The Unexpected Economics of AGI - with Christian Catalini, Tech Founder and Co-Creator of Libra

**Christian Catalini** (0:00)
I do think there are things that are fundamentally non-measurable, and that's where things get interesting, and all the do-mers, I think, are wrong on the, okay, we're just going to be completely displaced. Think about deep science, deep tech, things that we haven't figured out yet.
A lot of this science discovery that's going to happen over the next few years, I think it's essentially AI finding recombinations of things that have already been mapped. If you think about all the possible permutations between different disciplines, different topics, humans have only explored a tiny percentage of that, so the impact of AI on scientific discovery and innovation is going to be massive.
Hi everyone, I'm Christian Catalini. I'm a tech founder. We're roots in academia. Tried to break the financial system with a little project called Libra out of Facebook a few years ago. Then launched a startup called LightSpark, which is focused on using crypto for cross-border payments. Over the last year and a half, I've been obsessing like everybody else about AI and what this all means for society. We recently released a paper that tries to grapple with the question of, now that we're close to AGI, what does it mean for all of us? I look forward to discussing it with all of you today.

**Jeremy Utley** (1:01)
You mentioned the paper on AGI. For folks who aren't familiar, what's the thesis in a nutshell?

**Christian Catalini** (1:08)
Yeah. The paper was born out of an existential crisis. I've been being in crypto for more than a decade.
It was a very natural moment to look back and say, was this all for nothing? If you look at the landscape of where most of payments in crypto is going, it's getting more and more boring. In a sense, it's like a wave of enterprise sales. You have banks and other traditional financial institutions connecting to these networks. It's getting more concentrated, not less like in the original crypto days. It was really clear that AI would transform everything. It's going through every sector of the economy and really reshaping how it can be built, how it can be operated.
We had this question around, if we're near AGI and people have all conflicting definitions, but my favorite one is very simple. It's essentially something that is as good as human for most tasks, is a very useful type of intelligence. It may not be exactly like us, which is fine, but it's a peer of sorts. I do think we're relatively close to that. If you take that as a given, then the next question is, okay, what does it mean for society, for the economy, for things that we should be paying attention to, things that are going to be defensible, things that are not going to be defensible? The paper is almost like it's too applied for academics at this point, and probably too theoretical for people tinkering with open clause and the like. It was an attempt at really teasing out core economic principles behind this transformation. There's a lot of economic work in this area, but it tends to be a bit too detached from reality. I mean, that's why back in 2013, we wrote The Simple Economics of the Blockchain. Same idea, right? So you have this fuzzy new object that's coming your way. Economies are really bad at predictions, but they're good at isolating the fundamental forces behind some of these transformations. So the paper is just an attempt at saying, look, we're not going to get 100 percent of this right, but can we get 70 or 80 percent of this right on at least what the economic forces are?
What we concluded is that, look, intelligence is getting commodified, it's getting cheap, I think everybody agrees on that. But in economics, typically when something becomes cheap, something else becomes the bottleneck. It's exciting and depressing, which is like, oh yeah, age of abundance. Well, not quite yet. The bottleneck we identify in the paper is what we call verification. Now, we have a very precise definition of what verification is because there's a lot of cope going around. People talk about judgment, curation, taste as being kind of the holdouts for us humans.
We call it verification. And so I'm happy to unpack that, if useful.

**Jeremy Utley** (3:46)
Let's go, let's go. I mean, to me, two natural questions follow. One, how do you define verification? But two, why is verification not solvable by AGI as well?

**Christian Catalini** (3:56)
Yeah, absolutely. And those are excellent questions. So let me start with the first one and then we'll get to the second. We started from a very simple intuition, which it seems like most people building these models agree with, which is like, as soon as something can be measured, it can be automated. So AI can take over anything for which we have enough data, enough digital trails. You feel a lot of the progress over the last few years as being, we can also use AI to measure more things, which is amazing.