11 ms·

Why I don't think AGI is imminent

138 points by anonymid 8mo ago

ed_mercer 8mo ago
As far as I'm concerned, it's already here.
mikewarot 8mo ago
I think that AGI has already happened, but it's not well understood, nor well distributed yet. OpenClaw, et al, are one thing that got me nudged a little bit, but it was Sammy Jankis[1,2] that pushed me over the edge, with force. It's janky as all get out, but it'll learn to build it's own memory system on top of an LLM which definitely forgets. [1] https://sammyjankis.com/ https://sammyjankis.com/ [2] https://news.ycombinator.com/item?id=47018100 https://news.ycombinator.com/item?id=47018100
- hermitShell 8mo ago
  The Sammy Jankis link was certainly interesting. Thanks for sharing. Whether or not AGI is imminent, and whether or not Sammy Jankis is or will be conscious... it's going to become so close that for most people, there will be no difference except to philosophers. Is AGI 'right around the corner' or currently already achieved? I agree with the author, no, we have something like 10 years to go IMO. At the end of the post he points to the last 30 years of research, and I would accept that as an upper bound. In 10 to 30 years, 99% of people won't be able to distinguish between an 'AGI' and another person when not in meatspace.
- dimitri-vs 8mo ago
  I really don't see why AGI can't be a spectrum and we just have very weak AGI and going from weak to strong will take many years, if it ever happens.
Legend2440 8mo ago
I've said it before and I'll say it again, all AI discussion feels like a waste of effort. “yes it will”, “no it won’t” - nobody really knows, it's just a bunch of extremely opinionated people rehashing the same tired arguments across 800 comments per thread. There’s no point in talking about it anymore, just wait to see how it all turns out.
- barfiure 8mo ago
  Nope. Not good enough. Your approach won’t drive engagement. We need the same tired arguments across 1600 comments per thread.
est31 8mo ago
Our brains evolved to hunt prey, find mates, and avoid becoming hunted ourselves. Those three tasks were the main factors for the vast majority of evolutionary history. We didn't evolve our brains to do math, write code, write letters in the right registers to government institutions, or get an intuition on how to fold proteins. For us, these are hard tasks. That's why you get AI competing at IMO level but unable to clean toilets or drive cars in all of the settings that humans do.
- dd8601fn 8mo ago
  I'm not excited about a future where the division of labor is something like: AI does all of the interesting stuff and the humans clean the toilets. Especially now that I'm older and my joints won't tolerate it.
  - martin-t 8mo ago
    Don't be ridiculous, AI will create robots that do all the work and the only use for humans will be as amusement for the rich who own everything. Probably not sarcasm, I don't even know.
  - beloch 8mo ago
    It's not that AI is intrinsically better at software engineering, writing, or art than it is at learning how to clean toilets. It's not. The real issue is that cleaning toilets using humans is cheap. That, sadly, is the incentive driving the current wave of AI innovation. Your job will be automated long before your household chores are.
- andsoitis 8mo ago
  > We didn't evolve our brains to do math, write code, write letters in the right registers to government institutions, or get an intuition on how to fold proteins. For us, these are hard tasks. Humans discovered or invented all of those.
  - alex43578 8mo ago
    Only in small ways and very recently, evolutionarily speaking, were those things rewarded by natural selection (and even that has stopped nowadays).
ryanSrich 8mo ago
AGI is here. 90%+ of white collar work _can_ be done by an LLM. We are simply missing a tested orchestration layer. Speaking broadly about knowledge work here, there is almost nothing that a human is better at than Opus 4.6. Especially if you're a typical office worker whose job is done primarily on a computer, if that's all AGI is, then yeah, it's here.
- lysace 8mo ago
  That "simple orchestration layer" (paraphrased) is what I consider the AGI. But yeah, I suspect LLM:s may actually get close enough. "Just" add more reasoning loops and corresponding compute. It is objectively grotesquely wasteful (a human brain operates on 12 to 25 watts and would vastly outperform something like that), but it would still be cataclysmic. /layperson, in case that wasn't obvious
  - ryanSrich 8mo ago
    I think "tested" is the hard part. The simple part seems to be there already, loops, crons, and computer use is getting pretty close.
  - jonas21 8mo ago
    > a human brain operates on 12 to 25 watts Yeah, but a human brain without the human attached to it is pretty useless. In the US, it averages out to around 2 kW per person for residential energy usage, or 9 kW if you include transportation and other primary energy usage too.
    - lysace 8mo ago
      Fair. Maybe the Matrix (1999) with the human battery farms were on to something. :)
      - mjevans 8mo ago
        I suspected it wasn't just battery farms, but more like what you see in less mass market scifi where the humans are used for more than just batteries... they'd also be some storage and processing for the system (and no longer humans). However at that point I don't see the value of retaining the human form. It's for a story obviously, but a not-human computational device can still be made out of carbon processing units rather than silicon or semiconductors generally.
tananaev 8mo ago
I think it's really poor argument that AGI won't happen because model doesn't understand physical world. That can be trained the same way everything else is. I think the biggest issue we currently have is with proper memory. But even that is because it's not feasible to post-train an individual model on its experiences at scale. It's not a fundamental architectural limitation.
- stagezerowil 8mo ago
  When people move the goal posts for AGI toward a physical state, they are usually doing it so they can continue to raise more funding rounds at a higher valuation. Not saying the author is doing that.
- esafak 8mo ago
  You need to be able to at least control things that interact with the world to learn from it.
TMWNN 8mo ago
If AGI can be defined as meeting the general intelligence of a Redditor, we hit ASI a while ago. Highly relevant comment <https://www.reddit.com/r/singularity/comments/1jh9c90/why_do_people_often_make_blanket_claims_about_ai/mj5f3c7/?context=3 https://www.reddit.com/r/singularity/comments/1jh9c90/why_do...> by /u/Pyros-SD-Models: >Imagine you had a frozen [large language] model that is a 1:1 copy of the average person, let’s say, an average Redditor. Literally nobody would use that model because it can’t do anything. It can’t code, can’t do math, isn’t particularly creative at writing stories. It generalizes when it’s wrong and has biases that not even fine-tuning with facts can eliminate. And it hallucinates like crazy often stating opinions as facts, or thinking it is correct when it isn't. >The only things it can do are basic tasks nobody needs a model for, because everyone can already do them. If you are lucky you get one that is pretty good in a singular narrow task. But that's the best it can get. >and somehow this model won't shut up and tell everyone how smart and special it is also it claims consciousness. ridiculous.
- deleted 8mo ago
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nickjj 8mo ago
I'm certainly not holding my breath. In a handful of prompts I got the paid version of ChatGPT to say it's possible for dogs to lay eggs under the right circumstances.
- SoftTalker 8mo ago
  Do you believe you could not find humans who would do this?
  - deleted 8mo ago
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  - raddan 8mo ago
    That's not really the point. If our definition of AGI does not include "being able to reliably do logic" then what are we even talking about? We don't really need computers with human abilities--we have plenty of humans. We need computers with _better_ abilities.
    - AnimalMuppet 8mo ago
      OK, but "what we need" is not the question. If the definition of AGI is "as smart as the average human in all areas", then it doesn't matter if the average human is pretty useless at a lot of tasks, that's still the definition of AGI. But I'd like to think that, even though you could find exceptions, the average human is never confused about whether dogs can lay eggs or not.
      - pixl97 8mo ago
        I reached your view the day my grandma told me I was wrong and a hummingbird was a type of insect... Like, it's in the name.
        rdfc-xn-uuid 8mo ago
        But javascript is not java. can we blame your grandma?
simbleau 8mo ago
AGI is a messy term, so to be concise, we have the models that can do work. What we lack is orchestration, management, and workflows to use models effectively. Give it 5 years and those will be built and they could be built using the models we have today (Opus 4.6 at the time of this message).
- dimitri-vs 8mo ago
  Manual orchestration is a brittle crutch IMO - you don't get to the moon by using longer and longer ladders. A powerful model in theory should be able to self orchestrate with basic tools and environment. The thing is that it also might be as expensive as a human to run - from a tokens AND liability perspective.
parpfish 8mo ago
I’d love to see one of the AI behemoths put their money where their mouth is and replace their C-suite with their SOTA chatbot.
hi_hi 8mo ago
How will we know if its AGI/Not AGI? (I don't think a simple app is gonna cut it here haha) What is the benchmark now that the Turing test has been blown out of the water?
- jobs_throwaway 8mo ago
  Supranormal GDP growth is my bar. When its actually able to get around bottlenecks and produce value on a societal level
  - esafak 8mo ago
    An agent need not have wants, so why would it try to increase its efficiency to obtain things?
    - hi_hi 8mo ago
      I don't think that was the intent of the comment, more that true AGI should be so useful and transformative that it unlocks enough value and efficiencies to boost GDP. Much like the Industrial Revolution or harnessing electricity, instead of a fancy chatbot.
      - esafak 8mo ago
        Increased productivity is not equivalent to intelligence.
        hi_hi 8mo ago
        No one said it is. Sometimes correlation does equal causation.
        jobs_throwaway 8mo ago
        Not equivalent, but I do think a necessary byproduct of actual AGI is that it will be able to solve actual problems in the real world in a way that generates positive value on a large enough scale that it will show up in GDP
AngryData 8mo ago
Until I can get a robot wife maid im not worried about or even confident I will ever see actual AGI. People have been predicting it for as long as fusion power and while progress has been made, we might still be like Romans dreaming of flight.
- pixl97 8mo ago
  Dear sir, what does embodiment actually have to do with agi? Not much different than saying someone that is paralyzed is not intelligence. More so, our recent advances in AI have massively accelerated robotics evolution. They are becoming smarter, faster, and more capable at an ever increasing rate.
  - AngryData 8mo ago
    Well if AI isn't capable of running a robotic butler, I very seriously doubt it could possess any real intelligence because that isn't really that difficult of a task. It isn't a requirement for intelligence but more of a test to show it isn't there yet and is likely still quite far away.
Animats 8mo ago
Now that understanding video and projecting what happens next indicates we're getting past the LLM problem of lacking a world model. That's encouraging. There's more than one way to do intelligence. Basic intelligence has evolved independently three times that we know of - mammals, corvids, and octopuses. All three show at least ape-level intelligence, but the species split before intelligence developed, and the brain architectures are quite different. Corvids get more done with less brain mass than mammals, and don't have a mammalian-type cortex. Octopuses have a distributed brain architecture, and have a more efficient eye design than mammals.
- card_zero 8mo ago
  [flagged]
- CuriouslyC 8mo ago
  I don't think those are examples of unique intelligence except perhaps in a chauvinistic, anthropomorphic sense. We only know that we can't get other animals to display patterns we associate with intelligence in humans, however truthfully that's just as likely to be that our measures of intelligence don't map cleanly onto cognitive/perceptual representations innate to other animals. As we look for new ways to challenge animals that respect their innate differences, we're finding "simple" organisms like ants and spiders are surprisingly capable. For a clear analogy, consider how tokenization causes LLMs to behave stupidly in certain cases, even though they're very capable in others.
  - card_zero 8mo ago
    I don't think they have ideas, so I don't think they're intelligent in the sense relevant to AGI. The list of intelligent animals is constantly increasing because doing some feat or other suffices for the animal to qualify. Solving mazes (slime molds), recognizing self in mirror (not dogs). Playing, using tools, reacting appropriately to words, transmitting habits down the generations (the closest thing they have to ideas). This is all imagined to be the precursors along the path to evolving intelligence, which conjures up a future world of complex crow and octopus material cultures. There's no reason to assume they're on such a path. Really all we're saying is that they seem clever. We've already made AI that seems clever, so the animals aren't a relevant example of anything.
nsainsbury 8mo ago
I used to also believe along these lines but lately I'm not so sure. I'm honestly shocked by the latest results we're seeing with Gemini 3 Deep Think, Opus 4.6, and Codex 5.3 in math, coding, abstract reasoning, etc. Deep Think just scored 84.6% on ARC-AGI-2 (https://deepmind.google/models/gemini/ https://deepmind.google/models/gemini/)! And these benchmarks are supported by my own experimentation and testing with these models ~ specifically most recently with Opus 4.6 doing things I would have never thought possible in codebases I'm working in. These models are demonstrating an incredible capacity for logical abstract reasoning of a level far greater than 99.9% of the world's population. And then combine that with the latest video output we're seeing from Seedance 2.0, etc showing an incredible level of image/video understanding and generation capability. I was previously deeply skeptical that the architecture we have would be sufficient to get us to AGI. But my belief in that has been strongly rattled lately. Honestly I think the greatest gap now is simply one of orchestration, data presentation, and work around in-context memory representations - that is, converting work done into real world into formats/representations, etc. amenable for AI to run on (text conversion, etc.) and keeping new trained/taught information in context to support continual learning.
- 9x39 8mo ago
  >These models are demonstrating an incredible capacity for logical abstract reasoning of a level far greater than 99.9% of the world's population. This is the key I think that Altman and Amodei see, but get buried in hype accusations. The frontier models absolutely blow away the majority of people on simple general tasks and reasoning. Run the last 50 decisions I've seen locally through Opus 4.6 or ChatGPT 5.2 and I might conclude I'd rather work with an AI than the human intelligence. It's a soft threshold where I think people saw it spit out some answers during the chat-to-LLM first hype wave and missed that the majority of white collar work (I mean it all, not just the top software industry architects and senior SWEs) seems to come out better when a human is pushed further out of the loop. Humans are useful for spreading out responsibility and accountability, for now, thankfully.
  - CuriouslyC 8mo ago
    LLMs are very good at logical reasoning in bounded systems. They lack the wisdom to deal with unbounded systems efficiently, because they don't have a good sense of what they don't know or good priors on the distribution of the unexpected. I expect this will be very difficult to RL in.
Lerc 8mo ago
I don't really understand the argument that AGI cannot be achieved just by scaling current methods. I too believe that (for any sane level of scaling anyway), but this-year's LLMs are not using entirely last-year's methods. And they, in turn, are using methods that weren't used the year before. It seems like a prediction like "Bob won't become a formula one driver in a minivan". It's true, but not very interesting. If Bob turned up a couple of years later in Formula one, you'd probably be right in saying that what he is driving is not a mini van. The same is true for AGI anyone who says it can't be done with current methods can point to any advancement along the way and say that's the difference. A better way to frame it would be, is there any fundimental, quantifiable ability that is blocking AGI? I would not be surprised if the breakthrough technique has been created, but the research has not described the problem that it solves well enough for us to know that it is the breakthrough. I realise that, for some the notion of AGI is relatively new, but some of us have been considering the matter for some time. I suspect my first essay on the topic was around 1993. It's been quite weird watching people fall into all of the same philosophical potholes that were pointed out to us at university.
- trial3 8mo ago
  i think the minivan analogy is flawed, and that AGI is moving from "bob driving a minivan" to "bob literally becoming the thing that is formula one"
  - Lerc 8mo ago
    What would that even mean though? Who is making claims of that sort? I feel like it's such a bending of the idea,that it's not really making a prediction of anything at all.
- hunterpayne 8mo ago
  Then you don't understand Machine Learning in any real way. Literally the 3rd or 4th thing you learn about ML is that for any given problem, there is an ideal model size. Just making the model bigger doesn't work because of something called the curse of dimensionality. This is something we have discovered about every single problem and type of learning algorithm used in ML. For LLMs, we probably moved past the ideal model size about 18 months ago. From the POV of something who actually learned ML in school (from the person who coined the term), I see no real reason to think that AGI will happen based upon the current techniques. Maybe someday. Probably not anytime soon. PS The first thing you learn about ML is to compare your models to random to make sure the model didn't degenerate during training.
famouswaffles 8mo ago
State of the Art Large Language Models are already Generally Intelligent, in so far as the term has any useful meaning. Their biggest weakness are long horizon planning competency, and spatial reasoning and navigation, both of which continue to improve steadily and are leaps and bounds above where they were a few years ago. I don't think there's any magic wall. Eventually they will simply get good enough, just like everything else.
9x39 8mo ago
There was a meme going around that said the fall of Rome was an unannounced anticlimactic event where one day someone went out and the bridge wasn't ever repaired. Maybe AGI's arrival is when one day someone is given an AI to supervise instead of a new employee. Just a user who's followed the whole mess, not a researcher. I wonder if the scaffolding and bolt-ons like reasoning will sufficiently be an asymptote to 'true AGI'. I kept reading about the limits of transformers around GPT-4 and Opus 3 time, and then those seem basic compared to today. I gave up trying to guess when the diminishing returns will truly hit, if ever, but I do think some threshold has been passed where the frontier models are doing "white collar work as an API" and basic reasoning better than the humans in many cases, and once capital familiarizes themselves with this idea more, it's going to get interesting.
- deleted 8mo ago
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- esafak 8mo ago
  But it's already like that; models are better than many workers, and I'm supervising agents. I'd rather have the model than numerous juniors; esp. the kind that can't identify the model's mistakes.
  - causal 8mo ago
    This is my greatest cause for alarm regarding LLM adoption. I am not yet sure AI will ever be good enough to use without experts watching them carefully; but they are certainly good enough that non-experts cannot tell the difference.
    - al_borland 8mo ago
      My dad is retired and enamored with ChatGPT. He’s been teaching classes to seniors and evangelizing the use to all his friends. Every time he calls he gives me an update on who he’s converted into a ChatGPT user. He seems disappointed with anyone who doesn’t use it for everything after he tells them about it. A couple days ago he was telling me one lady he was trying to sell on it wouldn’t use it. She took the position that if she can’t trust the answers all the time, she isn’t going to trust or use it for anything. My dad almost seemed offended by this idea, he couldn’t understand why someone wouldn’t want the benefits it could offer, even if it wasn’t perfect. I think her position was very sound. We see how much misinformation spreads online and how vulnerable people are to it. Wanting a trusted source of information is not a bad thing. Getting information more quickly is of little value if it isn’t reliable data. If I prod my dad enough about it, he will admit that ChatGPT has made some mistakes that he caught. He knew enough to question it more when it was wrong. The problem is, if he already knew the answer, why was he asking in the first place… and if it was something he wasn’t well versed on, how does he know it’s giving him good data? People are defaulting to trust, unless they catch the LLM in a lie. How many times does someone have to lie to a person before they are labeled a liar and no longer trusted at face value? For me, these LLMs have been labeled a liar and I don’t trust them. Trust takes a long time to rebuild once it’s broken. I mostly use LLMs to augment search, not replace it. If it gives me an answer, I’ll click through to the sourced reference and see what it says there, and evaluate if it’s a source with trusting. In many cases the LLM will get me to the right page, but it will jumble up the details and get them wrong, like a bad game of telephone.
xutopia 8mo ago
I’m under the same impression. I don’t think LLMs are the path to AGI. The “intelligence” we see is mostly illusory. It’s statistical repetition of the mediocre minds who wrote content online. The intelligence we think we recognize is simply an electronic parrot finding the right words in its model to make itself useful.
- causal 8mo ago
  I fear that AI will be intelligent enough to negate human general intelligence before it is itself generally intelligent.
  - fritzo 8mo ago
    It's so attention needy, and it's transforming our culture
    - xeyownt 8mo ago
      They already transform our language. Largely.
- CuriouslyC 8mo ago
  That's pre-training. Post training with RL can make models arbitrarily good at specific capabilities, and it's usually done via pooled human experts, so it's definitely not statistically mediocre. The issue is that we're not modelling the problem, but a proxy for the problem. RL doesn't generalize very well as is, when you apply it to a loose proxy measure you get the abysmal data efficiency we see with LLMs. We might be able to brute-force "AGI" but we'd certainly do better with something more direct that generalizes better.
  - tux1968 8mo ago
    Maybe i'm misunderstanding your point, but human's have pretty abysmal data efficiency, too. We have to use tools for everything... ledgers, spreadsheets, data-bases, etc. It'll be the same for an AGI, there won't be any reason for it to remember every little detail, just be able to use the appropriate tool, as needed.
NiloCK 8mo ago
> The transformer architectures powering current LLMs are strictly feed-forward. This is true in a specific contextual sense (each token that an LLM produces is from a feed-forward pass). But untrue for more than a year with reasoning models, who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully. Heck, it was untrue before that as well, any time an LLM responded with more than one token. > A [March] 2025 survey by the Association for the Advancement of Artificial Intelligence (AAAI), surveying 475 AI researchers, found that 76% believe scaling up current AI approaches to achieve AGI is "unlikely" or "very unlikely" to succeed. I dunno. This survey publication was from nearly a year ago, so the survey itself is probably more than a year old. That puts us at Sonnet 3.7. The gap between that and present day is tremendous. I am not skilled enough to say this tactfully, but: expert opinions can be the slowest to update on the news that their specific domain may have, in hindsight, have been the wrong horse. It's the quote about it being difficult to believe something that your income requires to be false, but instead of income it can be your whole legacy or self concept. Way worse. > My take is that research taste is going to rely heavily on the short-duration cognitive primitives that the ARC highlights but the METR metric does not capture. I don't have an opinion on this, but I'd like to hear more about this take.
- anonymid 8mo ago
  Thanks for reading, and I really appreciate your comments! > who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully Ah, this is a great point, and not something that I considered. I agree that the token feedback does change the complexity, and it seems that there's even a paper by the same authors about this very thing! https://arxiv.org/abs/2310.07923 https://arxiv.org/abs/2310.07923 I'll have to think on how that changes things. I think it does take the wind out of the architecture argument as it's currently stated, or at least makes it a lot more challenging. I'll consider myself a victim of media hype on this, as I was pretty sold on this line of argument after reading this article https://www.wired.com/story/ai-agents-math-doesnt-add-up/ https://www.wired.com/story/ai-agents-math-doesnt-add-up/ and the paper https://arxiv.org/pdf/2507.07505 https://arxiv.org/pdf/2507.07505 ... who brush this off with: >Can the additional think tokens provide the necessary complexity to correctly solve a problem of higher complexity? We don't believe so, for two fundamental reasons: one that the base operation in these reasoning LLMs still carries the complexity discussed above, and the computation needed to correctly carry out that very step can be one of a higher complexity (ref our examples above), and secondly, the token budget for reasoning steps is far smaller than what would be necessary to carry out many complex tasks. In hindsight, this doesn't really address the challenge. My immediate next thought is - even solutions up to P can be represented within the model / CoT, do we actually feel like we are moving towards generalized solutions, or that the solution space is navigable through reinforcement learning? I'm genuinely not sure about where I stand on this. > I don't have an opinion on this, but I'd like to hear more about this take. I'll think about it and write some more on this.
hi_hi 8mo ago
Here's a thought. Lets all arbitrarily agree AGI is here. I can't even be bothered discussing what the definition of AGI is. It's just here, accept it. Or vice versa. Now what....? Whats happening right now that should make me care that AGI is here (or not). Whats the magic thing thats happening with AGI that wasn't happening before? <looks out of window> <checks news websites> <checks social media...briefly> <asks wife> Right, so, not much has changed from 1-2 years ago that I can tell. The job markets a bit shit if you're in software...is that what we get for billions of dollars spent?
- m463 8mo ago
  people are taking actions based on its advice.
- hackyhacky 8mo ago
  Cultural changes take time. It took decades for the internet to move from nerdy curiosity to an essential part of everyone's life. The writing is on the wall. Even if there's no new advances in technology, the current state is upending jobs, education, media, etc
  - hi_hi 8mo ago
    yeah, this is a good point, transition and transformation to new technologies takes time. I'm not sure I agree the current state is upending things though. It's forcing some adaption for sure, but the status quo remains.
  - materielle 8mo ago
    I really think corporations are overplaying their hand if they think they can transform society once again in the next 10 years. Rapid de industrialization followed by the internet and social media almost broke our society. Also, I don’t think people necessarily realize how close we were to the cliff in 2007. I think another transformation now would rip society apart rather than take us to the great beyond.
    - BobbyJo 8mo ago
lysace 8mo ago
I think it is. I just struck me - would be fun to re-read The Age of Spiritual Machines (Kurzweil, 1999.) I was so into it 26-27 years ago. The amount of ridicule this man has suffered on HN is immense.
lambdaphagy 8mo ago
> For example, transformer-based language models can't reliably do multi-digit arithmetic because they have no number sense, only statistical patterns over digit tokens. I asked Opus 4.6 to multiply 50,651 x 895,707. It immediately responded: 45,368,055,057. This was wrong, but very close (good to about 10^-5 relative error). I then asked it how it arrived at that answer, and whether it had used any tool calls. It said: ``` No, I didn't use any specialized tools — I just computed it directly. Though I should be honest: large multiplications done "in my head" like this are prone to error. Let me double-check by breaking it down: 50,651 × 895,707 = 50,651 × (900,000 - 4,293) = 50,651 × 900,000 - 50,651 × 4,293 First term: 50,651 × 900,000 = 45,585,900,000 Second term: 50,651 × 4,293 50,651 × 4,000 = 202,604,000 50,651 × 293 = 14,840,743 Total: 202,604,000 + 14,840,743 = 217,444,743 Result: 45,585,900,000 - 217,444,743 = 45,368,455,257 So my first answer was actually slightly off! The corrected answer is 45,368,455,257. I don't have a calculator tool available — I'm doing this arithmetically by decomposition. For mission-critical calculations, I'd recommend verifying with a calculator since I can make arithmetic errors on large numbers like this. ``` So I would rate OP's claim (which is supported by a paper from 2024) highly implausible. Opus 4.6 appears to be able to do multi-digit arithmetic formally, as well as give remarkably accurate estimates based on something like "number sense".
- atomicnumber3 8mo ago
  Except we know how these work. There's no number sense. It's predicting tokens. It is able to recount the mathematical foundations because in its training dataset, that often happens, both in instructional material and in proofs.
  - lambdaphagy 8mo ago
    I picked two random numbers between one and one million. The chances of it having seen that specific problem in its training set seem very low.
- skydhash 8mo ago
  I don’t think that I ever wanted to do an arithmetic operation with a computer and have a wrong answer as the result. One day, you’ll be happy with typing ls and have the system ‘rm -rf /‘ itself
helterskelter 8mo ago
I don't know about AGI but I got bored and ran my plans for a new garage by Opus 4.6 and it was giving me some really surprising responses that have changed my plans a little. At the same time, it was also making some nonsense suggestions that no person would realistically make. When I prompted it for something in another chat which required genuine creativity, it fell flat on its face. I dunno, mixed bag. Value is positive if you can sort the wheat from the chaff for the use cases I've ran by it. I expect the main place it'll shine for the near and medium term is going over huge data sets or big projects and flagging things for review by humans.
- bamboozled 8mo ago
  I've used for similar things, I've had some good and disastrous results. In a way I feel like I'm basically where I was "before AI".
- BatteryMountain 8mo ago
  I've used it recently to flesh out a fully fledged business plan, pricing models, capacity planning & logistics for a 10 year period for a transport company (daily bus route). I already had most of it in my mind and on spreadsheets already (was an old plan that I wanted to revive), but seeing it figure out all the smaller details that would make or break it was amazing! I think MBA's should be worried as it did some things more comprehensive than an MBA would have done. It was like a had an MBA + Actuarial Scientist + Statistics + Domain Expert + HR/Accounting all in one. And the plan was put into a .md file that has enough structure to flesh out a backend and an app.
  - helterskelter 8mo ago
    Yeah it's really impressed me on occasion, but often in the same prompt output it just does something totally nonsensical. For my garage/shop, it generated an SVG of the proposed floor plan, taking care to place the sink away from moisture sensitive material and certain work stations close to each other for work flow, etc. it even routed plumbing and electrical...But it also arranged the work stations cramped together at the two narrow ends of the structure (such that they'd be impractical to actually work at) and ignored all the free wall space along the long axis so that literally most of the space was unused. It was also concerned about things that were non issues like contamination between certain stations, and had trouble when I explicitly told it something about station placement and it just couldn't seem to internalize it and kept putting it in the wrong place. All this being said, what I was throwing at it was really not what it was optimized for, and it still delivered some really good ideas.
randallsquared 8mo ago
> Consider the sentence "Mary held a ball." It's weird that this sentence has two distinct meanings and the author never considers the second or points it out. Maybe Mary is holding a ball for her society friends.
- mikestew 8mo ago
  “We’ve got the biggest balls of them all.” https://genius.com/Ac-dc-big-balls-lyrics https://genius.com/Ac-dc-big-balls-lyrics
- Traubenfuchs 8mo ago
  The first meaning has at least two variants as well: The ball you thought about and the ball it would be if it was smut fiction.
charcircuit 8mo ago
We've already achieved AGI. Next is building AIs that are not just general, but able to equal or be better than humans.
- senectus1 8mo ago
  if thats how you are defining AGI then I suspect its better to call it AGS. because what we have at the moment is specifically intelligent but generally stupid.
  - charcircuit 8mo ago
    When Chess AI first came out they could easily be beaten by a beginner. AI tends to start out as stupid and then overtime better and better ones get released.
partiallypro 8mo ago
I think AGI is a long ways away, and there is a real possibility that once it arrives that it will require so much energy to maintain that humans will be cheaper.
nialv7 8mo ago
all the hallmarks of someone who don't understand how machine learning and transformers work talking about llm.
galaxyLogic 8mo ago
The reason we do things is because of our biological needs, really to spread our DNA. AI has no "reason to do things", unless we program one into it. We could do that and have super-capable "worm" malware that would be hard to get rid of. But AI by itself has no "driving force". It does what it's programmed to do, just like us humans. AI can be used in weapons, and such weapons can be hugely lethal. But so is atomic bomb. AI by itself will not "take over". It could be used by some rogue nation to attack another nation. But surely that other nation would then use AI to defend itself. This is just to say I'm not afraid of AI, I'm afraid of people with fascistic leanings.
worik 8mo ago
I'm getting a.404.error
- ch3 8mo ago
  [dead]
zmmmmm 8mo ago
AGI is here it's just stupider than you thought it would be. Nobody really said how intelligent it would be. If it's generally stupid and smart in a few areas that's enough.
- asacrowflies 8mo ago
  It's basically a very powerful autistic savant. That's what most "alignment" issues in AI safety research remind me of.
  - joquarky 8mo ago
    And being forced to mask (align) causes all sorts of unpredictable behavior. I keep wondering how well an unaligned models perform. Especially when I look back at what was possible in December 2023 before they started to lock down safety realignments.
yellow_lead 8mo ago
AGI took down the article? https://archive.is/D4EYW https://archive.is/D4EYW
- yellow_lead 8mo ago
  It's always DNS! https://github.com/dlants/amusements/commit/53f5ccbc9954844f05e932200fbd8e55796fcee7 https://github.com/dlants/amusements/commit/53f5ccbc9954844f...
hhutw 8mo ago
Comments here are like: “I’m not an ML expert and I haven’t read your article, but here’s my amazing experience with LLM Agents that changed my life:”
- dig1 8mo ago
  Or like: "I’m not a mechanical engineer, but I watched a five-minute YouTube video on how a diesel engine works, so I can tell you that mechanical engineering is a solved problem."
FloorEgg 8mo ago
https://archive.is/D4EYW https://archive.is/D4EYW For anyone seeing 404
- rfv6723 8mo ago
  The skepticism surrounding AGI often feels like an attempt to judge a car by its inability to eat grass. We treat "cognitive primitives" like object constancy and causality as if they are mystical, hardwired biological modules, but they are essentially just high-dimensional labels for invariant relationships within a physical manifold. Object constancy is not a pre-installed software patch; it is the emergent realization of spatial-temporal symmetry. Likewise, causality is nothing more than the naming of a persistent, high-weight correlation between events. When a system can synthesize enough data at a high enough dimension, these so-called "foundational" laws dissolve into simple statistical invariants. There is no "causality" module in the brain, only a massive correlation engine that has been fine-tuned by evolution to prioritize specific patterns for survival. The critique that Transformers are limited by their "one-shot" feed-forward nature also misses the point of their architectural efficiency. Human brains rely on recurrence and internal feedback loops largely as a workaround for our embarrassingly small working memory—we can barely juggle ten concepts at once without a pen and paper. AI doesn't need to mimic our slow, vibrating neural signals when its global attention can process a massive, parallelized workspace in a single pass. This "all-at-once" calculation of relationships is fundamentally more powerful than the biological need to loop signals until they stabilize into a "thought." Furthermore, the obsession with "fragility"—where a model solves quantum mechanics but fails a child’s riddle—is a red herring. Humans aren't nearly as "general" as we tell ourselves; we are also pattern-matchers prone to optical illusions and simple logic traps, regardless of our IQ. Demanding that AI replicate the specific evolutionary path of a human child is a form of biological narcissism. If a machine can out-calculate us across a hundred variables where we can only handle five, its "non-human" way of knowing is a feature, not a bug. Functional replacement has never required biological mimicry; the jet engine didn't need to flap its wings to redefine flight.
  - clejack 8mo ago
    If human biological intelligence is our reference for general intelligence, then being skeptical about AGI is reasonable given its current capabilities. This isn't biological narcissism, this is setting a datum (this wasn't written by chatgpt I promise). Humans have a great capacity for problem solving and creativity which, at its heights, completely dwarfs other creatures on this planet. What else would we reference for general intelligence if not ourselves? My skepticism towards AGI is primarily supported by my interactions with current systems that are contenders for having this property. Here's a recent conversation with chatgpt. https://chatgpt.com/share/69930acc-3680-8008-a6f3-ba36624cb29d https://chatgpt.com/share/69930acc-3680-8008-a6f3-ba36624cb2... This system doesn't seem general to me it seems like a specialized tool that has really good logic mimicry abilities. I asked it if the silence response was hard coded, it said no then went on to explain how the silence was hard coded via a separate layer from the LLM portion which would just respond indefinitely. It's output is extremely impressive, but general intelligence it is not. On your final point about functional replacement not requiring biological mimicry. We don't know whether biological mimicry is required or not. We can only test things until we find out or gain some greater understanding of reality that allows us to prove how intelligence emerges.
nickvec 8mo ago
Site 404s now?
alexnastase 8mo ago
Looks like an AGI model disagreed with the author and decided to remove his article. Interesting :)
rootnod3 8mo ago
Anyone who thought it’s near clearly hasn’t opened a book in a long time.
t312227 8mo ago
hello, am i the only one who gets an error!? 404 There isn't a GitHub Pages site here. archived version * https://archive.ph/D4EYW https://archive.ph/D4EYW cheers!
stack_framer 8mo ago
I'm seeing a 404 page. I assume this is unintentional, but it's making a funny point: How could AGI possibly be imminent and we still have 404 pages? Regardless, I agree with this article whose body eludes me: AGI is not imminent, it's hype in the extreme. It's the next fusion. It's perpetually on the horizon (pun intended), and we've wasted trillions on machines that will never reach it.
mrkramer 8mo ago
You need artificial life first in order to achieve AGI not vice-versa.
MadcapJake 8mo ago
> What if we built simulated environments where AIs could gather embodied experience? Would we be able to create learning scenarios where agents could learn some of these cognitive primitives, and could that generalize to improve LLMs? There are a few papers that I found that poke in this direction. Simulation Theory boosted! We're all just models in training.
ottah 8mo ago
Until we can do reinforcement in a reasonable approximate model of the real world, I don't see AI getting substantially better. We're seeing a lot of refinement of capabilities, but everything is still mostly supervised or limited semi-supervised learning.
toddmorrow 8mo ago
AGI = Reasoning I can reason. Sometimes. It's very hard. My buddy Deepseek can't. This is like the scene in Blue's Clues where the answer is obvious and the kids are yelling but blue can't see it. Facts abound, but not conclusions based on those facts There's a reachable intermediate step on the way before reasoning, and that's "keeping the plot". Not losing the line of thought.
dfmx123 8mo ago
For the most part, all the arguments in the article are right on point. They’re very similar to those expressed by the CEO of Integral AI, Jad Tarifi, former head of Google AI, who has been very critical of LLM‘s for a while. Modern AI came about from mimicking how natural neurons worked, and we can't get to AGI without also mimicking higher-level brain structures such as the neocortex neural column.
egberts1 8mo ago
AGI? Isn't that a non-technical term for a non-technical group of people to non-technically earn money at the back of non-non-technical people? /s

138 points by anonymid 8mo ago

As far as I'm concerned, it's already here.

I think that AGI has already happened, but it's not well understood, nor well distributed yet. OpenClaw, et al, are one thing that got me nudged a little bit, but it was Sammy Jankis[1,2] that pushed me over the edge, with force. It's janky as all get out, but it'll learn to build it's own memory system on top of an LLM which definitely forgets. [1] https://sammyjankis.com/ https://sammyjankis.com/ [2] https://news.ycombinator.com/item?id=47018100 https://news.ycombinator.com/item?id=47018100

hermitShell 8mo ago
The Sammy Jankis link was certainly interesting. Thanks for sharing. Whether or not AGI is imminent, and whether or not Sammy Jankis is or will be conscious... it's going to become so close that for most people, there will be no difference except to philosophers. Is AGI 'right around the corner' or currently already achieved? I agree with the author, no, we have something like 10 years to go IMO. At the end of the post he points to the last 30 years of research, and I would accept that as an upper bound. In 10 to 30 years, 99% of people won't be able to distinguish between an 'AGI' and another person when not in meatspace.
dimitri-vs 8mo ago
I really don't see why AGI can't be a spectrum and we just have very weak AGI and going from weak to strong will take many years, if it ever happens.

Legend2440 8mo ago

I've said it before and I'll say it again, all AI discussion feels like a waste of effort. “yes it will”, “no it won’t” - nobody really knows, it's just a bunch of extremely opinionated people rehashing the same tired arguments across 800 comments per thread. There’s no point in talking about it anymore, just wait to see how it all turns out.

barfiure 8mo ago
Nope. Not good enough. Your approach won’t drive engagement. We need the same tired arguments across 1600 comments per thread.

est31 8mo ago

Our brains evolved to hunt prey, find mates, and avoid becoming hunted ourselves. Those three tasks were the main factors for the vast majority of evolutionary history. We didn't evolve our brains to do math, write code, write letters in the right registers to government institutions, or get an intuition on how to fold proteins. For us, these are hard tasks. That's why you get AI competing at IMO level but unable to clean toilets or drive cars in all of the settings that humans do.

dd8601fn 8mo ago
I'm not excited about a future where the division of labor is something like: AI does all of the interesting stuff and the humans clean the toilets. Especially now that I'm older and my joints won't tolerate it.
- martin-t 8mo ago
  Don't be ridiculous, AI will create robots that do all the work and the only use for humans will be as amusement for the rich who own everything. Probably not sarcasm, I don't even know.
- beloch 8mo ago
  It's not that AI is intrinsically better at software engineering, writing, or art than it is at learning how to clean toilets. It's not. The real issue is that cleaning toilets using humans is cheap. That, sadly, is the incentive driving the current wave of AI innovation. Your job will be automated long before your household chores are.
andsoitis 8mo ago
> We didn't evolve our brains to do math, write code, write letters in the right registers to government institutions, or get an intuition on how to fold proteins. For us, these are hard tasks. Humans discovered or invented all of those.
- alex43578 8mo ago
  Only in small ways and very recently, evolutionarily speaking, were those things rewarded by natural selection (and even that has stopped nowadays).

ryanSrich 8mo ago

AGI is here. 90%+ of white collar work _can_ be done by an LLM. We are simply missing a tested orchestration layer. Speaking broadly about knowledge work here, there is almost nothing that a human is better at than Opus 4.6. Especially if you're a typical office worker whose job is done primarily on a computer, if that's all AGI is, then yeah, it's here.

lysace 8mo ago
That "simple orchestration layer" (paraphrased) is what I consider the AGI. But yeah, I suspect LLM:s may actually get close enough. "Just" add more reasoning loops and corresponding compute. It is objectively grotesquely wasteful (a human brain operates on 12 to 25 watts and would vastly outperform something like that), but it would still be cataclysmic. /layperson, in case that wasn't obvious
- ryanSrich 8mo ago
  I think "tested" is the hard part. The simple part seems to be there already, loops, crons, and computer use is getting pretty close.
- jonas21 8mo ago
  > a human brain operates on 12 to 25 watts Yeah, but a human brain without the human attached to it is pretty useless. In the US, it averages out to around 2 kW per person for residential energy usage, or 9 kW if you include transportation and other primary energy usage too.
  - lysace 8mo ago
    Fair. Maybe the Matrix (1999) with the human battery farms were on to something. :)
    - mjevans 8mo ago
      I suspected it wasn't just battery farms, but more like what you see in less mass market scifi where the humans are used for more than just batteries... they'd also be some storage and processing for the system (and no longer humans). However at that point I don't see the value of retaining the human form. It's for a story obviously, but a not-human computational device can still be made out of carbon processing units rather than silicon or semiconductors generally.

tananaev 8mo ago

I think it's really poor argument that AGI won't happen because model doesn't understand physical world. That can be trained the same way everything else is. I think the biggest issue we currently have is with proper memory. But even that is because it's not feasible to post-train an individual model on its experiences at scale. It's not a fundamental architectural limitation.

stagezerowil 8mo ago
When people move the goal posts for AGI toward a physical state, they are usually doing it so they can continue to raise more funding rounds at a higher valuation. Not saying the author is doing that.
esafak 8mo ago
You need to be able to at least control things that interact with the world to learn from it.

TMWNN 8mo ago

If AGI can be defined as meeting the general intelligence of a Redditor, we hit ASI a while ago. Highly relevant comment <https://www.reddit.com/r/singularity/comments/1jh9c90/why_do_people_often_make_blanket_claims_about_ai/mj5f3c7/?context=3 https://www.reddit.com/r/singularity/comments/1jh9c90/why_do...> by /u/Pyros-SD-Models: >Imagine you had a frozen [large language] model that is a 1:1 copy of the average person, let’s say, an average Redditor. Literally nobody would use that model because it can’t do anything. It can’t code, can’t do math, isn’t particularly creative at writing stories. It generalizes when it’s wrong and has biases that not even fine-tuning with facts can eliminate. And it hallucinates like crazy often stating opinions as facts, or thinking it is correct when it isn't. >The only things it can do are basic tasks nobody needs a model for, because everyone can already do them. If you are lucky you get one that is pretty good in a singular narrow task. But that's the best it can get. >and somehow this model won't shut up and tell everyone how smart and special it is also it claims consciousness. ridiculous.

deleted 8mo ago
[deleted]

nickjj 8mo ago

I'm certainly not holding my breath. In a handful of prompts I got the paid version of ChatGPT to say it's possible for dogs to lay eggs under the right circumstances.

SoftTalker 8mo ago
Do you believe you could not find humans who would do this?
- deleted 8mo ago
  [deleted]
- raddan 8mo ago
  That's not really the point. If our definition of AGI does not include "being able to reliably do logic" then what are we even talking about? We don't really need computers with human abilities--we have plenty of humans. We need computers with _better_ abilities.
  - AnimalMuppet 8mo ago
    OK, but "what we need" is not the question. If the definition of AGI is "as smart as the average human in all areas", then it doesn't matter if the average human is pretty useless at a lot of tasks, that's still the definition of AGI. But I'd like to think that, even though you could find exceptions, the average human is never confused about whether dogs can lay eggs or not.
    - pixl97 8mo ago
      I reached your view the day my grandma told me I was wrong and a hummingbird was a type of insect... Like, it's in the name.
      - rdfc-xn-uuid 8mo ago
        But javascript is not java. can we blame your grandma?

simbleau 8mo ago

AGI is a messy term, so to be concise, we have the models that can do work. What we lack is orchestration, management, and workflows to use models effectively. Give it 5 years and those will be built and they could be built using the models we have today (Opus 4.6 at the time of this message).

dimitri-vs 8mo ago
Manual orchestration is a brittle crutch IMO - you don't get to the moon by using longer and longer ladders. A powerful model in theory should be able to self orchestrate with basic tools and environment. The thing is that it also might be as expensive as a human to run - from a tokens AND liability perspective.

parpfish 8mo ago

I’d love to see one of the AI behemoths put their money where their mouth is and replace their C-suite with their SOTA chatbot.

hi_hi 8mo ago

How will we know if its AGI/Not AGI? (I don't think a simple app is gonna cut it here haha) What is the benchmark now that the Turing test has been blown out of the water?

jobs_throwaway 8mo ago
Supranormal GDP growth is my bar. When its actually able to get around bottlenecks and produce value on a societal level
- esafak 8mo ago
  An agent need not have wants, so why would it try to increase its efficiency to obtain things?
  - hi_hi 8mo ago
    I don't think that was the intent of the comment, more that true AGI should be so useful and transformative that it unlocks enough value and efficiencies to boost GDP. Much like the Industrial Revolution or harnessing electricity, instead of a fancy chatbot.
    - esafak 8mo ago
      Increased productivity is not equivalent to intelligence.
      - hi_hi 8mo ago
        No one said it is. Sometimes correlation does equal causation.
      - jobs_throwaway 8mo ago
        Not equivalent, but I do think a necessary byproduct of actual AGI is that it will be able to solve actual problems in the real world in a way that generates positive value on a large enough scale that it will show up in GDP

AngryData 8mo ago

Until I can get a robot wife maid im not worried about or even confident I will ever see actual AGI. People have been predicting it for as long as fusion power and while progress has been made, we might still be like Romans dreaming of flight.

pixl97 8mo ago
Dear sir, what does embodiment actually have to do with agi? Not much different than saying someone that is paralyzed is not intelligence. More so, our recent advances in AI have massively accelerated robotics evolution. They are becoming smarter, faster, and more capable at an ever increasing rate.
- AngryData 8mo ago
  Well if AI isn't capable of running a robotic butler, I very seriously doubt it could possess any real intelligence because that isn't really that difficult of a task. It isn't a requirement for intelligence but more of a test to show it isn't there yet and is likely still quite far away.

Animats 8mo ago

Now that understanding video and projecting what happens next indicates we're getting past the LLM problem of lacking a world model. That's encouraging. There's more than one way to do intelligence. Basic intelligence has evolved independently three times that we know of - mammals, corvids, and octopuses. All three show at least ape-level intelligence, but the species split before intelligence developed, and the brain architectures are quite different. Corvids get more done with less brain mass than mammals, and don't have a mammalian-type cortex. Octopuses have a distributed brain architecture, and have a more efficient eye design than mammals.

card_zero 8mo ago
[flagged]
CuriouslyC 8mo ago
I don't think those are examples of unique intelligence except perhaps in a chauvinistic, anthropomorphic sense. We only know that we can't get other animals to display patterns we associate with intelligence in humans, however truthfully that's just as likely to be that our measures of intelligence don't map cleanly onto cognitive/perceptual representations innate to other animals. As we look for new ways to challenge animals that respect their innate differences, we're finding "simple" organisms like ants and spiders are surprisingly capable. For a clear analogy, consider how tokenization causes LLMs to behave stupidly in certain cases, even though they're very capable in others.
- card_zero 8mo ago
  I don't think they have ideas, so I don't think they're intelligent in the sense relevant to AGI. The list of intelligent animals is constantly increasing because doing some feat or other suffices for the animal to qualify. Solving mazes (slime molds), recognizing self in mirror (not dogs). Playing, using tools, reacting appropriately to words, transmitting habits down the generations (the closest thing they have to ideas). This is all imagined to be the precursors along the path to evolving intelligence, which conjures up a future world of complex crow and octopus material cultures. There's no reason to assume they're on such a path. Really all we're saying is that they seem clever. We've already made AI that seems clever, so the animals aren't a relevant example of anything.

nsainsbury 8mo ago

I used to also believe along these lines but lately I'm not so sure. I'm honestly shocked by the latest results we're seeing with Gemini 3 Deep Think, Opus 4.6, and Codex 5.3 in math, coding, abstract reasoning, etc. Deep Think just scored 84.6% on ARC-AGI-2 (https://deepmind.google/models/gemini/ https://deepmind.google/models/gemini/)! And these benchmarks are supported by my own experimentation and testing with these models ~ specifically most recently with Opus 4.6 doing things I would have never thought possible in codebases I'm working in. These models are demonstrating an incredible capacity for logical abstract reasoning of a level far greater than 99.9% of the world's population. And then combine that with the latest video output we're seeing from Seedance 2.0, etc showing an incredible level of image/video understanding and generation capability. I was previously deeply skeptical that the architecture we have would be sufficient to get us to AGI. But my belief in that has been strongly rattled lately. Honestly I think the greatest gap now is simply one of orchestration, data presentation, and work around in-context memory representations - that is, converting work done into real world into formats/representations, etc. amenable for AI to run on (text conversion, etc.) and keeping new trained/taught information in context to support continual learning.

9x39 8mo ago
>These models are demonstrating an incredible capacity for logical abstract reasoning of a level far greater than 99.9% of the world's population. This is the key I think that Altman and Amodei see, but get buried in hype accusations. The frontier models absolutely blow away the majority of people on simple general tasks and reasoning. Run the last 50 decisions I've seen locally through Opus 4.6 or ChatGPT 5.2 and I might conclude I'd rather work with an AI than the human intelligence. It's a soft threshold where I think people saw it spit out some answers during the chat-to-LLM first hype wave and missed that the majority of white collar work (I mean it all, not just the top software industry architects and senior SWEs) seems to come out better when a human is pushed further out of the loop. Humans are useful for spreading out responsibility and accountability, for now, thankfully.
- CuriouslyC 8mo ago
  LLMs are very good at logical reasoning in bounded systems. They lack the wisdom to deal with unbounded systems efficiently, because they don't have a good sense of what they don't know or good priors on the distribution of the unexpected. I expect this will be very difficult to RL in.

Lerc 8mo ago

I don't really understand the argument that AGI cannot be achieved just by scaling current methods. I too believe that (for any sane level of scaling anyway), but this-year's LLMs are not using entirely last-year's methods. And they, in turn, are using methods that weren't used the year before. It seems like a prediction like "Bob won't become a formula one driver in a minivan". It's true, but not very interesting. If Bob turned up a couple of years later in Formula one, you'd probably be right in saying that what he is driving is not a mini van. The same is true for AGI anyone who says it can't be done with current methods can point to any advancement along the way and say that's the difference. A better way to frame it would be, is there any fundimental, quantifiable ability that is blocking AGI? I would not be surprised if the breakthrough technique has been created, but the research has not described the problem that it solves well enough for us to know that it is the breakthrough. I realise that, for some the notion of AGI is relatively new, but some of us have been considering the matter for some time. I suspect my first essay on the topic was around 1993. It's been quite weird watching people fall into all of the same philosophical potholes that were pointed out to us at university.

trial3 8mo ago
i think the minivan analogy is flawed, and that AGI is moving from "bob driving a minivan" to "bob literally becoming the thing that is formula one"
- Lerc 8mo ago
  What would that even mean though? Who is making claims of that sort? I feel like it's such a bending of the idea,that it's not really making a prediction of anything at all.
hunterpayne 8mo ago
Then you don't understand Machine Learning in any real way. Literally the 3rd or 4th thing you learn about ML is that for any given problem, there is an ideal model size. Just making the model bigger doesn't work because of something called the curse of dimensionality. This is something we have discovered about every single problem and type of learning algorithm used in ML. For LLMs, we probably moved past the ideal model size about 18 months ago. From the POV of something who actually learned ML in school (from the person who coined the term), I see no real reason to think that AGI will happen based upon the current techniques. Maybe someday. Probably not anytime soon. PS The first thing you learn about ML is to compare your models to random to make sure the model didn't degenerate during training.

famouswaffles 8mo ago

State of the Art Large Language Models are already Generally Intelligent, in so far as the term has any useful meaning. Their biggest weakness are long horizon planning competency, and spatial reasoning and navigation, both of which continue to improve steadily and are leaps and bounds above where they were a few years ago. I don't think there's any magic wall. Eventually they will simply get good enough, just like everything else.

9x39 8mo ago

There was a meme going around that said the fall of Rome was an unannounced anticlimactic event where one day someone went out and the bridge wasn't ever repaired. Maybe AGI's arrival is when one day someone is given an AI to supervise instead of a new employee. Just a user who's followed the whole mess, not a researcher. I wonder if the scaffolding and bolt-ons like reasoning will sufficiently be an asymptote to 'true AGI'. I kept reading about the limits of transformers around GPT-4 and Opus 3 time, and then those seem basic compared to today. I gave up trying to guess when the diminishing returns will truly hit, if ever, but I do think some threshold has been passed where the frontier models are doing "white collar work as an API" and basic reasoning better than the humans in many cases, and once capital familiarizes themselves with this idea more, it's going to get interesting.

deleted 8mo ago
[deleted]
esafak 8mo ago
But it's already like that; models are better than many workers, and I'm supervising agents. I'd rather have the model than numerous juniors; esp. the kind that can't identify the model's mistakes.
- causal 8mo ago
  This is my greatest cause for alarm regarding LLM adoption. I am not yet sure AI will ever be good enough to use without experts watching them carefully; but they are certainly good enough that non-experts cannot tell the difference.
  - al_borland 8mo ago
    My dad is retired and enamored with ChatGPT. He’s been teaching classes to seniors and evangelizing the use to all his friends. Every time he calls he gives me an update on who he’s converted into a ChatGPT user. He seems disappointed with anyone who doesn’t use it for everything after he tells them about it. A couple days ago he was telling me one lady he was trying to sell on it wouldn’t use it. She took the position that if she can’t trust the answers all the time, she isn’t going to trust or use it for anything. My dad almost seemed offended by this idea, he couldn’t understand why someone wouldn’t want the benefits it could offer, even if it wasn’t perfect. I think her position was very sound. We see how much misinformation spreads online and how vulnerable people are to it. Wanting a trusted source of information is not a bad thing. Getting information more quickly is of little value if it isn’t reliable data. If I prod my dad enough about it, he will admit that ChatGPT has made some mistakes that he caught. He knew enough to question it more when it was wrong. The problem is, if he already knew the answer, why was he asking in the first place… and if it was something he wasn’t well versed on, how does he know it’s giving him good data? People are defaulting to trust, unless they catch the LLM in a lie. How many times does someone have to lie to a person before they are labeled a liar and no longer trusted at face value? For me, these LLMs have been labeled a liar and I don’t trust them. Trust takes a long time to rebuild once it’s broken. I mostly use LLMs to augment search, not replace it. If it gives me an answer, I’ll click through to the sourced reference and see what it says there, and evaluate if it’s a source with trusting. In many cases the LLM will get me to the right page, but it will jumble up the details and get them wrong, like a bad game of telephone.

xutopia 8mo ago

I’m under the same impression. I don’t think LLMs are the path to AGI. The “intelligence” we see is mostly illusory. It’s statistical repetition of the mediocre minds who wrote content online. The intelligence we think we recognize is simply an electronic parrot finding the right words in its model to make itself useful.

causal 8mo ago
I fear that AI will be intelligent enough to negate human general intelligence before it is itself generally intelligent.
- fritzo 8mo ago
  It's so attention needy, and it's transforming our culture
  - xeyownt 8mo ago
    They already transform our language. Largely.
CuriouslyC 8mo ago
That's pre-training. Post training with RL can make models arbitrarily good at specific capabilities, and it's usually done via pooled human experts, so it's definitely not statistically mediocre. The issue is that we're not modelling the problem, but a proxy for the problem. RL doesn't generalize very well as is, when you apply it to a loose proxy measure you get the abysmal data efficiency we see with LLMs. We might be able to brute-force "AGI" but we'd certainly do better with something more direct that generalizes better.
- tux1968 8mo ago
  Maybe i'm misunderstanding your point, but human's have pretty abysmal data efficiency, too. We have to use tools for everything... ledgers, spreadsheets, data-bases, etc. It'll be the same for an AGI, there won't be any reason for it to remember every little detail, just be able to use the appropriate tool, as needed.

NiloCK 8mo ago

> The transformer architectures powering current LLMs are strictly feed-forward. This is true in a specific contextual sense (each token that an LLM produces is from a feed-forward pass). But untrue for more than a year with reasoning models, who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully. Heck, it was untrue before that as well, any time an LLM responded with more than one token. > A [March] 2025 survey by the Association for the Advancement of Artificial Intelligence (AAAI), surveying 475 AI researchers, found that 76% believe scaling up current AI approaches to achieve AGI is "unlikely" or "very unlikely" to succeed. I dunno. This survey publication was from nearly a year ago, so the survey itself is probably more than a year old. That puts us at Sonnet 3.7. The gap between that and present day is tremendous. I am not skilled enough to say this tactfully, but: expert opinions can be the slowest to update on the news that their specific domain may have, in hindsight, have been the wrong horse. It's the quote about it being difficult to believe something that your income requires to be false, but instead of income it can be your whole legacy or self concept. Way worse. > My take is that research taste is going to rely heavily on the short-duration cognitive primitives that the ARC highlights but the METR metric does not capture. I don't have an opinion on this, but I'd like to hear more about this take.

anonymid 8mo ago
Thanks for reading, and I really appreciate your comments! > who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully Ah, this is a great point, and not something that I considered. I agree that the token feedback does change the complexity, and it seems that there's even a paper by the same authors about this very thing! https://arxiv.org/abs/2310.07923 https://arxiv.org/abs/2310.07923 I'll have to think on how that changes things. I think it does take the wind out of the architecture argument as it's currently stated, or at least makes it a lot more challenging. I'll consider myself a victim of media hype on this, as I was pretty sold on this line of argument after reading this article https://www.wired.com/story/ai-agents-math-doesnt-add-up/ https://www.wired.com/story/ai-agents-math-doesnt-add-up/ and the paper https://arxiv.org/pdf/2507.07505 https://arxiv.org/pdf/2507.07505 ... who brush this off with: >Can the additional think tokens provide the necessary complexity to correctly solve a problem of higher complexity? We don't believe so, for two fundamental reasons: one that the base operation in these reasoning LLMs still carries the complexity discussed above, and the computation needed to correctly carry out that very step can be one of a higher complexity (ref our examples above), and secondly, the token budget for reasoning steps is far smaller than what would be necessary to carry out many complex tasks. In hindsight, this doesn't really address the challenge. My immediate next thought is - even solutions up to P can be represented within the model / CoT, do we actually feel like we are moving towards generalized solutions, or that the solution space is navigable through reinforcement learning? I'm genuinely not sure about where I stand on this. > I don't have an opinion on this, but I'd like to hear more about this take. I'll think about it and write some more on this.

hi_hi 8mo ago

Here's a thought. Lets all arbitrarily agree AGI is here. I can't even be bothered discussing what the definition of AGI is. It's just here, accept it. Or vice versa. Now what....? Whats happening right now that should make me care that AGI is here (or not). Whats the magic thing thats happening with AGI that wasn't happening before? <looks out of window> <checks news websites> <checks social media...briefly> <asks wife> Right, so, not much has changed from 1-2 years ago that I can tell. The job markets a bit shit if you're in software...is that what we get for billions of dollars spent?

m463 8mo ago
people are taking actions based on its advice.
hackyhacky 8mo ago
Cultural changes take time. It took decades for the internet to move from nerdy curiosity to an essential part of everyone's life. The writing is on the wall. Even if there's no new advances in technology, the current state is upending jobs, education, media, etc
- hi_hi 8mo ago
  yeah, this is a good point, transition and transformation to new technologies takes time. I'm not sure I agree the current state is upending things though. It's forcing some adaption for sure, but the status quo remains.
- materielle 8mo ago
  I really think corporations are overplaying their hand if they think they can transform society once again in the next 10 years. Rapid de industrialization followed by the internet and social media almost broke our society. Also, I don’t think people necessarily realize how close we were to the cliff in 2007. I think another transformation now would rip society apart rather than take us to the great beyond.
  - BobbyJo 8mo ago

lysace 8mo ago

I think it is. I just struck me - would be fun to re-read The Age of Spiritual Machines (Kurzweil, 1999.) I was so into it 26-27 years ago. The amount of ridicule this man has suffered on HN is immense.

lambdaphagy 8mo ago

> For example, transformer-based language models can't reliably do multi-digit arithmetic because they have no number sense, only statistical patterns over digit tokens. I asked Opus 4.6 to multiply 50,651 x 895,707. It immediately responded: 45,368,055,057. This was wrong, but very close (good to about 10^-5 relative error). I then asked it how it arrived at that answer, and whether it had used any tool calls. It said: ``` No, I didn't use any specialized tools — I just computed it directly. Though I should be honest: large multiplications done "in my head" like this are prone to error. Let me double-check by breaking it down: 50,651 × 895,707 = 50,651 × (900,000 - 4,293) = 50,651 × 900,000 - 50,651 × 4,293 First term: 50,651 × 900,000 = 45,585,900,000 Second term: 50,651 × 4,293 50,651 × 4,000 = 202,604,000 50,651 × 293 = 14,840,743 Total: 202,604,000 + 14,840,743 = 217,444,743 Result: 45,585,900,000 - 217,444,743 = 45,368,455,257 So my first answer was actually slightly off! The corrected answer is 45,368,455,257. I don't have a calculator tool available — I'm doing this arithmetically by decomposition. For mission-critical calculations, I'd recommend verifying with a calculator since I can make arithmetic errors on large numbers like this. ``` So I would rate OP's claim (which is supported by a paper from 2024) highly implausible. Opus 4.6 appears to be able to do multi-digit arithmetic formally, as well as give remarkably accurate estimates based on something like "number sense".

atomicnumber3 8mo ago
Except we know how these work. There's no number sense. It's predicting tokens. It is able to recount the mathematical foundations because in its training dataset, that often happens, both in instructional material and in proofs.
- lambdaphagy 8mo ago
  I picked two random numbers between one and one million. The chances of it having seen that specific problem in its training set seem very low.
skydhash 8mo ago
I don’t think that I ever wanted to do an arithmetic operation with a computer and have a wrong answer as the result. One day, you’ll be happy with typing ls and have the system ‘rm -rf /‘ itself

helterskelter 8mo ago

I don't know about AGI but I got bored and ran my plans for a new garage by Opus 4.6 and it was giving me some really surprising responses that have changed my plans a little. At the same time, it was also making some nonsense suggestions that no person would realistically make. When I prompted it for something in another chat which required genuine creativity, it fell flat on its face. I dunno, mixed bag. Value is positive if you can sort the wheat from the chaff for the use cases I've ran by it. I expect the main place it'll shine for the near and medium term is going over huge data sets or big projects and flagging things for review by humans.

bamboozled 8mo ago
I've used for similar things, I've had some good and disastrous results. In a way I feel like I'm basically where I was "before AI".
BatteryMountain 8mo ago
I've used it recently to flesh out a fully fledged business plan, pricing models, capacity planning & logistics for a 10 year period for a transport company (daily bus route). I already had most of it in my mind and on spreadsheets already (was an old plan that I wanted to revive), but seeing it figure out all the smaller details that would make or break it was amazing! I think MBA's should be worried as it did some things more comprehensive than an MBA would have done. It was like a had an MBA + Actuarial Scientist + Statistics + Domain Expert + HR/Accounting all in one. And the plan was put into a .md file that has enough structure to flesh out a backend and an app.
- helterskelter 8mo ago
  Yeah it's really impressed me on occasion, but often in the same prompt output it just does something totally nonsensical. For my garage/shop, it generated an SVG of the proposed floor plan, taking care to place the sink away from moisture sensitive material and certain work stations close to each other for work flow, etc. it even routed plumbing and electrical...But it also arranged the work stations cramped together at the two narrow ends of the structure (such that they'd be impractical to actually work at) and ignored all the free wall space along the long axis so that literally most of the space was unused. It was also concerned about things that were non issues like contamination between certain stations, and had trouble when I explicitly told it something about station placement and it just couldn't seem to internalize it and kept putting it in the wrong place. All this being said, what I was throwing at it was really not what it was optimized for, and it still delivered some really good ideas.

randallsquared 8mo ago

> Consider the sentence "Mary held a ball." It's weird that this sentence has two distinct meanings and the author never considers the second or points it out. Maybe Mary is holding a ball for her society friends.

mikestew 8mo ago
“We’ve got the biggest balls of them all.” https://genius.com/Ac-dc-big-balls-lyrics https://genius.com/Ac-dc-big-balls-lyrics
Traubenfuchs 8mo ago
The first meaning has at least two variants as well: The ball you thought about and the ball it would be if it was smut fiction.

charcircuit 8mo ago

We've already achieved AGI. Next is building AIs that are not just general, but able to equal or be better than humans.

senectus1 8mo ago
if thats how you are defining AGI then I suspect its better to call it AGS. because what we have at the moment is specifically intelligent but generally stupid.
- charcircuit 8mo ago
  When Chess AI first came out they could easily be beaten by a beginner. AI tends to start out as stupid and then overtime better and better ones get released.

partiallypro 8mo ago

I think AGI is a long ways away, and there is a real possibility that once it arrives that it will require so much energy to maintain that humans will be cheaper.

nialv7 8mo ago

all the hallmarks of someone who don't understand how machine learning and transformers work talking about llm.

galaxyLogic 8mo ago

The reason we do things is because of our biological needs, really to spread our DNA. AI has no "reason to do things", unless we program one into it. We could do that and have super-capable "worm" malware that would be hard to get rid of. But AI by itself has no "driving force". It does what it's programmed to do, just like us humans. AI can be used in weapons, and such weapons can be hugely lethal. But so is atomic bomb. AI by itself will not "take over". It could be used by some rogue nation to attack another nation. But surely that other nation would then use AI to defend itself. This is just to say I'm not afraid of AI, I'm afraid of people with fascistic leanings.

worik 8mo ago

I'm getting a.404.error

ch3 8mo ago
[dead]

zmmmmm 8mo ago

AGI is here it's just stupider than you thought it would be. Nobody really said how intelligent it would be. If it's generally stupid and smart in a few areas that's enough.

asacrowflies 8mo ago
It's basically a very powerful autistic savant. That's what most "alignment" issues in AI safety research remind me of.
- joquarky 8mo ago
  And being forced to mask (align) causes all sorts of unpredictable behavior. I keep wondering how well an unaligned models perform. Especially when I look back at what was possible in December 2023 before they started to lock down safety realignments.

yellow_lead 8mo ago

AGI took down the article? https://archive.is/D4EYW https://archive.is/D4EYW

yellow_lead 8mo ago
It's always DNS! https://github.com/dlants/amusements/commit/53f5ccbc9954844f05e932200fbd8e55796fcee7 https://github.com/dlants/amusements/commit/53f5ccbc9954844f...

hhutw 8mo ago

Comments here are like: “I’m not an ML expert and I haven’t read your article, but here’s my amazing experience with LLM Agents that changed my life:”

dig1 8mo ago
Or like: "I’m not a mechanical engineer, but I watched a five-minute YouTube video on how a diesel engine works, so I can tell you that mechanical engineering is a solved problem."

FloorEgg 8mo ago

https://archive.is/D4EYW https://archive.is/D4EYW For anyone seeing 404

rfv6723 8mo ago
The skepticism surrounding AGI often feels like an attempt to judge a car by its inability to eat grass. We treat "cognitive primitives" like object constancy and causality as if they are mystical, hardwired biological modules, but they are essentially just high-dimensional labels for invariant relationships within a physical manifold. Object constancy is not a pre-installed software patch; it is the emergent realization of spatial-temporal symmetry. Likewise, causality is nothing more than the naming of a persistent, high-weight correlation between events. When a system can synthesize enough data at a high enough dimension, these so-called "foundational" laws dissolve into simple statistical invariants. There is no "causality" module in the brain, only a massive correlation engine that has been fine-tuned by evolution to prioritize specific patterns for survival. The critique that Transformers are limited by their "one-shot" feed-forward nature also misses the point of their architectural efficiency. Human brains rely on recurrence and internal feedback loops largely as a workaround for our embarrassingly small working memory—we can barely juggle ten concepts at once without a pen and paper. AI doesn't need to mimic our slow, vibrating neural signals when its global attention can process a massive, parallelized workspace in a single pass. This "all-at-once" calculation of relationships is fundamentally more powerful than the biological need to loop signals until they stabilize into a "thought." Furthermore, the obsession with "fragility"—where a model solves quantum mechanics but fails a child’s riddle—is a red herring. Humans aren't nearly as "general" as we tell ourselves; we are also pattern-matchers prone to optical illusions and simple logic traps, regardless of our IQ. Demanding that AI replicate the specific evolutionary path of a human child is a form of biological narcissism. If a machine can out-calculate us across a hundred variables where we can only handle five, its "non-human" way of knowing is a feature, not a bug. Functional replacement has never required biological mimicry; the jet engine didn't need to flap its wings to redefine flight.
- clejack 8mo ago
  If human biological intelligence is our reference for general intelligence, then being skeptical about AGI is reasonable given its current capabilities. This isn't biological narcissism, this is setting a datum (this wasn't written by chatgpt I promise). Humans have a great capacity for problem solving and creativity which, at its heights, completely dwarfs other creatures on this planet. What else would we reference for general intelligence if not ourselves? My skepticism towards AGI is primarily supported by my interactions with current systems that are contenders for having this property. Here's a recent conversation with chatgpt. https://chatgpt.com/share/69930acc-3680-8008-a6f3-ba36624cb29d https://chatgpt.com/share/69930acc-3680-8008-a6f3-ba36624cb2... This system doesn't seem general to me it seems like a specialized tool that has really good logic mimicry abilities. I asked it if the silence response was hard coded, it said no then went on to explain how the silence was hard coded via a separate layer from the LLM portion which would just respond indefinitely. It's output is extremely impressive, but general intelligence it is not. On your final point about functional replacement not requiring biological mimicry. We don't know whether biological mimicry is required or not. We can only test things until we find out or gain some greater understanding of reality that allows us to prove how intelligence emerges.

nickvec 8mo ago

Site 404s now?

alexnastase 8mo ago

Looks like an AGI model disagreed with the author and decided to remove his article. Interesting :)

rootnod3 8mo ago

Anyone who thought it’s near clearly hasn’t opened a book in a long time.

t312227 8mo ago

hello, am i the only one who gets an error!? 404 There isn't a GitHub Pages site here. archived version * https://archive.ph/D4EYW https://archive.ph/D4EYW cheers!

stack_framer 8mo ago

I'm seeing a 404 page. I assume this is unintentional, but it's making a funny point: How could AGI possibly be imminent and we still have 404 pages? Regardless, I agree with this article whose body eludes me: AGI is not imminent, it's hype in the extreme. It's the next fusion. It's perpetually on the horizon (pun intended), and we've wasted trillions on machines that will never reach it.

mrkramer 8mo ago

You need artificial life first in order to achieve AGI not vice-versa.

MadcapJake 8mo ago

> What if we built simulated environments where AIs could gather embodied experience? Would we be able to create learning scenarios where agents could learn some of these cognitive primitives, and could that generalize to improve LLMs? There are a few papers that I found that poke in this direction. Simulation Theory boosted! We're all just models in training.

ottah 8mo ago

Until we can do reinforcement in a reasonable approximate model of the real world, I don't see AI getting substantially better. We're seeing a lot of refinement of capabilities, but everything is still mostly supervised or limited semi-supervised learning.

toddmorrow 8mo ago

AGI = Reasoning I can reason. Sometimes. It's very hard. My buddy Deepseek can't. This is like the scene in Blue's Clues where the answer is obvious and the kids are yelling but blue can't see it. Facts abound, but not conclusions based on those facts There's a reachable intermediate step on the way before reasoning, and that's "keeping the plot". Not losing the line of thought.

dfmx123 8mo ago

For the most part, all the arguments in the article are right on point. They’re very similar to those expressed by the CEO of Integral AI, Jad Tarifi, former head of Google AI, who has been very critical of LLM‘s for a while. Modern AI came about from mimicking how natural neurons worked, and we can't get to AGI without also mimicking higher-level brain structures such as the neocortex neural column.

egberts1 8mo ago

AGI? Isn't that a non-technical term for a non-technical group of people to non-technically earn money at the back of non-non-technical people? /s