DIGITAL TRANSFORMATION

FROMAN: End of last year you wrote an essay, Machines of Loving Grace, that talked about some of the upside. How one could achieve a decade's worth of progress in biology, for example, in a year, how the machines were going to be as smart as all the Nobel Prize winners, which probably depresses some of them. Tell us the upside. Tell us your best-case scenario as to what AI is going to produce.

AMODEI: Yeah. So I go back by starting with the exponential. You know, if we go back to 2019, the models were barely able to give a coherent sentence or a coherent paragraph. People like me, of course, thought that was an amazing accomplishment that models were not capable of. And, you know, we had these predictions that five years from now, you know, the models are going to be generating billions of dollars of revenue. They're going to be helping us code. We can talk to them like they're—like they're human beings. They'll know as much as human beings do. And there were all these unprincipled objections of why that couldn't happen.

You know, the same exponential trends, the same arguments that predicted that, predict that if we go forward another two years, three years, maybe four years, we will get to all of this. We will get to models that are as intelligent as Nobel Prize winners across a whole bunch of fields. You won't just chat with them. They'll be able to do anything you can do on a computer. Basically, any remote work that humans do, any modality, being able to, you know, do tasks that take days, weeks, months. The kind of evocative phrase that I used for it in Machines of Loving Grace was it's like having a country of geniuses in a datacenter. Like, a country of genius remote workers, which they can't do everything, right? There are restrictions in the physical world.

And I think this still sounds crazy to many people but, you know, look back on previous exponential trends. You know, look at—look at the early days of the internet and how wild the prediction seemed, and what actually came to pass. I'm not sure of this. I would say I'm maybe 70 or 80 percent confidence. You know, it could very well be that the technology stops where it is, or stops in a few months and, you know, the essays that I've written and things I've said, you know, in events like this, people spend the next ten years laughing at me. But that would not be my bet.

FROMAN: Let's just build on that one, because on the issue of jobs, and the impact that AI is likely to have on employment, there's a pretty big debate. Where are you on the spectrum—well, before I get there—how long will it take for AI, let's say, to replace the head of a think tank? I'm asking for a friend. (Laughter.) Actually, how—we'll get to that one. That's too—where are you on the spectrum of everyone's going to be able do some really cool things, and they're going to be able to do so many more things than they're able to do now, versus everyone's going to be sitting on their sofa collecting UBI?

AMODEI: Yeah. So I think it's going to be a really multifaceted mix of those two things, that also depends on the policy choices that we make.

FROMAN: You can also answer the think-tank question if you like, but—(laughter).

AMODEI: Yeah. So, I mean, I guess I didn't—I kind of, you know, ended my answer to the last question without saying all the great things that will happen. So honestly, the thing that makes me most optimistic, before I get to jobs, is things in the biological sciences—biology, health, neuroscience. You know, I think if we look at what's happened in biology in the last hundred years, what we've solved are simple diseases. Solving viral and bacterial diseases is actually relatively easy because it's the equivalent of repelling a foreign invader in your body. Dealing with things like cancer, Alzheimer's, schizophrenia, major depression, these are system-level diseases.

If we can solve these with AI at a baseline, regardless of kind of the job situation, we will have a much better world. And I think we will even—if we get to the mental illness side of it—have a world where it is at least easier for people to find meaning. So I'm very optimistic about that.

But now, getting to kind of the job side of this, I do have a fair amount of concern about this. On one hand, I think comparative advantage is a very powerful tool. If I look at coding, programming, which is one area where AI is making the most progress, what we are finding is we are not far from the world—I think we'll be there in three to six months—where AI is writing 90 percent of the code. And then in twelve months, we may be in a world where AI is writing essentially all of the code. But the programmer still needs to specify, you know, what are the conditions of what you're doing, what is the overall app you're trying to make, what's the overall design decision? How do we collaborate with other code that's been written? You know, how do we have some common sense on whether this is a secure design or an insecure design?

So as long as there are these small pieces that a programmer, a human programmer, needs to do, the AI isn't good at, I think human productivity will actually be enhanced. But on the other hand, I think that eventually all those little islands will get picked off by AI systems. And then we will eventually reach the point where, you know, the AIs can do everything that humans can. And I think that will happen in every industry. I think it's actually better that it happens to all of us than that it happens—you know, that it kind of picks people randomly. I actually think the most societally divisive outcome is if randomly 50 percent of the jobs are suddenly done by AI, because what that means—the societal message is we're picking half—we're randomly picking half of people and saying, you are useless, you are devalued, you are unnecessary.

FROMAN: And instead we're going to say, you're all useless? (Laughter.)

AMODEI: Well, we're all going to have to have that conversation, right? Like, we're going to—we're going to have to—we're going to have to look at what is technologically possible and say, we need to think about usefulness and uselessness in a different way than we have before, right? Our current way of thinking has not been tenable. I don't know what the solution is, but it's got to be—it's got to be different than, we're all useless, right? We're all useless is a nihilistic answer. We're not going to get anywhere with that answer. We're going to have to come up with something else.

FROMAN: That's not a very optimistic picture. (Laughter.) Is what it is?

AMODEI: I actually—I would actually challenge that. You know, I think about a lot of the things that I do—you know, I spend a lot of time, for example, swimming. I spend time playing video games. I look at, like, human chess champions. You know, you might think when Deep Blue beat Kasparov, and that was almost thirty years ago, that after that it would be, like, chess would be seen as a pointless activity. But exactly the opposite has happened. Human chess champions, like Magnus Carlsen, are celebrities. I think he's even, like, a fashion model. He's like this kind of hero.

So I think there's something there where we can build—we can build a world where, you know, human life is meaningful, and humans, perhaps with the help of AIs, perhaps working with AIs, build really great things. So I am not that—I am actually not that pessimistic. But if we handle it wrongly, I think there's maybe not that much room for error.