# AGI may be a learner, not a finished mind

Markdown copy of https://realalpha.app/s/agi-may-be-a-learner-not-a-finished-mind/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Ilya Sutskever](https://realalpha.app/builders/ilya-sutskever/) · Ilya Sutskever is cofounder of SSI. · Co-founded Safe Superintelligence in 2024 and leads it as CEO, following earlier breakthroughs beginning with AlexNet and work at OpenAI.
- Host: [Dwarkesh Patel](https://realalpha.app/people/dwarkesh-patel/) · Host, Dwarkesh Podcast · Co-wrote The Scaling Era, an oral history of AI; named to TIME100 AI.
- Show: Dwarkesh Podcast
- Episode: [Ilya Sutskever – We're moving from the age of scaling to the age of research](https://realalpha.app/v/aR20FWCCjAs/), Nov 25, 2025
- Moment: 47:14 to 54:04 of the episode (7 min), playing on https://realalpha.app/s/agi-may-be-a-learner-not-a-finished-mind/; the episode from there: https://realalpha.app/v/aR20FWCCjAs/?t=2834

## Quote

> “a human being lacks a huge amount of knowledge instead **we rely on continual learning**”
> — [Ilya Sutskever](https://realalpha.app/builders/ilya-sutskever/) at [49:47](https://realalpha.app/v/aR20FWCCjAs/?t=2987) · Dwarkesh Podcast

## Takeaways

1. AGI need not arrive with every skill already mastered.
2. Deployment could involve a trial-and-error learning period.
3. Broad deployment may produce rapid, uneven economic growth.

## About this segment

Ilya Sutskever argues that a human-level learner could enter work before knowing every job. Deployment could let it build skills continuously, potentially driving rapid economic growth.

From “Ilya Sutskever – We're moving from the age of scaling to the age of research”, Nov 25, 2025. [Watch the full episode](https://realalpha.app/v/aR20FWCCjAs/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[47:27](https://realalpha.app/v/aR20FWCCjAs/?t=2847) So one of the things, so I'll give you another example of how language affects thinking. And in this case, it is going to be two words, two words that have shaped everyone's thinking, I maintain. First word, AGI. Second word, pre-training. Let me explain. So the term AGI, why does this term exist? It's a very particular term. Why does it exist? There's a reason. The reason that the term AGI exists is, in my opinion, not so much because it's like a very important essential descriptor of some end state of intelligence.

[48:11](https://realalpha.app/v/aR20FWCCjAs/?t=2891) but because it is a reaction to a different term that existed and the term is narrow ai if you go back to ancient history of game plane ai of checkers ai chess ai computer games ai everyone would say look at this narrow intelligence sure the chess ai can beat casper off but it can't do anything else it is so narrow artificial narrow intelligence so in response as a reaction to this some people said well this is not good it is so narrow what we need is general ai general ai an ai that can just do all the things the second and and that term just got a lot of traction yeah the second thing that got a lot of is pre-training.

[49:07](https://realalpha.app/v/aR20FWCCjAs/?t=2947) Specifically, the recipe of pre-training. I think the way people do RL now is maybe undoing the conceptual imprint of pre-training. But pre-training had the property. You do more pre-training and the model gets better at everything, more or less uniformly. General AI. Pre-training gives AGI. but the thing that happened with AGI and pre-training is that in some sense they overshot the target because by the kind if you think about the term AGI you will realize and especially in the context of pre-training you will realize that a human being is not an AGI because a human being yes there is definitely a foundation of skills a human being a human being lacks a huge amount of knowledge instead we rely on continual learning we rely on continual learning and so then when you think about okay so let's suppose that we

[50:13](https://realalpha.app/v/aR20FWCCjAs/?t=3013) achieve success and we produce a safe super some kind of safe super intelligence the question is but how do you define it where on the curve of continual learning is going to be i produce like a super intelligent 15 year old that's very eager to go and you say okay I'm going to they don't know very much at all the great student very eager you go and be a programmer you go and be a doctor go and learn so you could imagine that the deployment itself will involve some kind of a learning trial and error period it's a process as opposed to you drop the finished thing okay i i see so you're you're suggesting that the thing you're pointing out with super intelligence is not some finished mind which knows how to do every single job in the economy because the way say the original i think open ai charter or whatever defines agi is like it can do

[51:10](https://realalpha.app/v/aR20FWCCjAs/?t=3070) every single job that every single thing a human can do you're proposing instead a mind which can learn to do every single job. Yes. And that is super intelligence. But once you have the learning algorithm, it gets deployed into the world the same way a human laborer might join an organization. And it seems like one of these two things might happen. Maybe neither of these happens. One, this super efficient learning algorithm becomes superhuman, becomes as good as you and potentially even better at the task of ML research.

[51:49](https://realalpha.app/v/aR20FWCCjAs/?t=3109) And as a result, the algorithm itself becomes more and more superhuman. The other is, even if that doesn't happen, if you have a single model, I mean, this is explicitly your vision. If you have a single model or instances of a model which are deployed through the economy, doing different jobs, learning how to do those jobs, continually learning on the job, picking up all the skills that any human could pick up, but actually picking them all up at the same time and then amalgamating the learnings.

[52:16](https://realalpha.app/v/aR20FWCCjAs/?t=3136) You basically have a model which functionally becomes super intelligent, even without any sort of recursive self-improvement in software. Right? Because you now have one model that can do every single job in the economy. And humans can't merge our minds in the same way. And so do you expect some sort of intelligence explosion from broad deployment? I think that it is likely that we will have rapid economic growth.

[52:40](https://realalpha.app/v/aR20FWCCjAs/?t=3160) i think the broad deployment like there are two arguments you could make which are conflicting one is that look if indeed you get once indeed you get to a point where you have an ai that can learn to do things quickly and you have many of them then they will then there will be a strong force to deploy them in the economy unless there will be some kind of a regulation that stops it which by the way there might be but i think the idea of very rapid economic growth for some time i think it's very possible from broad deployment the other question is how rapid it's going to be so i think this is hard to know because on the one hand you have this very efficient worker on the other hand there is the world is just really big and there's a lot of stuff and that stuff moves at a different speed but then on the other hand now the ai could you know so i think

[53:48](https://realalpha.app/v/aR20FWCCjAs/?t=3228) very rapid economic growth is possible and we will see like all kinds of things like different