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> I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation.

Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.

Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.

The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.

The main difference is humans are learning all the time and models learn batch wise and forget whatever happened in a previous session unless someone makes it part of the training data so there is a massive lag.

Whoever cracks the continuous customized (per user, for instance) learning problem without just extending the context window is going to be making a big splash. And I don't mean cheats and shortcuts, I mean actually tuning the model based on received feedback.

> Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation.

While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions. You kinda want an artefact with a version stamp that doesn't change itself before you release the update, especially as this isn't like normal software where specific features can be toggled on or off in isolation of everything else.

* I think. Also, I'm saying "mammal" because of an absence of evidence (to my *totally amateur* skill level) not evidence of absence.

You could have continual learning on text and still be stuck in the same "remixing baseline human communications" trap. It's a nasty one, very hard to avoid, possibly even structurally unavoidable.

As for the "just put a vision LLM in a robot body" suggestion: People are trying this (e.g. Physical Intelligence) and it looks like it's extraordinarily hard! The results so far suggest that bolting perception and embodiment onto a language-model core doesn't produce any kind of causal understanding. The architecture behind the integration of sensory streams, persistent object representations, and modeling time and causality is critically important... and that's where world models come in.

yes those are bottlenecks that world models don't solve. but the promise of world models is, unlike LLMs, they might be able to learn things about the world that humans haven't written. For example, we still don't fully know how insects fly. A world model could be trained on thousands of videos of insects and make a novel observation about insect trajectories. The premise is that despite being here for millenia, humans have only observed a tiny fraction of the world.

So I do buy his idea. But I disagree that you need world models to get to human level capabilities. IMO there's no fundamental reason why models can't develop human understanding based on the known human observations.

The fact that models aren't continually updating seems more like a feature. I want to know the model is exactly the same as it was the last time I used it. Any new information it needs can be stored in its context window or stored in a file to read the next it needs to access it.

Especially they will require even more compute to get anything close to usable output. Human brains are super efficient at learning and producing output. We will need exponentially more compute for real time learning from video + audio + haptic data.

I never understood why we believe humans don't backprop. Isn't it that during the day we fill up our context (short term memory) and sleep is actually where we use that to backprop? Heck, everyone knows what "sleep on it" means.

LeCun is a researcher.

From his point of view, there are not much research left on LLM. Sure we can still improve them a bit with engineering around, but he's more interested in basic research.

It's pretty simple... the word circle and what you can correlate to it via english language description has somewhat less to do with reality than a physical 3D model of a circle and what it would do in an environment. You can't just add more linguistic description via training data to change that. It doesn't really matter that you can keep back propagating because what you are back propagating over is fundamentally and qualitatively less rich.

If your model is poor, no amount of learning can fix it. If you don't think your model architecture is limited, you aren't looking hard enough.

I don't understand why online learning is that necessary. If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know (meaning no online learning), that's still a very useful AGI. A hippocampus is a nice upgrade to that, but not super obviously on the critical path.

I don’t understand your view. Reality is that we need some way to encode the rules of the world in a more definitive way. If we want models to be able to make assertive claims about important information and be correct, it’s very fair to theorize they might need a more deterministic approach than just training them more. But it’s just a theory that this will actually solve the problem.

Ultimately, we still have a lot to learn and a lot of experiments to do. It’s frankly unscientific to suggest any approaches are off the table, unless the data & research truly proves that. Why shouldn’t we take this awesome LLM technology and bring in more techniques to make it better?

A really, really basic example is chess. Current top AI models still don’t know how to play it (https://www.software7.com/blog/ai_chess_vs_1983_atari/) The models are surely trained on source material that include chess rules, and even high level chess games. But the models are not learning how to play chess correctly. They don’t have a model to understand how chess actually works — they only have a non-deterministic prediction based on what they’ve seen, even after being trained on more data than any chess novice has ever seen about the topic. And this is probably one of the easiest things for AI to stimulate. Very clear/brief rules, small problem space, no hidden information, but it can’t handle the massive decision space because its prediction isn’t based on the actual rules, but just “things that look similar”

(And yeah, I’m sure someone could build a specific LLM or agent system that can handle chess, but the point is that the powerful general purpose models can’t do it out of the box after training.)

Maybe more training & self-learning can solve this, but it’s clearly still unsolved. So we should definitely be experimenting with more techniques.

Iirc LeCunn talks about a self organizing hierarchy of real world objects and imo this is exactly how the human brain actually learns

Who knows? Perhaps attention really is all you need. Maybe our context window is really large. Or our compression is really effective. Perhaps adding external factors might be able to indirectly teach the models to act more in line with social expectations such as being embarrassed to repeat the same mistake, unlocking the final piece of the puzzle. We are still stumbling in the dark for answers.

The reason LLMs fail today is because there’s no meaning inherent to the tokens they produce other than the one captured by cooccurrence within text. Efforts like these are necessary because so much of “general intelligence” is convention defined by embodied human experience, for example arrows implying directionality and even directionality itself.

Agents have the ability of continual learning.

> virtual simulation of 3d world

Virtual simulations are not substitutable for the physical world. They are fundamentally different theory problems that have almost no overlap in applicability. You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how.

Physical world dynamics are metastable and non-linear at every resolution. The models we do build are created from sparse irregular samples with large error rates; you often have to do complex inference to know if a piece of data even represents something real. All of this largely breaks the assumptions of our tidy sampling theorems in mathematics. The problem of physical world inference has been studied for a couple decades in the defense and mapping industries; we already have a pretty good understanding of why LLM-style AI is uniquely bad at inference in this domain, and it mostly comes down to the architectural inability to represent it.

Grounded estimates of the minimum quantity of training data required to build a reliable model of physical world dynamics, given the above properties, is many exabytes. This data exists, so that is not a problem. The models will be orders of magnitude larger than current LLMs. Even if you solve the computer science and theory problems around representation so that learning and inference is efficient, few people are prepared for the scale of it.

(source: many years doing frontier R&D on these problems)

I guess you need two things to make that happen. First, more specialization among models and an ability to evolve, else you get all instances thinking roughly the same thing, or deer in the headlights where they don't know what of the millions of options they should think about. Second, fewer guardrails; there's only so much you can do by pure thought.

The problem is, idk if we're ready to have millions of distinct, evolving, self-executing models running wild without guardrails. It seems like a contradiction: you can't achieve true cognition from a machine while artificially restricting its boundaries, and you can't lift the boundaries without impacting safety.

Re 1: You experience the world in real time (or close enough) via your senses, which combine to form a spatiotemporal sense: A sense of being a bounded entity in space and time. The LLM has none of that. They experience the world via stale old text and text derivatives.

Re 2: There's something tremendous in the fact, staring us right in the face, that LLMs are unable to meaningfully contribute to academic/medical research. I'm not saying that they need to perform on the level of a one-in-a-million Maxwell, DaVinci, or whatever. But as Dwarkesh asked one year ago: "What do you make of the fact that these things have basically the entire corpus of human knowledge memorized and they haven't been able to make a single new connection that has led to a discovery?"

Re 3: Sure, you can hold it by the hand and spoonfeed it. You can also create for it a mirror reality which doesn't exist, which is pure fiction. Given how limited these systems are, I don't suppose it makes much of a difference. There's no way for it to tell. The "human in the loop" is its interaction with the world. And a pale, meager interaction it is.

Re 4: Static, old images/video that they were trained on some months ago. That, too, is no way of interacting with the world.

Um, why would anyone be "holding the bag" and who needs protecting by society? He's not taking out a loan, he's getting capital investment in a startup. People are gambling that he will do well and make money for them. If they gamble wrong, that's on them. Society won't be doing anything either way because investors in startups that fail don't get anything.

W Brian Arthur's book "The Nature of Technology" provides a framework for classifying new technology as elemental vs innovative that I find helpful. For example the Huntley-Mcllroy diff operates on the phenomenon that ordered correspondence survives editing. That was an invention (discovery of a natural phenomenon and a means to harness it). Myers diff improves the performance by exploiting the fact that text changes are sparse. That's innovation. A python app using libdiff, that's engineering. And then you might say in terms of "descendants": invention > innovation > engineering. But it's just a perspective.

Novel things can be incremental. I don't think LLMs can do that either, at least I've never seen one do it.

Suno is transformer-based; in a way it's a heavily modified LLM.

You can't get Suno to do anything that's not in its training data. It is physically incapable of inventing a new musical genre. No matter how detailed the instructions you give it, and even if you cheat and provide it with actual MP3 examples of what you want it to create, it is impossible.

The same goes for LLMs and invention generally, which is why they've made no important scientific discoveries.

You can learn a lot by playing with Suno.

Genuinely novel discovery or invention?

Einstein’s theory of relativity springs to mind, which is deeply counter-intuitive and relies on the interaction of forces unknowable to our basic Newtonian senses.

There’s an argument that it’s all turtles (someone told him about universes, he read about gravity, etc), but there are novel maths and novel types of math that arise around and for such theories which would indicate an objective positive expansion of understanding and concept volume.

Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence?

It could potentially reach super-dolphin level intelligence

> Imagine that we made an LLM out of all dolphin songs ever recorded, would such LLM ever reach human level intelligence? Obviously and intuitively the answer is NO.

Not so fast. People have built pretty amazing thought frameworks out of a few axioms, a few bits, or a few operations in a Turing machine. Dolphin songs are probably more than enough to encode the game of life. It's just how you look at it that makes it intelligence.

I mean no offense here, but I really don't like this attitude of "I thought for a bit and came up with something that debunks all of the experts!". It's the same stuff you see with climate denialism, but it seems to be considered okay when it comes to AI. As if the people that spend all day every day for decades have not thought of this.

Dataset limitations have been well understood since the dawn of statistics-based AI, which is why these models are trained on data and RL tasks that are as wide as possible, and are assessed by generalization performance. Most of the experts in ML, even the mathematically trained ones, within the last few years acknowledge that superintelligence (under a more rigorous definition than the one here) is quite possible, even with only the current architectures. This is true even though no senior researcher in the field really wants superintelligence to be possible, hence the dozens of efforts to disprove its potential existence.

I really hate the world model terminology, but the actual low level gripe between LeCunn and autoregressive LLMs as they stand now is the fact that the loss function needs to reconstruct the entirety of the input. Anything less than pixel perfect reconstruction on images is penalized. Token by token reconstruction also is biased towards that same level of granularity.

The density of information in the spatiotemporal world is very very great, and a technique is needed to compress that down effectively. JEPAs are a promising technique towards that direction, but if you're not reconstructing text or images, it's a bit harder for humans to immediately grok whether the model is learning something effectively.

I think that very soon we will see JEPA based language models, but their key domain may very well be in robotics where machines really need to experience and reason about the physical the world differently than a purely text based world.

https://medium.com/state-of-the-art-technology/world-models-...

> One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”

Diffusion models are not autoregressive but have the same limitations

Sure, but don't conflate the representation format with the structure of what's being represented.

Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!)

A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing itself, that which generates those descriptions.

You can tokenize an image just as easily as a sentence, sure, but a pile of images and text won't give you a relation between the system and the world. A world model, in theory, can. I mean, we ought to be sufficient proof of this, in a sense...

Can a token represent concentration, will?

Ironically, your comment is practically incomprehensible.

Yeah I don't even think you'd need to train it. You could probably just explain how SVG works (or just tell it to emit coordinates of lines it wants to draw), and tell it to draw a horse, and I have to imagine it would be able to do so, even if it had never been trained on images, svg, or even cartesian coordinates. I think there's enough world model in there that you could simply explain cartesian coordinates in the context, it'd figure out how those map to its understanding of a horse's composition, and output something roughly correct. It'd be an interesting experiment anyway.

But yeah, I can't imagine that LLMs don't already have a world model in there. They have to. The internet's corpus of text may not contain enough detail to allow a LLM to differentiate between similar-looking celebrities, but it's plenty of information to allow it to create a world model of how we perceive the world. And it's a vastly more information-dense means of doing so.

You must be new here

Care to explain what led to this reaction?

There's two points here. The first is that a strategy of monetizing models to fund the goal of reaching AI is indistinguishable from just running a business selling LLM model access, you don't actually need to be trying to reach AGI you can just run an LLM company and that is probably what these companies are largely doing. The AGI talk is just a recruiting/marketing strategy.

Secondly, it's not clear that the current LLMs are a run up to AGI. That's what LeCun is betting - that the LLM labs are chasing a local maxima.

I mean, Sutskevar and Carmack are trying to one-shot AGI. We just don't talk about them as much as we do the labs with products because their labs aren't selling products.

> Trying to go straight for the goal is risky.

That's the point of it. You need to take more risk for different approach. Same as what OpenAI did initially.

For instance, under Yann's direction Meta FAIR produced the ESM protein sequence model, which is less hyped than AlphaFold, but has been incredibly influential. They achieved great performance without using multiple alignments as an input/inductive bias. This is incredibly important for large classes of proteins where multiple alignments are pretty much noise.

I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did.

Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.

Do not keep bad results in context. You have to purge them to prevent them from effecting the next output. LLMs deceptively capable, but they don’t respond like a person. You can’t count on implicit context. You can’t count on parts of the implicit context having more weight than others.

> - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough.

If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.

You should ignore Llama because by his own admission,

>My only contribution was to push for Llama 2 to be open sourced.

Did he work on those vision models?

Llama wasn’t Yann LeCun’s work and he was openly critical of LLMs, so it’s not very relevant in this context.

Source: himself https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.

Its a matter of needing more time, which is a resource even SV VCs are scared to throw around. Look at the timeline of all these advancements and how long it took

Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022

This makes alot of people think that things are just accelerating and they can be along for the ride. But its the years and years of foundational research that allows this to be done. That toll has to be paid for the successsors of LLMs to be able to reason properly and operate in the world the way humans do. That sowing wont happen as fast as the reaping did. Lecun was to plant those seeds, the others who onky was to eat the fruit dont get that they have to wait

I was thinking the same, are all people he hires LeSomething like those working at Bolson Construction having -son as a suffix.

LeBron is missing out an opportunity to invest

Or LeX

He just made a joke

We do worry much more about animal well-being than we worry about our "lumps of metal" (as a cousin comment fittingly put it). As we should, and generally I think we should worry much more about animal welfare. I find concerns for AI system welfare voiced by people like Thomas Metzinger wildly misguided.

And while they don't have language like we do, dogs can understand basic commands and they aren't even the smartest animals.

That's the direction the field is already going with "agents". People want autonomous AI agents that are capable of acting independently and that have more and more capabilities. For example, something like Claude code, but that acts as a sidekick that is constantly running, and able to act without being prompted. That's what people are imagining when they talk about teams of agents. You act as a manager, but your coding agents are off working on various features and only check in periodically.

They won't have sentience because it will be antithetical to capitalist business ideology. There's no good business value proposition for having the AI daydream like humans do, or 'sleep' while 'on', or have inspirational thought that might be seen as 'wrong' or useless. If that behavior ever manifests, it will probably be stamped out in a future release.

You can't justify to the board the wasted money to have the android dream.

Yeah, call me when Yann incorporates the four humors and the elemental force of fire, from which we draw life. Metal lacks the nature for this purpose.

Says the bag of lipids and proteins :)

I think the more likely retort will be that we can't be smart, by the AI's standard.

Lol. A lump of flesh can't be sentient

https://www.mit.edu/people/dpolicar/writing/prose/text/think...

Have you published anything about your health time series model? Sounds interesting!

Appreciate your work! Healthcare is a regulated industry. Everything (Research, proposals, FDA submissions, Compliance docs, Accreditation Standards, etc.) is documented and follows a process, which means there's a lot of thesis. You can't sneak in anything unverified or unreliable. Why does healthcare need a JEPA\World model?

I've been working to understand the potential uses for JEPA. Outside of video, has anyone made a list of any type (geared towards dummies like me)?

Hm, Singapour looks more like "one of their base"; they will have offices in Paris, Montréal, Singapour and New York (according to both this article and the interview Yann Le Cun did this morning on France Inter, the most listened radio in France).

Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.

Probably just a satellite office.

Might be to be close to some of Yann's collaborators like Xavier Bresson at NUS

That's a Singaporian newspaper, though; not sure if it's objectively their main base, or just one of them

Which would be a good idea, as a European. I'd hate to see the investment go to waste on taxes that are spent on stupid shit anyway. Should go into R&D not fighting bureaucracy.

"Show me the incentive and I will show you the outcome."

Almost certainly the IP will be held in Singapore for tax reasons.

> they are setting up in Singapore as their base

Europe in general has been tightening up their rules / taxes / laws around startups / companies especially tech and remote.

It's been less friendly. these days.

It's sufficient to think that there is a chance that they will not be, however, for there to be a non-zero value to fund other approaches.

And even if you think the chance is zero, unless you also think there is a zero chance they will be capable of pivoting quickly, it might still be beneficial.

I think his views are largely flawed, but chances are there will still be lots of useful science coming out of it as well. Even if current architectures can achieve AGI, it does not mean there can't also be better, cheaper, more effective ways of doing the same things, and so exploring the space more broadly can still be of significant value.

I think LeCun has been so consistently wrong and boneheaded for basically all of the AI boom, that this is much, much more likely to be bad than good for Europe. Probably one of the worst people to give that much money to that can even raise it in the field.

Whenever I see claims about AGI being reachable through large language models, it reminds me of the miasma theory of disease. Many respectable medical professionals were convinced this was true, and they viewed the entire world through this lens. They interpreted data in ways that aligned with a miasmatic view.

Of course now we know this was delusional and it seems almost funny in retrospect. I feel the same way when I hear that 'just scale language models' suddenly created something that's true AGI, indistinguishable from human intelligence.

Just because you raise 1 billion dollars to do X doesn't mean you can't pivot and do Y if it is in the best interest of your mission.

I won't comment on Yann LeCun or his current technical strategy, but if you can avoid sunk cost fallacy and pivot nimbly I don't think it is bad for Europe at all. It is "1 billion dollars for an AI research lab", not "1 billion dollars to do X".

It's been 6 months away for 5 years now. In that time we've seen relatively mild incremental changes, not any qualitative ones. It's probably not 6 months away.

> RSI

Wait, we have another acronym to track. Is this the same/different than AGI and/or ASI?

Hard disagree, military might isn't going to secure anybody into the future, modern society and our economies will only get more vulnerable as time goes on and large wars or engagements will just push economies closer to collapse. And without a solid modern economy to back up the military, modern military will fall apart.

Right, they really need a military industrial complex to be "competitive" :eyeroll. Are you suggesting regressing to the stone age?

can you elaborate more, also isn't this necessary for a Lab that wants to compete with highly funded entities (like OpenAI, Anthropic)?

Erm, ... OpenAI has hyped when it started and it took 6 years to take off. It's way to early to declare the SSI and Thinking Machines have failed.

You underestimate academia. Any academic that reads these two sentences only focuses on the first one: He has a named chair at Courant. In Germany, being a a Prof is added to your ID card/passport and becomes part of your official name, like knighthood in other countries.

It's not comparing him to anyone. He has an endowed professorship. This is standard in academia, and you give the name because a) it's prestigious for the recipient and b) it strokes the ego of the donor.

https://cims.nyu.edu/dynamic/news/1441/

This is just the official name of a chair at NYU. I'm not even sure Jacob T. Schwartz is more well known than Yann LeCun

That’s not a comparison to another person. That’s his job title. It is not uncommon for universities to have distinguished chairs within departments named after a notable person—in this case, the founder of NYU’s Department of Computer Science.

Eh, that paragraph reads perfectly normal to me.

Either you have not read enough Wikipedia pages, or you have too much to complain about. (Or both.)

Probably related to the reasoning behind: https://arstechnica.com/tech-policy/2026/02/wikipedia-bans-a...

Or you're using Cloudflare DNS.

Huh, it's working for me (on Firefox).

>The fundamental problem with today's LLMs that will prevent them from achieving human level intelligence, and creativity, is that they are trained to predict training set continuations, which creates two very major limitations:

I am of the opinion that imagination and creativity comes from emotion, hence a machine that cannot "feel" will never be truly intelligent.

One can go ahead and ask, but you are just a lump of meat, if you can feel, then so a computer of similar structure can.

If we assume that physical reality is fundamental, then that might make sense. But what if consciousness is fundamental and reality plays on consciousness?

Then randomness, and in-turn ideas come from the attributes of the fundamental reality that we are in.

I ll try to simplify it. Imagine you having an idea that extends your life for a day. Then from all the possible worlds, in some worlds, you find yourselves living in the next day (in others you are dead). But this "idea" you had, was just one among the infinite sea of possibilities, and your consciousness inside one such world observes you having that idea and survive for a day!

If you want to create a machine that can do that, it implies that you should be a consciousness inside a world in it (because the machine cannot pick valid worlds from infinite samples, but just enables consciousness to exists such suitable worlds). So it cannot be done in our reality!

Mayyyyy be "Quantum Darwinism" is what I am trying to describe here..

RL on LLMs has changed things. LLMs are not stuck in continuation predicting territory any more.

Models build up this big knowledge base by predicting continuations. But then their RL stage gives rewards for completing problems successfully. This requires learning and generalisation to do well, and indeed RL marked a turning point in LLM performance.

A year after RL was made to work, LLMs can now operate in agent harnesses over 100s of tool calls to complete non-trivial tasks. They can recover from their own mistakes. They can write 1000s of lines of code that works. I think it’s no longer fair to categorise LLMs as just continuation-predictors.

We have a pretty good answer to your questions, they are called IQ tests. It’s not like measuring intelligence is uncharted territory.

https://www.scientificamerican.com/article/i-gave-chatgpt-an...

https://www.reddit.com/r/singularity/comments/1p5f0b1/gemini...

Gemini 3 Pro has an IQ of 130 now but we keep moving the goalposts and being like “not THAT intelligence, we mean this other intelligence”. I suspect, and history shows us this will be the case, that humans will judge AIs as not human and not intelligent and not needing rights way past the point where they should have rights, even when vastly superior to human intelligence.

Was just a grift

> So I wonder if you just think you will be one of the few who benefit at the expense of others

It's not a zero sum game, IMO. It will benefit some, be neutral for others, negative for others.

For instance, improved productivity could be good (and doesn't have to result in layoffs, Jevon's paradox will come into play, IMO, with increased demand). Easier/better/faster scientific research could be good too. Not everyone would benefit from those, but not everyone has to for it to be generally good.

Autonomous AI-powered drone swarms could be bad, or could result in a Mutually Assured Destruction stalemate.

Where have you been in the last 2 decades?

>isn't US the one missing out here?

Why would the US miss out here? The US invests in something = the US owns part of something.

This isn't a zero sum game.

Not at all. A quick google turns up evidence of 4. There may be more but I think probably not many.

For a foundation AI lab with a world famous AI researcher at the helm though, it's not so impressive. Won't even touch the sides of the hardware costs they'd need to be anywhere near competitive

It is not free, we just pay taxes.

Free healthcare and retirement ?

„free“

actually, they raised $1.03 billion at a $3.5 billion valuation.

That's a bonfire of capital into a gaping hole in the ground with zero chance outside of "military pork" and "overcharging the taxpayer" to ever make their money back. The brain capital loss here is what's going to spook investors.