Ilya Sutskever – We're moving from the age of scaling to the age of research

Dwarkesh Patel (Host) 00:00.140

the finish thing. Okay, I I 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

Dwarkesh Patel (Host) 00:20.860

defines AGI is like it can do every single job that a every single thing a human can do. You're proposing instead a mind which can learn to do any single every single job. Yes. And that is super intelligence. And then but once you have the learning algorithm it gets deployed

Dwarkesh Patel (Host) 00:38.700

into the world the same way a human

Ilya Sutskever (Co-founder and Chief Scientist) 00:40.500

labor or might join an organization.

Dwarkesh Patel (Host) 00:43.180

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. And as a result, the algorithm itself becomes

Dwarkesh Patel (Host) 01:04.620

more and more superhuman. The other is, even if that doesn't happen, if you have a single model, I mean, this is explicit 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,

Dwarkesh Patel (Host) 01:18.660

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. You basically have a model which functionally becomes super intelligent even without any sort of

Dwarkesh Patel (Host) 01:33.440

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 like intelligence explosion

Dwarkesh Patel (Host) 01:45.080

from broad deployment?

Ilya Sutskever (Co-founder and Chief Scientist) 01:46.520

I think that it is likely that people have rapid economic growth.

Ilya Sutskever (Co-founder and Chief Scientist) 01:55.240

I think the broad deployment like there are two arguments you could make which are conflicting.

Ilya Sutskever (Co-founder and Chief Scientist) 02:04.560

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 for to deploy them in the economy unless there will be some kind of a regulation that

Ilya Sutskever (Co-founder and Chief Scientist) 02:27.840

stops it, which by the way there might be. But

Ilya Sutskever (Co-founder and Chief Scientist) 02:32.600

I think the idea of very rapid economic growth for some time, I think it's very possible from broad deployment.

Ilya Sutskever (Co-founder and Chief Scientist) 02:39.840

The other question is how rapid it's going to be?

Ilya Sutskever (Co-founder and Chief Scientist) 02:43.360

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.

Ilya Sutskever (Co-founder and Chief Scientist) 02:56.680

But then on the other hand, now the AI could you know, so I think very rapid economic growth is possible.

Ilya Sutskever (Co-founder and Chief Scientist) 03:02.400

And we will see like all kinds of things like different countries with different rules and the ones which have the friendlier rules, the economic growth will be faster. Hard to predict.

Dwarkesh Patel (Host) 03:12.160

Some people in our audience like to read the transcripts instead of listening to the episode. And so we put a ton of effort into making the transcripts read like they are standalone essays. The problem is that If you just transcribe a conversation verbatim using speech to text

Dwarkesh Patel (Host) 03:27.080

model, it'll be full of all kinds of fits and starts and confusing phrasing. We mentioned this problem to Label Box and they asked if they could take a stab. Working with them on this is probably the reason that I'm most excited to recommend Label Box to people. It wasn't just

Dwarkesh Patel (Host) 03:41.080

oh hey tell us what kind of data you need and we'll go get it. They walked us through the entire process from helping us identify what kind of data we needed in the first place to assembling a team of expert aligners to generate it. Even after After we got all the data back,

Dwarkesh Patel (Host) 03:54.160

Labelbox stayed involved. They helped us choose the right base model and set up auto QA on the model's output so that we could tweak and refine it. And now we have a new transcriber tool that we can use for all our episodes moving forward. This is just one example of how

Dwarkesh Patel (Host) 04:09.560

Labelbox meets their customers at the ideas level and partners with them through their entire journey. If you want to learn more or if you want to try out the transcriber tool yourself, go to labelbox.com/dwarkash.

Dwarkesh Patel (Host) 04:25.680

It seems to me that this is a very precarious situation to be in where looking the limit, we know that this should be possible because if you have something that is as good as a human at learning, but which can merge its brains, merge their different instances in a way that

Dwarkesh Patel (Host) 04:42.240

humans can't merge. Already, this seems like a thing that should physically be possible. Humans are possible. Digital computers are possible. You just need both of those combined to produce this thing. And it also seems like this kind of thing is extremely powerful and economic

Dwarkesh Patel (Host) 05:00.160

growth is one way to put it. I mean Dyson Sphere is a lot of economic growth, but another way to put it is just like you will have potentially a very short period of time because a human on the job can you know you you're hired people to SSI in six months they're like net

Dwarkesh Patel (Host) 05:12.480

productive probably, right? A human like learns really fast and so this thing is becoming smarter and smarter very fast. What is how do you think about making that go well? And why is SSI position to that one? What is the size plan there basically? So I'm trying to ask.