Jagged AGI: o3, Gemini 2.5, and everything after

The capabilities of AI post gpt3 have become extraordinary and clearly in many cases superhuman.

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.

AGI is a annonymous good model coming around the corner with no company and no LLM researchers attached. AGI is when the LLM hype train threads are replaced with CEOs and let go researchers demanding UBI.

I suspect AGI will be one of those things that you can't describe it exactly, but you'll know it when you see it.

Yeah formal agreement seems exceedingly unlikely. Since there isn’t even agreement on the defintion of “Artifical Intelligence”.

I suspect everyone will call it a stochastic parrot because it did this one thing not right. And this will continue into the far far future even when it becomes sentient we will completely miss it.

> but you'll know it when you see it.

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.

This is part of what the article is about

I still can't have an earnest conversation or bounce ideas off of any LLM - all of them seem to be a cross between a sentient encyclopedia and a constraint solver.

They might get more powerful but I feel like they're still missing something.

> 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.

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.

Why are you not able to have an earnest conversation with an LLM? What kind of ideas are you not able to bounce of LLMs? These seem to be the type of use cases where LLMs have generally shined for me.

Once it pushed out most humans from white collar labor so the remaining humans work in blue collar jobs people wont say its just a stochastic parrot.

It's easy to treat AGI as one thing — I did so myself before everyone's differing reaction to LLMs made me realise we all mean different things by each of the three letters of the initialism, and that none of those initials are really boolean valued.

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.

I think a reasonable definition of intelligence is the application of reason on knowledge. An example of a system that is highly knowledgeable but has little to no reason would be an encyclopedia. An example of a system that is highly reasonable, but has little knowledge would be a calculator. Intelligent systems demonstrate both.

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-...

It's more than that but less than intelligence.

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.

Which part of "General Intelligence" requires replacing white collar workers? 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. IMO GPT-3 was AGI, just a pretty crappy one.

> 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.

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.

> 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?

Similarity match. For that you need to understand reflexively how you think and write.

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.

This is the forum that fell the hardest for the superconductor hoax a few years ago. HN has no superiority leg to stand on.

Low agreeableness will actually be extremely useful in many use cases, such as scientific discovery and of course programming assistance. It's amazing that this venue hasn't been explored more deeply.

Its much easier to sell an agreeable assistant than a disagreeable one, so it isn't that strange the alternative isn't explored.

The exact definition of AGI is pretty much the least interesting thing about AGI. It's basically bike-shedding at this point: arguing about something easy to understand instead of tackling the really hard questions like "how competent can AI get before it's too dangerous to be in the hands of flakey tech companies?"

I've always felt that trying to pin down the precise definition of AGI is as useless as trying to pin down "what it means to truly understand". It's a mental trap for smart people, that distracts them from focusing on the impacts of hard-to-define concepts like AGI.

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.

There's no way to be sure in either case, but I suspect their impressions of the technology ten or twenty years ago would be not so different from my experience of first using LLMs a few years ago...

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.

I spent some amount of time trying to create a stock/option trading bot to exploit various market inefficiencies that persist, and did a bunch of code and idea bouncing off these LLMs. What I fund is that even all the various incarnations of GPT 4+ and GPT o+ routinely kept falling for the "get rich quick" option strategies all over the internet that don't work.

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.

It's definitely a trap for those who aren't familiar with the existing academic work in philosophy, cognition, and neuroscience. There are no definitive answers but there are lots of relatively well developed ideas and concepts that everyone here on HN seems completely ignorant of, even though some of the ideas were developed by industry giants like Marvin Minsky.

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.

When it can replace a polite, diligent, experienced 120 IQ human in all tasks. So it has a consistent long-term narrative memory, doesn't "lose the plot" as you interact longer and longer with it, can pilot robots to do physical labor without much instruction (what is current state of the art is not that, a trained human will still do much better, can drive cars, etc), generate images without goofy non-human style errors, etc.

Maybe, maybe not. Power loom pushed a lot of humans out of the textile factory jobs, yet noone claims power loom is the AGI.

I felt this way until I tried gemini 2.5. Imo it fully passes the turing test unless youre specifically utilizing tricks that LLMs are known to fall for.

Excellent article and analysis. Surprised I missed it.

It is very hard to argue with Norvig’s arguments that AGI has been around since at least 2023.

We have all seen it and are now just in severe denial.

Until you can boot one up, give it access to a VM video and audio feeds and keyboard and mouse interfaces, give it an email and chat account, tell it where the company onboarding docs are and expect them to be a productive team member, they're not AGI. So long as we need special protocols like MCP and A2A, rather than expecting them to figure out how to collaborate like a human, they're not AGI.

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.

I think the thing missing would be memory. The knowledge of current models is more or less static save for whatever you can cram into their context window. I think if they had memory and thus the ability to learn - “oh hey, I’ve already tried to solve a bug in these ways maybe I won’t get stuck in loop on them!” Would be the agi push for me. Real time incorporating new knowledge into the model is the missing piece.

Or become shocked to realize humans are basically statistical parrots too.

So they are only as smart as most humans.

Compared to someone who graduated middle school on November 30th, 2022 (2.5 years ago, would you say that today's gemini 2.5 pro has NOT gained intelligence faster?

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.

I don't think so, and here's my simple proof:

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.

This is actually how a supreme court justice defined the test for obscenity.

> 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

> would you say that today's gemini 2.5 pro has NOT gained intelligence faster?

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.

Not a lot, I mean basically everyone, to the point where most companies doesn't need to pay humans to think anymore.

Just because we rely on vision to interface with computer software doesn't mean it's optimal for AI models. Having a specialized interface protocol is orthogonal to capability. Just like you could theoretically write code in a proportional font with notepad and run your tools through windows CMD - having an editor with syntax highlighting and monospaced font helps you read/navigate/edit, having tools/navigation/autocomplete etc. optimized for your flow makes you more productive and expands your capability, etc.

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.

> I don't think so, and here's my simple proof:

That isn't a proof since you haven't ran that test, it is just a thought experiment.

> experienced 120 IQ human in all tasks.

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.

> clearly in many cases superhuman

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.

The reason why it's so famous though (and why some people tend to use it in a tongue in cheek manner) is because "you know it when you see it" is a hilariously unhelpful and capricious threshold, especially when coming from the Supreme Court. For rights which are so vital to the fabric of the country, the Supreme Court recommending we hinge free speech on—essentially—unquantifiable vibes is equal parts bizarre and out of character.

Well, I'm too lazy to look up how many weavers were displaced back then and that's why I said a lot. Maybe all of them, since they weren't trained to operate the new machines.

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.

my 2c on this is that if you interact with any current llm enough you can mentally 'place' its behavior and responses. when we truly have AGI+/ASI my guess is that it will be like that old adage of blind men feeling & describing an elephant for the first time. we just wont be able to fully understand its responses. it would always be something left hanging and then eventually we'll just stop trying. that would be time when the exponential improvement really kicks in.

it should suffice to say we are nowhere near that and I dont even believe LLMs are the right architecture for that.

> Gemini 2.5 pro the model has not gained any intelligence since it is a static model.

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.

Agreed, but I also think to be called AGI, they should be capable of working through human interfaces rather than needing to have special interfaces created for them to get around their lack of AGI.

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 an incredibly specific test/definition of AGI - particularly remembering that I would probably say an octopus classes as an intelligent being yet can't use outlook...

I've been accused a few times of being an AI, even here.

(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.

Indeed, on both. Even IQ 85 would make a painful dent in the economy via unemployment statistics. But the AI we have now is spikey, in ways that make it trip up over mistakes even slighly below average humans would not make, even though it can also do Maths Olympiad puzzles, the bar exam, leetcode, etc.

> Gemini 2.5 pro the model has not gained any intelligence since it is a static model.

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.

> how competent can AI get before it's too dangerous to be in the hands of flakey tech companies?

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.

Even worse, there isn’t even a working definition of "Intelligence"—neither in computer science nor biology.

Eh, I am torn on this. I had some great conversations on random questions or conceptual ideas, but also some where the models instructions shone through way too clearly. Like, when you ask something like "I’m working on the architecture of this system, can you let me know what you think and if there’s anything obvious to improve on?"—the model will always a) flatter me for my amazing concept, b) point out the especially laudable parts of it, and c) name a few obvious but not-really-relevant parts (e.g. "always be careful with secrets and passwords"). However, it will not actually point out higher level design improvements, or alternative solutions. It’s always just regurgitating what I’ve told it about. That is semi-useful, most of the time.

It needs some kind of low latency world model. Or at least more agent inspired pretraining

(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

You can't do that with most of the world's human population. Does that imply that most humans haven't reached AGI?

With MCP/tool use you can tell it to save state into an MD file, simulating this. How much that counts is left as an exercise to the reader.

Where A stands for artificial, I don't think most humans have "reached" that, no.

An LLM is arguably more "intelligent" then people with an IQ of less than 80.

If we call people with an IQ of less than 80 an intelligent life form, why can't we call an LLM that?

I'd bet that for any discipline you chose, one could find an AI that can trick any of today's post-enlightenment humans.

Why would a bad attitude be helpful in those domains? Are the human partners wont to deliver more effort when you’re mean to them?

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!

[0] https://en.m.wikipedia.org/wiki/Agreeableness

> Just because we rely on vision to interface with computer software doesn't mean it's optimal for AI models

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.

By this logic disabled people would not class as "Generally Intelligent" because they might have physical "interface" limitations.

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.

The emotional way that humans think when buying products is similarly unfair. Only the 90th percentile is truly 'satisfactory'. The implied question is when would Joe Average and everyone else stop moving the goal posts to the question, "Do we have AI yet"?

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'.

Given the gigantic amount of compute power and storage needed to train and run LLMs, this is certainly not true. It is absolutely feasible for government to check every data center capable of advancing the state of the art in AI to ensure that no such research is taking place.

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.

If you just bow out of the AI race you are handing it to other countries where practices might not be subjected to the same standards. It's suicide to do this.

> Similarly I wouldn't be "Generally Intelligent" by this definition if you sat me at a Cyrillic or Chinese keyboard

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?

If all we needed was general intelligence, we would be hiring octopuses. Human skills, like fluency in specific languages, are implicit in our concept of AGI.

Yes, thar is the same feelingg I have. Giving it some json and describe how a website should look? Super fast results and amazing capabilities. Trying to get it to translate my unit tests from Xunit to Tunit, where the latter is new and does not have a ton of blog posts? Forget about it. The process is purely mechanical and it is easy after RTFM, but it falls flat on its face

Forums shed and gain members over time. Much of that cohort is gone.

Because it spits out the most probable answer, which is based on endless copycat articles online written by marketers for C-level decision makers to sell their software.

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?

You presumably understand the posters underlying point though - that the definition of 'general intelligence' does not need to be 'at above-average human level' and humans can be intelligent without being able to use a computer or do some sort of job on a VM.

We don't have this for humans either, other than the vague pseudoscience of IQ. As I've travelled more, and just grown older in general, I've come to accept a lot of humans as barely sentient.

How come we’re discussing if they’re artificial general intelligence then?

This argument needlessly anthropomorphizes the models. They are not humans nor living entities, they are systems.

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?

It's not: whatever the way you define AGI, you cannot just ignore the key letter of the three letters acronym: G stands for “General”.

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.

But its the AI researchers that made it smarter, it isn't a self contained system like a child. If you fired the people maintaining it and it just interacted with people it would stop improving.

> Surely that's an irrelevant distinction, from the point of view of a hiring manager?

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.

It wont get smart enough without the researchers making architectural updates though, current architecture wont learn to become a white collar worker just from user feedback.

> Have you not experienced being on the recieving end of such accusations?

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.

Because some believe that to be intelligence while others believe it requires more than that.

> but you can disagree agreeably, right?

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.

LK-99? The room temperature superconductor made by some guys in a small lab in Korea after 20 years of attempts - who doesn't want that to be real.

Won´t say people fell for it though it was just the current happening at the time.

> 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."

Your own comment was a movement of the goalposts.

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".

It sounds like you have a different definition of "general" in the context of intelligence from the one I shared. What is it?

Its the same middle schooler, nobody gave a time limit for how long it takes the middle schooler to solve the problem. These AI models wont solve it no matter how much time spent, you have to make new models, like making new kids.

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.

Superhuman is an interesting term because it's very vague. In some sense, computers have always been superhuman. I don't know anyone who could do a 10 by 10 digit multiplication in a fraction of a second yet even the earliest computers were capable of that. Does that make them superhuman?

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?

So is your argument is that all skeptics are unreasonable people that can't ever be convinced based on being called an AI once? Don't you see who is the unreasonable one here?

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.

That's only if countries can't agree on this. I just gave the example of human cloning, which has been banned globally. You can also look at nuclear non-proliferation, which has been largely successful (though not completely) despite huge incentives for any country to defy it.

Uh… what do you call mass downvoting anything reasonably skeptical? I saw comments saying “I’ll wait until the data can be replicated to believe it” turn grey almost immediately on most threads. And after we knew it didn’t replicate it took the community the better part of a week to grieve and admit that the “it’s not a superconductor” results were real and not operator error.

This is why I suspect that slapping “Artificial” in the acronym is rather forward of us as a species. If we do end up eventually with something we consider intelligent, there won’t be anything artificial about it.

Artificial means human made, if we made an intelligent being then it is artificial. What do you think artificial meant here?

Although I think if you asked people 20 years ago to describe a test for something AGI would do, they would be more likely to say “writing a poem” or “making art” than “turning Xunit code to Tunit”

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!

Tell them humans need to babysit it and doublecheck its answers to do anything since it isn't as reliable as a human then no they wouldn't call it an AGI back then either.

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.

1. The child didn't learn algebra on its own either. Aside from Blaise Pascal, most children learned those skills by having experienced humans teach them.

2. How likely is it that we're going to fire everyone maintaining those models in the next 7.5 years?

General intelligence means it can do the same intellectual tasks as humans can, including learning to do different kinds of intellectual jobs. Current AI can't learn to do most jobs like a human kid can, so its not AGI.

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.

Why do human programmers need code review then if they are intelligent?

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.

> Why do human programmers need code review then if they are 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.

Now that’s a big if.

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.

Well AGI can write unit tests, write application code then run the tests and iterate - agents in cursor are doing this already.

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.

What intellectual tasks can humans do that language models can't? Particularly agentic language model frameworks.

> The child didn't learn algebra on its own either. Aside from Blaise Pascal, most children learned those skills by having experienced humans teach them.

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.

A normal software engineering job? You have access to email and can send code etc. No current model manages anything close to that. Even much simpler jobs can't be automated like that by them.

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.

> Put another way, I’m not a strong dev but good LLMs can write lots of code with less bugs than me!

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.

> who doesn't want that to be real.

I think that's exactly the point the person you're responding to is calling out. That's a massive bias.

Good point, but calling a woman overweight isn't necessarily a disagreement.

If anything it's been replaced with a far more naive and gullible cohort, not a more skeptical one.

> You have access to email and can send code etc. No current model manages anything close to that.

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"?

The brain of a child is not self-contained either. Neither is the entire complete child themselves — "It takes a village to raise a child", to quote the saying.

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.

It doesn't have any memory _you're aware of_. A semiconductor can hold state, so it has memory.

The blue collar jobs are more entertaining anyways, provided you take the monetary inequality away.

Why would it be a tooling issue? AI has access to email, IDEs, and all kinds of systems. It still cannot go and build software on its own by speaking to stakeholders, taking instructions from a PM, understanding it needs to speak to DevOps to release its code, suggesting to product team that feature is better developed as part of core product, objecting to SA about the architecture, and on and on…

(If it was a tooling issue, AGI could build the missing tools)

> 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.

Weird spiky things that are hard to characterise even within one specific model, and where the ability to reliably identify such things itself causes subsequent models to not fail so much.

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/

> Just because we rely on vision to interface with computer software doesn't mean it's optimal for AI models.

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.

People already share viral clips of AI recognising other AI, but I've not seen real scientific study of if this is due to a literary form of passing a mirror test, or if it's related to the way most models openly tell everyone they talk to that they're an AI.

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.

Right, and also the ability to know when it's stuck. It should be able to take a problem, work on it for a few hours, and if it decides it's not making progress it should be able to ping back asynchronously, "Hey I've broken the problem down into A, B, C, and D, and I finished A and B, but C seems like it's going to take a while and I wanted to make sure this is the right approach. Do you have time to chat?" Or similarly, I should be able to ask for a status update and get this answer back.

> It's optimal for beings that have general purpose inteligence [Sic].

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?

So I am a blind human. I cannot drive a car or use a camera/robot to do housework (I need my hands to see!) Am I not a general intelligence?

> Do you really think that clicking buttons through a GUI is the one true optimal way to use a computer?

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.

It's only the USA, Russia, France, China, the UK, India, Pakistan, Israel, and North Korea that have nuclear weapons, from the entire world. Iran has been working on it for decades and they still ultimately haven't gotten them. This is a huge success given the gigantic advantage nuclear weapons give strategically.

> There are some tasks you can't do without vision...

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.

> I can't think of anything where you require vision that having a tool (a sighted person) you protocol with (speak) wouldn't suffice.

Okay, are you volunteering to be the guide passenger while I drive?

Of course not. The visual part is window dressing on the argument. The real point is, before declaring AGI, I think the way we interact with these agents needs to be more like human to human interaction. Right now, agents generally accept a command, figure out which from a small number of MCPs that have been precoded for it to use, do that thing you wanted right or wrong, the end. If it does the right thing, huge confirmation bias that it's AGI. Maybe the MCP did most of the real work. If it doesn't, well, blame the prompt or maybe blame the MCPs are lacking good descriptions or something.

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.

Strictly speaking, it can, but its ability to do so is limited by its context size.

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.

Your brain does rewire itself as you learn.

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?

The "meaningless praise" part is basically American cultural norms trained into the model via RLHF. It can be largely negated with careful prompting, though.

Tastes differ.

Thank you for making my point:

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.

Read a bunch of books not present in the training data on a specific topic, and learn something from it.

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.

> Have you actually tried agentic LLM based frameworks that use tool calling for long term memory storage and retrieval,

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.

> 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).

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?

> Its the same middle schooler, nobody gave a time limit for how long it takes the middle schooler to solve the problem.

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.

> 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.

I think you are off topic here. You agree these models can't replace those humans, hence you agree they aren't AGI, the rest of your post somehow got into whether companies would hire 11 year olds or not.

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.

For two years during covid that's how people worked and how kids attended school. So I'd say generally using a computer to communicate, understand expectations, and perform for complex tasks is something well within most humans' ability.

Reread it, I edit stuff while composing, and hadn't finished until at least 13 minutes after your comment.

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.

Humans have general intelligence. A network of humans have better general 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.

I replied this to another comment, but I'll put it here: your limitation is physical. You have standard human intelligence, but you're lacking a certain physical input (vision). As a generally intelligent being, you will compensate for the lack of vision by using other senses.

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?

More like all humans, most of the time.

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.

> LLMs have general intelligence.

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.

It actually works really well in my experience. But it eats up context. Using 500-600k token windows per inquiry isn’t cheap.

Unless it's from LessWrong, I usually read these with a grain of salt.