AI Just Gave You Superpowers — Now What?

**Eddy Lazzarin** (0:00)
You've just been told you have superpowers. You've just been told you can have multiple employees for $200 a month. What do you do? If I was a young person today, starting off my career, I would try to convince my parents to give me some money to harness a huge swarm of computers and see like, can I spend $5,000 of compute productively? That's the challenge. We've been talking about a meme sort of in tech world for years now, the idea of like the one person billion dollar startup, right?

**Christian Catalini** (0:28)
Yeah.

**Eddy Lazzarin** (0:28)
Is this not how that happens? What we're describing is exactly how that happens. There's a new surplus, learn to exploit it. That is the lesson for a young person.

**Christian Catalini** (0:37)
The apprenticeship might be dead, but the real work is beginning.

**Robert Hackett** (0:47)
Hi everybody, we're here with Christian Catalini, who is the co-founder of LightSpark and founder of the MIT Crypto Economics Lab, as well as Eddy Lazzarin. And we're here to talk about a new economics paper that Christian published, called Some Simple Economics of AGI. Christian, I think the title of this paper is slightly misleading, in that it's actually not incredibly simple. It's more than 100 pages long, and there are many complex mathematical formulae involved. Maybe some of the insights you've managed to distill down into a simple kind of framework for people to understand things. But over the course of 100 pages, there is a lot of complexity also in your analysis. So I'd love to ask, what began you on this journey to investigate the economic relationship of AI and the world we live in right now, the robots and the humans?

**Christian Catalini** (1:41)
Yeah, I would say it was born like probably many others at the same time out of a semi-existential crisis. We're grappling with the fast pace of progress and just how quickly everything is moving. I'm an optimist, so I look at all of this and can see at the end of the arc, really amazing things. But the fundamental question was like, what are we going to do? What should we focus on? What's worth of our attention, effort, and time, especially in this phase where we still, I think, have a meaningful shot at influencing the trajectory and really the technology. So we wrote actually some months ago, a piece on measurement. And the basic idea of that piece was like, anything that can be measured will be automated, which doesn't sound like good news. But this second paper was really centered around, okay, if that is true, let's take that initial assumption to the limit. What would the economy look like?
What will the nature of labor look like? What should startups do? What should incumbents do? And essentially, what will the future look like? Now, we did a similar exercise back in 2013 when I went down the crypto rabbit hole. We wrote A Simple Economics of the Blockchain. The simple in the title is just a trick. If you make it too intimidating, people will not read it. But very much like that time, look, some things will be right, some things will be wrong. Hopefully, we got it directionally right. And part of the exciting phase right now is, it's in the wild, and people are seeing what resonates and what doesn't.

**Robert Hackett** (3:10)
Even so, you have managed to distill down the findings in a way that people can get a handle on pretty reliably well. You even have little short branded ways of understanding the existential crises that we all face, such as the codifiers curse and several other of these kinds of labels that you've invented to describe the world we're entering. Let me just ask you though, you said this stemmed from an existential crisis. How are you feeling psychologically? What is your state?

**Christian Catalini** (3:40)
Great.

**Robert Hackett** (3:40)
Are you sweating? Are you happy? You feel good?

**Christian Catalini** (3:44)
Absolutely.
I think it was a long journey. It was many, many months of kind of thinking about some of these fundamental concepts. It came out, and I think my coders too, with a feeling that first of all, this is a technology that is under our control, still at this point. Second, the upside, as I already kind of hinted at, is many orders of magnitude greater than what the do-mers would want you to believe. Third, I think there's a playbook. There's a playbook that all of us can look at. We can think about where are we adding value? What are the sort of things that we do within our job? Jobs tend to be bundles of different tasks, and people get always very nervous when certain tasks, or certain parts of their job get automated. I think right now coding is going through that experience where there are many talented individuals that have identified as developers, that have written elegant, fantastic code over the last few decades, look and say, oh wow, this is doing what I do. I think that's both true and not true. In a sense, as we kind of surface in the paper, these tools, which for now are tools, but I think will become a lot more than just simple tools, are taking out the groundwork, they're taking out a lot of the exploration within what is known. But we're still, I think, at the top, thinking through, okay, well, what is not known? Where can we push beyond the boundaries of what's being recorded, measured, digitized? And so those decisions, although they seem smaller, I think they have much higher leverage than we had before.