That's not the definition they have been using. The definition was "$100B in profits". That's less than the net income of Microsoft. It would be an interesting milestone, but certainly not "most of the jobs in an economy".
It ties the definition to economic value, which I think is the best definition that we can conjure given that AGI is otherwise highly subjective. Economically relevant work is dictated by markets, which I think is the best proxy we have for something so ambiguous.
And then I think coming up with the right metric is just as subjective on this field as the technological one.
Deep scientific discoveries are also cognitively demanding, but are not really valued (see the precarious work environment in academia).
Another point: a lot of work is rather valued in the first place because the work centers around being submissive/docile with regard to bullshit (see the phenomenon of bullshit jobs). You really know better, but you have to keep your mouth shut.
e.g. average cost to complete a set of representative tasks
Huh. Source? I mean, typical OpenAI bullshit, but would love to know how they defined it.
Wow. Maybe they spelled it out as aggregate gross income :P.
Apple, Alphabet, Amazon, NVIDIA, Samsung, Intel, Cisco, Pfizer, UnitedHealth , Procter & Gamble, Berkshire Hathaway, China Construction Bank, Wells Fargo, ...
A self-running massive corporation with no people that generates billions in profit, no matter what you call it, would completely upend all previous structural assumptions under capitalism
That's a relevent aspect of the AGI concept.
[0] https://techcrunch.com/2024/12/26/microsoft-and-openai-have-...
if you think drone targeting in Ukraine is scary now, wait until AGI is on it...
ditto for exploiting vulns via mythos
I responded to the below quoted question you dumb fuck. Can you figure out basic website navigation. Or is that too complex for you?
----- ' They redefined AGI to be an economical thing Huh. Source? I mean, typical OpenAI bullshit, but would love to know how they defined it. '
BTW I didn't downwote you (I hate it, if many people downvote a comment it's harder to read), I was just trying to explain why others did. On second thought, my comment was wrong, because your answer was related to the question but it wasn't really the intended one.
I don't think your original comment deserve to be downvoted. (Calling someone illiterate, on the other hand.)
But the "it" I was asking about was "AGI" as "an economical thing." You technically correctly answered how OpenAI defines AGI in public, i.e. with no reference to profits. But it did not address the economic definition OP initially alluded to.
For what it's worth, I could have been clearer in my ask.
But originally I was just trying to be helpful by quoting their charter on what they consider "agi" now.
"OpenAI has only achieved AGI when it develops AI systems that can generate at least $100 billion in profits."
Given that the definition of AGI is beyond meaningless, it is clear that the "I" in AGI stands for IPO.
[0] https://finance.yahoo.com/news/microsoft-openai-financial-de...
I don't get why HN commenters find this so hard to understand. I have a sense they are being deliberately obtuse because they resent OpenAI's success.
The current estimation on the time between this is fairly small, bottlenecked most likely by compute constraints, risk aversion, and need to implement safeguards. Metaculus puts it at about 32 months
https://www.metaculus.com/questions/4123/time-between-weak-a...
I don’t really buy into the ”one part equals another”, we are very quick to make those assumptions but they are usually far from the science fiction promised. Batteries and self driving cars comes to mind, and organic or otherwise crazy storage technologies, all ”very soon” for multiple decades.
It’s very possible that white collar jobs get automated to a large degree and we’ll be nowhere closer to AGI than we were in the 70’s, I would actually bet on that outcome being far more likely.
The leap between Opus 4.7/GPT 5.5 and what would be sufficient for AGI seems smaller than the leap between The invention of the Transformer model (2017) and today, thus by a very conservative estimate I think it will take no more time between then and now as it will between now and an AI model as smart as any human in all respects (so by 2035). I think it will be shorter though because the amount of money being put into improving and scaling AI models and systems is 100000x greater than it was in 2017.