by Cole Wagner
The story of foreign aid is usually told as a story of insufficient resources. Bill Easterly thinks that misses the point. In this episode, Ben sits down with Bill Easterly, NYU economist and author of four books on development, to dig into why aid so often fails, and why the problem runs deeper than most people are willing to admit. Topics covered: From disappointment to concern: how Easterly's view of foreign aid evolved from "zero effect" to actively harmful, particularly through its support of autocrats The accountability problem: why aid agencies are accountable to US foreign policy goals rather than to the people they're meant to help, and how that shapes everything Why even well-intentioned private philanthropy ends up in the same traps The "benevolent autocrat" fallacy: why GDP growth under coercive regimes doesn't constitute development Why development as freedom, not just material income is the right framework, and what historical cases reveal about the limits of GDP-first thinking The problem with "we": who gets to be included in that word, what it conceals about power and paternalism Coercion vs. exploitation About Bill Easterly: Bill Easterly is a former World Bank economist, professor of economics at NYU and co-director of the Development Research Institute. Check out Bill’s books: The Elusive Quest for Growth The White Man’s Burden The Tyranny of Experts Violent Saviors 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
The standard debate about AI and jobs is a rich-country debate. But for billions of people in low- and middle-income countries, the stakes and the story look completely different. In this episode, Ben sits down with Deena Mousa, program officer at Coefficient Giving and writer on AI and global development, to explore what gets missed when we only think about AI's impact through a Western lens. Topics covered: Why there are still so many radiologists, and what the gap between benchmark performance and real-world clinical performance tells us about AI adoption more broadly How automation demand works in reverse: why making scans faster and cheaper expands the complexity of what radiologists are asked to do, not just the volume Why low-income countries are in a fundamentally different situation with both their access to AI and how it can address their needs. The service export threat: why the BPO and outsourcing sectors in countries like the Philippines and India may face a more immediate AI disruption than any manufacturing sector Why "supply creates its own demand" doesn't automatically work for development, and the Malawi example of why breakout growth is the exception, not the rule The difference between low-income and middle-income countries, and why bundling them into "LMICs" obscures the very different things they need to do Why some Western social progress movements can inadvertently harm the workers they're meant to protect About Deena Mousa: Deena Mousa is a program officer at Coefficient Giving, formerly Open Philanthropy, where she focuses on the intersection of AI and global development. She writes on Substack about how transformative technology reshapes economic opportunity across the world. Check out Deena’s substack Check out Deena on 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
The debate about immigration is usually framed around politics and labor markets. But the deeper story includes mobility. Who gets ahead, how, and why where they start determines so much about where you end up. In this episode, Ben sits down with Leah Boustan, professor at Yale and co-author of Streets of Gold, to dig into what the data on immigration reveals about economic opportunity across generations. Topics covered: Why immigration is an underappreciated driver of global mobility The Norway brothers study: why migrants earned double what their brothers who stayed earned, and why it had little to do with changing occupations What immigrants bring back when they return home and how that shapes sending countries How firms fit into immigrant assimilation Location and mobility: why immigrants have historically moved to high-productivity cities, whether that's changing, and what remote work means for this pattern The gap between public sentiment toward immigration and what economists know about its effects About Leah Boustan: Leah Boustan is a professor of economics at Yale University and a research associate at the NBER. She is the co-author, with Ran Abramitzky, of Streets of Gold: America's Untold Story of Immigrant Success, which uses newly digitized historical records to trace the economic trajectories of immigrants and their descendants across generations. Check out Leah's book Streets of Gold 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
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.