One of my reports just sent me a giant design doc that Claude enthusiastically generated packed with plausible looking technical detail. Unfortunately the problem it's trying to solve is completely misguided and we shouldn't be doing it at all. So I'd say as answer to the question posed by this title: a while.
Yeah. I find a lot of the gripes people have a bout LLMs, these days, could also be said about employees.
Like, "the agent went and did a bunch of unrelated changes for the task I asked it to do!"... Have you ever worked with other engineers? This happens all the time
The current HN submission title ("AGI timelines shift with whichever lab is dominant") is very bad. It is neither the title of the article nor is it the thrust of the content.
The title of the article is "How long until AI automates all cognitive labor?"
The main point of the article is summarized by its intro: "Recently, though, I noticed that many great researchers have now published two or more precise forecasts, all using similar definitions of AGI, and all providing confidence intervals. So I was able to visualize how their forecasts changed over time."
The closest the article comes to saying the HN submitted title is:
> And every single person who updated their timelines from January 2026 to April 2026 has moved their timeline to say AGI is coming sooner, myself included.
> So I think the data supports the impression I got from Daniel, Eli, and the AI Futures team. One way I could characterize it is: in the ChatGPT era, people updated towards AI coming sooner. Then in the xAI, Meta, and Gemini era, people updated towards it coming later. Then in the Anthropic era, people updated towards AI coming sooner. Take from that what you will.
There was a ChatGPT era, and now an Anthropic era (less so though than the initial boom was 'ChatGPT dominated'), but there never was an xAI, Meta, and Gemini era.
Original title took one framing from the back half of the post (3 update cycles that can loosely be called the "ChatGPT era, then xAI/Meta/Gemini era, then Anthropic era"), but definitely not the point here. Thanks for flagging
> The overlapping AGI definition I use here is "Most purely cognitive labor is automatable at better quality, speed, and cost than humans".
That's a poor definition. Nowhere have I seen cheapness as being a requirement to count as AGI. If we have something that can do everything people can do and more, but it costs a lot means it's not AGI?
Author here, I drew on this from AI 2027. Yes, a very-expensive AGI, e.g. $1 million / day to simulate a smart human, would be a huge deal. But it would have meaningfully different effects than a cheap one.
Here's one definition AI 2027 used [1]: "Superhuman coder (SC): An AI system for which the company could run with 5% of their compute budget 30x as many agents as they have human research engineers..."
I've got no problem with your concept, and even think it's useful. I just don't think that concept and AGI are the same thing. Economically useful has no relation to what has been called AGI before.
If it's merely human equivalent but somehow costs a lot more than actual humans, then it's actually pretty marginal until the cost comes down. There are a lot of humans.
So you could technically have AGI without entering a true AGI era. "95% as good as an average Harvard graduate across the board, but it costs $5 million/year to run" is impressive and scientifically interesting, but not economically transformative.
But if it costs $50,000/year to run, then everything changes really fast. And not necessarily in a good way.
Plenty of C-level executives have salaries around that number. Replacing them with AGI would be cost-effective. Cost is contingent, shouldn't be part of the AGI definition.
Well, let's look at someone like Einstein. Just for argument's sake let's say he has a flat salary demand of $5 million dollars. It's not cost effective to hire Einstein to write your CRUD apps in this situation. That doesn't mean there isn't somewhere that he would have a value of $5 million.
It's also a very lame definition. Intelligence - and humans - are more than just labor.
(You'll forgive me for conflating humanity and intelligence - we are homo spaiens, after all. Thinking man.)
I'm not _confused_ why these "AI" "Labs" are using that definition though. It's extremely clear they're trying to eliminate the need for the non-owner class. They're not selling LLMs (some companies are, but not these companies). These companies are selling the idea of labor without laborers to people who hate and fear laborers - and their utter dependence on them - more than anything else in their lives.
Really looking forward to the scam collapsing. Crypto wasn't very satisfying to me because too many of the victims were just idiots. This time, it's class warfare.
> The overlapping AGI definition I use here is "Most purely cognitive labor is automatable at better quality, speed, and cost than humans". For some of these researchers, saying they use this definitions is a bit of a stretch, but I included everyone who I judged as close enough to be informative.
Seems "AGI" is on the same level as "art" or "love" in that everyone knows what we're talking about but no one can nail down unanimously what it is.
AGI is simple: the model does not need to be endlessly trained, I can hand it a PDF about a brand new programming language, and the next person to talk to the same model should get an answer at the same speed and knowledge as if it were trained. We are clearly nowhere near this, we're in a state where we can 100% fake this, but nobody has shown this to be the case yet. I think its certainly possible, but I am also convinced that it will require rethinking how we do LLMs today.
So find the group that cares about the collection of capabilities you care to talk about. Regardless of whatever line is drawn for AGI, it's obvious that should some tech advances come to pass, we'll all care about the threshold of many jobs going away. Does that mean AGI? The people who care about jobs won't quibble, they care about the jobs.
If the issue you care about is jobs going away then I think you'll find a growing movement with a common base of beliefs.
I have no idea why this "AGI is not even well defined" meme gained so much traction recently.
AGI is something that can do everything better than humans. Write a novel, seduce someone, prove a theorem, fix a pipe, whatever. And it's clear right now we don't have it.
Your definition is closer to ASI than AGI. And that's the explanation for your first sentence: it's not well defined because you ask 10 people and get 12 different definitions. And it gets even worse if you ask experts in the field :)
Then you have the process of drifting definitions (or, more colloquially moving the goalposts). Hassabis has said this himself: his definition of AGI has shifted. And we know that's true, because we have his definition from 2010 when he started DeepMind. His definition then was much much "simpler", and there are arguments to be made that we already have that. But, alas, he's changed the definition. As did most of us. Seeing the progress will do that to you.
Even going by your definition, even adjusting it for "General" instead of "Super", it's still not clear. What's better? Is a poem written by a nobel laureate better than one written by a lit student? Probably. Is one written by a nobel laureate better than another written by another nobel laureate? Maybe? Is the one scribbled on a card by your 5yo for your birthday better? It most certainly is better for you. And so on...
We're not dealing with easy to define things here. Hell, I could make arguments that every word in Artificial General Intelligence is so hard to define or ambiguous that you'd never reach a consensus between a group of people. There are good arguments to be made in ever each direction. That makes it by definition not well defined. It's all ... relative :)
I don’t think the definition is that clear cut at all. A human can remember the smell of something and invoke it right then and there even if only for half a second. Are we expecting an AGI to do the same?
Then again, transformers seem super-human in some ways already. Who do you know who can more or less recite and make associations from (even if not always intelligently) hundreds of billions of text fragments? Transformers already are better at math than your average human.
My bet is we’ll land in a weird place in between where these systems clearly have some superhuman intelligent capabilities but still are far from “do everything better than humans”.
It's been a big problem for a while. The big Metaculus question about AGI has depends on the game "Montezuma's revenge" (!), and there have been many debates about this going back to at least 2020: https://www.metaculus.com/questions/3479/date-weakly-general...
Which humans? "Humans" are not fungible objects, no matter what the gray-wool-suit set says. The LLMs are already replacing human workers on the bottom of the food chain. Are they perfect? No. Are the humans they are replacing perfect? No. At that point it becomes about tradeoffs.
If AGI is "better at every human at everything" that is ASI, which is a different breed of cat.
You make a good point that not having it is easy to spot. But what precisely would flip the switch?
Seducing someone for example, how often would that have to work? On all people? Maybe that was just thrown out as an example but it points to how subjective these goal posts are.
It's not just that, it can also learn without having to be retrained. Which goes back to the issue, the real issue is people like Scam Altman can claim AGI is near, but then later say "well my view of AGI was that it is x, y and z" if not pressed to define what they think AGI is in that exact moment they're commenting on AGI, they can just later redefine it.
I really liked Dario's metaphor that in the 80's, we could have said someday we'll have "supercomputers", which can do all the calculations we did except WAY faster. When, in reality, the AI's just get smarter over time, even if the frontier is jagged. AGI is just vibes only for "smart enough, consistently enough".
AGI means no needing to retrain the model, it should be able to learn on the fly. That's the true meat of AGI. Any CEO or exec saying any remark about AGI should be forced to define what their definition of AGI is in that moment, or be completely shunned by the industry, since it seems they can just reframe what they meant by AGI later if they don't define it in that moment.
So it's not a human intelligence. The transformer works very differently. We're trying to emulate human intelligence on a very different architecture.
Although, for the most part, what we actually seem to care about is that the job gets done. It's just that all the training data we have is "guy shaped" (linear), not transformer shaped. We haven't actually figured out how to train a transformer yet.
Amodei still predicts 2028, the same year when we'll have full self driving and Mars settlements.
So far all he has is this little code stealing application that could be replaced by git clone and sed for stripping the license.
The times before the Internet when Scientology people had to go into the streets to recruit people were nice. I wish we could put him and his ilk on some Claudology remote island, cut all Internet cables and enjoy the world without dorks and criminals that have been given a megaphone.
It's a specific subtype of Gell-Mann Amnesia effect. They "know" AI cant do their own jobs, but it seems pretty good at summarizing what others do without understanding the nuance. It seems to really apply to the AI Lab CEOs who appear "shocked" everyone isn't simply replaced with LLMs by now so the timelines get kicked.
i like this and i think it's adjacent to gell-man effect rather than a subtype of it. Any CEO claiming AGI is here will never say, "AGI is here because it can automate what I do today." They are saying AGI is here because they have some loose understanding of what other humans seem to be doing and think the LLMs can also do this. In a way it also makes me think that these CEOs are kind of operating like LLMs - they sound confident, they don't have the full nuanced picture (its impossible to have a nuanced understanding of everything), they are not doing the actual labor that they want to replace.
The article misses an important clarification for a general audience: current LLM architecture is not AGI by most scientists working on intelligence and cognition, even if its impact is already extraordinary and in many tasks exceeds human performance. AGI implies a broader set of traits.
One of my reports just sent me a giant design doc that Claude enthusiastically generated packed with plausible looking technical detail. Unfortunately the problem it's trying to solve is completely misguided and we shouldn't be doing it at all. So I'd say as answer to the question posed by this title: a while.
>Unfortunately the problem it's trying to solve is completely misguided and we shouldn't be doing it at all
Isn't that like 90% of project plans by pre-LLM managers too?
Yeah. I find a lot of the gripes people have a bout LLMs, these days, could also be said about employees.
Like, "the agent went and did a bunch of unrelated changes for the task I asked it to do!"... Have you ever worked with other engineers? This happens all the time
Maybe - but this means that cognitive labor is still needed.
Yeah but these models are the worst they’ll ever be! They’ll get better! You need to give it better context! Did you try multiple agents?
Sarcasm aside, I am just so tired.
The current HN submission title ("AGI timelines shift with whichever lab is dominant") is very bad. It is neither the title of the article nor is it the thrust of the content.
The title of the article is "How long until AI automates all cognitive labor?"
The main point of the article is summarized by its intro: "Recently, though, I noticed that many great researchers have now published two or more precise forecasts, all using similar definitions of AGI, and all providing confidence intervals. So I was able to visualize how their forecasts changed over time."
The closest the article comes to saying the HN submitted title is:
> And every single person who updated their timelines from January 2026 to April 2026 has moved their timeline to say AGI is coming sooner, myself included.
> So I think the data supports the impression I got from Daniel, Eli, and the AI Futures team. One way I could characterize it is: in the ChatGPT era, people updated towards AI coming sooner. Then in the xAI, Meta, and Gemini era, people updated towards it coming later. Then in the Anthropic era, people updated towards AI coming sooner. Take from that what you will.
There was a ChatGPT era, and now an Anthropic era (less so though than the initial boom was 'ChatGPT dominated'), but there never was an xAI, Meta, and Gemini era.
You're right, just updated.
Original title took one framing from the back half of the post (3 update cycles that can loosely be called the "ChatGPT era, then xAI/Meta/Gemini era, then Anthropic era"), but definitely not the point here. Thanks for flagging
Author here, I agree, I'd be happy if admins want to change the title of this submission to the title of the piece.
> The overlapping AGI definition I use here is "Most purely cognitive labor is automatable at better quality, speed, and cost than humans".
That's a poor definition. Nowhere have I seen cheapness as being a requirement to count as AGI. If we have something that can do everything people can do and more, but it costs a lot means it's not AGI?
Author here, I drew on this from AI 2027. Yes, a very-expensive AGI, e.g. $1 million / day to simulate a smart human, would be a huge deal. But it would have meaningfully different effects than a cheap one.
Here's one definition AI 2027 used [1]: "Superhuman coder (SC): An AI system for which the company could run with 5% of their compute budget 30x as many agents as they have human research engineers..."
[1] https://ai-2027.com/research/timelines-forecast
I've got no problem with your concept, and even think it's useful. I just don't think that concept and AGI are the same thing. Economically useful has no relation to what has been called AGI before.
If it's merely human equivalent but somehow costs a lot more than actual humans, then it's actually pretty marginal until the cost comes down. There are a lot of humans.
So you could technically have AGI without entering a true AGI era. "95% as good as an average Harvard graduate across the board, but it costs $5 million/year to run" is impressive and scientifically interesting, but not economically transformative.
But if it costs $50,000/year to run, then everything changes really fast. And not necessarily in a good way.
Plenty of C-level executives have salaries around that number. Replacing them with AGI would be cost-effective. Cost is contingent, shouldn't be part of the AGI definition.
Well, let's look at someone like Einstein. Just for argument's sake let's say he has a flat salary demand of $5 million dollars. It's not cost effective to hire Einstein to write your CRUD apps in this situation. That doesn't mean there isn't somewhere that he would have a value of $5 million.
It's also a very lame definition. Intelligence - and humans - are more than just labor.
(You'll forgive me for conflating humanity and intelligence - we are homo spaiens, after all. Thinking man.)
I'm not _confused_ why these "AI" "Labs" are using that definition though. It's extremely clear they're trying to eliminate the need for the non-owner class. They're not selling LLMs (some companies are, but not these companies). These companies are selling the idea of labor without laborers to people who hate and fear laborers - and their utter dependence on them - more than anything else in their lives.
Really looking forward to the scam collapsing. Crypto wasn't very satisfying to me because too many of the victims were just idiots. This time, it's class warfare.
how long until i stop seeing this nonsense shoveled at me from every direction
> The overlapping AGI definition I use here is "Most purely cognitive labor is automatable at better quality, speed, and cost than humans". For some of these researchers, saying they use this definitions is a bit of a stretch, but I included everyone who I judged as close enough to be informative.
Seems "AGI" is on the same level as "art" or "love" in that everyone knows what we're talking about but no one can nail down unanimously what it is.
AGI is simple: the model does not need to be endlessly trained, I can hand it a PDF about a brand new programming language, and the next person to talk to the same model should get an answer at the same speed and knowledge as if it were trained. We are clearly nowhere near this, we're in a state where we can 100% fake this, but nobody has shown this to be the case yet. I think its certainly possible, but I am also convinced that it will require rethinking how we do LLMs today.
So find the group that cares about the collection of capabilities you care to talk about. Regardless of whatever line is drawn for AGI, it's obvious that should some tech advances come to pass, we'll all care about the threshold of many jobs going away. Does that mean AGI? The people who care about jobs won't quibble, they care about the jobs.
If the issue you care about is jobs going away then I think you'll find a growing movement with a common base of beliefs.
I have no idea why this "AGI is not even well defined" meme gained so much traction recently.
AGI is something that can do everything better than humans. Write a novel, seduce someone, prove a theorem, fix a pipe, whatever. And it's clear right now we don't have it.
Your definition is closer to ASI than AGI. And that's the explanation for your first sentence: it's not well defined because you ask 10 people and get 12 different definitions. And it gets even worse if you ask experts in the field :)
Then you have the process of drifting definitions (or, more colloquially moving the goalposts). Hassabis has said this himself: his definition of AGI has shifted. And we know that's true, because we have his definition from 2010 when he started DeepMind. His definition then was much much "simpler", and there are arguments to be made that we already have that. But, alas, he's changed the definition. As did most of us. Seeing the progress will do that to you.
Even going by your definition, even adjusting it for "General" instead of "Super", it's still not clear. What's better? Is a poem written by a nobel laureate better than one written by a lit student? Probably. Is one written by a nobel laureate better than another written by another nobel laureate? Maybe? Is the one scribbled on a card by your 5yo for your birthday better? It most certainly is better for you. And so on...
We're not dealing with easy to define things here. Hell, I could make arguments that every word in Artificial General Intelligence is so hard to define or ambiguous that you'd never reach a consensus between a group of people. There are good arguments to be made in ever each direction. That makes it by definition not well defined. It's all ... relative :)
I don’t think the definition is that clear cut at all. A human can remember the smell of something and invoke it right then and there even if only for half a second. Are we expecting an AGI to do the same?
Then again, transformers seem super-human in some ways already. Who do you know who can more or less recite and make associations from (even if not always intelligently) hundreds of billions of text fragments? Transformers already are better at math than your average human.
My bet is we’ll land in a weird place in between where these systems clearly have some superhuman intelligent capabilities but still are far from “do everything better than humans”.
It's been a big problem for a while. The big Metaculus question about AGI has depends on the game "Montezuma's revenge" (!), and there have been many debates about this going back to at least 2020: https://www.metaculus.com/questions/3479/date-weakly-general...
Is seducing someone a cognitive task? In a way I guess it is but often there are a lot of meatspace factors at play as well.
Maybe a bit off topic but your comment made me wonder.
I think generally we don’t have a good definition of what intelligence is.
Which humans? "Humans" are not fungible objects, no matter what the gray-wool-suit set says. The LLMs are already replacing human workers on the bottom of the food chain. Are they perfect? No. Are the humans they are replacing perfect? No. At that point it becomes about tradeoffs.
If AGI is "better at every human at everything" that is ASI, which is a different breed of cat.
You make a good point that not having it is easy to spot. But what precisely would flip the switch?
Seducing someone for example, how often would that have to work? On all people? Maybe that was just thrown out as an example but it points to how subjective these goal posts are.
Well, the 2nd one requires a human form (I think? Or at least video), and the 3rd one requires robotics.
By the 3rd example we won't have AGI until we have plumber-level robotics, and by the 2nd example we won't have AGI until the plumber is really hot.
It's not just that, it can also learn without having to be retrained. Which goes back to the issue, the real issue is people like Scam Altman can claim AGI is near, but then later say "well my view of AGI was that it is x, y and z" if not pressed to define what they think AGI is in that exact moment they're commenting on AGI, they can just later redefine it.
Given your definition what's the difference between AGI and superintelligence?
AGI should at least match, not surpass humans in every cognitive task.
I really liked Dario's metaphor that in the 80's, we could have said someday we'll have "supercomputers", which can do all the calculations we did except WAY faster. When, in reality, the AI's just get smarter over time, even if the frontier is jagged. AGI is just vibes only for "smart enough, consistently enough".
AGI means no needing to retrain the model, it should be able to learn on the fly. That's the true meat of AGI. Any CEO or exec saying any remark about AGI should be forced to define what their definition of AGI is in that moment, or be completely shunned by the industry, since it seems they can just reframe what they meant by AGI later if they don't define it in that moment.
So it's not a human intelligence. The transformer works very differently. We're trying to emulate human intelligence on a very different architecture.
Although, for the most part, what we actually seem to care about is that the job gets done. It's just that all the training data we have is "guy shaped" (linear), not transformer shaped. We haven't actually figured out how to train a transformer yet.
Amodei still predicts 2028, the same year when we'll have full self driving and Mars settlements.
So far all he has is this little code stealing application that could be replaced by git clone and sed for stripping the license.
The times before the Internet when Scientology people had to go into the streets to recruit people were nice. I wish we could put him and his ilk on some Claudology remote island, cut all Internet cables and enjoy the world without dorks and criminals that have been given a megaphone.
The timelines you’re quoting are too short but “ai is just git clone and sed” is not a good faith argument in 2026.
Any chance there is a prediction market for this that we can use, since research has shown they tend to be more accurate than experts?
It's a specific subtype of Gell-Mann Amnesia effect. They "know" AI cant do their own jobs, but it seems pretty good at summarizing what others do without understanding the nuance. It seems to really apply to the AI Lab CEOs who appear "shocked" everyone isn't simply replaced with LLMs by now so the timelines get kicked.
i like this and i think it's adjacent to gell-man effect rather than a subtype of it. Any CEO claiming AGI is here will never say, "AGI is here because it can automate what I do today." They are saying AGI is here because they have some loose understanding of what other humans seem to be doing and think the LLMs can also do this. In a way it also makes me think that these CEOs are kind of operating like LLMs - they sound confident, they don't have the full nuanced picture (its impossible to have a nuanced understanding of everything), they are not doing the actual labor that they want to replace.
That is an absolutely beautiful infographic and should become the standard for time series change!
Thank you! Tok me a few hours, without Claude Code I don't think I would have even attempted this.
somewhat relevant longread:
https://paoloanzn.github.io/2026/04/26/agi-will-always-be-on...
The article misses an important clarification for a general audience: current LLM architecture is not AGI by most scientists working on intelligence and cognition, even if its impact is already extraordinary and in many tasks exceeds human performance. AGI implies a broader set of traits.