Dario Amodei

Economists are familiar with studying the productivity increases that came from the computer revolution and internet revolution. And generally those productivity increases were underwhelming. They were less than you might imagine. There was a quote from Robert Solow, you see the computer revolution everywhere except the productivity statistics. So why is this the case?

people point to the structure of firms, the structure of enterprises, how slow it's been to roll out our existing technology to very poor parts of the world, which I talk about in the essay, right? How do we get these technologies to the poorest parts of the world that are behind on cell phone technology, computers, medicine, let alone newfangled AI that hasn't been invented yet?

So you could have a perspective that's like, well, this is amazing technically, but it's all a nothing burger. I think Tyler Cowen, who wrote something in response to my essay, has that perspective. I think he thinks the radical change will happen eventually, but he thinks it'll take 50 or 100 years. And you could have even more static perspectives on the whole thing.

I think there's some truth to it. I think the timescale is just too long. And I can see it. I can actually see both sides with today's AI. So, you know, a lot of our customers are large enterprises who are used to doing things a certain way. I've also seen it in talking to governments, right? Those are prototypical, you know, institutions, entities that are slow to change.

But the dynamic I see over and over again is, yes, it takes a long time to move the ship. Yes, there's a lot of resistance and lack of understanding. But the thing that makes me feel that progress will in the end happen moderately fast is

not incredibly fast, but moderately fast, is that you talk to... What I find is I find over and over again, again, in large companies, even in governments, which have been actually surprisingly forward-leaning, you find two things. that move things forward.

One, you find a small fraction of people within a company, within a government who really see the big picture, who see the whole scaling hypothesis, who understand where AI is going, or at least understand where it's going within their industry. And there are a few people like that within the current US government who really see the whole picture.

And those people see that this is the most important thing in the world, and so they agitate for it. And they alone are not enough to succeed because they're a small set of people within a large organization. But...

As the technology starts to roll out, as it succeeds in some places, in the folks who are most willing to adopt it, the specter of competition gives them a wind at their backs because they can point within their large organization. They can say, look, these other guys are doing this, right? You know, one bank can say, look, this newfangled hedge fund is doing this thing.

They're going to eat our lunch. In the U.S., we can say we're afraid China is going to get there before we are. Uh, and that combination, the specter of competition, plus a few visionaries within these, you know, within these, the organizations that in many ways are, are sclerotic, you put those two things together and it actually makes something happen. I mean, it's interesting.

It's a balanced fight between the two because inertia is very powerful, but, but, but eventually over enough time, the innovative approach breaks through, uh, And I've seen that happen. I've seen the arc of that over and over again. And it's like the barriers are there. The barriers to progress, the complexity, not knowing how to use the model, how to deploy them are there.

And for a bit, it seems like they're going to last forever, like change doesn't happen. But then eventually change happens and always comes from a few people. I felt the same way when I was an advocate of the scaling hypothesis within the AI field itself and others didn't get it. It felt like no one would ever get it. Then it felt like we had a secret almost no one ever had.

And then a couple of years later, everyone has the secret. And so I think that's how it's going to go with deployment to AI in the world. It's going to – the barriers are going to fall apart gradually and then all at once. And so I think this is going to be more – and this is just an instinct. I could easily see how I'm wrong.

I think it's going to be more like five or ten years, as I say in the essay, than it's going to be 50 or 100 years. I also think it's going to be five or ten years – more than it's going to be, you know, five or 10 hours. Uh, uh, because I've just, I've just seen how human systems work.

And I think a lot of these people who write down the differential equations who say AI is going to make more powerful AI who can't understand how it could possibly be the case that these things won't, won't change so fast. I think they don't understand these things.

It's almost... It's so... No, no. This was the feeling I had when I was writing it, that it's like, this would be such a beautiful future if we can just make it happen, right? If we can just get the landmines out of the way and make it happen. There's so much... There's so much... beauty and elegance and moral force behind it. And it's something we should all be able to agree on.

As much as we fight about all these political questions, is this something that could actually bring us together?

um but you were asking when when when when when do you think what's just so putting numbers on so you know this this is of course the thing i've been grappling with for many years and i'm not i'm not at all confident every time if i say 2026 or 2027 there will be like a zillion like people on twitter who will be like aico said 2026 2020 and it'll be repeated for like the next two years that like this is definitely when i think it's going to happen um so

whoever's exerting these clips will crop out the thing I just said and only say the thing I'm about to say. But I'll just say it anyway. Have fun with it. So if you extrapolate the curves that we've had so far, right? If you say, well, I don't know, we're starting to get to like PhD level. And last year we were at undergraduate level.

And the year before we were at like the level of a high school student. Again, you can quibble with at what tasks and for what. We're still missing modalities, but those are being added. Like computer use was added. Like image in was added. Like image generation has been added.