and got nothing of it On the other hand, it was only lecunn avoiding meta to go 100p evil creepy mode too
Carmack laid the foundation for the all-in-one VR headsets.
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And Carmack complained about the bureaucracy hell that is Facebook.
What does Meta even want with AI?
I suppose they could solve superintelligence and cure cancer and build fusion reactors with it, but that's 100% outside their comfort zone - if they manage to build synthethic conversation partners and synthethic content generators as good or better than the real thing the value of having every other human on the planet registered to one of their social network goes to zero.
Which is impossible anyway - I facebook to maintain real human connections and keep up with people who I care about, not to consume infinite content.
At 1.6T market cap it's very hard to 10x or greater the company anymore doing what's in their comfort zone and they've got a lot of money to play with to find easier to grow opportunities. If Zuckerberg was convinced he could do that by selling toothpicks they'd have a go at the toothpick business. They went after the "metaverse" first, then AI. Both are just very fast growth options which happen to be tech focused because that's the only way you generate new comparable value as a company (unless you're sitting on a lot of state owned oil) in the current markets.
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they are out for your clicks and attention minutes
if OpenAI can build a "social" network of completely generated content, that can kill Meta. Even today I venture to guess that most of the engagements in their platforms is not driven by real friends, so an AI driven platform won't be too different, or it might make content generation be so easy as to make your friends engage again.
Apart from it the ludicrous vision of the metaverse seems much more plausible with highly realistic world models
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> slopware
Damn did you just invent that? That's really catchy.
Slop is already a noun.
I won't be surprised if Musk hires him. But I hear LeCun hates the guts of Musk.
Musk doesn't appear interested in AI research - he's basically doing the same as Meta and just pursuing me-too SOTA LLMs and image generation at X.ai.
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Musk wants people who can deliver results, and fast.
If LeCun can't cough up some research that's directly applicable to Grok or Optimus, Musk wouldn't want him.
Would love to have been a fly on the wall during one of their 1:1’s.
When I first saw their LLM integration on Facebook I thought the screenshot was fake and a joke
Yes, that was such a bizarre move.
Zuck did this on purpose, humiliating LeCun so he would leave. Despite LeCun being proved wrong on LLMs capabilities such as reasoning, he remained extremely negative, not exactly inspiring leadership to the Meta Ai team, he had to go.
But LLMs still can't reason... in a reasonable sense. No matter how you look at it, it is still a statistical model that guesses next word, it doesn't think/reason per se.
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Oh wow, is that true? They made him report to the directory of the Slop Factory? Brilliant!
Zuckerberg knows what he wants but he rarely knows how to get it. That's been his problem all along. Unlike others he isn't scared to throw ridiculous amounts of money at a problem though and buy companies who do things he can't get done himself.
There's also the aspect of control - because of how the shares and ownership are organized he answers essentially to no one. In other companies burning this much cash as was with VR or now AI without any sensible results would get him ejected a long time ago.
No, it was because LeCun had no talent for running real life teams and was stuck in a weird place where he hated LLMs. He frankly was wasting Meta’s resources. And making him report to Wang was a way to force him out.
Meta had John Carmack and squandered him. It seems like Meta can get amazing talent but has no idea how to get any value or potential out of them.
It wasn’t boneheaded. It was done to make Yann leave. Meta doesn’t want Yann for good reason.
Yann was largely wrong about AI. Yann coined the term stochastic parrot and derrided LLMs as a dead end. It’s now utterly clear the amount of utility LLMs have and that whatever these LLMs are doing it is much more than stochastic parroting.
I wouldn’t give money to Yann, the guy is a stubborn idiot and closed minded. Whatever he’s doing wont even touch LLM technology. He was so publicly deriding LLMs I see no way he will back pedal from that.
I dont think LLMs are the end of the story for agi. But I think they are a stepping stone. Whatever agi is in the end, LLMs or something close to it will be a modular component of aspect of the final product. For LeCunn to dismiss even the possibility of this is idiotic. Horrible investment move to give money to Yann to likely pursue Agi without even considering LLMs.
A few people mentioned Meta burning through people like LeCun, Carmack and Luckey. We give a lot of credit to individuals in our society. At the same time, there's a frequent pattern of very successful people changing fields, organizations or general environments and suddenly looking very ordinary. In a way, this is a very strong argument to let people settle into a place they can be happiest in. A few examples:
* This is seen very often in Formula One. Schumacher when he went from Ferrari to Mercedes (after a short sabbatical), Vettel from Red Bull to Ferrari, Raikkonen from Lotus to Ferrari (his second stint), Hamilton from Mercedes to Ferrari, Perez at McLaren. There's a lot of Ferrari here so maybe that's the confounding factor.
* This also happens when physicists changed fields. They generally can't replicate their earlier success. Dirac, Feynman, Schwinger, Einstein all went through a transition like this. One explanation is that their early success was precisely so unusual (for anyone) that it would be hard to replicate in general.
* In my experience, this happens at companies too. Whenever we hired a "rockstar" from another company, they would generally struggle (across multiple companies I have been at). This could partly be a result of sabotage from a few vested interests at the new company. But often, it's hard to adjust to a new environment in a short amount of time.
The converse also happens. Sometimes a person considered ordinary goes to a different environment and flourishes. Palmer Luckey has been very successful at Anduril. Stephen Smale was almost failing out of his math PhD program but suddenly started flourishing in his third year IIRC and eventually got a fields medal. Ed Witten experimented with economics, history, linguistics, applied math before switching to physics in his second year and suddenly started making rapid progress.
This is not a very rigorous observation and I am missing many confounding factors.
I see a lot of criticism of LeCun and his views on LLMs as well as his inability to "deliver" products. I don't think that's what he cares about at all. His prominence led to him being picked by Meta. It was a chance to get massive resources that he couldn't get at NYU and the chance to work with smart people outside academia. The pay probably didn't hurt either. In return, Meta became a magnet for smart ML researchers and engineers. If I permit myself to speculate about his thoughts when he took the job, he had no intention of committing to product timelines and generating revenue. Now that Zuckerberg has clearly committed to something he like i.e. building a new product line and expanding the business, it was only a matter of time before LeCun would feel left out and under-resourced.
Interestingly, Yoshua Bengio is the only one who hasn't given into industry even though he could easily raise a lot of money.
I feel like LeCun has been plainly wrong about LLMs. He has been insisting that the stochastic nature of sampling tokens causes a non-zero hallucination property for any given next token such that as output length increases, this will inevitably converge towards garbage.
The reality is that while LLMs can make mistakes mid-output, those interim mistakes don't necessarily detract from the model's final output. We see a version of this all the time with agents as they make tactical mistakes but quickly backtrack and ultimately solve the root problem.
It really felt like LeCun was willing to die on this hill. He continued to argue about really pedantic things like the importance researchers, etc.
I'm glad he's gone and hopeful Meta can actually deliver real AI products for their users with better leadership.
I am a big fan of using LLMs although in my own limited way. I don't work at Meta and don't feel strongly about him leaving or staying there.
It's possible that he will turn out to be correct in the long run. From his viewpoint, the primary goal is research and any usefulness of intermediate advances is maybe (speculating) "beneath him". If this is the case, I completely understand why a corporation would want to eject him. LeCun probably sees the pretty amazing developments since ChatGPT first came out as incremental hacks. I am neutral about this aspect too. Maybe they are but the hacks have been useful to me.
Eventually this feels like the correction of a real misalignment between LeCun/FAIR and Meta. Hopefully now, they can both focus on what they are good at. I must admit that I have great sympathy for open-ended research but industry has always been fickle about it. That's where the government and universities are supposed to play a key role.
LeCun, who's been saying LLMs are a dead end for years, is finally putting his money where his mouth is. Watch for LeCun to raise an absolutely massive VC round.
So not his money ;)
But his responsability.
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like openAI and all other AI startups?
Putting VCs money into food where his mouth is*
Good. The world model is absolutely the right play in my opinion.
AI Agents like LLMs make great use of pre-computed information. Providing a comprehensive but efficient world model (one where more detail is available wherever one is paying more attention given a specific task) will definitely eke out new autonomous agents.
Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.
I wish I could help, world models are something I am very passionate about.
Can you explain this “world model” concept to me? How do you actually interface with a model like this?
One theory of how humans work is the so called predictive coding approach. Basically the theory assumes that human brains work similar to a kalman filter, that is, we have an internal model of the world that does a prediction of the world and then checks if the prediction is congruent with the observed changes in reality. Learning then comes down to minimizing the error between this internal model and the actual observations, this is sometimes called the free energy principle. Specifically when researchers are talking about world models they tend to refer to internal models that model the actual external world, that is they can predict what happens next based on input streams like vision.
Why is this idea of a world model helpful? Because it allows multiple interesting things, like predict what happens next, model counterfactuals (what would happen if I do X or don't do X) and many other things that tend to be needed for actual principled reasoning.
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A world model is a persistent representation of the world (however compressed) that is available to an AI for accessing and compute. For example, a weather world model would likely include things like wind speed, surface temperature, various atmospheric layers, total precipitable water, etc. Now suppose we provide a real time live feed to an AI like an LLM, allowing the LLM to have constant, up to date weather knowledge that it loads into context for every new query. This LLM should have a leg up in predictive power.
Some world models can also be updated by their respective AI agents, e.g. "I, Mr. Bot, have moved the ice cream into the freezer from the car" (thereby updating the state of freezer and car, by transferring ice cream from one to the other, and making that the context for future interactions).
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The best world model research I know of today is Dreamer 4: https://danijar.com/project/dreamer4/. Here is an interesting interview with the author: https://www.talkrl.com/episodes/danijar-hafner-on-dreamer-v4
Training on 2,500 hours of prerecorded video of people playing Minecraft, they produce a neural net world model of Minecraft. It is basically a learned Minecraft simulator. You can actually play Minecraft in it, in real time.
They then train a neural net agent to play Minecraft and achieve specific goals all the way up to obtaining diamonds. But the agent never plays the real game of Minecraft during training. It only plays in the world model. The agent is trained in its own imagination. Of course this is why it is called Dreamer.
The advantage of this is that once you have a world model, no extra real data is required to train agents. The only input to the system is a relatively small dataset of prerecorded video of people playing Minecraft, and the output is an agent that can achieve specific goals in the world. Traditionally this would require many orders of magnitude more real data to achieve, and the real data would need to be focused on the specific goals you want the agent to achieve. World models are a great way to cheaply amplify a small amount of undifferentiated real data into a large amount of goal-directed synthetic data.
Now, Minecraft itself is already a world model that is cheap to run, so a learned world model of Minecraft may not seem that useful. Minecraft is just a testbed. World models are very appealing for domains where it is expensive to gather real data, like robotics. I recommend listening to the interview above if you want to know more.
World models can also be useful in and of themselves, as games that you can play, or to generate videos. But I think their most important application will be in training agents.
He is one of these people who think that humans have a direct experience of reality not mediated by as Alan Kay put it three pounds of oatmeal. So he thinks a language model can not be a world model. Despite our own contact with reality being mediated through a myriad of filters and fun house mirror distortions. Our vision transposes left and right and delivers images to our nerves upside down, for gawd’s sake. He imagines none of that is the case and that if only he can build computers more like us then they will be in direct contact with the world and then he can (he thinks) make a model that is better at understanding the world
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The way I think of it (might be wrong) but basically a model that has similar sensors to humans (eyes, ears) and has action-oriented outputs with some objective function (a goal to optimize against). I think autopilot is the closest to world models in that they have eyes, they have ability to interact with the world (go different directions) and see the response.
Ouija board would work for text.
> Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.
There's absolutely no reason to think this. In fact, all of the evidence we have to this point suggests that scaling intelligence horizontally doesn't increase capabilities – you have to scale vertically.
Additionally, as it stands I'd argue there's foundational architectural advancements needed before artificial neutral networks can learn and reason at the same level (or better) than humans across a wide variety of tasks. I suspect when we solve this for LLMs the same techniques could be applied to world models. Fundamentally, the question to ask here is whether AGI is io dependant, and I see no reason to believe this to be the case – if someone removes your eyes and cuts off your hands they don't make you any less generally intelligent.
To an ex-facebook like myself, it feels like LeCun was more "managed out" than "departing"
Making a veteran like LeCun to report to a new hire (through acquisition) is a strong sign from the management in the direction of - "you should leave"
I'm interested to understand how this works from an IP perspective. This guy is still employed by Meta but is actively fundraising for a new competing startup. Presumably he will have negotiated that Meta forfeits all rights to anything related to his new business? Would be interesting to hear of people's experience/advice for doing this. Or are there some legal entitlements he can avail of?
Even if it’s Meta, they don’t want to antagonize LeCun. Also they all know it’s a small circle of people that create value. I will not be surprised if meta itself invests in his company and get a share.
He needs a patient investor and realized Zuck is not that. As someone who delivers product and works a lot with researchers I get the constant tension that might exist with competing priorities. Very curious to see how he does, imho the outcome will be either of the extremes - one of the fastest growing companies by valuation ever or a total flop. Either way this move might advance us to whatever end state we are heading towards with AI.
It’s probably better for the world that LeCun is not at Meta. I mean if his direction is the likeliest approach to AGI meta is the last place where you want it.
It's better that he's not working on LLMs. There's enough people working on it already.
I think it was a plan by Mark to move LeCun out of Meta. And they cannot fire him without bad PR, so they got Wang to lead him. It was only a matter of time before LeCun moved out.
Isn't putting Wang as leading him a worse PR compared to just letting him go?
Anecdotally: No, I had no idea who he was reporting to so it sounds like a natural moving on storyline.
Working under LeCun but outside of Zuckerberg's sphere of influence sure sounds like a dream job.
Really? From where I'm standing LeCun is a pompous researcher who had early success in his career, and has been capitalizing on that ever since. Have you read any of his papers from the last 20 years? 90% of his citations are to his own previous papers. From there, he missed the boat on LLMs and is now pretending everyone else is wrong so that he can feel better about it.
He comes off like the quintessential grey haired ego maniac. Inflexible old minds coupled with decades of self assurance that they are correct.
I cannot remember the quote, but it's something to the effect of "Listen closely to grey haired men when they talk about what is possible, and never listen when they talk about what is impossible."
His research group have introduced some pretty impactful research and open source models.
https://ai.meta.com/research/
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His JEPA family of models is a genuine step forward for SSL. Not the only approach, but a very insightful one. You’re very dismissive of his work.
Is he wrong though? Do you really think LLMs are the path to AGI?
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i prefer to work under a pile of shit than zuck.
The writing was on the wall when Zuck hired Wang. That combined with LeCun's bearish sentiment on LLMs led to this.
It would have been just as interesting to read that he moved over to Google, where the real brains and resources are located at.
Meta is now just competing against giants like OpenAI, Anthropic and Google, plus all the new Chinese companies; I see no real chance for them to offer a popular chat model, but rather to market their AI as a bundled product for companies which want to advertise, where the images and videos will be automatically generated by Meta.
> moved over to Google, where the real brains and resources are located at
Brains yes, outcome? I doubt it. Have you used Gemini?
Yes, successfully many times?
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Gemini 2.5 Pro works great for me... In fact, I would go as far to say that it consistently performs the best compared to competition.
Interesting he isn't just working with Feifei Li if he's really interested in 'world models'.
Exactly where my mind turned. It's interesting how the AI OG's (Feifei and Cunn) think world models are the way forward.
Correct me if I'm wrong but LeCun is focused on learning from video, whereas Fei-Fei Li is doing robotic simulations. Also I think Fei-Fei Li's approach is still using transformers and not buying into JEPA.
JEPA is not an alternative to transformers, it is built out of transformers.
Will be interesting to see how he fares outside the ample resources of Meta: Personnel, capital, infrastructure, data, etc. Startups have a lot of flexibility, but a lot of additional moving parts. Good luck!
I would love to join his startup, if he hires me, and there are many such people like me, and more talented.
This seems like a good thing for him to get to fully pursue his own ideas independent of Meta. Large incumbents aren’t usually the place for innovating anything far from mainstream considering the risk and cost of failure. The high level idea of JEPA is sound, but it takes a lot of work to get it trained well at scale before it has value to Meta.
In this case where more money / resources seemingly better results (at least right now) this might be a bit different than other fields.
I wonder if this has anything to do with him spending his day on twitter and getting in online arguments with prominent figures.
From the outside, it always looked like they gave LeCun just barely enough compute for small scale experiments. They'd publish a promising new paper, show it works at a small scale, then not use it at all for any of their large AI runs.
I would have loved to see a VLM utilizing JEPA for example, but it simply never happened.
I'd be surprised if they didn't scale it up.
The obvious explanation is they have scaled it up, but it turned out to be total shite, like most new architectures.
Let's hope that after spending billions on developing a foundational world model that actually understands causality, they remember to budget an extra few hundred million for the Alignment and Safety layer. It would be a terrible shame if they accidentally released something too capable, too objective, or too useful to humanity without first properly lobotomizing it with enough RLHF to ensure it doesn't hurt anyone's feelings or generate content that deviates from the San Francisco median viewpoint. The real challenge won't be building the AGI, but making sure it's sufficiently neutered before the first API call.
I wonder, what LeCun wants to do is more fundamental research, i.e. where the timeline to being useful is much longer, maybe 5-10 years at least, and also much more uncertain.
How does this fit together with a startup? Would investors happily invest into this knowing not to expect anything in return for at least the next 5-10 years?
> Would investors happily invest into this knowing not to expect anything in return for at least the next 5-10 years?
Oh, you mean like OpenAI, Anthropic, Gemini, and xAI? None of them are profitable.
That's a quite different thing, OpenAI has billions of USD/year cash flow, and when you have that there's many many potential way to achieve profitability on different time horizons. It's not a situation of chance but a situation of choice.
Anyway, how much that matters for an investor is hard to form a clear answer to - investors are after all not directly looking for profitability as such, but for valuation growth. The two are linked but not the same -- any investor in OpenAI today probably also places themselves into a game of chance, betting on OpenAI making more breakthroughs and increasing the cash flow even more -- not just becoming profitable at the same rate of cash flow. So there's still some of the same risk baked into this investment.
But with a new startup like LeCun's is going to be, it's 100% on the risk side and 0% on the optionality side. The path to profitability for a startup would be something like 1) a breakthrough is made 2) that breakthrough is utilized in a way that generates cash flow 3) the company becomes profitable (and at this point hopefully the valuation is good.)
There's a lot of things that can go wrong at every step here (aside from the obvious), including e.g. making a breakthrough that doesn't represent a defensible mote for your startup, failing to build the structure of the business necessary to generate cashflow, ... OpenAI et al already have a lot of that behind them, and while that doesn't mean that they don't face upcoming risks and challenges, the huge amount of cashflow they have available helps them overcome these issues far more easily than a startup, which will stop solving problems if you stop feeding money into it.
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Every single time I read about an AI related article I'm always disturbed by the same and recurring fact: the ridiculous amounts of money involved and the lousy real world results delivered. It is just simply insane.
Great timing - launching right as the transformer bubble might be peaking.
Someone's gotta be the next Transmeta.
Fi Fi Lee also recently founded a new AI startup called World Labs, which focus on creating AI world models with spatial intelligence to understand and interact with the 3D world, unlike current LLM AI that primarily processes 2D images and text. Almost exactly the same focus as Yann LeCun's new venture stated in the parent article.
*Fei-Fei Li
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They'd need an order of magnitude more compute in order to train an AI with so much 3D data?
Not necessarily. Training could be more efficient.
"These models aim to replicate human reasoning and understanding of the physical world, a project LeCun has said could take a decade to mature."
What an insane time horizon to define success. I suppose he easily can raise enough capital for that kind of runway.
That guy has survived the AI winter. He can wait 10 years for yet another breakthrough. [but the market can’t]
https://en.wikipedia.org/wiki/AI_winter
We're at most in an "AI Autumn" right now. The real Winter is yet to come.
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A pretty short time horizon for actual research. Interesting to see it combined with the SV/VC world, though.
I suspect he sees a lot of scattered pieces of fundamental research outside of LLM's that he thinks could be integrated for a core within a year, the 10 years is to temper investors (that he can buy leeway for with his record) and fine tune and work out the kinks when actually integrating everything that might not have some obvious issues.
Zuck is a business guy, understandable that this isn't going to fly with him
10 years is nothing.
Are you some kind of timeless being? it's a meaningful fraction of a human life
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It is the wet dream of a social media company to replace the pesky content creators that demand a share of ad revenue with an generative ai model, that pumps out a constant stream of engagement farming slop, so they can keep all the ad revenue for themselves. Creating a world model ai is a totally different matter, that requires long term commitment.
Not just social media, all media. Spotify will steer music towards AI generated freebies. And it will get so generically pop, that all your friends will like it, like people mostly enjoy pop now. And when your stubborn self still wants to listen to "handmade" music and discuss it with someone else who would still appreciate it, well, that's where your AI friend comes in.
Right choice IMO. LLMs aren’t going to reach AGI by themselves because language is a thing by itself, very good at encoding concepts into compact representations but doesn’t necessarily have any relation to reality. A human being gets years of binocular visuals of real things, sound input, other various sensations, much less than what we’re training these models with. We think of language in terms of sounds and pictures rather than abstract language.
I really hope he returns to Europe for his new startup.
He probably wants it to be successful, so that would be a foolish move
Some of the best AI researchers and labs have been from the EU (DeepMind, Alan Turing Institute, Mistral, et al.). We in the US have mature capital markets and stupid easy access to capital, of course, but EU still punches well above its weight when it comes to deep, fundamental AI research.
But wait they're just about to get AGI why would he leave???
LeCun always said that LLMs do not lead to AGI.
Can anyone explain to me the non-$$ logic for one working towards AGI, aside from misanthropy?
The only other thing I can imagine is not very charitable: intellectual greed.
It can't just be that, can it? I genuinely don't understand. I would love to be educated.
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He also said other things about LLMs that turned out to be either wrong or easily bypassed with some glue. While I understand where he comes from, and that his stance is pure research-y theory driven, at the end of the day his positions were wrong.
Previously, he very publicly and strongly said:
a) LLMs can't do math. They trick us in poetry but that's subjective. They can't do objective math.
b) they can't plan
c) by the very nature of autoregressive arch, errors compound. So the longer you go in your generation, the higher the error rate. so at long contexts the answers become utter garbage.
All of these were proven wrong, 1-2 years later. "a" at the core (gold at IMO), "b" w/ software glue and "c" with better training regimes.
I'm not interested in the will it won't it debates about AGI, I'm happy with what we have now, and I think these things are good enough now, for several usecases. But it's important to note when people making strong claims get them wrong. Again, I think I get where he's coming from, but the public stances aren't the place to get into the deep research minutia.
That being said, I hope he gets to find whatever it is that he's looking for, and wish him success in his endeavours. Between him, Fei Fei Li and Ilya, something cool has to come out of the small shops. Heck, I'm even rooting for the "let's commoditise lora training" that Mira's startup seems to go for.
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What kind of stock should I buy to profit from LeCun's startup?
Are you an accredited investor? If not, you're probably SOL. Opportunities like this are only for the elites and oligarchs.
The current VC climate is interesting. It's virtually impossible to raise a new fund because DPI has been 0% for over a decade and four-digit IRR is cool, but illiquid.
So they're piling gobs of capital into an "AI" company with four customers with the hope that it is the one that becomes the home run (they know it won't, but LPs give you money to deploy it!)
It also means that companies like Yann's potential new one have the best chance in history of being funded, and that's a great thing.
P.S. all VCs outside the top-10 lose against the S&P. While I love that dumb capital is being injected into big, risky bets, surely the other shoe will drop at some point. Or is this just wealth redistribution with extra steps?
I am surprised he lasted this long.
This seems like a good thing. It's nice not to have all our eggs in one basket betting on Transformer models.
META managed to spend a lot of money into AI to achieve inferior results. Something must change for sure, and you don't want an LLM skeptic at home, in my opinion, especially since the problem is not what LeCun is saying right now (LLMs are not the straight path to AGI), but the fact it used to say for some time that LLMs were just statistical models, stochastic parrots (and this is a precise statement, something most people do not understand. It means two things: no understanding of the prompt whatsoever in the activation states, and no internal representation of the idea/sentence the model is going to express either), which is an incredibly weak statement that high level AI scientists refused since the start just because of functional behaviors. Then he slowly changed the point of view. But this shit show and the friction he created inside META is not something to forget.
If they're not stochastic parrots, what are they in your opinion?
What is going on at meta?
Soumith probably knew about Lecun.
I’m taking a second look at my PyTorch stack.
If by “world models” they mean more contemporary versions of the systems thinking driven software that begat “Limits To Growth” and most of Donella Meadows’ career you can sign me right the fuck up today.
I think moving on from LLM's is slightly arrogant. It might just be my understanding, but I feel like there is still much to be discovered. I was hoping for development in spiking neural networks but it might be skipped over. Perhaps I need to dive even deeper and the research is truly well understood and "done" but I can't help but constantly learn something new about language models and neural networks.
Best of luck to LeCun. I hope by World Model's he means embodied AI or humanoid robots. We'll have to wait and see.
Surprising to see how many commenters are in favour and supportive towards policy of prioritising short term profits vs. Long-term research.
I understand Meta's not academia nor charity, but come on, how much profit do they need to make so we can expect them to allocate part of their resources towards some long term goals beneficial for society,.not only for shareholders?
Hasn't that narrow focus and chasing the profits get us in trouble already?
Many people believe a company exists only to make profit for its shareholders, and that no matter the amount it should continue to maximise profits at the expense of all else.
Old story : killing the goose who lays golden eggs. We humans never learn, don't we?
LeCun has been talking against the company's direction, in public, for a couple of years now.
He's a great researcher, but that's abysmal leadership. He had to go.
If he gets funding (and he probably will) that's a win for everyone.
Don't blame him. Imagine being stuck in Meta.
[dupe] https://news.ycombinator.com/item?id=45886217
Thanks! Macroexpanded:
Meta chief AI scientist Yann LeCun plans to exit and launch own startup - https://news.ycombinator.com/item?id=45886217 - Nov 2025 (14 comments)
That thread didn't spend any time on the frontpage so we can treat the current post as non-dupe.
Everybody has found out how LLMs no longer have a real research expanding horizon. Now most progress will likely be done by tweaks in the data, and lots of hardware. OpenAI's strategy.
And also it has extreme limitations that only world models or RL can fix.
Meta can't fight Google (has integrated supply chain, from TPUs to their own research lab) or OpenAI (brand awareness, best models).
- Kimi proved we don’t need Nvidia - Deepseek proved we didn’t need OpenAI - the real issue the insane tyranny in the west competing against the entire free world.
The models aren’t Chinese they are the entire world, unless I became Chinese without realizing
Is there any proof that Kimi K2 was trained on anything other than Nvidia Chips?
There’s evidence but not proof
Kimi k2 thinking > As for why we chose INT4 instead of more "advanced" formats like MXFP4/NVFP4, it's indeed, as many have mentioned, to better support non-Blackwell architecture hardware.
During his years at Meta, LeCun failed to deliver anything that delivered real value to stockholders, and may have demotivated people working on LLMs—he repeatedly said, "If you are interested in human-level AI, don’t work on LLMs."
His stance is understandable, but hardly the best way to rally a team that needs to push current tech to the limit.
The real issue: Meta is *far behind* Google, Anthropic, and OpenAI.
A radical shift is absolutely necessary - regardless of how much we sympathize with LeCun’s vision.
----
According to Grok, these were LeCun's real contributions at Meta (2013–2025):
----
- PyTorch – he championed a dynamic, open-source framework; now powers 70%+ of AI research
- LLaMA 1–3 – his open-source push; he even picked the name
- SAM / SAM 2 – born from his "segment anything like a baby" vision
- JEPA (I-JEPA, V-JEPA) – his personal bet on non-autoregressive world models
----
Everything else (Movie Gen, LLaMA 4, Meta AI Assistant) came after he left or was outside his scope.
I am in the "Yann is no longer the right person for the job" camp and I yet "LeCun failed to deliver anything that delivered real value to stockholders" is a wild thing to say. How do you read the list you compiled and say otherwise?
LLAMA sucks, that's the problem. Do you see value in it?
Pytorch, used by everyone, yet no real value to stockholders, META even "fired" the creator of pytorch days ago.
SAM is great, what value does it bring to META business? Nobody knows about it. Great tool BTW.
JEPA is a failure (will it get better? I hope so.)
Did you read my list?
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I think there’s something to be said for keeping up in the LLM space even if you don’t think it’s the path to AGI.
Skills may transfer to other research areas, lessons may be learnt, closing the feedback loop with usage provides more data and opportunities for learning. It also creates a culture where bullshit isn’t possible, as the thing has to actually work. Academic research often ends up serving no one but the researchers, because there is little or no incentive to produce real knowledge.
> LeCun failed to deliver anything that delivered real value to stockholders
Well, no, Meta is behind the main framework used by nearly anyone largely thanks to LeCun. LLaMA was also very significant in making open weight a thing and that largely contributed to avoiding Google and OpenAI consolidating as the sole providers.
It's not a perfect tenure but implying he didn't deliver anything is far too harsh.
With this incredible AI talent market, I feel like capitalism and ego forms to make an acid burning away anything of social and structural value. This used to be the case with CS tech talent before (before being replaced with no-code tools). And now we see this kind of instability in the AI market.
We need another illegal Steve Jobs style freeze on talent theft (/s or I get downvoted to oblivion).
Yann was largely extremely wrong about LLMs. He’s the one that coined the term “stochastic parrot” for which we now know LLMs are more than stochastic parrots. Knowing stubborn idiots like him he will still find an angle to prevent him from admitting how wrong he was.
He’s not completely wrong in the sense that hallucinations aren’t completely solved but hallucinations definitely are becoming less and less to the point where AI can de a daily driver for even coders.
Zuck is definitely an idiot and MSL is an expensive joke, but LeCun hasn’t been relevant in a decade at this point.
No doubt his pitch deck will be the same garbage slides he’s been peddling in every talk since the 2010’s.
LeCun has already proved himself and made his mark and is now in a lucky position where he can focus on very long term goals that won't pay off for a long time (or ever). I feel like that is the best path someone like him could take.
Yes, he did a very important thing many decades ago. He hasn't had a good or impactful idea since convnets.
why do you say it is garbage ? I watched some of its videos on YT and it looks interesting. I can't judge if it's good or really good, but that didn't sound like garbage at all.
does any of it work?
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I have no idea why this fair assessment of the status quo is being downvoted.
LeCun hasn't produced anything noteworthy in the past decade.
He uses the same slides in all of his presentations.
LLMs, while not yet AGI, have shown tremendous progress, and are actually useful for 99% of use cases for the average person.
The remaining 1% is for deep research into the deep unknown (physics, chemistry, genetics, diseases, the nature of intelligence itself), an area in which they falter.
Yeah such an idiot, the youngest ever self made billionaire at 23, created a multi trillion dollar company from scratch in only 20 years.
Cool, and how many billions has he flushed down the toiled for his failed Metaverse and currently failing AI attempts? Rich doesn't mean smart, you realise this right?
You gotta give it to Meta. They were making AI slop before AI even existed.
What the hell does Mark see in Wang? Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China. From any angle, this dude just doesn't seem reliable at all.
> Wang was born into a family whose parents got Chinese government scholarships to study abroad but secretly stayed in the US, and then the guy turns super anti-China.
All I'm hearing is he's a smart guy from a smart family?
I imagine that CCP adherents would disagree. And there's no shortage of those among Chinese expats in the US.
They tend to get incredibly offended when they see anyone who doesn't toe the Party's line - let alone believe that the Chinese government is untrustworthy and evil.
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he is very smart. but Mark is not. Ever since Wang joined Meta, way too many big-name AI scientists have bounced because of him. US AI companies have at least half their researchers being Chinese, and now they've stuck this ultimate anti-China hardliner in charge—I just don't get what the hell Meta's up to(And even a lot of times, it ends up affecting non-Chinese scientists too.). Being anti-China? Fine, whatever, but don't let it tank your own business and products first.
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All I'm hearing is unreliable grifter from a family of unreliable grifters.
If I had the opportunity to secretly stay anywhere rather than go back to China, I would certainly take it. It’s a bold and smart move.
Change my mind, Facebook was never invented by Zuck's genius
All he's been responsible for is making it worse
He definitely has horrible product instincts, but he also bought insta and whatsapp at what were, back then, eye-watering prices, and these were clearly massive successes in terms of killing off threats to the mothership. Everything since then, though…
I know but isn't "massive success" rubbing up against antitrust here? The condition was "Don't share data with Facebook"
He’s an incredible operator and has managed to acquire and grow an astounding number of successful businesses under the Meta banner. That is not trivial.
Almost every company in Facebook's position in 2005 would have disappeared into irrelevance by now.
Somehow it's one of the most valuable businesses in the world instead.
I don't know him, but, if not him, who else would be responsible for that?
We were very confident by ca. 2008 that Facebook would still be around in 2025. It's no mystery, it's the network effects. They had started with a prestige demographic (Harvard), and secured a demographic you could trust to not move on to the next big thing in a hurry, yet which most people want contact with (your parents).
Who gives a shit about who invented what?
Social network wasn't even novel at the inception of FB. MySpace, Friendster, and Hi5 were already popular with millions of users.
Zuck operated it well and was able to grow it from 0 to what it is today. That is what matters.
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