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?
What's happening with AGI depends on what you mean by AGI so "can't even be bothered discussing what the definition" means you can't say what's happening.
My usual way of thinking about it is AGI means can do all the stuff humans do which means you'd probably after a while look out the window and see robots building houses and the like. I don't think that's happening for a while yet.
One of the most impactful books I ever read was Alvin Toffler's Future Shock.
Its core thesis was: Every era doubled the amount of technological change of the prior era in one half the time.
At the time he wrote the book in 1970, he was making the point that the pace of technological change had, for the first time in human history, rendered the knowledge of society's elders - previously the holders of all valuable information - irrelevant.
The pace of change has continued to steadily increase in the ensuing 55 years.
> Here's a thought. Lets all arbitrarily agree AGI is here.
A slightly different angle on this - perhaps AGI doesn't matter (or perhaps not in the ways that we think).
LLMs have changed a lot in software in the last 1-2 years (indeed, the last 1-2 months); I don't think it's a wild extrapolation to see that'll come to many domains very soon.
> Lets all arbitrarily agree AGI is here. I can't even be bothered discussing what the definition of AGI is.
There is a definition of AGI the AI companies are using to justify their valuation. It's not what most people would call AGI but it does that job well enough, and you will care when it arrives.
They define it as an AI that can develop other AI's faster than the best team of human engineers. Once they build one of those in house they outpace the competition and become the winner that takes all. Personally I think it's more likely they will all achieve it at a similar time. That would mean the the race will continues, accelerating as fast as they can build data centres and power plants to feed them.
It will impact everyone, because the already dizzying pace of the current advances will accelerate. I don't know about you, but I'm having trouble figuring out what my job will be next year as it is.
An AI that just develops other AI's could hardly be called "general" in my book, but my opinion doesn't count for much.
If AGI is already here actions would be so greatly accelerated humans wouldn’t have time to respond.
Remember that weather balloon the US found a few years ago that for days was on the news as a Chinese spy balloon?
Well whether it was a spy balloon or a weather balloon but the first hint of its existence could have triggered a nuclear war that could have already been the end of the world as we know it because AGI will almost certainly be deployed to control the U.S. and Chinese military systems and it would have acted well before any human would have time to intercept its actions.
That’s the apocalyptic nuclear winter scenario.
There are many other scenarios.
An AGI which has been infused with a tremendous amount of ethics so the above doesn’t happen, may also lead to terrible outcomes for a human. An AGI would essentially be a different species (although a non biological one). If it replicated human ethics even when we apply them inconsistently, it would learn that treating other species brutally (we breed, enslave, imprison, torture, and then kill over 80 billion land animals annually in animal agriculture, and possibly trillions of water animals). There’s no reason it wouldn’t do that to us.
Finally, if we infuse it with our ethics but it’s smart enough to apply them consistently (even a basic application of our ethics would have us end animal agriculture immediately), so it realizes that humans are wrong and doesn’t do the same thing to humans, it might still create an existential crisis for humans as our entire identity is based on thinking we are smarter and intellectually superior to all other species, which wouldn’t be true anymore. Further it would erode beliefs in gods and other supernatural BS we believe which might at the very least lead humans to stop reproducing due to the existential despair this might cause.
Depends on the cost to run it.say it costs 5k to do a years worth of something intellectual with it. That means the price ceiling on 90% of lawyer/accountant/radiologist/low to middle management is 5k now. It will be epic and temporarily terrible when it happens as long as reasonably competent models are opensource. I also don't think we are near that at all though
I do strongly agree on the framing, but I'd argue with the conclusion
Yeah, it really doesn't matter if AGI has happened, is going to happen, will never happen, whatever. No matter what sort of definition we make for it, someone's always doing to disagree anyway. For a looong time, we thought the Turing test was the standard, and that only a truly intelligent computer could beat it. It's been blown out of the water for years now, and now we're all arguing about new definitions for AGI
At the end of the day, like you say, it doesn't matter a bit how we define terms. We can label it whatever we want, but the label doesn't change what it can DO
What it can DO is the important part. I think a lot of software devs are coming to terms with the idea that AI will be able to replace vast chunks of our jobs in the very near future.
If you use these things heavily, you can see the trajectory.
6 months ago I'd only trust them for boiler plate code generation and writing/reviewing short in-line documentation.
Today, with the latest models and tools, I'm trusting them with short/low impact tasks (go implement this UI fix, then redeploy the app locally, navigate to it, and verify the fix looks correct).
6 months from now, my best guess is that they'll continue to become more capable of handling longer + more complex tasks on their own.
5 years from now, I'm seeing a real possibility that they'll be handling all the code, end to end.
Doesn't matter if we call that AGI or not. It very much will matter whose jobs get cut, because one person with AI can do the work of 20 developers
I think you are missing the point: If we assume that AGI is *not* yet here, but may be here soon, what will change when it arrives? Those changes could be big enough to affect you.
I've been writing code for 20 years. AI has completely changed my life and the way I write code and run my business. Nothing is the same anymore, and I feel I will be saying that again by the end of 2026. My productive output as a programmer in software and business have expanded 3x *compounding monthly*.
> 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.
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
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/ and the paper 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.
You run it again, with a bigger input. If it needs to do a loop to figure out what the next token should be (Ex. The result is: X), it will fail. Adding that token to the input and running it again is too late. It has already been emitted. The loop needs to occur while "thinking" not after you have already blurted out a result whether or not you have sufficient information to do so.
> 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
Not sure I follow. Are you saying that AI researchers would be out of a job if scaling up transformers leads to AGI? How? Or am I misunderstanding your point.
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.
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.
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.
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.
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.
I've recently come to the understanding that LLMs don't have intelligence in any way. They have language, which in humans is a downstream product of intelligence. But thats all they have. There's no little being sitting at the center of the Chinese room. Trying to classify LLMs as intelligent is going upstream and doesn't work.
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.
"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."
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.
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.
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.
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/)! 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.
>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.
> 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 yet they have trouble knowing that a person should take their car to a car wash.
I also saw a college professor who put various AI models through all his exams for a freshman(?) level class. Most failed, I think one barely passed, if I remember correctly.
I’ve been reading about people being shocked by how good things are for years now, but while there may be moments of what seems like incredible brilliance, there are also moments of profound stupidity. AI optimists seem to ignore these moments, but they very real.
If someone on my team performed like AI, I wouldn’t trust them with anything.
> 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
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".
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
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.
>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.
Until recently, philosophy of artificial intelligence seemed to be mostly about arguments why the Turing test was not a useful benchmark for intelligence. Pretty much everyone who had ever thought about the problem seriously had come to the same conclusion.
The fundamental issue was the assumption that general intelligence is an objective property that can be determined experimentally. It's better to consider intelligence an abstraction that may help us to understand the behavior of a system.
A system where a fixed LLM provides answers to prompts is little more than a Chinese room. If we give the system agency to interact with external systems on its own initiative, we get qualitatively different behavior. The same happens if we add memory that lets the system scale beyond the fixed context window. Now we definitely have some aspects of general intelligence, but something still seems to be missing.
Current AIs are essentially symbolic reasoning systems that rely on a fixed model to provide intuition. But the system never learns. It can't update its intuition based on its experiences.
Maybe the ability to learn in a useful way is the final obstacle on the way towards AGI. Or maybe once again, once we start thinking we are close to solving intelligence, we realize that there is more to intelligence than what we had thought so far.
To my knowledge Turing test has not been blown out of the water. The forms I saw were time limited and participants were not pushed hard to interrogate.
I would consider something generally intelligent that is capable of sustaining itself. So... self-sufficiency? I don't see why the bar would be much lower than that. And before people chime in about kids not being self-sufficient so by that definition I wouldn't consider them generally intelligent which is obviously false... to that I would say... they're still in pre-training.
Humans will never accept we created AI, they'll go so far as to say we were not intelligent in the first place. That is the true power of the AI effect.
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.
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.
I don't see how you can come to that conclusion if you've actually used e.g. Opus 4.6 on a hard problem. Either you're not using it, or you're not using it right. And I don't mean simple web dev stuff. In a few hours Claude built me a fairly accurate physics simulation for a game I've been working on. It searched for research papers, grabbed constants for the different materials, implemented the tests and the physics and... it worked. It would have taken me weeks. Yes, I guided it here and there, especially by telling it about various weird physics behavior that I observed, but I didn't write one line of code.
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.
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.
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.
> 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.
That seems like a massive oversimplification of the things our brains evolved to do.
> 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.
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.
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.
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.
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.
> 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.
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.
> 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's a tautology - obviously advancements come through newer, refined methods.
I believe they mean that AGI can't be achieved by scaling the current approach; IOW, this strategy is not scalable, not this method is not scalable.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
I've long been terrified of the existence of adversarial prompts that can get me to say anything, that dogs can lay eggs, that there are five lights, that here's my bank info
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.
Opus is the very best and I still throw away most of what it produces. If I did not carefully vet its work I would degrade my code bases so quickly.
To accurately measure the value of AI you must include the negative in your sum.
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.
I ran a quick experiment with Claude and Perplexity, both free versions. I input some retirement info (portfolios balances etc), my age, my desired retirement age etc. Simple stuff that a financial planner would have no issue with. Perplexity was very very good on the surface. Rarely made an obvious blunder or error, and was fast. Claude was much slower and despite me inputting my exact birthdate, kept messing up my age by as much as 18 months. This obviously screws up retirement planning. I also asked some questions about how RMDs would affect my taxes, and asked for some strategies. Perplexity was convinced that I should do a Roth conversion to max up to the 22% bracket, while Claude thought that the tax savings would be minimal.
Mind you, I used the EXACT same prompts. I don't know which model Perplexity was using since the free version has multiple it chooses from (including Claude 3.0).
AGI is when it can do all intellectual work that can be done by humans. It can improve its own intelligence and create a feedback loop because it is as smart as the humans who created it.
API Opus 4.6 will tell you it's still 2025, admit it's wrong then revert back to being convinced it's 2025 as it nears it's context limit.
I'll go so far as to say LLM agents are AGI-lite but saying we "just need the orchestration layer" is like saying ok we have a couple neurons, now we just need the rest of the human.
> there is almost nothing that a human is better at than Opus 4.6.
Lolwut. I keep having to correct Claude at trivial code organization tasks. The code it writes is correct; it’s just ham-fisted and violates DRY in unholy ways.
hi_hi|14 days ago
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?
hackyhacky|14 days ago
The writing is on the wall. Even if there's no new advances in technology, the current state is upending jobs, education, media, etc
tim333|13 days ago
My usual way of thinking about it is AGI means can do all the stuff humans do which means you'd probably after a while look out the window and see robots building houses and the like. I don't think that's happening for a while yet.
CamperBob2|14 days ago
After enlightenment^WAGI: chop wood, fetch water, prepare food
keernan|13 days ago
Its core thesis was: Every era doubled the amount of technological change of the prior era in one half the time.
At the time he wrote the book in 1970, he was making the point that the pace of technological change had, for the first time in human history, rendered the knowledge of society's elders - previously the holders of all valuable information - irrelevant.
The pace of change has continued to steadily increase in the ensuing 55 years.
Edit: grammar
jwilliams|14 days ago
A slightly different angle on this - perhaps AGI doesn't matter (or perhaps not in the ways that we think).
LLMs have changed a lot in software in the last 1-2 years (indeed, the last 1-2 months); I don't think it's a wild extrapolation to see that'll come to many domains very soon.
rstuart4133|13 days ago
There is a definition of AGI the AI companies are using to justify their valuation. It's not what most people would call AGI but it does that job well enough, and you will care when it arrives.
They define it as an AI that can develop other AI's faster than the best team of human engineers. Once they build one of those in house they outpace the competition and become the winner that takes all. Personally I think it's more likely they will all achieve it at a similar time. That would mean the the race will continues, accelerating as fast as they can build data centres and power plants to feed them.
It will impact everyone, because the already dizzying pace of the current advances will accelerate. I don't know about you, but I'm having trouble figuring out what my job will be next year as it is.
An AI that just develops other AI's could hardly be called "general" in my book, but my opinion doesn't count for much.
hshdhdhj4444|14 days ago
Remember that weather balloon the US found a few years ago that for days was on the news as a Chinese spy balloon?
Well whether it was a spy balloon or a weather balloon but the first hint of its existence could have triggered a nuclear war that could have already been the end of the world as we know it because AGI will almost certainly be deployed to control the U.S. and Chinese military systems and it would have acted well before any human would have time to intercept its actions.
That’s the apocalyptic nuclear winter scenario.
There are many other scenarios.
An AGI which has been infused with a tremendous amount of ethics so the above doesn’t happen, may also lead to terrible outcomes for a human. An AGI would essentially be a different species (although a non biological one). If it replicated human ethics even when we apply them inconsistently, it would learn that treating other species brutally (we breed, enslave, imprison, torture, and then kill over 80 billion land animals annually in animal agriculture, and possibly trillions of water animals). There’s no reason it wouldn’t do that to us.
Finally, if we infuse it with our ethics but it’s smart enough to apply them consistently (even a basic application of our ethics would have us end animal agriculture immediately), so it realizes that humans are wrong and doesn’t do the same thing to humans, it might still create an existential crisis for humans as our entire identity is based on thinking we are smarter and intellectually superior to all other species, which wouldn’t be true anymore. Further it would erode beliefs in gods and other supernatural BS we believe which might at the very least lead humans to stop reproducing due to the existential despair this might cause.
snapplebobapple|7 days ago
generallyjosh|9 days ago
Yeah, it really doesn't matter if AGI has happened, is going to happen, will never happen, whatever. No matter what sort of definition we make for it, someone's always doing to disagree anyway. For a looong time, we thought the Turing test was the standard, and that only a truly intelligent computer could beat it. It's been blown out of the water for years now, and now we're all arguing about new definitions for AGI
At the end of the day, like you say, it doesn't matter a bit how we define terms. We can label it whatever we want, but the label doesn't change what it can DO
What it can DO is the important part. I think a lot of software devs are coming to terms with the idea that AI will be able to replace vast chunks of our jobs in the very near future.
If you use these things heavily, you can see the trajectory.
6 months ago I'd only trust them for boiler plate code generation and writing/reviewing short in-line documentation.
Today, with the latest models and tools, I'm trusting them with short/low impact tasks (go implement this UI fix, then redeploy the app locally, navigate to it, and verify the fix looks correct).
6 months from now, my best guess is that they'll continue to become more capable of handling longer + more complex tasks on their own.
5 years from now, I'm seeing a real possibility that they'll be handling all the code, end to end.
Doesn't matter if we call that AGI or not. It very much will matter whose jobs get cut, because one person with AI can do the work of 20 developers
copx|14 days ago
Havoc|14 days ago
tsukurimashou|14 days ago
munchler|14 days ago
m463|13 days ago
dyauspitr|13 days ago
otabdeveloper4|14 days ago
That's Trump's economy, not LLMs.
skeptic_ai|14 days ago
Many people slowly losing jobs and can’t find new ones. You’ll see effects in a few years
znnajdla|14 days ago
xhcuvuvyc|14 days ago
Has it runaway yet? Not sure, but is it currently in the process of increasing intelligence with little input from us? Yes.
Exponential graphs always have a slow curve in the beginning.
NiloCK|14 days ago
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|14 days ago
> 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
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/ and the paper 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.
vrighter|14 days ago
You run it again, with a bigger input. If it needs to do a loop to figure out what the next token should be (Ex. The result is: X), it will fail. Adding that token to the input and running it again is too late. It has already been emitted. The loop needs to occur while "thinking" not after you have already blurted out a result whether or not you have sufficient information to do so.
wavemode|14 days ago
Not sure I follow. Are you saying that AI researchers would be out of a job if scaling up transformers leads to AGI? How? Or am I misunderstanding your point.
helterskelter|14 days ago
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|14 days ago
BatteryMountain|14 days ago
9x39|14 days ago
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.
esafak|14 days ago
unknown|14 days ago
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beej71|14 days ago
Animats|14 days ago
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.
xyzsparetimexyz|14 days ago
CuriouslyC|14 days ago
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|14 days ago
[deleted]
hhutw|14 days ago
“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|14 days ago
"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."
randallsquared|14 days ago
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.
Traubenfuchs|14 days ago
mikestew|14 days ago
https://genius.com/Ac-dc-big-balls-lyrics
zmmmmm|14 days ago
asacrowflies|14 days ago
FloorEgg|14 days ago
For anyone seeing 404
rfv6723|14 days ago
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.
nsainsbury|14 days ago
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/)! 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|14 days ago
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.
lostmsu|14 days ago
al_borland|13 days ago
And yet they have trouble knowing that a person should take their car to a car wash.
I also saw a college professor who put various AI models through all his exams for a freshman(?) level class. Most failed, I think one barely passed, if I remember correctly.
I’ve been reading about people being shocked by how good things are for years now, but while there may be moments of what seems like incredible brilliance, there are also moments of profound stupidity. AI optimists seem to ignore these moments, but they very real.
If someone on my team performed like AI, I wouldn’t trust them with anything.
lambdaphagy|14 days ago
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".
FromTheFirstIn|13 days ago
skydhash|14 days ago
atomicnumber3|14 days ago
TMWNN|14 days ago
>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.
unknown|14 days ago
[deleted]
hi_hi|14 days ago
What is the benchmark now that the Turing test has been blown out of the water?
jltsiren|14 days ago
The fundamental issue was the assumption that general intelligence is an objective property that can be determined experimentally. It's better to consider intelligence an abstraction that may help us to understand the behavior of a system.
A system where a fixed LLM provides answers to prompts is little more than a Chinese room. If we give the system agency to interact with external systems on its own initiative, we get qualitatively different behavior. The same happens if we add memory that lets the system scale beyond the fixed context window. Now we definitely have some aspects of general intelligence, but something still seems to be missing.
Current AIs are essentially symbolic reasoning systems that rely on a fixed model to provide intuition. But the system never learns. It can't update its intuition based on its experiences.
Maybe the ability to learn in a useful way is the final obstacle on the way towards AGI. Or maybe once again, once we start thinking we are close to solving intelligence, we realize that there is more to intelligence than what we had thought so far.
beej71|14 days ago
lostmsu|14 days ago
latentsea|14 days ago
jobs_throwaway|14 days ago
pixl97|14 days ago
Humans will never accept we created AI, they'll go so far as to say we were not intelligent in the first place. That is the true power of the AI effect.
ed_mercer|14 days ago
yellow_lead|14 days ago
https://archive.is/D4EYW
yellow_lead|14 days ago
https://github.com/dlants/amusements/commit/53f5ccbc9954844f...
xutopia|14 days ago
The intelligence we think we recognize is simply an electronic parrot finding the right words in its model to make itself useful.
causal|14 days ago
CuriouslyC|14 days ago
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.
pendenthistory|13 days ago
alexnastase|14 days ago
nialv7|14 days ago
rootnod3|14 days ago
dfmx123|12 days ago
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.
est31|14 days 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.
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|14 days ago
nozzlegear|14 days ago
That seems like a massive oversimplification of the things our brains evolved to do.
andsoitis|14 days ago
Humans discovered or invented all of those.
famouswaffles|14 days ago
partiallypro|14 days ago
tananaev|14 days ago
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.
esafak|14 days ago
stagezerowil|14 days ago
MadcapJake|13 days ago
Simulation Theory boosted! We're all just models in training.
Lerc|14 days ago
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.
lelanthran|14 days ago
It's a tautology - obviously advancements come through newer, refined methods.
I believe they mean that AGI can't be achieved by scaling the current approach; IOW, this strategy is not scalable, not this method is not scalable.
trial3|14 days ago
hunterpayne|14 days ago
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.
ottah|13 days ago
AngryData|14 days ago
pixl97|14 days ago
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.
worik|14 days ago
ch3|14 days ago
[deleted]
lysace|14 days ago
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.
galaxyLogic|14 days ago
mikewarot|14 days ago
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/
[2] https://news.ycombinator.com/item?id=47018100
hermitShell|14 days ago
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|14 days ago
Legend2440|14 days ago
“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|14 days ago
charcircuit|14 days ago
senectus1|14 days ago
because what we have at the moment is specifically intelligent but generally stupid.
stack_framer|14 days ago
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.
simbleau|14 days ago
dimitri-vs|14 days ago
mrkramer|13 days ago
toddmorrow|12 days ago
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.
nickjj|14 days ago
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|14 days ago
fritzo|14 days ago
t312227|14 days ago
am i the only one who gets an error!?
404 There isn't a GitHub Pages site here.
archived version
* https://archive.ph/D4EYW
cheers!
nickvec|14 days ago
parpfish|14 days ago
egberts1|12 days ago
ryanSrich|14 days ago
causal|14 days ago
lysace|14 days ago
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
greedo|14 days ago
Mind you, I used the EXACT same prompts. I don't know which model Perplexity was using since the free version has multiple it chooses from (including Claude 3.0).
JSDave|14 days ago
dimitri-vs|14 days ago
I'll go so far as to say LLM agents are AGI-lite but saying we "just need the orchestration layer" is like saying ok we have a couple neurons, now we just need the rest of the human.
loloquwowndueo|14 days ago
Lolwut. I keep having to correct Claude at trivial code organization tasks. The code it writes is correct; it’s just ham-fisted and violates DRY in unholy ways.
And I’m not even a great coder…
tayo42|14 days ago