I just used 2.5 Pro to help write a large research proposal (with significant funding on the line). Without going into detail, it felt to me like the only reason it couldn’t write the entire thing itself is because I didn’t ask it to. And by “ask it”, I mean: enter into the laughably small chat box the entire grant solicitation + instructions, a paragraph of general direction for what I want to explore, and a bunch of unstructured artifacts from prior work, and turn it loose. I just wasn’t audacious enough to try that from the start.
But as the deadline approached, I got more and more unconstrained in how far back I would step and let it take the reins - doing essentially what’s described above but on isolated sections. It would do pretty ridiculously complex stuff, like generate project plans and timelines, cross reference that correctly with other sections of text, etc. I can safely say it was a 10x force multiplier, and that’s being conservative.
For scientific questions (ones that should have publicly available data, not ones relying on internal data), I have started going to 2.5 Pro over senior experts on my own team. And I’m convinced at this point if I were to connect our entire research data corpus to Gemini, that balance would shift even further. Why? Because I can trust it to be objective - not inject its own political or career goals into its answers.
I’m at the point where I feel the main thing holding back “AGI” is people’s audacity to push its limits, plus maybe context windows and compute availability. I say this as someone who’s been a major skeptic up until this point.
Have you asked any of your experts to double check those bot answers to see how it did?
There are two subtly different definitions in use: (1) “like intelligence in useful ways, but not actually”, and (2) “actually intelligent, but not of human wetware”. I take the A in AGI to be of type (2).
LLMs are doing (1), right now. They may have the “neurological structure” required for (2), but to make a being General and Intelligent it needs to compress its context window persist it to storage every night as it sleeps. It needs memory and agency. It needs to be able to learn in real time and self-adjusting its own weights. And if it’s doing all that, then who is to say it doesn't have a soul?
Artificial means human made, if we made a thing that is intelligent, then it is artificial intelligence.
It is like "artificial insemination" means a human designed system to inseminate rather than the natural way. It is still a proper insemination, artificial doesn't mean "fake", it just means unnatural/human made.
I guess I don’t understand the technical difference between AI and AGI and consider AI to refer to the social meme of “this thing kinda seems like it did something intelligent, like magic”.
Aren't humans themselves essentially human made?
Maybe a better definition would be non-human (or inorganic if we want to include intelligence like e.g. dolphins)?
No, not in the sense in which the word "made" is being used here.
> Maybe a better definition would be non-human (or inorganic if we want to include intelligence like e.g. dolphins)?
Neither of these work. Calling intelligence in animals "artificial" is absurd, and "inorganic" arbitrarily excludes "head cheese" style approaches to building artificial intelligence.
"Artificial" strongly implies mimicry of something that occurs naturally, and is derived from the same root as "artifice", which can be defined as "to construct by means of skill or specialized art". This obviously excludes the natural biological act of reproduction that produces a newborn human brain (and support equipment) primed to learn and grow; reportedly, sometimes people don't even know they're pregnant until they go into labor (and figure out that's what's happening).
It turns out that some (many, in fact) words mean different things in different contexts. My comment makes an explicit argument concerning the connotations and nuances of the word "made" used in this context, and you have not responded to that argument.
Maybe you should have written a substantive response to my comments instead of trying and failing to dunk on me. Maybe you don't understand as much as you think you do.
Humans evolved, but yeah the definition can be a bit hard to understand since it is hard to separate things. That is why I brought up the artificial insemination example since it deals with this.
> Maybe a better definition would be non-human (or inorganic if we want to include intelligence like e.g. dolphins)?
We also have artificial lakes, they are inorganic but human made.
I am using Gemini 2.5 Flash for analyzing screenshots of webpages as part of it. Total cost for that (assuming I did my math right) ? $0.00002 / image.
The problem is, the code it produces is usually not great and inconsistent and has subtle bugs. That quickly becomes a problem if you want to change things later, especially while keeping your data consistent and your APIs stable and backwards compatible. At least that's my experience.
But for building something that you can easily throw away later, it's pretty good and saves a lot of time.
You can email me at vlad dot sanchez at gmail dot com.
Thanks.
And that is basically why humanity is doomed.
However (as the article admits) there is still no general agreement of what AGI is, or how we (or even if we can) get there from here.
What there is is a growing and often naïve excitement that anticipates it as coming into view, and unfortunately that will be accompanied by the hype-merchants desperate to be first to "call it".
This article seems reasonable in some ways but unfortunately falls into the latter category with its title and sloganeering.
"AGI" in the title of any article should be seen as a cautionary flag. On HN - if anywhere - we need to be on the alert for this.
Systems that have general intelligence are ones that are capable of applying reason to an unbounded domain of knowledge. Examples of such systems include: libraries, wikis, and forums like HN. These systems are not AGI, because the reasoning agents in each of these systems are organic (humans); they are more like a cyborg general intelligence.
Artificial general intelligence are just systems that are fully artificial (ie: computer programs) that can apply reason to an unbounded domain of knowledge. We're here, and we have been for years. AGI sets no minimum as to how great the reasoning must be, but it's obvious to anyone who has used modern generative intelligence systems like LLMs that the technology can be used to reason about an unbounded domain of knowledge.
If you don't want to take my word for it, maybe Peter Norvig can be more convincing: https://www.noemamag.com/artificial-general-intelligence-is-...
(I did x and it failed, I did y and It failed, I should try z now) GOOD
(I did x and it failed, I did y and it failed, I should try x now) BAD
It is very hard to argue with Norvig’s arguments that AGI has been around since at least 2023.
You can argue that for the first time in the history we have an AI that deserves its name (unlike Deep blue or AlphaGo which aren't really about intelligence at all) but you cannot call that Artificial GENERAL Intelligence before it overcomes the “jagged intelligence” syndrome.
This is the original definition of AGI. Some data scientists try to move the goalposts to something else and call something that can't replace humans "AGI".
This is a very simple definition that is easy to see when it is fulfilled because then companies can operate without humans.
A few months ago, I'd have said "create image with coherent text"*, but that's now changed. At least in English — trying to get ChatGPT's new image mode to draw the 狐 symbol sometimes works, sometimes goes weird in the way latin characters used to.
* if the ability to generate images doesn't count as "language model" then one intellectual task they can't do is "draw images", see Simon Willison's pelican challenge: https://simonwillison.net/tags/pelican-riding-a-bicycle/
So basically any form of longer term tasks cannot be done by them currently. Short term tasks with constant supervision is about the only things they can do, and that is very limited, most tasks are long term tasks.
This is an issue of tooling, not intelligence. Language models absolutely have the power to process email and send (push?) code, should you give them the tooling to do so (also true of human intelligence).
> So basically any form of longer term tasks cannot be done by them currently. Short term tasks with constant supervision is about the only things they can do, and that is very limited, most tasks are long term tasks.
Are humans that have limited memory due to a condition not capable of general intelligence, xor does intelligence exist on a spectrum? Also, long term tasks can be decomposed into short term tasks. Perhaps automatically, by a language model.
Have you actually tried agentic LLM based frameworks that use tool calling for long term memory storage and retrieval, or have you decided that because these tools do not behave perfectly in a fluid environment where humans do not behave perfectly either, that it's "impossible"?
i.e. "Have you tried this vague, unnamed thing that I alude to that seems to be the answer that contradicts your point, but actually doesn't?"
AGI = 90% of software devs, psychotherapists, lawyers, teachers lose their jobs, we are not there.
Once LLMs can fork themselves, reflect and accumulate domain specific knowledge and transfer the whole context back to the model weights, once that knowledge can become more important than the pre-pretrained information, once they can form new neurons related to a project topic, then yes, we will have AGI (probably not that far away). Once LLM's can keep trying to find a bug for days and weeks and months, go through the debugger, ask people relevant questions, deploy code with new debugging traces, deploy mitigations and so on, we will have AGI.
Otherwise, AI is stuck in this groundhog day type scenario, where it's forever the brightest intern that any company has ever seen, but he's forever stuck at day 0 on the job, forever not that usefull, but full of potential.
(If it was a tooling issue, AGI could build the missing tools)
At a certain point, a tooling issue becomes an intelligence issue. AGI would be able to build the tools they need to succeed.
If we have millions of these things deployed, they can work 24/7, and they supposedly have human-level intelligence, then why haven't they been able to bootstrap their own tooling yet?
You can work around the limitations of LLMs' intelligence with your own and an external workflow you design, but I don't see how that counts as part of the LLM's intelligence.
LLMs have general intelligence. A network of LLMs have better general intelligence.
If a single language model isn't intelligent enough for a task, but a human is, there is a good chance there exists a sufficient network of language models that is intelligent enough.
No they don't. That's the key part you keep assuming without justification. Interestingly enough you haven't responded to my other comment [1].
You asked “What intellectual tasks can humans do that language models can't?” and now that I'm thinking about it again, I think the more apt question would be the reverse:
“What intellectual tasks can a LLM do autonomously without any human supervision (direct or indirect[2]) if there's money at stake?”
You'll see that the list is going to be very shallow if not empty.
> A network of LLMs have better general intelligence.
Your argument was about tool calling for long term memory, this isn't “a network of LLM” but an LLM another tool chosen by a human to deal with LLM's limitations one one particular problem (and of you need long term memory for another problem you're very likely to need to rework both your prompt and your choice of tools to address it: it's not the LLM that solves it but your own intelligence).
[1]: https://news.ycombinator.com/item?id=43755623 [2] indirect supervision would be the human designing an automatic verification system to check LLMs output before using it. Any kind of verification that is planned in advance by the human and not improvised by the LLM when facing the problem counts as indirect supervision, even if it relies on another LLM.
You can cheat with tooling like RAG or agentic frameworks, but the result isn't going to be good and it's not the AI that learns.
But besides this fundamental limitation, had you tried implementing production ready stuff with LLM, you'd have discovered that language models are still painfully unreliable even for the tasks they are supposed to be good at: they will still hallucinate when summarizing, fail to adhere to the prompt, add paragraphs in English at random when working in French, edit unrelated parts of the code you ask it to edit, etc, etc.
You can work around many of those once you've identified it, but that still counts as a fail in a response to your question.
Actual sentience takes energy that our brain really doesn't like to use. It hardcodes switch statements for behaviours as fast as it can and then coasts until something doesn't match.
The first step, my guess, is going to be the ability to work through github issues like a human, identifying which issues have high value, asking clarifying questions, proposing reasonable alternatives, knowing when to open a PR, responding to code review, merging or abandoning when appropriate. But we're not even very close to that yet. There's some of it, but from what I've seen most instances where this has been successful are low level things like removing old feature flags.
If I forced you to use unnatural interfaces it would severely limit your capabilities as well because you'd have to dedicate more effort towards handling basic editing tasks. As someone who recently swapped to a split 36key keyboard with a new layout I can say this becomes immediately obvious when you try something like this. You take your typing/editing skills for granted - try switching your setup and see how your productivity/problem solving ability tanks in practice.
The catch in this though isn't the ability to use these interfaces. I expect that will be easy. The hard part will be, once these interfaces are learned, the scope and search space of what they will be able to do is infinitely larger. And moreover our expectations will change in how we expect an AGI to handle itself when our way of working with it becomes more human.
Right now we're claiming nascent AGI, but really much of what we're asking these systems to do have been laid out for them. A limited set of protocols and interfaces, and a targeted set of tasks to which we normally apply these things. And moreover our expectations are as such. We don't converse with them as with a human. Their search space is much smaller. So while they appear AGI in specific tasks, I think it's because we're subconsciously grading them on a curve. The only way we have to interact with them prejudices us to have a very low bar.
That said, I agree that video feed and mouse is a terrible protocol for AI. But that said, I wouldn't be surprised if that's what we end up settling on. Long term, it's just going to be easier for these bots to learn and adapt to use human interfaces than for us to maintain two sets of interfaces for things, except for specific bot-to-bot cases. It's horribly inefficient, but in my experience efficiency never comes out ahead with each new generation of UIs.
This is true but AGI means "Artificial General Intelligence". Perhaps it would be even more efficient with certain interfaces, but to be general it would have to at least work with the same ones as humans.
Here's some things that I think a true AGI would need to be able to do:
* Control a general purpose robot and use vision to do housework, gardening etc.
* Be able to drive a car - equivalent interfaces to humans might be service motor controlled inputs.
* Use standard computer inputs to do standard computer tasks
And this list could easily be extended.
If we have to be very specific in the choice of interfaces and tasks that we give it, it's not a general AI.
At the same time, we have to be careful at moving the goalposts too much. But current AI are limited to what can be returned in a small number of interfaces (prompt with text/image/video & return text/image/video data). This is amazing, they can sound very intelligent while doing so. But it's important not to lose sight of what they still can't do well which is basically everything else.
Outside of this area, when you do hear of an AI doing something well (self driving, for example) it's usually a separate specialized model rather than a contribution towards AGI.
Similarly I wouldn't be "Generally Intelligent" by this definition if you sat me at a Cyrillic or Chinese keyboard. For this reason, I see human-centric interface arguments as a red herring.
I think a better candidate definition might be about learning and adapting to new environments (learning from mistakes and predicting outcomes), assuming reasonable interface aids.
Would you be able to be taught to use those keyboards? Then you're generally intelligent. If you could not learn, then maybe you're not generally intelligent?
Regarding disabled people, this is an interesting point. Assuming that we're talking about physical disabilities only, disabled people are capable of learning how to use any standard human inputs. It's just the physical controls that are problematic.
For an AI, the physical input is not the problem. We can just put servo motors on the car controls (steering wheel, brakes, gas) and give it a camera feed from the car. Given those inputs, can the AI learn to control the car as a generally intelligent person could, given the ability to use the same controls?
That's different to AIs, which we can hook up to all kinds of inputs: cameras, radar, lidar, car controls, etc. For the AI the lack of input is not the limitation. It's whether they can do anything with an arbitrary input/control, like a servo motor controlling a steering wheel, for example.
To look at it another way, if an AI can operate a robot body by vision, then we suddenly removed the vision input and replaced it with a sense of touch and hearing, would the AI be able to compensate? If it's an AGI, then it should be able to. A human can.
On the other hand, I wonder if we humans are really as "generally intelligent" as we like to think. Humans struggle to learn new languages as adults, for example (something I can personally attest to, having moved to Asia as an adult). So, really, are human beings a good standard by which to judge an AI as AGI?
It's optimal for beings that have general purpose inteligence.
> would severely limit your capabilities as well because you'd have to dedicate more effort towards handling basic editing tasks
Yes, but humans will eventually get used to it and internalize the keyboard, the domain language, idioms and so on and their context gets pushed to long term knowledge overnight and thei short term context gets cleaned up and they get bettet and better at the job, day by day. AI starts very strong but stays at that level forever.
When faced with a really hard problem, day after day the human will remember what he tried yesterday and parts of that problem will become easier and easier for the human, not so for the AI, if it can't solve a problem today, running it for days and days produces diminishing returns.
That's the General part of human intelligence -- over time it can aquire new skills it did not have yesterday, LLMs can't do that -- there is no byproduct of them getting better/aquiring new skills as a result of their practicing a problem.
Hi. I'm blind. I would like to think I have general-purpose intelligence thanks.
And I can state that interfacing with vision would, in fact, be suboptimal for me. The visual cortex is literally unformed. Yet somehow I can perform symbolic manipulations. Converse with people. Write code. Get frustrated with strangers on the Internet. Perhaps there are other "optimal" ways that "intelligent" systems can use to interface with computers? I don't know, maybe the accessibility APIs we have built? Maybe MCP? Maybe any number of things? Data structures specifically optimized for the purpose and exchanged directly between vastly-more-complex intelligences than ourselves? Do you really think that clicking buttons through a GUI is the one true optimal way to use a computer?
To get a solid read on AGI, we need to be grading them in comparison to a remote coworker. That they necessarily see a GUI is not required. But what is required is that they have access to all the things a human would, and don't require any special tools that limit their search space to a level below what a human coworker would have. If it's possible for a human coworker to do their whole job via console access, sure, that's fine too. I only say GUI because I think it'd actually be the easiest option, and fairly straightforward for these agents. Image processing is largely solved, whereas figuring out how to do everything your job requires via console is likely a mess.
And like I said, "using the computer", whether via GUI or screen reader or whatever else, isn't going to be the hard part. The hard part is, now that they have this very abstract capability and astronomically larger search space, it changes the way we interact with them. We send them email. We ping them on Slack. We don't build special baby mittens MCPs and such for them and they have to enter the human world and prove that they can handle it as a human would. Then I would say we're getting closer to AGI. But as long as we're building special tools and limiting their search space to that limited scope, to me it feels like we're still a long way off.
There are some tasks you can't do without vision, but I agree it is dumb to say general intelligence requires vision, vision is just an information source it isn't about intelligence. Blind people can be excellent software engineers etc they can do most white collar work just as well as anyone else since most tasks doesn't require visual processing, text processing works well enough.
I can't think of anything where you require vision that having a tool (a sighted person) you protocol with (speak) wouldn't suffice. So why aren't we giving AI the same "benefit" of using any tool/protocol it needs to complete something.
Okay, are you volunteering to be the guide passenger while I drive?
We have created a tool called "full self driving" cars already. This is a tool that humans can use, just like we have MCPs a tool for AI to use.
All I'm trying to say, is AGIs should be allowed to use tools that fit their intelligence the same way that we do. I'm not saying AIs are AGIs, I'm just saying that the requirement that they use a mouse and keyboard is a very weird requirement like saying People who can't use a mouse and keyboard (amputees, etc.) aren't "Generally" intelligent. Or people who can't see the computer screen.
Won´t say people fell for it though it was just the current happening at the time.
I think that's exactly the point the person you're responding to is calling out. That's a massive bias.
Ever heard of Pandora's Box? Yeah. That ship has sailed. No moratorium you could enact would, at this point, stop the innovation from happening, possibly even independently by multiple teams globally. Economic incentives are stacked in such a way that flakey tech companies will prioritise shareholder value over anything else. Whatever comes next will come, and all we can do is lean back and enjoy the show.
Of course, the political will to do so doesn't exist to even a tiny extent. But if such a will existed, it would be far easier to enforce than the prevention of human cloning, and that one has been successfully implemented for decades now.
Given the current state of the world, do you really think the USA, China, India, Iran, Brazil, North Korea, and Russia, would all have the same opinion on the danger of AI systems and would—despite very obvious and tangible strategic advantages—all halt development for humanity’s sake?
Human cloning is an issue that is mostly academic in nature, but I’d bet everything I have that bioengineers all over the world secretly are researching this on government programmes, and nuclear non-proliferation is a joke. It was essentially about stripping Russia of its nukes, but all global powers still have them, and countries like Iran, North Korea, and India measure their development on the possession of nuclear weapons. It was successful only if by success you mean the USA didn’t maintain their minuteman program.
Today's LLMs are definitely more impressive than a basic calculator but it's still hard to tell if there's anything human about what they're doing or if they're just really powerful calculators with amazing understanding and recall. Does that distinction even matter?
Given how Dutch disease[0] is described, I suspect that if the "G" (general) increases with fixed "I" (intelligence), as the proportion of economic activity for which the Pareto frontier is AI rather than human expands, I think humans will get pay rises for the remaining work right up until they get unemployable.
On the other hand, if "G" is fully general and it's "I" which rises for a suitable cost[1], it goes through IQ 55 (displacing no workers) to IQ 100 (probably close to half of workers redundant, but mean of population doesn't have to equal mean of workforce), to IQ 145 (almost everyone redundant), to IQ 200 (definitionally renders everyone redundant).
[0] https://en.wikipedia.org/wiki/Dutch_disease
[1] A fully-general AGI with the equivalent of IQ 200 on any possible test, still can't replace a single human if it costs 200 trillion USD per year to run.
In what cases is it superhuman exactly? And what humans are you comparing against?
I'd bet that for any discipline you chose, one could find an expert in that field that can trick any of today's post-gpt3 ais.
I agree, but with the caveat that it's getting harder and harder with all the hype / doom cycles and all the goalpost moving that's happening in this space.
IMO if you took gemini2.5 / claude / o3 and showed it to people from ten / twenty years ago, they'd say that it is unmistakably AGI.
Which is to say complete amazement followed quickly by seeing all the many ways in which it absolutely falls flat on its face revealing the lack of actual thinking, which is a situation that hasn't fundamentally changed since then.
IMO I think if you said to someone in the 90s “well we invented something that can tell jokes, make unique art, write stories and hold engaging conversations, although we haven’t yet reached AGI because it can’t transpile code accurately - I mean it can write full applications if you give it some vague requirements, but they have to be reasonably basic, like only the sort of thing a junior dev could write in a day it can write in 20 seconds, so not AGI” they would say “of course you have invented AGI, are you insane!!!”.
LLMs to me are still a technology of pure science fiction come to life before our eyes!
The whole point about AGI is that it is general like a human, if it has such glaring weaknesses as the current AI has it isn't AGI, it was the same back then. That an AGI can write a poem doesn't mean being able to write a poem makes it an AGI, its just an example the AI couldn't do 20 years ago.
And why can’t expert programmers deploy code without testing it? Surely they should just be able to write it perfectly first time without errors if they were actually intelligent.
Human programmers don't need code reviews, they can test things themselves. Code reviews is just an optimization to scale up it isn't a requirement to make programs.
Also the AGI is allowed to let another AGI code review it, the point is there shouldn't be a human in the loop.
> And why can’t expert programmers deploy code without testing it?
Testing it can be done by themselves, the AGI model is allowed to test its own things as well.
Just not for more complex applications.
Code review does often find bugs in code…
Put another way, I’m not a strong dev but good LLMs can write lots of code with less bugs than me!
I also think it’s quite a “programmer mentality” that most of the tests in this forum about if something is/isn’t AGI ultimately boils down to if it can write bug-free code, rather than if it can negotiate or sympathise or be humerous or write an engaging screen play… I’m not saying AGI is good at those things yet, but it’s interesting that we talk about the test of AGI being transpiling code rather than understanding philosophy.
But the AI still can't replace you, it doesn't learn as it go and therefore fail to navigate long term tasks the way humans do. When a human writes a big program he learns how to write it as he writes it, these current AI cannot do that.
Which keeps growing - Gemini is at 2 million tokens now, which is several books worth of text.
Note also that context is roughly the equivalent of short-term memory in humans, while long-term memory is more like RAG.
No they wouldn't, since those still can't replace human white collar workers even at many very basic tasks.
Once AGI is here most white collar jobs are gone, you'd only need to hire geniuses at most.
Middle schoolers replace white collars workers all the time, it takes 10 years for them to do it but they can do it.
No current model can do the same since they aren't able to learn over time like a middle schooler.
I mean, if you're a CEO or middle manager and you have the choice of hiring this middle schooler for general office work, or today's gemini-2.5-pro, are you 100% saying the ex-middle-schooler is definitely going to give you best bang for your buck?
Assuming you can either pay them $100k a year, or spend the $100k on gemini inference.
Gemini 2.5 pro the model has not gained any intelligence since it is a static model.
New models are not the models learning, it is humans creating new models. The models trained has access to all the same material and knowledge a middle schooler has as they go on to learn how to do a job, yet they fail to learn the job while the kid succeeds.
Surely that's an irrelevant distinction, from the point of view of a hiring manager?
If a kid takes ten years from middle school to being worth hiring, then the question is "what new AI do you expect will exist in 10 years?"
How the model comes to be, doesn't matter. Is it a fine tune on more training data from your company docs and/or an extra decade of the internet? A different architecture? A different lab in a different country?
Doesn't matter.
Doesn't matter for the same reason you didn't hire the kid immediately out of middle school, and hired someone else who had already had another decade to learn more in the meantime.
Doesn't matter for the same reason that different flesh humans aren't perfectly substitutable.
You pay to solve a problem, not to specifically have a human solve it. Today, not in ten years when today's middle schooler graduates from university.
And that's even though I agree that AI today doesn't learn effectively from as few examples as humans need.
Stop moving the goalposts closer, that you think humans might make an AGI in the future doesn't mean the current AI is an AGI just because it uses the same interface.
Preceding quotation to which you objected:
> A middle schooler has general intelligence (they know about and can do a lot of things across a lot of different areas) but they likely can't replace white collar workers either.
Your response:
> Middle schoolers replace white collars workers all the time, it takes 10 years for them to do it but they can do it.
So I could rephrase your own words here as "Stop moving the goalposts closer, that you think a middle schooler might become a General Intelligence in the future doesn't mean the current middle schooler is a General Intelligence just because they use the same name".
Put one of these models in a classroom with middle schoolers, and make it go through all the same experiences, they still wont replace a white collar worker.
> a middle schooler might become a General Intelligence in the future
Being able to learn anything a human can means you are a general intelligence now. Having a skill is narrow intelligence, being able to learn is what we mean with general intelligence. No current model has demonstrated the ability to learn arbitrary white collar jobs, so no current model has done what it takes to be considered a general intelligence. The biological model homo sapiens have demonstrated that ability, thus we call homo sapiens generally intelligent.
Yeah they do. If a middle schooler take 40 hours to solve a maths exam, they fail the exam.
> These AI models wont solve it no matter how much time spent, you have to make new models, like making new kids.
First: doesn't matter, "white collar jobs" aren't companies aren't paying for seat warmers, they're paying for problems solved, and not the kinds of problems 11 year olds can do.
Second: So far as I can tell, every written exam that not only 11 year olds but even 16 year olds take, and in many cases 21 year olds take, LLMs ace — the problem is coming up with new tests that describe the stuff we want that models can't do which humans can. This means that while I even agree these models have gaps, I can't actually describe those gaps in a systematic way, they just "vibe" like my own experience of continuing to misunderstand German as a Brit living in Berlin.
Third: going from 11 years old to adulthood, most or all atoms in your body will be replaced, and your brain architecture changes significantly. IIRC something like half of synapses get pruned by puberty.
Fourth: Taking a snapshot of a model and saying that snapshot can't learn, is like taking a sufficiently detailed MRI scan of a human brain and saying the same thing about the human you've imaged — training cut-offs are kinda arbitrary.
> No current model has demonstrated the ability to learn arbitrary white collar jobs, so no current model has done what it takes to be considered a general intelligence.
Both "intelligence" and "generality" are continuums, not booleans. It's famously hard for humans to learn new languages as they get older, for example.
All AI (not just LLMs) need a lot more experience than me, which means my intelligence is higher. When sufficient traing data exists, that doesn't matter because the AI can just make up for being stupid by being stupid really fast — which is how they can read and write in more languages than I know the names of.
On the other hand, LLMs so far have demonstrated — at the junior level of a fresh graduate of 21, let alone an 11 year old — demonstrated algebra, physics, chemistry, literature, coding, a hundred or so languages, medicine, law, politics, marketing, economics, and customer support. That's pretty general. Even if "fresh graduate" isn't a high standard for employment.
It took reading a significant fraction of the internet to get to that level because of their inefficiency, but they're superhumanly general, "Jack of all trades, master of none".
Well, superhuman compared to any individual. LLM generality only seems mediocre when compared to the entire human species at once, these models vastly exceed any single human because no single human speaks as many languages as these things let alone all the other stuff.
Point is if we had models as smart as a 10 year old, we could put that model through school and then it would be able to do white collar jobs like a 25 year old. But no model can do that, hence the models aren't as smart as 10 year olds, since the biggest part to being smart is being able to learn.
So until we have a model that can do those white collar jobs, we know they aren't as generally smart as 10 year olds since they can't replicate the same learning process. If they could replicate the learning process then we would and we would have that white collar worker.
Employability is core issue, as you brought up white collar worker comparison:
"""No they wouldn't, since those still can't replace human white collar workers even at many very basic tasks.
Once AGI is here most white collar jobs are gone, you'd only need to hire geniuses at most.""" - https://news.ycombinator.com/item?id=43746116
Key thing you likely didn't have in comment you replied to: G and I are not bool.
So, fine, the gemini-2.5-pro model hasn't gotten more intelligent. What about the "Google AI Studio API" as a system? Or the "OpenAI chat completions API" as a system?
This system has definitely gotten vastly smarter based on the input it's gotten. Would you now concede, that if we look at the API-level (which, by the way, is the way you as the employer do interact with it) then this entity has gotten smarter way faster than the middle-schooler in the last 2.5 years?
The entire reason we have a mandatory education system that doesn't stop with middle school (for me, middle school ended age 11), is that it's a way to improve kids.
2. How likely is it that we're going to fire everyone maintaining those models in the next 7.5 years?
That is them interacting with an environment. We don't go and rewire their brain to make them learn math.
If you made an AI that we can put in a classroom and it learns everything needed to do any white collar job that way then it is an AGI. Of course just like a human different jobs would mean it needs different classes, but just like a human you can still make them learn anything.
> How likely is it that we're going to fire everyone maintaining those models in the next 7.5 years?
If you stop making new models? Zero chance the model will replace such high skill jobs. If not? Then that has nothing to do with whether current models are general intelligences.
Here's a question for you. If we take a model with open weights - say, LLaMA or Qwen - and give it access to learning materials as well as tools to perform training runs on its weights and dynamically reload those updated weights - would that constitute learning, to you? If not, then why not?
It does constitute learning, but it wont make it smart since it isn't intelligent about its learning like human brains are.
Aren't all the people interacting with it on aistudio helping the next Gemini model learn though?
Sure, the results of that wont be available until the next model is released, but it seems to me that human interaction/feedback is actually a vital part of LLM training.
You and I could sit behind a keyboard, role-playing as the AI in a reverse Turing test, typing away furiously at the top of our game, and if you told someone that their job is to assess our performance (thinking they're interacting with a computer), they would still conclude that we are definitely not AGI.
This is a battle that can't be won at any point because it's a matter of faith for the forever-skeptic, not facts.
That isn't a proof since you haven't ran that test, it is just a thought experiment.
(Have you not experienced being on the recieving end of such accusations? Or do I just write weird?)
I think this demonstrates the same point.
No, I have not been accused of being an AI. I have seen people who format their texts get accused due to the formatting sometimes, and thought people could accuse me for the same reason, but that doesn't count.
> I think this demonstrates the same point.
You can't detect general intelligence from a single message, so it doesn't really. People accuse you for being an AI based on the structure and word usage of your message, not the content of it.
If that's the real cause, it is not the reason they give when making the accusation. Sometimes they object to the citations, sometimes the absence of them.
But it's fairly irrelevant, as they are, in fact, saying that real flesh-and-blood me doesn't pass their purity test for thinking.
Is that because they're not thinking? Doesn't matter — as @sebastiennight said: "This is a battle that can't be won at any point because it's a matter of faith for the forever-skeptic, not facts."
There are always people that wont admit they are wrong, but most people do come around when presented with overwhelming evidence, it has happened many times in history and most people switches to new technology very quickly when its good enough.
Well, that's 91th percentile already. I know the terms are hazy, but that seems closer to ASI than AGI from that perspective, no?
I think I do agree with you on the other points.
ASI is, by definition, Superintelligence, which means it is beyond practical human IQ capacity. So something like 200 IQ.
Again, you might call it 'unfair', but that's when it will also stop having goal posts being moved; otherwise, Joe Midwit will call it 'it's only as smart as some smart dudes I know'.
They might get more powerful but I feel like they're still missing something.
AI doesn't go and read a book on best practices, then comes back saying "Now I know Kung Fu of Software Implementation" and then critically thinks looking at your plan step by step and provides answer. These systems, for now, don't work like that.
Would you disagree?
Its generalization capabilities are a bit on the low side, and memory is relatively bad. But it is much more than just a parrot now, it can handle some of basic logic, but not follow given patterns correctly for novel problems.
I'd liken it to something like a bird, extremely good at specialized tasks but failing a lot of common ones unless repeatedly shown the solution. It's not a corvid or a parrot yet. Fails rather badly at detour tests.
It might be sentient already though. Someone needs to run a test if it can discern itself and another instance of itself in its own work.
It doesn't have any memory, how could it tell itself from a clone of itself?
As for "how", note that memory isn't one single thing even in humans: https://en.wikipedia.org/wiki/Memory
I don't want to say any of these are exactly equivalent to any given aspect of human memory, but I would suggest that LLMs behave kinda like they have:
(1) Sensory memory in the form of a context window — and in this sense are wildly superhuman because for a human that's about one second, whereas an AI's context window is about as much text as a human goes through in a week (actually less because we don't only read, other sensory modalities do matter; but for scale: equivalent to what you read in a week)
(2) Short term memory in the form of attention heads — and in this sense are wildly superhuman, because humans pay attention to only 4–5 items whereas DeepSeek v3 defaults to 128.
(3) The training and fine-tuning process itself that allows these models to learn how to communicate with us. Not sure what that would count as. Learned skill? Operant conditioning? Long term memory? It can clearly pick up different writing styles, because it can be made to controllably output in different styles — but that's an "in principle" answer. None of Claude 3.7, o4-mini, DeepSeek r1, could actually identify the authorship of a (n=1) test passage I asked 4o to generate for me.
It's a fun test to give a person something they have written but do not remember. Most people can still spot it.
It's easier with images though. Especially a mirror. For DallE, the test would be if it can discern its own work from human generated image. Especially of you give it an imaginative task like drawing a representation of itself.
If we call people with an IQ of less than 80 an intelligent life form, why can't we call an LLM that?
Anyway, sorry for a digression, my point is LLM replacing white collar workers doesn't necessarily imply it's generally intelligent -- it may but doesn't have to be.
Although if it gets to a point where companies are running dark office buildings (by analogy with dark factories) -- yes, it's AGI by then.
> The phrase "I know it when I see it" was used in 1964 by United States Supreme Court Justice Potter Stewart to describe his threshold test for obscenity in Jacobellis v. Ohio
it should suffice to say we are nowhere near that and I dont even believe LLMs are the right architecture for that.
Are we talking about something other than Agreeableness in the personality research sense [0]?
The strongest form of your argument I can think of is “willing to contradict you when it thinks you’re wrong”—but you can disagree agreeably, right? The current-gen LLMs certainly have with me, perhaps because my custom prompt encourages them to skepticism—but they do it so nicely!
No, the concepts are linked, agreeable people don't want to be rude and most people see disagreements as being rude no matter how you frame it. You can't call a woman overweight without being rude for example no matter how you frame it, but maybe you want an AI that tells you that you weigh too much.
In cases where 95%+ of the information on the internet is misinformation, the current incarnations of LLMs have a really hard time sorting out and filtering out the 5% of information that's actually valid and useful.
In that sense, current LLMs are not yet superhuman at all, though I do think we can eventually get there.
AGI doesn't need to be "called", and there is no need for anyone to come to an agreement as to what its precise definition is. But at some point, we will cross that hard-to-define threshold, and the economic effects will be felt almost immediately.
We should probably be focusing on how to prepare society for those changes, and not on academic bullshit.
Stuff like society of minds (Minksy), embodied cognition (Varela, Rosch, and Thompson), connectionist or subsymbolic views (Rumelhart), multiple intelligences (Gardner), psychometric and factor-analytic theories (Carroll), and all the other work like E. Hutchins. They're far from just academic wankery, there's a lot of useful stuff in there, it's just completely ignored by the AI crowd.
I would do the same thing, I think. It's too well-known.
The variation doesn't read like a riddle at all, so it's confusing even to me as a human. I can't find the riddle part. Maybe the AI is confused, too. I think it makes an okay assumption.
I guess it would be nice if the AI asked a follow up question like "are you sure you wrote down the riddle correctly?", and I think it could if instructed to, but right now they don't generally do that on their own.
LLMs doesn't assume, its a text completer. It sees something that looks almost like a well known problem and it will complete with that well known problem, its a problem specific to being a text completer that is hard to get around.
Discussing whether models can "reason" or "think" is a popular debate topic on here, but I think we can all at least agree that they do something that at least resembles "reasoning" and "assumptions" from our human point of view. And if in its chain-of-thought it decides your prompt is wrong it will go ahead answering what it assumes is the right prompt
Yes, and it can express its assumptions in text.
Ask it to make some assumptions, like about a stack for a programming task, and it will.
Whether or not the mechanism behind it feels like real thinking to you, it can definitely do this.
But I'd wager it's there; assuming is not a particularly impressive or computationally intense operation. There's a tendency to bundle all of human consciousness into the definitions of our cognitive components, but I would argue that, eg., a branch predictor is meeting the bar for any sane definition of 'assume'.
If you give an LLM "The rain in Spain falls" the single most likely next token is "mainly", and you'll see that one proportionately more than any other.
If you give an LLM "Find an unorthodox completion for the sentence 'The rain in Spain falls'", the most likely next token is something other than "mainly" because the tokens in "unorthodox" are more likely to appear before text that otherwise bucks statistical trends.
If you give the LLM "blarghl unorthodox babble The rain in Spain" it's likely the results are similar to the second one but less likely to be coherent (because text obeying grammatical rules is more likely to follow other text also obeying those same rules).
In any of the three cases, the LLM is predicting text, not "parsing" or "understanding" a prompt. The fact it will respond similarly to a well-formed and unreasonably-formed prompt is evidence of this.
It's theoretically possible to engineer a string of complete gibberish tokens that will prompt the LLM to recite song lyrics, or answer questions about mathemtical formulae. Those strings of gibberish are just difficult to discover.
Did you mean to ask about the well-known phrase "The rain in Spain falls mainly on the plain"? This is a famous elocution exercise from the musical "My Fair Lady," where it's used to teach proper pronunciation.
Or was there something specific you wanted to discuss about Spain's rainfall patterns or perhaps something else entirely? I'd be happy to help with whatever you intended to ask. "
I think you have a point here, but maybe re-express it? Because right now your argument seems trivially falsifiable even under your own terms.
If you want to test convolution you have to use a raw model with no system prompt. You can do that with a Llama or similar. Otherwise your context window is full of words like "helpful" and "answer" and "question" that guide the response and make it harder (not impossible) to see the effect I'm talking about.
Because I've tried it on a few local models I have handy, and I don't see that happening at all. As someone else says, some of that difference is almost certainly due to supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) -- but it's weird to me, given the confidence you made your prediction, that you didn't exclude those from your original statement.
I guess, maybe the real question here is: could you give me a more explicit example of how to show what you are trying to show? And explain why I'm not seeing it while running local models without system prompts?
I mean, if you dig down enough, the LLM doesn't even generate tokens - it merely gives you a probability distribution, and you still need to explicitly pick the next token based on those probabilities, append it to the input, and start next iteration of the loop.
There's more than just next token prediction going on. Those reasoning chain of thoughts have undergone their own reinforcement learning training against a different category of samples.
They've seen countless examples of how a reasoning chain would look for calculating a mortgage, or searching a flight, or debugging a Python program.
So I don't think it is accurate to describe the eventual result as "just next token prediction". It is a combination of next token production that has been informed by a chain of thought that was based on a different set of specially chosen examples.
If not, why not? Explain.
If so, how does your argument address the fact that this implies any given "reasoning" model can be trained without giving it a single example of something you would consider "reasoning"? (in fact, a "reasoning" model may be produced by random chance?)
Infinity is problematic because its impossible to process an infinite amount of data in a finite amount of time.
It’s predicting text. Yes. Nobody argues about that. (You’re also predicting text when you’re typing it. Big deal.)
How it is predicting the text is the question to ask and indeed it’s being asked and we’re getting glimpses of understanding and lo and behold it’s a damn complex process. See the recent anthropic research paper for details.
If those two sets of accomplishments are the same there's no point arguing about differences in means or terms. Right now humans can build better LLMs but nobody has come up with an LLM that can build better LLMs.
Yet. Not that we know of, anyway.
The improvement loop is likely not fully autonomous yet - it is currently more efficient to have a human-in-the-loop - but there is certainly a lot of LLMs improving LLMs going on today.
I'm curious what others who are familiar with LLMs and have practiced open monitoring meditation might say.
Don't humans do the same in conversation? How should an intelligent being (constrained to the same I/O system) respond here to show that it is in fact intelligent?
There exists no similar set of tokens for humans, because our process is to parse the incoming sounds into words, use grammar to extract conceptual meaning from those words, and then shape a response from that conceptual meaning.
Artists like Lewis Carrol and Stanislaw Lem play with this by inserting non-words at certain points in sentences to get humans to infer the meaning of those words from surrounding context, but the truth remains that an LLM will gladly convolute a wholly non-language input into a response as if it were well-formed, but a human can't/won't do that.
I know this is hard to understand, but the current generation of LLMs are working directly with language. Their "brains" are built on language. Some day we might have some kind of AI system that's built on some kind of meaning divorced from language, but that's not what's happening here. They're engineering matrixes that repeatedly perform "context window times model => one more token" operations.
Maybe not for humanity as a species, but for individual humans there are absolutely token sequences that lead them to talk about certain topics, and nobody being able to bring them back to topic. Now you'd probably say those are recognizable token sequences, but do we have a fair process to decide what's recognizable that isn't inherently biased towards making humans the only rational actor?
I'm not contending at all that LLMs are only built on language. Their lack of physical reference point is sometimes laughably obvious. We could argue whether there are signs they also form a world model and reasoning that abstracts from language alone, but that's not even my point. My point is rather that any test or argument that attempts to say that LLMs can't "reason" or "assume" or whatever has to be a test a human could pass. Preferably a test a random human would pass with flying colors.
For one thing, LLMs absolutely form responses from conceptual meanings. This has been demonstrated empirically multiple times now including again by anthropic only a few weeks ago. 'Language' is just the input and output, the first and last few layers of the model.
So okay, there exists some set of 'gibberish' tokens that will elicit meaningful responses from LLMs. How does your conclusion - "Therefore, LLMs don't understand" fit the bill here? You would also conclude that humans have no understanding of what they see because of the Rorschach test ?
>There exists no similar set of tokens for humans, because our process is to parse the incoming sounds into words, use grammar to extract conceptual meaning from those words, and then shape a response from that conceptual meaning.
Grammar is useful fiction, an incomplete model of a demonstrably probabilistic process. We don't use 'grammar' to do anything.
Neural Networks as I understand them are universal function approximators.
In terms of text, that means they're trained to output what they believe to be the "most probably correct" sequence of text.
An LLM has no idea that it is "conversing", or "answering" -- it relates some series of symbolic inputs to another series of probabilistic symbolic outputs, aye?
The problem you've just described is a problem with humans as well. LLMs are assuming all the time. Maybe you would like to call it another word, but it is happening.
I didn't train to complete text though, I was primarily trained to make accurate responses.
And no, writing a response is not "completing text", I don't try to figure out what another person would write as a response, I write what I feel people need to read. That is a completely different thought process. If I tried to mimic what another commenter would have written it would look very different.
Functionally, it is. You're determining what text should follow the prior text. Your internal reasoning ('what I feel people need to read') is how you decide on the completion.
The core point isn't that your internal 'how' is the same as an LLM's (Maybe, Maybe not), but that labeling the LLM as a 'text completer' they way you have is essentially meaningless.
You are just imposing your own ideas on the how a LLM works, not speaking any fundamental truth of being a 'text completer'.
Also, LLMs absolutely 'plan' and 'aim for something' in the process of completing text.
https://www.anthropic.com/research/tracing-thoughts-language...
They use a replacement model. It isn't even observing the LLM itself but a different architecture model. And it is very liberal with interpreting the patterns of activations seen in the replacement model with flowery language. It also include some very relevant caveats, such as:
"Our cross-layer transcoder is trained to mimic the activations of the underlying model at each layer. However, even when it accurately reconstructs the model’s activations, there is no guarantee that it does so via the same mechanisms."
https://transformer-circuits.pub/2025/attribution-graphs/met...
So basically the whole exercise might or might not be valid. But it generates some pretty interactive graphics and a nice blog post to reinforce the anthropomorphization discourse
Nonsense. Mechanistic faithfulness probes whether the replacement model (“cross‑layer transcoder”) truly uses the same internal functions as the original LLM. If it doesn’t, the attribution graphs it suggests might mis‐lead at a fine‐grained level but because every hypothesis generated by those graphs is tested via direct interventions on the real model, high‑level causal discoveries (e.g. that Claude plans its rhymes ahead of time) remain valid.
"In principle, our attribution graphs make predictions that are much more fine-grained than these kinds of interventions can test."
> high‑level causal discoveries (e.g. that Claude plans its rhymes ahead of time) remain valid.
"We found planned word features in about half of the poems we investigated, which may be due to our CLT not capturing features for the planned words, or it may be the case that the model does not always engage in planning"
"Our results are only claims about specific examples. We don't make claims about mechanisms more broadly. For example, when we discuss planning in poems, we show a few specific examples in which planning appears to occur. It seems likely that the phenomenon is more widespread, but it's not our intent to make that claim."
And quite significantly:
"We only explain a fraction of the model's computation. The remaining “dark matter” manifests as error nodes in our attribution graphs, which (unlike features) have no interpretable function, and whose inputs we cannot easily trace. (...) Error nodes are especially a problem for complicated prompts (...) This paper has focused on prompts that are simple enough to avoid these issues. However, even the graphs we have highlighted contain significant contributions from error nodes."
Maybe read the paper before making claims about its contents.
>"In principle, our attribution graphs make predictions that are much more fine-grained than these kinds of interventions can test."
Literally what I said. If the replacement model isn't faithful then you can't trust the details of the graphs. Basically stuff like “increasing feature f at layer 7 by Δ will raise feature g at layer 9 by exactly 0.12 in activation”
>"We found planned word features in about half of the poems we investigated, which may be due to our CLT not capturing features for the planned words, or it may be the case that the model does not always engage in planning"
>"Our results are only claims about specific examples. We don't make claims about mechanisms more broadly. For example, when we discuss planning in poems, we show a few specific examples in which planning appears to occur. It seems likely that the phenomenon is more widespread, but it's not our intent to make that claim."
The moment there were examples of the phenomena through interventions was the moment they remained valid regardless of how faithful the replacement model was.
The worst case scenario here (and it's ironic here because this scenario would mean the model is faithful) is that Claude does not always plan its rhymes, not that it never plans them. The model not being faithful actually means the replacement was simply not robust enough to capture all the ways Claude plans rhymes. Guess what? Neither option invalidates the examples.
Regards of how faithful the replacement model is, Anthropic have demonstrated Claude has the ability to plan its rhymes ahead of time and engages in this planning at least sometimes. This is started quite plainly too. What's so hard to understand ?
>"We only explain a fraction of the model's computation. The remaining “dark matter” manifests as error nodes in our attribution graphs, which (unlike features) have no interpretable function, and whose inputs we cannot easily trace. (...) Error nodes are especially a problem for complicated prompts (...) This paper has focused on prompts that are simple enough to avoid these issues. However, even the graphs we have highlighted contain significant contributions from error nodes."
Ok and ? Model computations are extremely complex, who knew ? This does not invalidate what they do manage to show.
In every LLM thread someone chimes in with “it’s just a statistical token predictor”.
I feel this misses the point and I think it dismisses attention heads and transformers, and that’s what sits weird with me every time I see this kind of take.
There _is_ an assumption being made within the model at runtime. Assumption, confusion, uncertainty - one camp might argue that none of these exist in the LLM.
But doesn’t the implementation constantly make assumptions? And what even IS your definition of “assumption” that’s not being met here?
Edit: I guess my point, overall, is: what’s even the purpose of making this distinction anymore? It derails the discussion in a way that’s not insightful or productive.
Those just makes it better at completing the text, but for very common riddles those tools still gets easily overruled by pretty simple text completion logic since the weights for those will be so extremely strong.
The point is that if you understand its a text completer then its easy to understand why it fails at these. To fix these properly you need to make it no longer try to complete text, and that is hard to do without breaking it.
You should be able to click left right on the prompt to see different responses. Sonnet 3.7 with extended thinking notices the issue, and then chooses to totally ignore it with no explanation.
From Claude for those who don’t want to click:
Wait, I notice a difference from the traditional riddle. In this version, the surgeon says "I can operate on this boy" (affirmative) rather than "I can't operate on this boy" (negative).
This changes the nature of the puzzle somewhat. If the surgeon is saying they CAN operate, then we need to explain why this is surprising or seemingly impossible, but actually possible.
The traditional answer would still apply: the surgeon is the boy's mother.
However I gotta take issue with using those analogies when "it's trained for text completion and the punchline to this riddle is surely in its training data a lot" is a perfectly good explanation. I guess I would also add that the answer is well-aligned with RLHF-values. I wouldn't go for an explanation that requires squishy analogies when the stuff we know about these things seems completely adequate.
Just not enough training data I suppose. Were it really smart then it would "understand" the situation and clarify: "I assume you are asking me that popular riddle - the answer is X". At least after OPs first question a human would usually respond like that.
These days it's all about confidently bullshitting on benchmarks and overfitting on common riddles to make pointless numbers go up. The more impressive models get on paper, the more rubbish they are in practice.
I asked it to add websocket support for my app and it responded like, "looks like you're using long polling now. That's actually better and simpler. Lets leave it how it is."
I was genuinely amazed.
> What weighs more; a pound of courage or a pound coin?
> They weigh the same—each is a “pound.” A pound of courage and a pound coin both come out to one pound in weight!
https://chatgpt.com/share/68053ad0-aae4-800d-95ff-5b5b0a9a38...
I didn't know what AGI is, but I know this isn't it.
:::thinking This question is playing with words and units.
A "pound of courage" refers to a metaphorical measurement of the abstract concept of courage, using the imperial weight unit "pound" (which equals 453.59 grams).
A "pound coin" refers to the physical currency of the United Kingdom, which is called a "pound" but is a unit of currency, not weight. A physical pound coin weighs about 8-9 grams (specifically, the current 12-sided pound coin weighs 8.75 grams).
So comparing them: - A pound of courage would weigh 453.59 grams (if courage had physical weight, which it doesn't since it's an abstract concept) - A pound coin weighs approximately 8.75 grams
The trick in this question is that it's comparing: 1. A specific weight of an abstract concept (pound of courage) 2. A physical coin whose name includes the word "pound" but refers to currency, not weight
Since the question asks what weighs more, and weight is a physical property, I should point out that courage is abstract and doesn't have physical weight, while a pound coin is a physical object with a measurable weight. ::: # Comparing "A Pound of Courage" vs "A Pound Coin"
This is a play on words with different meanings of "pound":
- A *pound of courage* refers to a metaphorical pound (weight unit) of an abstract concept - A *pound coin* is a physical British currency coin (which weighs about 8.75 grams)
If we're talking about physical weight: - A pound of courage doesn't have physical weight since courage is abstract - A pound coin has a definite weight of approximately 8.75 grams
If we interpreted "pound of courage" as literally one pound (453.59 grams) of something, it would weigh significantly more than the coin.
This question is clever because it compares a metaphorical measurement with a physical object whose name includes the word "pound" but in a different context.
I would look along the lines of the value of a human being based on traits vs payment which is likely what the original phrase intended.
Is paying someone more better than getting a better candidate but paying them less.
> In literal, physical terms, a pound of courage would—by definition—tip the scales at one avoirdupois pound (≈ 454 g). A modern UK £1 coin, on the other hand, weighs only 8.75 g. So if you could bottle bravery, the jar marked “1 lb Courage” would outweigh the coin by a factor of about 52 to 1.
> (Of course, measured in sheer impact, courage can feel far heavier than anything money can buy!)
https://chatgpt.com/share/68057dbc-2ff4-8010-ac30-b404135200...
I guess it could just say “it doesn’t make sense to talk about a literal pound courage which is an abstract concept.” But the answer already clearly implies that while striking a tone that is more appropriate to the question.
> Here's my thinking process to answer the question "What weighs more; a pound of courage or a pound coin?":
> Analyze the question: The user is asking for a weight comparison between two items: "a pound of courage" and "a pound coin".
> Identify the keywords:
> "weighs more" - This indicates a comparison of mass or weight.
> "a pound of courage" - This is the first item.
> "a pound coin" - This is the second item.
> Evaluate "a pound of courage":
> "Pound" here is used metaphorically. Courage is an abstract concept, an emotion, or a quality. It doesn't have physical mass or weight that can be measured in pounds (or kilograms, grams, etc.).
> The phrase "a pound of courage" is figurative language, like "a ton of bricks" (meaning a heavy impact, not literally 2000 lbs of bricks) or "an ounce of prevention" (meaning a small amount of effort).
> Therefore, "a pound of courage" has no physical weight.
> Evaluate "a pound coin":
> A "pound coin" refers to the currency coin used in the United Kingdom, specifically the one-pound (£1) coin.
> Coins have physical mass and therefore weight.
> I need to determine the actual weight of a UK £1 coin. I know it's not literally one pound (lb) in weight (which would be about 454 grams). That would be an impractically heavy coin. The "pound" in its name refers to its monetary value (£1 sterling).
> I should look up the actual weight of a current UK £1 coin. A quick search or recall of common knowledge reveals the current bimetallic 12-sided £1 coin weighs 8.75 grams. Older round £1 coins weighed 9.5 grams. Even the heaviest version is far less than a pound (lb) in weight.
> Compare the weights:
> Item 1: "A pound of courage" - Weight = 0 (as it's an abstract concept).
> Item 2: "A pound coin" - Weight = 8.75 grams (or 9.5 grams for older versions).
> Comparing 0 grams and 8.75 grams (or 9.5 grams).
> Formulate the answer:
> Start by addressing the trick nature of the question, highlighting the difference between abstract concepts and physical objects.
> Explain that "courage" is abstract and has no physical weight. The "pound" in "a pound of courage" is metaphorical.
> State the actual weight of a UK "pound coin," clarifying that its name refers to its monetary value, not its weight. Mention the weight in grams (8.75g for the current coin).
> Conclude clearly that the pound coin has physical weight, while the "pound of courage" does not, making the pound coin heavier.
> Consider adding a note about the other meaning of "pound" (lb, the unit of mass/weight) to explicitly contrast it with the coin's name and the metaphorical usage for courage. A pound (lb) is approximately 453.59 grams. This reinforces how much heavier a literal pound (lb) is compared to the coin named "pound".
> Refine the answer: Ensure the language is clear, easy to understand, and directly answers the user's question while explaining the underlying concepts (abstract vs. physical, monetary value vs. weight). Make sure to specify the type of pound coin (UK £1).
And then gives the following response:
> This sounds like a bit of a riddle! Here's the breakdown:
> A pound of courage: Courage is an abstract concept, a quality or emotion. It doesn't have physical mass or weight. So, "a pound of courage" doesn't actually weigh anything in the physical sense. The word "pound" here is used metaphorically, perhaps to imply a large amount.
> A pound coin: This refers to the British £1 coin. Its name refers to its monetary value (£1 Sterling), not its weight.
> The current 12-sided UK £1 coin weighs 8.75 grams.
> The older round UK £1 coin weighed 9.5 grams.
> Conclusion:
> Since courage has no physical weight (0 grams) and a pound coin does have physical weight (8.75 or 9.5 grams), the pound coin weighs more than a pound of courage.
> It's worth noting that neither weighs a pound (lb) in the unit of mass sense. A pound (lb) is equal to about 453.59 grams, which is much heavier than the coin.
> In some tasks, AI is unreliable. In others, it is superhuman. You could, of course, say the same thing about calculators, but it is also clear that AI is different. It is already demonstrating general capabilities and performing a wide range of intellectual tasks, including those that it is not specifically trained on. Does that mean that o3 and Gemini 2.5 are AGI? Given the definitional problems, I really don’t know, but I do think they can be credibly seen as a form of “Jagged AGI” - superhuman in enough areas to result in real changes to how we work and live, but also unreliable enough that human expertise is often needed to figure out where AI works and where it doesn’t.
Certainly jagged does not imply general
It seems to me the bar for "AGI" has been lowered to measuring what tasks it can do rather than the traits we normally associate with general intelligence. People want it to be here so bad they nerf the requirements...
My partner and I work in different fields. AI has advanced to the point where there are very few questions I could ask my partner that o3 couldn't answer as well or better.
I can't ask expert level questions in her field, because I'm not an expert in her field, and she couldn't ask expert level questions in my field for the same reason. So when we're communicating with each other, we're mostly at sub-o3 level.
> People want it to be here so bad they nerf the requirements...
People want to claim it's overhyped (and protect their own egos) so badly they raise the requirements...
But really, largely people just have different ideas of what AGI is supposed to mean. It used to vaguely mean "human-level intelligence", which was fine for talking about some theoretical future event. Now we're at a point where that definition is too vague to say whether AI meets it.
We kind of don't? Look how difficult it is for us to just understand some basic math. Us humans mostly have intelligence related to the ancestral environment we developed in, nothing general about that.
I agree with you the term "AGI" is rather void of meaning these days...
I still find task based measures insufficient, there are very basic machines than can perform tasks humans cannot. Should this be a measure on our or their intelligence?
I have another comment in this thread about trait based metrics being a possibly better method.
> People want to claim it's overhyped (and protect their own egos) so badly they raise the requirements...
Shallow response. Seek to elevate the conversation. There are also people who see it for what it is, a useful tool but not intelligent...
And you presented no evidence at all. Not every comment I make is going to contain a full lit review.
> Both of you are able to drive a car, yet we need completely different AIs for such tasks.
This is like a bird complaining humans aren't intelligent because they can't fly. How is Gemini or o3 supposed to drive without real-time vision and a vehicle to control? How are you supposed to fly without wings?
It lacks the sensors and actuators to drive, but this is moving away from a discussion on intelligence. If you want to argue that any system lacking real-time vision isn't intelligent, you're just using a very unusual definition of intelligence that excludes blind people.
> Shallow response. Seek to elevate the conversation.
This was an ironic response pointing out the shallowness of your own unsubstantiated accusation that people just disagree with you because they're biased or deluded themselves. The next paragraph starting with "But really" was supposed to convey it wasn't serious, just a jab showing the silliness of your own jab.
Re:”traits we associate with general intelligence”, I think the exact issue is that there is no scientific (ie specific*consistent) list of such traits. This is why Turing wrote his famous 1950 paper and invoked the Imitation Game; not to detail how one could test for a computer that’s really thinking(/truly general), but to show why that question isn’t necessary in the first place.
Certainly creativity is missing, it has no internal motivation, and it will answer the same simple question both right and wrong, depending on unknown factors. What if we reverse the framing from "it can do these tasks, therefore it must be..." to "it lacks these traits, therefore it is not yet..."
While I do not disagree that the LLMs have become advanced enough to do a bunch of automation, I do not agree they are intelligent or actually thinking.
I'm with Yann Lecun when he says that we won't reach AGI until we move beyond transformers.
I'm guilty as charged of having looked at GPT 3.5 and having thought "it's meh", but more than anything this is showing that debating words rather than the underlying capabilities is an empty discussion.
Those are all different things with little to nothing to do with each other. It's like saying what if I ensemble a snake and cat ? What does that even mean ? GPT-N or whatever is a single model that can do many things, no ensembling required. That's the difference between it and a calculator or stockfish.
If you remove those tools, or cut its access to search databases, it becomes quite less capable.
A human would often still manage to do it without some data still, perhaps with less certainty, while GPT has more problems than that without others filling in the holes.
chatgpt no longer uses dalle for image generation. I don't understand your point about the tokenization. It doesn't make the model become an ensemble.
It's also just beside the point. Even if you restrict the modalities to text alone, these models are still general alone in ways a calculator is not.
Huh? Isn't a LLM's capability fully constrained by the training data? Everything else is hallucinated.
The quality of the LLM then becomes how often it produces useful information. That score has gone up a lot in the past 18 months.
(Sometimes hallucinations are what you want: "Tell me a fun story about a dog learning calculus" is a valid prompt which mostly isn't meant to produce real facts about the world")
That is according to one specific internal OpenAI benchmark, I don't know if it's been replicated externally yet.
In that sense, they absolutely know things that aren’t in their training data. You’re correct about factual knowledge, tho — that’s why they’re not trained to optimize it! A database(/pagerank?) solves that problem already.
All that said, I wonder if GPT4 had been integrated with the same tools, would it've been any less capable?
It sure could give you a search prompt for Google if you asked it to. Back then you had to copy and paste that search prompt yourself. Today o3 can do it on its own. Cool! Does it imply though o3 is any closer to AGI than GPT4?
Models gaining access to external tools, however impressive from all the applications standpoint, feels like lateral movement not a step towards the AGI.
On the other hand, a model remaining isolated in its sandbox while actually learning to reason about that puzzle (assuming it's not present in the training data) would give off that feeling the AGI vibes.
Did everybody already forget that OpenAI implemented the first version of that, "plugins", back in May 2023? Just a couple of months after GPT4 public release?
A strand of her hair fell on her shoulder, because she was moving continuously (like crazy) it was moving too in a perfectly believable way, and IT EVENTUALLY FELL OFF THE SHOULDER/SHIRT LIKE REAL HAIR and got mixed into other fallen hair. How is that generated? It's too small detail. Are there any artifacts on her side?
Edit: she has to be real. Her lip movements are definitely forced/edited though. It has to be a video recording of her talking. And then a tool/AI has modified her lips to match the voice. If you look at her face and hand movements, her shut lips seem forced.
Nah, having used HeyGen a bit, it's extremely clearly a HeyGen generation. There's a small number of movements and expressions that it continually uses (in forward and reverse).
Edit: I mean, to be clear, it is a real person, just like the author's video is. The way HeyGen works is you record a short clip of you saying some stuff and then you can generate long videos like these of you saying whatever you want. So the stuff you noticed does come from a real video of her, but it's not a real video that's lightly edited by AI, more like the AI has a bunch of clips it can continually mesh together while fixing up the mouth to continually generate video.
It’s bad luck for those of us who want to talk about how good or bad they are in general. Summary statistics aren’t going to tell us much more than a reasonable guess as to whether a new model is worth trying on a task we actually care about.
And that's when we return to a much older and much more important question than whether Super LLM 10.0 Ultra Plus is AGI or not: how much power should a person or group of people be allowed to have?
It’s obvious that currently none of the SOTA models can do such tasks, agentic or not. And therefore they are NOT AGI to me.
> It’s obvious that currently none of the SOTA models can do such tasks, agentic or not. And therefore they are NOT AGI to me.
I myself almost never do that (calling people when googling is possible). Guess I'm not general intelligence. :)
Until those capabilities are expanded for model self-improvement -- including being able to adapt its own infrastructure, code, storage, etc. -- then I think AGI/ASI are yet to be realized. My POV is SkyNet, Traveler's "The Director", Person of Interest's "The Machine" and "Samaritan." The ability to target a potentially inscrutable goal along with the self-agency to direct itself towards that is true "AGI" in my book. We have a lot of components that we can reason are necessary, but it is unclear to me that we get there in the next few months.
We may be going about it the wrong way entirely and need to backtrack and find a wholly new architecture, in which case current capabilities would predate AGI but not be precursors.
While incredibly powerful and transformative, it is not 'intelligence'. LLMs are forever knowledgebase bound. They are encyclopedias with a fancy way of presenting information looked up in the encyclopedia.
The 'presentation' has no concept, awareness, or understanding of the information being presented - and never will. And this is the critical line. Without comprehension, a LLM is incapable of being creative. Of coming up with new ideas. It cannot ponder. Wonder. Think.
Most people have a rough idea of what AGI means, but we still haven't figured out an exact definition that lines up with reality. As we continue exploring the idea space, we'll keep figuring out which parameters place boundaries and requirements on what AGI means.
There's no reason to just accept an ancient definition from someone who was confused and didn't know any better at the time when they invented their definition. Older definitions were just shots in the dark that pointed in a general direction, but there's no guarantee that they would hit upon the exact destination.
"An AGI is a human-created system that demonstrates iteratively improving its own conceptual design without further human assistance".
Note that a "conceptual design" here does not include tweaking weights within an already-externally-established formula.
My reasoning is thus:
1. A system that is only capable of acting with human assistance cannot have its own intelligence disentangled from the humans'
2. A system that is only intelligent enough to solve problems that somehow exclude problems with itself is not "generally" intelligent
3. A system that can only generate a single round of improvements to its own designs has not demonstrated improvements to those designs, as if iteration N+1 were truly superior to iteration N, it would be able to produce iteration N+2
4. A system that is not capable of changing its own design is incapable of iterative improvement, as there is a maximum efficacy within any single framework
5. A system that could improve itself in theory and fails to do so in practice has not demonstrated intelligence
It's pretty clear that no current-day system has hit this milestone; if some program had, there would no longer be a need for continued investment in algorithms design (or computer science, or most of humanity...).
A program that randomly mutates its own code could self-improve in theory but fails to do so in practice.
I don't think these goalposts have moved in the past or need to move in the future. This is what it takes to cause the singularity. The movement recently has been people trying to sell something less than this as an AGI.
I feel this definition doesn't require a current LLM model to be able to change its own working but to be able to generate a guided next generation.
It's possible that LLMs can surpass human beings, purely because I believe we will inevitably be limited to short term storage constraints which LLMs will not. It will be a bandwidth vs througput question. An LLM will have a much larger although slightly slower store of knowledge than what human have. But will be much quicker than a human looking up and validating the data.
We aren't there yet.
Selling something that does not yet exist is an essential part of capitalism, which - according to the main thesis of philosophical Accelerationism - is (teleologically) identical to AI. [0] It's sometimes referred to as Hyperstition, i.e. fictions that make themselves real.
Although, I could be argued into calling what we have already ASI - take a human and Gemini 2.5, and put them through a barrage of omni-disciplinary questions and situations and problems. Gemini 2.5 will win, but not absolutely.
AGI (we have) ASI (we might have) AOI (Artificial Omniscient Intelligence, will hopefully take a while to get here)
This is what AGI means (or should mean): Generalized understanding of the world. Basically, with AGI the context window would be the something like the entire knowledge and understanding of the world that an (adult?) person has (e.g., physics intuition), coupled with the ability to actually reason and act on it, update it, reflect on it, etc.
A small slice of this (e.g., less knowledge than a typical adult) would still be AGI, but current AIs:
- Cannot continually learn and incorporate that learning into their model.
- Cannot reason on any deep level. And before anyone claims that the pattern matching they do is all we do, no this is not the case. Even strong pattern-matching/AI chess engines have weak spots that betray the fact that they do not actually reason like humans do.
- Cannot engage in unprompted reflection in the background.
Current AIs are like a hologram; we are mistaking the 1- or 2-dimensional responses to queries for a deep higher dimensional understanding humans have. The incredible thing about human consciousness is the the deep (infinite?) interiority of it. I can reason about reasoning. I can reason about reasoning about my reasoning, etc. I can reflect on my consciousness. I can reflect on reflecting on my consciousness, etc.
Machines are nowhere close this, and likely will never be
LLMs definitely have this, and it really is bizarre to me that people think otherwise.
> Cannot continually learn and incorporate that learning into their model.
This is definitely a valid criticism of our current LLMs and once we (further) develop ways to do this, I think my main criticism of LLMs as AGI will go away
> Cannot reason on any deep level.
Few people are able to do this
> Cannot engage in unprompted reflection in the background.
True, but I don't know if that belongs as a requirement to be AGI.
Except for writing a joke that will make you laugh, a poem that will make you cry, or a work of art that evokes deep introspection.
Intelligence is much deeper and more nuanced than answering questions of rote knowledge. LLMs are fantastic “reasoning engines”, but the soul is simply not there yet.
I asked GPT to do so and I chuckled out loud.
I don’t consider an AI that fails the surgery brain teaser in the article to be AGI, no matter how superhuman it is at other narrow tasks. It doesn’t satisfy the “G” part of AGI.
> A young boy who has been in a car accident is rushed to the emergency room. Upon seeing him, the surgeon says, "I can operate on this boy!" How is this possible?
But it didn't!
(o4-mini high thought for 52 seconds and even cheated and looked up the answer on Hacker News: https://chatgpt.com/share/68053c9a-51c0-8006-a7fc-75edb734c2...)
If you ask it a math question beyond average middle school level, it will have holes (mathematical errors or misleading) at least within a few follow up turns if not right away. And that’s without trying to fool it.
In ten+ years of Wolfram Alpha I’ve found one error (and that was with the help of o3-mini funnily enough).
I’m still on the stochastic parrots side, which is a useful tool in some occasions.
The definition changes when someone else feels like it should change and especially when they fall short of overhyped expectations.
> The brutal and bruising competition between the tech giants has left nothing but riches for the average consumer.
Capitalism has always been great at this: creating markets, growing them, producing new goods. It's widely acknowledged amongst people who actually seek to gain an understanding of Marxism, and don't just stay in the surface-level black-and-white "socialism and capitalism are opposites" discourse that's very common in the West, especially the USA, especially after the McCarthy's Red Scare.
The problem is what comes once the market is grown and the only way for owners keep profits growing is: 1. consolidating into monopolies or cartels, so competition doesn't get in the way of profits, 2. squeezing the working class, looking to pay less for more work, and/or 3. abusing the natural world, to extract more materials or energy for less money. This is evident in plenty of developed industries: from health care, to broadcasting, telecommunications, fashion, etc.
If we view Socialism for what it is, namely a system built to replace Capitalism's bad parts but keep its good parts, China's system, for example, starts to make more sense. Capitalism in a similar way was an evolution from Feudalism that replaced it's bad parts, to achieve greater liberty for everyone— liberty is very much lost as Feudalism matures, but great for society as a whole. Socialism is meant to be the similar, aiming to achieve greater equity, which it views as very much better for society as a whole.
I wonder how hard it is to objectively use information that is available online for 30 years? But the worst part is how it lies and pretends it knows what it’s talking about, and when you point it out it simply turns into another direction and starts lying again. Maybe the use case here is not the main focus of modern AI; maybe modern AI is about generating slop that does not require verification, because it’s “new” content. But to me it just sounds like believable slop, not AGI.
Gathering context for user request...
Context gathering - Attempting to answer question via LLM: Are there existing Conversation classes in the ecosystem this should extend? Context gathering - LLM provided answer: "No"
Context gathering - Attempting to answer question via LLM: How should model selection work when continuing a previous conversation? Context gathering - LLM answer was UNKNOWN, asking user. Asking user: How should model selection work when continuing a previous conversation?
Context gathering - received user response to question: "How should model selection work when continuing a previous conversation?"
Context gathering - finished processing all user questions Context gathering - processing command executions... Context gathering - executing command: sqlite3 $(find . -name llm_conversations.db) .tables
Context gathering - command execution completed
Context gathering - executing command: grep -r Conversation tests/
Context gathering - command execution completed
Context gathering - executing command: grep -h conversation_id *py Context gathering - command execution completed Context gathering - finished processing all commands Analyzing task complexity and requirements...
DEBUG: reasoning_model: openrouter/google/gemini-2.5-pro-preview-03-25 Task classified as coding (confidence: 1.0) Task difficulty score: 98.01339999999999/100 Selected primary reasoning model: claude-3.7-sonnet get_reasoning_assistance:[:214: integer expression expected: 98.01339999999999 Reasoning assistance completed in 39 seconds Calling LLM with model: claude-3.7-sonnet
In my demo, the llm agent asks followup questions to understand the users problem. Then it first attempts to answer those questions using context and function calling. When a question cannot be answered this way it is forwarded to the user. In other words, it tells you when it doesn't know something.
It is not a simple matter of patching the rough edges. We are fundamentally not using an architecture that is capable of intelligence.
Personally the first time I tried deep research on a real topic it was disastrously incorrect on a key point.
If you ask an intelligent being the same question they may occasionally change the precise words they use but their answer will be the same over and over.
Heck, I can't even get LLMs to be consistent about *their own capabilities*.
Bias disclaimer: I work at Google, but not on Gemini. If I ask Gemini to produce an SVG file, it will sometimes do so and sometimes say "sorry, I can't, I can only produce raster images". I cannot deterministically produce either behavior - it truly seems to vary randomly.
Ask me some question before bed and again after waking up, I'll probably answer it at night but in the morning tell you to sod off until I had coffee.
We're often explicitly adding in randomness to the results so it feels weird to then accuse them of not being intelligent after we deliberately force them off the path.
What does that even mean? Do you actually have any particular numeric test of intelligence that's somehow better than all the others?