ECON OF WORK - Private feed for review
Season 1
by Cole Wagner
Five years after the pandemic reshaped where and how we work, where have we actually landed? In this episode, Ben sits down with Nick Bloom, professor of economics at Stanford and the world's leading researcher on remote and hybrid work. Drawing on surveys of hundreds of thousands of workers across many countries,Nick unpacks what the data actually shows about productivity, innovation, and the future of the office. Topics covered: Where work-from-home rates have settled since the pandemic peak Why culture, not technology, explains why some countries went back to the office while others didn't The "personal trainer effect": why being in the same room still matters for concentration and collaboration Why forcing people back five days a week drives up attrition by a third and costs firms more than they save The U-shaped relationship between age and office preference, and what it means for how companies should design their policies What good management actually looks like, and why so much bad management persists even when better practices are well understood The "donut effect": how hybrid work is reshaping cities and suburbs About Nick Bloom: Nick Bloom is the William D. Eberle Professor of Economics at Stanford University and co-founder of WFH Research. He is one of the most cited economists in the world on the subjects of management practices, and the future of work, and is the author of the upcoming book The Triple Win. Follow Nick on LinkedIn and on X Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
What makes a transaction repugnant? And why does society allow some controversial markets to flourish while banning others that seem far less harmful? In this episode, Ben sits down with Al Roth, Nobel laureate and professor of economics at Stanford, to explore the hidden moral architecture beneath the markets we take for granted, and the ones we don't allow at all. Drawing on his new book Moral Economics, Al makes the case that good policy can't be built on moral intuition alone. Topics covered: What "repugnant" actually means in relation to transactions Surrogacy, gene editing, and AI companions: where the line between protection and paternalism blurs The coercion vs. exploitation distinction: is banning a market for poor people's benefit sometimes just denying them an opportunity? How public opinion and legislation diverge Why labor markets are fundamentally different from commodity markets How the internet (and now AI) has flooded job markets with applications and destroyed the information value of applying What the economics job market's "signaling" system can teach LinkedIn, dating apps, and corporate hiring alike Al Roth is a Professor of Economics at Stanford University and a recipient of the Nobel Memorial Prize in Economic Sciences. He is the author of Who Gets What — and Why and Moral Economics, and is one of the world's leading researchers in market design and matching theory. Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
The backlash against AI among young people appears codependent as they rely on it for more and more, while also being some of its most vocal opponents. In this episode, Ben sits down with Jasmine Sun, writer and journalist covering the AI economy, to explore what youth sentiment toward AI reveals about jobs, inequality, and the world being built around us. Topics covered: Why young people's hostility to AI is rational and what it has to do with affordability, distrust of institutions, and declining faith in upward mobility How AI sentiment differs across countries The Silicon Valley worldview: why the push for AGI and generality reflects specific beliefs about human limitations and who gets to build the future The "permanent underclass" concept: what has to be true for it to happen, why some in AI actually believe it, and why even the moderate version is alarming Today's teenagers as the first true "AI Generation" About Jasmine Sun: Jasmine Sun is a writer and journalist focused on the economics of AI, work, and inequality. She writes on Substack and has reported extensively on how AI is reshaping early career labor markets, Silicon Valley culture, and the future of economic mobility. Check out Jasmine on Substack and X Follow Ben on LinkedIn Follow us on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
What does it take to connect the worlds of academia, government, and industry around workforce development? The answer requires someone fluent in all three. In this episode, Ben sits down with Rachel Lipson, researcher at Harvard, fellow at Brookings and the Aspen Institute, and author, to explore what's working (and what isn't) in America's approach to training workers for the jobs of today and tomorrow. Topics covered: Why business, education, and policy need to operate together The CHIPS Act as a workforce policy outlier: how it flipped the government's default from antipoverty lens to competitiveness and innovation Why demand-side workforce policy is so rare in the U.S. The Tesla and Austin Community College case: how a company-college partnership went from a soft PR commitment to a proven productivity driver Why subsidizing firm-specific training isn't a corporate giveaway What community colleges can offer that on-the-job hiring can't How to think about preparing the next generation for jobs that don't exist yet About Rachel Lipson: Rachel Lipson is a researcher and policy entrepreneur working across Harvard, Brookings, and the Aspen Institute, with a focus on workforce development, social mobility, and the relationship between education and economic opportunity. She is the author of a forthcoming book examining how employers and training institutions can better work together to build a skilled workforce. Follow Rachel on LinkedIn Follow Ben on LinkedIn Follow us on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
What actually happens to a job when AI touches it? The answer depends on something most frameworks aren't designed to measure. In this episode, Ben sits down with Daniel Rock, assistant professor at Penn and co-founder of Work Helix, to dig into AI exposure. Topics covered: What "AI exposure" actually measures Why tasks are "assemblages," not atoms, and what that means for how we think about job change The task chaining paper: why the sequence in which tasks are automated matters as much as which tasks get automated Why the handoff costs of breaking work into steps also have handoff benefits and when human checkpoints create value rather than friction Jobs as equilibrium objects: why there may never be a complete theory of how tasks get bundled into jobs, and what we can learn from the attempt What academics can learn from entrepreneurs and vice versa Why the green shoots of AI's impact on science and medicine point toward something much bigger than productivity gains at work About Daniel Rock: Daniel Rock is an assistant professor at the Wharton School of the University of Pennsylvania and co-founder of Work Helix. His research sits at the intersection of economics, organizational behavior, and artificial intelligence, with a focus on how technology changes work and how firms can measure and manage that change. Follow Daniel on LinkedIn Papers discussed: Weak Bundle, Strong Bundle Task Chaining Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
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 here Follow Isabella on LinkedIn Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
Finance has net present value. Operations has bottleneck theory. What does HR have? In this episode, Ben sits down with John Boudreau, professor emeritus at USC and one of the most influential thinkers in the history of human resource management, to explore a question he has spent 40 years trying to answer: why do leaders who make rigorous, model-driven decisions about financial and operational assets continue to rely on gut instinct when it comes to people? Topics covered: Why leaders who would never make a capital investment without a discounted cash flow model routinely make multi-million dollar talent decisions on instinct The bottleneck problem: why investing equally in every role is as irrational as improving every stage of a production line simultaneously Why economists were studying tasks long before HR was Why AI pilots aren't experiments: the difference between watching what happens and actually measuring it What it would take to create generally accepted principles for people decisions, and why codified principles matter more than codified measures Why the word "job" may be the single biggest obstacle to clear thinking about the future of work About John Boudreau: John Boudreau is Professor Emeritus and Senior Research Scientist at the University of Southern California's Marshall School of Business. He is the author of more than 200 articles and ten books, including Beyond HR, Retooling HR, and Reinventing Jobs, and is widely regarded as one of the founding thinkers of the people analytics movement. Follow John on LinkedIn Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
Most conversations about the future of work focus on technology. This one focuses on people. In this episode, Ben sits down with Alexis Fink, organizational psychologist and president of SIOP — the Society for Industrial-Organizational Psychology — to explore what over a century of behavioral science actually tells us about how work gets done, why organizations succeed or fail, and what leaders get dangerously wrong about their own people. Alexis brings a perspective that's rarely heard in economics conversations: one grounded not in incentives and market forces, but in how humans actually behave under pressure, in teams, and inside complex organizational systems. Topics covered: What industrial-organizational psychology is Why meetings are almost always done wrong and what a genuinely good one looks like Why AI adoption metrics are measuring the wrong metrics The elevator analogy: why the real potential of AI isn't doing existing things faster, it's enabling things that were never possible before "Brain fry" and cognitive vigilance, and how pushing people harder with AI tools may produce diminishing, even negative, returns Why strategic workforce planning is one of the most underrated practices in business, and why so few companies actually do it well About Alexis Fink: Alexis Fink is an organizational psychologist, people analytics leader, and president of SIOP, the Society for Industrial-Organizational Psychology. She has led people analytics and workforce strategy functions at some of the world's largest tech companies and is one of the leading voices on the intersection of behavioral science, organizational design, and the future of work. Follow Alexis on LinkedIn Follow us on LinkedIn Follow Ben on LinkedIn Sign up for our Newsletter Visit our website for more information Get in touch with us at info@reveliolabs.com
In this episode, Ben sits down with David Autor, professor of economics at MIT, to explore how technology transforms work at every level from individual tasks to entire industries. Topics covered: Why transformative technologies require organizational reinvention, not just adoption The "expertise framework": how the same automation can be a force multiplier for one worker and a threat to another, depending on where their specialized skills sit Occupational licensing as a double-edged sword: consumer protection vs. a barrier to adaptation Whether AI will complement high-skilled workers, substitute for low-skilled ones, or eventually do both, and what the evidence actually shows so far The skills that will remain valuable Why we are dangerously under-invested in helping workers transition David Autor is Ford Professor of Economics at MIT and co-director of the MIT Work of the Future task force. He is among the most cited economists in the world on the topics of labor markets, inequality, and technological change.