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I cannot express how annoyed I am a researcher could use such a shitty definition.
It only makes sense to say "most" if you have a clear idea of what constitutes the majority. "Most people are male" yeah, fine..50% + epsilon of humans are males. That's more or less decidable (maybe a little vague because of intersex folks). I believe it's false because there are slightly more females but it's obviously measurable.
Now, most cognitive labor...what does that mean? Is it most of the time? Most of the tasks? Most of the value? Most of the job descriptions?
If I am a developer, and the majority of my code is written by AI, but I'm still in the driver's seat, is that most of my cognitive labor? Probably not. Ok, what if my company fires 60% of its developers, does that mean most development cognitive labor is automated? Well, it's most of the expense, and most of the butt in chair time, and it's most of the individual jobs, but it's not most of the job descriptions.
Of course, there's no way that all these researchers making pronouncements are giving consistent answers to what they mean by "most". They're probably not using his phrasing either.
Edit: The four options I threw out above: time, tasks, value, job descriptions are each interesting in their own way. My point is not that they're bad questions to be asking, it's that they're all separate questions that matter in different ways.
Most of the time? Well it includes the word most, so yes.
Most of the tasks? Well it includes the word most, so yes.
Such is a common way of writing. Think of it as a kind of compression. Researchers consider the rhetoric more than you want to give them credit for with your knee jerk "I don't personally understand so these researchers are idiots" ad hominem.
Models do contain a mathematical happy path to answer most questions that have been asked and answered when the model was trained. The issue is not whether those answers exist but finding them. That's what the bulk of the bleeding edge of model work is focused on atm.
Isn't that like 90% of project plans by pre-LLM managers too?
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
Sarcasm aside, I am just so tired.
Look, you might be the most knowledgeable person on this planet but we cannot verify. Your “report” might be brilliant and you are just a dinosaur. I mean, it’s impossible to tell.
Also, deciding on whether something is a good idea for the business is not a thing LLMs are currently trained for. We are busy automating development which will in turn accelerate all other automation. Deciding whether some line of thinking is a good idea does not strike me as particularly outside the range of capabilities we are witnessing them exhibiting today.
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.
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
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?
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
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.
They get this much money not because their work is worth this much. It's just how the system is set up.
AGI couldn't be CEO BECASUE it can't receive millions of dollars in compensation the same way potato can't. Getting this much money is what the CEO does. Apart from that they do very little when summed up.
Now, if you have an AGI that can reliably and repeatedly do Einstein-level science, then I'd argue that we're starting to talk about ASI, aka, "superintelligence." Which would be providing something that humans can't consistently produce at any cost. So cost becomes much less relevant.
But if the best you can do is replace an ordinary smart human for $5 million/year, you have to compete with ordinary smart humans. Who are abundant and who very rarely cost more than $500,000/year, if you're willing to shop around and gamble a bit.
(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.
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 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.
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 :)
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”.
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.
If AGI is "better at every human at everything" that is ASI, which is a different breed of cat.
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.
That was never the concept (which predates LLMs).
AGI was something that can think like a human.
Not necessarily better, and not necessarily do everything any human can do.
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.
AGI should at least match, not surpass humans in every cognitive task.
Nothing in AGI implies "surpass humans in every cognitive task".
Not even "match in every cognitive task" is really required. There are humans that by definition have "general intelligence" that still don't match other humans "in every cognitive task", just in some.
Why should AGI need to match ALL humans in EVERY congitive task then? An AGI just needs to be as good as an average (or even slightly below average) human, in human-like cognition.
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.
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.
Research and problem-solving in these fields may still need cognitive work, but the day-to-day practice of jobs does not. AI will take all of that work soon.
You can also now use AI forecasters like FutureSearch [1] (disclaimer: I work there), which are competitive with the best humans / teams of humans. And since you aren't depending on a human crowd, you can ask any variation of AGI questions with any definition, even ask conditional questions.
[1] https://futuresearch.ai/app
https://paoloanzn.github.io/2026/04/26/agi-will-always-be-on...
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.
Even if LLMs become incredibly, undeniably brilliant 1000000 IQ, they cannot keep track of what's going across long horizons. Imagine a supergenius, but in Memento.
No amount of MD scribbling or embeddings will remove that limitation, but it may obfuscate it further and make it seem like progress is being made.
At the end of the day, being fully autonomous means that something can keep track of context, goals, complex and shifting relationships, over LONG time horizons without drift. If you need to be there to prompt, it is not truly automating. Until the continuity becomes real instead of simulated, context no longer has to compact, and weights update on-demand, you will always need a prompt wrangler leading the effort. And prompt wrangling is cognitive labor.