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159 turns · 41 min[upbeat music] Is AI gonna cause a white-collar bloodbath, or is that narrative itself part of the problem? And does the way we talk about technology actually shape the technology that ends up being built? My guest today is Isabella Loaiza, researcher at MIT, whose work challenges some of the most critical assumptions about how AI will affect workers and jobs. Here's our talk. [upbeat music] Welcome to The Economics of Work. I'm excited to welcome Isabella Loaiza. Did I get that right?
Yes, that's correct.
Whoo. Nice. Isabella has been influential in changing the debate and adding very important nuance, I think, to the debate about how AI is affecting work. So Isabella, welcome to the show.
Thank you so much for having me, Ben. I'm so excited to be here.
Yeah. So, so, so let's break down this debate about how AI is changing work and what you don't like about it. So feel free to throw some stones. What are we getting wrong in how we talk about AI and jobs, AI and work?
Oh, wow. So many things, uh, to discuss here, right? So I think just to begin, I will choose two different things that I often think about. One of them is the narrative about how AI is going to replace all workers, and it's going to be a white-collar bloodbath kind of situation. That's the one thing I think we're getting mostly wrong for two reasons. One of them is that, first of all, technology will do what we want it to do. So if we build for replacement, then that's what we're going to get. If we build for augmentation, then we are probably going to get some augmentation. And yes, definitely some roles will be replaced because that happens with all technological changes. But, um, how we design the technology is going to have a very large impact on how we actually see it having an effect in the labor force.
Yeah. Yeah. Let me, let me, [laughs] let me drill into both of those points.
Sure.
So, so really interesting points, but, but I promise we'll, we'll circle back. So this idea of like, you know, it being a white-collar bloodbath, that's more like a-- I mean, that's a prediction-
Yes
... which I also think is probably wrong, but I'd love to find out like where, where in the chain of reasoning is that wrong? And then on the other thing, I just wanna note it to maybe circle back to it, this idea of, of us being in control of what we build and how, how the technology that we build will affect jobs, that sounds hard. I mean, it's not organized by some central planner. There's a lot of forces which determine like how the technology will evolve. I wonder if that is in anyone's control.
Mm-hmm.
You can answer those or circle back, whatever you want.
I agree with you. I mean, technology is not centrally designed, and, uh, we're happy for that not to be the case because we want innovation everywhere. We want people to be able to create and build things that they think are necessary. I'm a big fan of that for sure. However, I guess from my position being at MIT and also having gotten my PhD at the Media Lab, I very closely work with people who actually build the technology. So because I'm in this environment where a lot of these folks are the ones who are creating the ideas and potentially
Episode notes
The dominant framework for measuring AI's impact on jobs may be asking the wrong question entirely.
In this episode, Ben sits down with Isabella Loaiza, economist and researcher at MIT, to challenge some of the most widely accepted assumptions in the AI and work debate. From the concept of "exposure" to the narrative of an incoming white-collar bloodbath, Isabella makes the case we're missing a big part of the picture around AI.
At the center of the conversation is EPOCH, a framework Isabella developed with her coauthor Roberto to capture the human capabilities that AI is least equipped to replace: Empathy, Presence, Opinion, Creativity, and Hope.
Topics covered:
- Why the "white collar bloodbath" narrative gets AI wrong
- The difference between automation and augmentation
- Why "exposure" should probably be called "automation potential"
- The EPOCH framework around AI and its relation to humanity
- The burnout problem: why offloading routine tasks to AI may eliminate the cognitive rest that knowledge workers rely on to sustain performance
- Whether AI empathy is real empathy
- Dream jobs vs. meaningful jobs: why they're not always the same thing
- Why measuring task content across countries matters and what a global labor market taxonomy might actually need to capture
Find [the paper by Isabella Loaiza and Roberto Rigobon about the EPOCH framework