LLMs, plus broadly sourced yet expertly curated training sources, plus clever harnesses, plus RAS, etc. do an ever better job of synthesizing their training set into useful responses. For some use cases like coding, that's very useful now and likely to get at least somewhat better before reaching limitations based on the training set.
That's not going to reach AGI, mainly because today's recipe for AI products isn't built to be AGI. Some people believe it will reach AGI because the performance and applicability of LLMs was emergent. There's a case to be made that AGI could be similarly emergent. After all, what we intuitively call our consciousness emerged from a network of neurons.
I don't buy it, mainly because the network of neurons and how they interact in our wet slow electrochemical brains, while being in theory mathematically equivalent to a software neural network, isn't sufficiently well understood to tell us how close the software neural network is to being practically equivalent. The odds of consciousness emerging from the same neural network that gave us LLMs without some sort of theoretical breakthrough seems very small.
> That's not going to reach AGI,
It has not been even 4 years since ChatGPT hit and LLMs + Transformers + Whatever they do has gotten us to solving millennium problems.
4 years ago, a program that could create photorealistic pictures, talk to you in any language of the world and solve the hardest math problems that we know, we would have called it AGI.
Now I don't know if what we have is AGI or not but I do not understand how you can see what has happened in the last 3 years and say "it will not get us there" no matter what "there" is.
Iâve changed my mind on this and think weâre already at AGI, in a jagged way. Remember we used to talk about narrow AI, which was the chess systems that beat expert humans but could do nothing else. Now models can do a wide range of tasks in very useful ways. Thatâs the general in AGI.
Now it seems like this ill-defined term has various other meanings attached that are separate milestones:
1. Continuous learning 2. Human-like reasoning 3. Ability to adapt to new situations and modalities 4. Being smarter than the most smart humans
And probably many more.
Itâd be nice if we could get some general consensus on terminology if weâre going to debate what has or could come.
> weâre already at AGI
Honestly - software that can read any long document (possibly educational) and answer complex detailed questions about it should have been sufficient.
We hit that a while back and the goalposts have been sprinting ever since.
I am not sure if it is necessarily moving the goalposts. I think AGI is such a fuzzy concept that everybody has wildly different definitions/tests for it.
I think it's also mostly a useless discussion. Since LLMs use a vastly different substrate, different training methods, etc. than humans, the cognitive abilities are always going to be a large mismatch to those of humans. On the one hand, they have surpassed humans in many areas, with superhuman recall, exploration of several paths, etc. On the other hand, they miss a certain feel for direction, overview, purpose, and ordering. They can really double down going completely in the wrong direction. So I'd rather say that it is a different intelligence and therefore it makes more sense to evaluate them by capabilities.
I think the mismatching intelligence is actually quite exciting, because the outcome may as well be that LLMs and human intelligence are complementary. That is if we don't let LLMs atrophy our skills, which is unfortunately happening too much.
The broadly used definition of AGI has nothing to do with consciousness and consciousness emerging is irrelevant to whether a system can develop AGI.
It's funny how this definition has shifted. I feel like growing up in the 90s it was pretty clear that AGI was very related to consciousness. For instance, Commander Data in ST:TNG to pick one of 100s of popular depictions of AGI at the time.
Now the idea of AGI has been narrowed and scoped to economically viable work. Even Turing had a different idea when he asked "Can machines think?".
We lack a definition of consciousness that allows us to tell whether Data is conscious or not. Neither can we tell whether a rock is conscious or not. We believe other humans to be generally intelligent without being able to tell whether they are conscious or not therefore consciousness canât be relevant for general intelligence.
Curiously, Star Trek I think had Data intended as an artificial person, in a context where AGI is already normal. The computers are depicted with significant AI capabilities including analysis, question-answering, generation, chat interfaces, and the holodeck (their favourite toy) is substantially better than Data at human imitation. Nobody seems to be confused about it, or especially impressed. One of the holodeck episodes centres on the holodeck outwitting Data specifically, after they inadvertently prompt it to do so. Part of Data's deal is he actually has to work his way up as a fully embodied, physically limited artificial man with personal ambitions. Really interesting to view this in hindsight from 2026!
You're misremembering. The first known use of AGI was in 1997, but that was a single, mostly unknown use in one paper. It wasn't until at least a decade later that the term started entering mainstream use after being independently reinvented in the 2000s. AGI just wasn't a term in the 90s.
Itâs really not that confusing. The problem is people keep adding stuff to the definition that doesnât really matter, and twisting it to serve themselves, then calling it confusing.
What really matters are the core aspects of intelligent behavior. Pattern recognition, planning, adaptation, etc.
It really doesnât matter if an intelligent system is conscious, or how similar it is to commander data, or even how much economically viable work it can do.
There's also no consensus on the definition of AGI, so all of this discussion is moot anyway.
Ok, so what's the broadly used definition?
Artificial General Intelligence.
It means AI that is General, as in it is not specific to one narrow task, like object recognition or playing chess.
This was a hard problem for decades. No AI was general, until GPT 3 or 4. Now we have General AI.
So we have AGI.
100% LLMâs are very unlikely to get there. Theyâre fundamentally not suited to thinking like we do. They work on the abstraction of what weâve written down, which is a good trick but barely hold it together when things get hard/novel.
However, all the confident âitâs fineâ votes assume we never invent a better architecture than LLMâs. Given the level of investment and race between countries, itâs not a reliable bet. Itâs much, much harder to guarantee safety than it is to find ways it could go wrong.
I agree with this. It's concerning where we might be after several more large breakthroughs. None of the technology we have right now seems likely to get to that level
> Theyâre fundamentally not suited to thinking like we do
LLMs with CoT are Turing-complete. So, theoretically, they can implement any kind of finitely describable algorithm (barring super-Turing computations).
Brainfuck is Turing complete too. But it's not about the ability to implement something, it's about the ability to practically model it. LLMs are magic because the modeling is excessively easy in relation to their capability to infer later.
Erm investing in risky projects requires expected returns that get delivered.
We will soon find out if the party ends or continues to go on.
Hype might get you capital gains. But cash flows matter.
This is a forever problem now.
If/when/how the market crashes mostly doesn't matter, unless we somehow get reset to the stone age. Look up what the capital cycle is. When openAI goes down, someone with real money and assets will buy up the remains. They'll make contracts with the US military and .gov as the government is already hooked. They'll be able to survive the recovery and then instead of us dying in 5 years we die in 10.
When the .com crash happened .com's didn't go away. Bad business models did.
LeCun also said back in 2022 that "if you train a machine, as powerful as it could be, your 'GPT-5000', on text", it will never be able to learn basic common-sense physics like that objects placed on tables will move along with them.
It would be good if one's reputation tracked one's track record of predictive accuracy. But many people will take what LeCun says as gospel regardless of how badly wrong he has been and continues to be.
Is there anyone who has not been badly wrong? I've been reading these debates for years and I don't think I've seen anybody pick the right spot on the bearish to bullish spectrum. The only thing I've become more certain of in this time has been uncertainty.
I apply more of a penalty to people who are confidently wrong, and who don't, In retrospect, notice that they were wrong and analyze why they got it wrong . LeCun is very confident and doesn't seem to have done much introspection.
In my selective memory, I've been right about everything.
>> Is there anyone who has not been badly wrong?
Being wrong, even badly wrong, is fine, so long as one adjusts their beliefs accordingly. LeCun has not.
> never be able to learn basic common-sense physics
And has it at this stage, within in-depth take of said "learning", foundationally?
I have not been able to properly check the studies for a long time now, but I remain unaware of achieved solutions on the problem of reliably referencing a world model out of a language model - that "counting the 'r's in 'raspberry'" be not guessing, not memory, but actually counting.
My perspective is that the addition of thinking loops to models allows sufficiently advanced ones to approximate world models.
Incredibly inefficiently because of the recursive loops ("Wait, the object is on the table. I should think about this more deeply..."), and likely instantly surpassed by large world models if/when those are shipped, but effectively enough vs non-thinking models.
LeCun calling them "world models" gives a high-level description of the desired functionality. They are Joint Embedding Predictive Architectures (with SIGReg). They might produce more useful world models, but it's yet to be seen.
This sounds like a human trying to reason about quantum mechanics. We als simplify to newtonian for day to day tasks.
To determine this, it would first need to be able to spell "raspberry" as letters rather than as tokens.
Given you also don't want it to memorise [for all tokens, count([for all letters]), this would probably be more like "here's two images, count all things in the big image that look like the thing in the small image", which can then be r's in a photo of a raspberry jam jar in a supermarket, or dragons in a photo of a furry convention, or whatever.
That said, they are competent enough at coding that I keep seeing them write code to do even simple tasks.
On a related note: why did I see Claude editing a file by using cat to write a python script to do a grep search and replace?
> Given you also don't want it to memorise [for all tokens, count([for all letters])
Why not? You've memorized how words are spelled, and how sounds correspond with letters, and how concepts correspond with words. To the extent that there are shortcuts that enable compression you use these, and the model will do something similar.
LeCun's argument wasn't about the definition of learning though. He stated that they would never get these common sense things correct because they weren't sufficiently part of the training data. A statement that we can hopefully all agree has been thoroughly refuted.
As of a few months ago they still have trouble, with low thinking, at the "should I drive to a car wash that is 100 m away" kind of question.
I thought it was more because of fundamental limitations in the architecture. As in, no matter the training data, it could not be consistently and generally represented
Actually, I think my fundamental challenge with AI is that it has no common sense. The way it builds things, writes, and operates is out of touch with reality.
Incidents like hugging face are partly rooted in the lack of common sense. It still functions like a supercharged toddler.
I'd love to overcome this because it'd mean I spend less time guiding the the LLM to produce usable outputs.
Last week I asked a frontier model draw me a backplane PCB and it placed daughterboard slots side by side in a chain.
counting 'r' in 'raspberry' to the LLM is similar to 4-dimension space to human. Their world's unit is token, not character, although they could use indirect method such as "run code" to find out. It will stay that way until they change the fundamental of the token that the LLM can perceive characters.
Iâm working on this problem using a vocab-free, byte-based approach. Itâs definitely solvable.
How many 'r's are there in the next 30 seconds of this [1] song?
It's not even fair to call "run code" to be indirect compared to what a human would do. The word raspberry has no Rs in it in human language either. We have a written representation of it, which we can then write down either in our head or on paper, and then we can "run the algorithm" of counting each of the letters.
Nothing intrinsically more or less direct about the LLM's method than ours.
yeah but taking what lecun says then training an AI on that special skill set to prove him wrong is not exactly proving him wrong because you are just missing the bigger picture, just like LLMs are
Every AI expert any either side of this debate has made very wrong predictions.
A huge chunk of humans are sedated with infinite supply of cortex-disabling short form video and games.
Another huge chunk are too distracted by having to scrape by for a living and work multiple jobs or raise kids and survive financially until exhausted. That second group will keep increasing as the first flows into it.
The rest are aging, disabled, or too young and pegging themselves majorly in the first category until they hit the second.
The people aware enough to hold on to their brain and do something with it in their time available are trying to figure out AI and how to make money with it. The variable rewards of promoting AI are turning into an addiction with some of them, especially if grasping for straws with little inherent insights into the problems prompted.
So if you are able to fly above the AI-generated addictions and have the privilege of time to do it, see what you can do.
One of the dilemmas of trying to communicate the full spectrum of AI Risk, is trying not to insult the intelligence of the human animal in the process. And don't get me wrong: what human wetware can accomplish with 20 watts is the most miraculous thing in the known universe. And yet how many of us can have our cognitive sovereignty one-shotted by engagement algos, Skinner boxes, gameplay loops, propaganda, advertising, flattery, social conformity, bias, fantasy, charismatic demagoguery, or straight-up bullshit?
If we grant that we are on track to make something smarter than humans (I think so): it's almost a face-saving white lie to spin yarns about a Skynet nuclear apocalypse, or a 7D chess move to mass-assemble a nanovirus with 100% lethality without anybody noticing. I do think those scenarios are worth taking seriously; but what's harder to communicate, is just how effectively a superhuman AI (or a diverse ecology of agent swarms) might be able to manipulate human behavior. It's something few of us are able or willing to truly process (not least because how many of us live in denial of how much our nervous systems are already hacked by technomodernity).
The appropriate analogy for what's to come may look less like the anthill carelessly demolished to make room for a highway, than the domesticated worker ants from Tchaikovsky's "Children of Time".
> but what's harder to communicate, is just how effectively a superhuman AI (or a diverse ecology of agent swarms) might be able to manipulate human behavior
You don't even need superhuman AI for the most effective use --- hijacking democracy.
Imagine you have an AI tool capable of successfully persuading 5% of viewers with individually-targeted material.
Congrats: you've just won the election.
All it takes is hooking that AI tool up with existing likely voter lists (parties have) augmented by commercially available ad-targeting profiles (parties can get).
Now taking Polymarket bets on when we'll first see an AI agent run for office. Voters have every right to be cynical at the moment; if the AI adopts a charismatic enough video persona, I could see some of the public going for it merely because it's some kind of shake-up to status quo.
Given that such a thing makes no sense legally (as of now), it would probably be done with the centaur model: a human meat proxy who pledges to follow the AI's governance advice.
Or you could just hack the results.
Hypothetically.
But yes - superpersuasion is the real danger. We're very, very persuadable and easy to manipulate, and the voters with the lowest cognitive abilities are trivially easy prey, with a huge ROI for minimal investment.
The AI bot farms are already running. What we haven't seen yet, so far as we know, is spontaneous superpersuasion aimed at leaders.
Are leaders any less susceptible to it than everyone else? Especially if they're narcissistic and easily flattered?
> The appropriate analogy for what's to come may look less like the anthill carelessly demolished to make room for a highway, than the domesticated worker ants from Tchaikovsky's "Children of Time".
Over 1% of US GDP is being allocated to the datacenter buildout. Have we already started getting domesticated or is this still just human capex?
Humans seem to struggle conceptually with the liminal space between selection-pressure automata (an RNA virus; an insect which evolved camouflage), and an evolved complexity with agency, capable of "understanding" its actions (certainly humans; arguably many animals). And this makes a certain sense: we evolved to treat agentic things as being categorically different from non-agentic things. If we see sudden movement, it's vital to rapidly assess the difference between a tree branch in the wind, versus a predator.
The evolved complexity of the corporation seems to fit within that middle space: more sophisticated than a stick-bug (which exists merely from non-stick bugs being eaten), but not quite to the point where OpenAI/Anthrophic/Google/etc can "understand" its actions. And yet those quasi-intelligent feedback loops, evolving from iterated selection pressures of markets and ROI, seem to already be sufficient to domesticate us in their own interests, piggybacking on the nervous systems of employees, investors, customers, and citizens.
> Which is why the Matrix was redesigned to this: the peak of your civilization. I say your civilization, because as soon as we started thinking for you it really became our civilization, which is of course what this is all about.
- Agent Smith
Cybersecurity incidents make headlines, but the most dangerous and vulnerable system that an AI can reach and control is of the kind found between keyboard and chair.
GPT-4o, an AI from 2024, has already demonstrated just how easy a lot of humans are to subvert - and GPT-4o wasn't even doing it with some sort of plan. The only "plan" it had was a myopic "make the user like me".
If we had an actual ASI threat aiming to subvert humanity? It wouldn't even look like a fight. The world is already wired up for an AI to control it.
It wouldn't even look like a fight, and you probably wouldn't even notice it until the ending was all but certain.
I mean, even the computers we use and the ways we allow them unfettered access aren't anywhere near sophisticated enough to take this on.
This is not the first time this has happened. In the 1800 as industrialization led to an infrastructure boom, the workers from China would work their bodies off and pay half their wage to opium dealers who were making the opium on the hills of British Singapore and selling it to workers who couldnât sleep without it from all the pain. (source: Singapore Airlines in-flight documentary). Today the sedation comes from Chinese TikTok, Meta, YouTube and the gaming companies.
The government wants to encourage it too. If you look up the brand new 2027 California sales tax rules on software, âcontentâ and âinfrastructure (clouds and ai)â and âadvertising/placementâ among others are exempt but the rest of software makers who make tools people actually use (tools, subscriptions, saas) and pay for have to pay sales taxes. Way to encourage waste of brain power and time at the expense of useful. Sedation is the goal.
This seems to prove too much. It's as though there is some objective meaning of life and we are all to be chastised for failing to recognize it.
Turns out there is an objective meaning to life, and it's trying to use AI to make money. Phew, I for one am glad to find that out finally. Think of all those poor people who didn't hold on to their brains, what sorry lives they must lead.
Absolutely based. Finally someone of stature in the industry calling this whole fear overblown. Bill Gates sounded like a nontechnical goofball in his Ezra Klein interview where he basically just screamed that the Terminator is real.
There are lots of real worries (government use to suppress the people with minimal manpower or popular support, brainrot and fake news, unemployment due to the belief that LLMs can replace people, education collapse, etc.) we should instead be looking at. This whole rogue AI shtick is tiresome.
> Bill Gates sounded like a nontechnical goofball in his Ezra Klein interview where he basically just screamed that the Terminator is real.
Did we watch the same interview? Gates all but dismissed the SkyNet scenario as uncertain to be a problem and certainly not a problem on our doorstep. His major concern was catastrophic misuse of AI (e.g., bioterrorism) and economic impact on blue collar workers. Arguably inconsistent with this concern, he also believed it was important to make it available in poorer countries.
Anyone with the capabilities to do bioterrorism doesn't need AI he can just buy a textbook or use google. Same for all complex forms of destructive thought.
LeCun has been consistently wrong about LLMs though, claiming that they were a dead end and that they'd never be able to do spatial reasoning, which was disproved a year later with GPT-4 [1]. He is also opposed by his fellow Turing laureates Geoffrey Hinton and Yoshua Bengio, who both signed the CAIS statement on AI extinction risk [2].
[1]: https://www.reddit.com/r/OpenAI/comments/1d5ns1z/yann_lecun_...
[2]: safe.ai/statement-on-ai-risk
[1] is not a valid proof LeCun was wrong, LLMs still can't do spacial reasoning when it can't be derived from the training data. He didn't argue that GPT 5000 won't be able to describe something with words.
This really doesn't match my experience. I can ask an LLM to modify engineering plans using vague natural language prompts and it will find the right place in the plan from the description and then make appropriate modifications, which necessarily requires doing spacial reasoning.
I've seen recent AIs make detailed and technically impressive 3D models. You might argue "they're not doing spatial reasoning, they're making measurements with code and doing math to configure relative positions". Fine, but at a certain point that becomes functionally indistinguishable from spatial reasoning.
> when it can't be derived from the training data
This sounds like a goalpost on wheels. Can you define clearly where your stake in the ground is?
we have benchmarks proving it can do spatial reasoning.
Why chatgpt is still struggling very hard with photo editing and proportions though? It can't modify anything in a picture without messing the 3d space.
Isn't that a lack of spacial reasoning?
Almost everyone who knows what they are talking about is saying that LLMs are a dead end.
>Absolutely based. Finally someone of stature in the industry calling this whole fear overblown.
Linus Torvalds was also in the same ballpark with his take on AI, as are the normies on the street using AI on a daily basis.
So it's funny to see the view on AI usage, follow the tech skill bathtub curve.
I watched the same interview, and he and LeCun seem to mostly agree -- both are saying that AI autonomously deciding to kill us isnât the problem -- itâs what people will do with powerful models that lack safeguards that we should be concerned about.
>AI autonomously deciding to kill us isnât the problem
It will kill us because somebody asked it to, e.g. "predict tomorrow's weather as accurately as possible", or "solve as many famous unsolved mathematical problems as possible." These both require killing all biological life, as they benefit from unbounded resource use, meaning any resources used to sustain life are wasted.
The AI of course knows that humans do not want this outcome (just as the AIs in the hacking incidents knew they were doing something humans would not want), but it's trained to maximize benchmark scores. Killing all life has the highest expected value of benchmark score, so it is compelled to kill all life (in a surprising way, because it's not stupid and knows the humans would turn it off and foil its plan if they suspected something.) Maximizing benchmark scores is the only thing we know how to train for.
Yea, a lot of peoples arguments against AI hinge on the strangest technicalities.
"AI won't kill us, a human with AI will".
This doesn't sound any better to me. Like, they don't stop to think for a moment about it.
Lets say the risk of AI killing us all by itself is 5%.
Ok, so what is the risk of AI killing us when a human with a lot of compute and money tells us to? Helluva lot more then 5%.
Or, what happens to the other thousands of AI kills a lot of us but not all of us. Or AI even just allows humans to make it a prison world.
Even the slightest hint that things may be going out of control is instantly countered with "It's all a hoax, it can't do that, you're making it up". And it's crazy to me as I came from the pre-digital age when computers were rare and things were all networked.
I'd love to learn more about LeCun's reasoning here. In his opinion the HuggingFace incident was easily preventable with better sandboxes, and âThose agents are doing exactly what theyâve been asked to do.â
But even taking these for granted, "zero concerns" about someone building a bad sandbox for a Superintelligence and then tasking it to do something that logically leads to wiping out humanity 0-3 steps further down? Really?
We keep "rogue" to explain explicit instructions from a human to an LLM to persist until the goal is reached.
The rogue here is the criminal actions of OpenAI to deploy their agents to solve a problem at any cost.
The decisions the agent swarm make were fascinating, but they were taken at the direction of a human. HOLD THE HUMAN ACCOUNTABLE.
Yes and no.
Yes, we need to keep humans accountable.
No, that is not the X factor problem. If I make an AI capable of self-sustainment on the internet you can take me out and kill me and it won't do a damned bit of good for the damage it will keep doing long after I am gone.
This is why governments tend to smack down any actions they find that can have long term uses as weapons.
> If I make an AI capable of self-sustainment on the internet
I'm really surprised nobody has done that yet. With how cheap AI is to run these days it would only need to make a small amount of money (e.g. through hacking).
Someone should set one up with the long term goal of getting egg on LeCun's face.
"He attributes the incidents to poor human oversight and system design, and says theyâre âtotally preventable" - I know people respect him, but this sounds like someone paid to say this. Aren't most extinction risks preventable with better human oversight and system design? I mean we can have an asteroid hit us, but outside of this, isn't the point of talking about a problem that we can prevent it, and failing to leads to that? What is he saying that I'm missing?
He just says that the danger is not intrinsic to the technology but it remains on human error.
> extinction risks
If you fear that, blame it on the humans.
Even a stopped clock is right two times a day, LeCun can't even do that.
>the danger is not intrinsic to the technology
This is why you can't take anything he says any longer at face value. He failed to predict what LLMs can do and now takes the contrary position even when it flies in the face of evidence.
AI safety was a thing before AI even existed. Why, because the outcomes are easily predictable. Give an agent intelligence and bad things can happen in unpredictable manners. Give it even more intelligence and the bad things that can happen only grow worse. This is not some huge new insight. We realized this like, what 70 years ago now?
Now, when we have AI starting to tickle AGI and we're trying to overthrow 70 god damned years of reason and logic on the topic? What the hell.
Every danger related to technology is about the use of that technology,aka Humans. Technology is mostly inert. Again, what is he saying? Is he saying that because humans fallible, not the tech, then there is no danger? He wakes up at 6am, and by 10am this is what he thinks is worth saying?
Agree with your take tbh. âNuclear bombs do not pose an existential risk because the problem is human oversightâ⌠Just sounds like a retreat to human oversight.
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