The Economics of Work with Ben Zweig

Επεισόδια

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  Mike Spence - What a Nobel Laureate Thinks AI Does to Education 30.09.2026 1ώ

  The theory of labor market signaling changed how economists think about education, hiring, and information. Fifty years later, AI is rewriting the conditions that made the theory work.In this episode, Ben sits down with Mike Spence, Nobel laureate and former dean at both Harvard and Stanford, to explore what AI means for signaling, growth, education, and the structure of markets.Topics covered:The CV screening war: how AI-generated applications met AI-powered filters, and why the net result may be less signal rather than moreWhy AI is better understood as a continuation of the internet than a ruptureWhat AI does to the signaling value of elite degreesAI as tutor: the most powerful educational tool ever built, and why using it as a substitute for your own thinking is problematicSupply chains as an early proof of concept: why volatile, complex environments are where AI shows its clearest valueWhy the models economists built for a stable world need to be rebuiltAbout Mike Spence: Mike Spence is a Nobel laureate in economics, awarded the 2001 prize for his work on information asymmetry and labor market signaling. He served as dean of Harvard's Faculty of Arts and Sciences and later as dean of Stanford's Graduate School of Business. He is a senior fellow at the Hoover Institution and has written extensively on economic growth, development, and the implications of AI for advanced and emerging economies.Follow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Anna Stansbury - Worker Power, Wages, and What's Gone Wrong in the American Labor Market 23.09.2026 49λ

  The labor share of income has dropped from roughly two thirds to below sixty percent since the early 1980s. The US looks like an outlier even among rich countries.In this episode, Ben sits down with Anna Stansbury, MIT economist and co-author with Larry Summers of influential work on worker power, to dig into what's driving wage stagnation and which institutions might fix it.Topics covered:The three theories of the declining labor share: globalization, technology, and the fall of worker power, and why the US looks different from almost everywhere elseWhy monopsony's wage effect is probably 5 to 10 percent below competitive wages, not the 30 to 50 percent implied by some elasticity estimatesWhy the more important story may be rent attribution: who captures firm profits when competition declines, and why that share has shifted toward capitalWhat unions are for, which of their functions are worth preserving, and why the answer depends heavily on what you think the underlying structure of the economy actually isSectoral bargaining in Germany and Scandinavia, why those systems are more resilient than US-style firm-level organizing, and the Swedish mine case that shows what happens when AI reclassifies a job mid-negotiationWhy what workers get from work can't be reduced to wages, and why the human dimensions of jobs remain the most underestimated piece of the puzzleAbout Anna Stansbury: Anna Stansbury is an assistant professor at MIT Sloan School of Management and a faculty research fellow at the NBER. Her research focuses on labor markets, worker power, and the macroeconomics of wages and inequality. She is co-author, with Lawrence Summers, of influential work on the role of worker power in explaining the declining labor share of income.Follow Anna on Twitter/X: https://x.com/annastansburyFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Paul Osterman - The Disposable Worker: How Firms Learned to Stop Investing in People 16.09.2026 52λ

  The unspoken employment contract, characterized by stable jobs, career ladders, wages that rose with productivity, didn't dissolve overnight. Firms chipped away at it deliberately, and the result is a labor market organized around workers who were never meant to stay.Topics covered:Contractors, freelancers, and marginal workers: the three categories of disposable workWhy using staffing firms drives wages downThe McKinsey statistic that says the quiet part out loud: 95% of a firm's value comes from 5% of its employeesWhy "at will" employment is a spectrum, not a switch, and why disposable workers sit at a different end of it entirelyThe hidden cost firms pay: contractors and marginal workers report substantially less organizational commitment and willingness to put in extra effortWhat Walmart, Google, and Activision show about the levers that have moved firm behavior when political pressure became realCheck out Paul's new book, Disposable Workers: https://a.co/d/05hPXELlAbout Paul Osterman: Paul Osterman is professor emeritus at the MIT Sloan School of Management and one of the leading figures in labor economics and workforce policy. His research spans internal labor markets, job quality, low-wage work, and workforce development. He is the author of Disposable Workers and several other foundational books on work and employment in America.Research mentioned:Susan Houseman's work on flexible staffing arrangements:https://scholar.google.com/citations?view_op=view_citation&hl=en&user=jSDQZyMAAAAJ&citation_for_view=jSDQZyMAAAAJ:u5HHmVD_uO8CFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  David Deming - What's Happening to Young Workers, and What College Should Do About It 09.09.2026 48λ

  For the first time in recent history, new college graduates have a higher unemployment rate than the general population. The causes are tangled, the implications are serious, and universities are caught in the middle.In this episode, Ben sits down with David Deming, Harvard economist and Dean of Harvard College, to work through what's driving the shift and what it means for how we think about education, expertise, and careers.Topics covered:Why the graduate unemployment rate is elevated and why remote work, AI, and post-pandemic labor hoarding are all suspects that probably worked togetherHow remote work accelerated AI automation by first digitizing and codifying knowledge workWhy you can beat AI on depth but not breadth, and how deep expertise still produces something AI doesn't have: taste and judgmentWhat taste actually is, why doing the work yourself is the only way to develop it, and why taking the summary first destroys itWhy college needs to move toward applied learning, and why that's not the same thing as vocational trainingHow academia is secretly closer to entrepreneurship than most people expectAbout David Deming: David Deming is a professor of economics at Harvard University and Dean of Harvard College. His research focuses on education, skills, and the labor market. He writes the Substack Fork Lightning and hosts the podcast The Context Window.Check out David’s substack: https://forklightning.substack.com/Check out The Context Window Podcast: https://thecontextwindowpodcast.substack.com/Follow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Ioana Marinescu - Why Workers Are Underpaid, and What We Could Do About It 02.09.2026 51λ

  Most people earn less than they're worth. Not because they're not productive, but because the labor market isn't nearly as competitive as it could be.In this episode, Ben sits down with Ioana Marinescu, professor at Penn and one of the leading researchers on labor market power, to dig into what monopsony actually means, why it matters, and what it would take to fix it.Topics covered:What a perfectly competitive labor market would look like and how far we are from itWhy the problem isn't just low wages but also hours, conditions, scheduling, and the terms of work that workers can't individually negotiateWhy unions and competition aren't simply substitutesHow to define a labor marketThe sleepy monopolist hypothesis: whether product market concentration reduces firms' incentive to hire the best peopleNon-compete agreementsJob search and online platforms: how the structure of matching markets shapes who finds work, at what wage, and how quicklyWhy AI in public policy may be one of the most important and underrated frontiersAbout Ioana Marinescu: Ioana Marinescu is a professor of economics at the University of Pennsylvania and a research associate at the NBER. Her research spans labor market competition and monopsony, online job search, non-compete agreements, and the labor market effects of basic income programs. She is one of the leading voices applying antitrust frameworks to labor markets.Check out Ioana's websiteFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Roberto Rigobon - Why We're Getting Worse at Measuring 26.08.2026 57λ

  The economy is changing faster than our ability to track it. And the data sources we rely on most, like surveys, official statistics, and government indices, were designed for a world that no longer exists.In this episode, Ben sits down with Roberto Rigobon, MIT economist and founder of the Billion Prices Project, to explore what gets lost when measurement fails.Topics covered:Why survey response rates have collapsedDesigned data vs. organic dataWhy statistical agencies aren't slow out of ignorance but because the cost of a measurement error is catastrophicWhy CO2 emissions are almost certainly being underestimated by a huge marginWhy globalization turned workers into widgetsThe monopoly problem: why the US has quietly stopped having real markets in sector after sector, and what that has to do with political polarizationWhy authentic human collective action is what Roberto believes AI will never fully replicateAbout Roberto Rigobon: Roberto Rigobon is a professor of applied economics at MIT's Sloan School of Management and co-founder of the Billion Prices Project, which pioneered the use of online prices to construct real-time inflation indices. His research spans international finance, economic measurement, labor market ethics, and the economics of human trafficking, CO2 emissions, and workplace behavior.Check out the Billion Prices ProjectFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Deena Mousa - What AI Means for the Developing World 19.08.2026 37λ

  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 broadlyHow automation demand works in reverse: why making scans faster and cheaper expands the complexity of what radiologists are asked to do, not just the volumeWhy 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 sectorWhy "supply creates its own demand" doesn't automatically work for development, and the Malawi example of why breakout growth is the exception, not the ruleThe difference between low-income and middle-income countries, and why bundling them into "LMICs" obscures the very different things they need to doWhy some Western social progress movements can inadvertently harm the workers they're meant to protectAbout 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 substackCheck out Deena on XFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Luis Garicano - Why the Most Valuable Work Is the Hardest to Automate 12.08.2026 50λ

  The jobs most protected from AI aren't necessarily the most credentialed ones. They're the ones where tasks are so tightly bundled together that pulling one thread unravels the whole thing.In this episode, Ben sits down with Luis Garicano, LSE professor and author of Messy Jobs, to dig into what makes some work durable, how organizations need to reconfigure around AI, and why Europe may be in serious trouble.Topics covered:Clean vs. messy jobs: why single-task, easily reinforced work is what AI takes firstThe unbundling productivity trade-off: why breaking jobs apart unlocks more growth but also drives wages down as labor supply floods a narrower set of tasksWhy technology without business process reconfiguration produces almost no productivity gainsThe junior professional problem in the current landscapeWhy reduced job mobility might inadvertently push firms back toward German-style on-the-job trainingEurope's stagnation trapAbout Luis Garicano: Luis Garicano is a professor of economics and strategy at the London School of Economics and a former member of the European Parliament. His research focuses on the economics of organizations, technology, and productivity.Check out Luis’s book Messy Jobs here: https://a.co/d/0hvVSmnSCheck out Luis’s substack: https://substack.com/@luisgaricanoFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Ronnie Chatterji - How The Labor Market Looks From Inside Open AI 05.08.2026 44λ

  What does it look like to do economics from inside the AI revolution with access to data nobody else has, and questions nobody has figured out how to answer yet?In this episode, Ben sits down with Ronnie Chatterji, chief economist at OpenAI, to pull back the curtain on how economic research gets done at a frontier AI lab.Topics covered:Why the right time horizon for AI economics is a mix of real-time, medium-term, and scenario planningWhy you can't afford to be late to recursive self-improvement even when it feels far awayWhat OpenAI's own labor data is showingThe four-category job framework: which roles are at risk, which will be reorganized, which will expand as prices fall, and which are largely insulatedThe Codex paper: what OpenAI's internal data on agentic coding tool adoption reveals about how long it takes different functions to catch up with one anotherExtensive vs. intensive margin: why 2025 was about getting firms to adopt AI at all, and why 2026 is about measuring how deeply they're using itAI and entrepreneurship: whether the falling cost of starting a business is producing more solo firms, thicker startup markets, or just more noiseWhether AI favors small firms that can now punch above their weight, or large firms with the data, governance, and training capacity to go deeperB2B Signals: what OpenAI's enterprise data set reveals about the gap between power users and median usersThe globalization of AI: which countries are growing fastest in AI usageAbout Ronnie Chatterji: Ronnie Chatterji is the chief economist at OpenAI, where he leads research on AI's impact on jobs, growth, and enterprise. He previously served as chief economist at the U.S. Department of Commerce and as a professor at Duke University's Fuqua School of Business. His research spans entrepreneurship, innovation, and the economics of technology adoption.Check out Ronnie's research at openai.com/signalsFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Bill Easterly - What We Get Wrong About Foreign Aid 30.07.2026 35λ

  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 autocratsThe 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 everythingWhy even well-intentioned private philanthropy ends up in the same trapsThe "benevolent autocrat" fallacy: why GDP growth under coercive regimes doesn't constitute developmentWhy development as freedom, not just material income is the right framework, and what historical cases reveal about the limits of GDP-first thinkingThe problem with "we": who gets to be included in that word, what it conceals about power and paternalismCoercion vs. exploitationAbout 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 GrowthThe White Man’s BurdenThe Tyranny of ExpertsViolent SaviorsFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Isabella Loaiza - Beyond Exposure 23.07.2026 41λ

  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 wrongThe difference between automation and augmentationWhy "exposure" should probably be called "automation potential"The EPOCH framework around AI and its relation to humanityThe burnout problem: why offloading routine tasks to AI may eliminate the cognitive rest that knowledge workers rely on to sustain performanceWhether AI empathy is real empathyDream jobs vs. meaningful jobs: why they're not always the same thingWhy measuring task content across countries matters and what a global labor market taxonomy might actually need to captureFind the paper by Isabella Loaiza and Roberto Rigobon about the EPOCH framework hereFollow Isabella on LinkedInFollow us on LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Jasmine Sun - The Youth's Love/Hate Relationship with AI 16.07.2026 46λ

  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 mobilityHow AI sentiment differs across countriesThe Silicon Valley worldview: why the push for AGI and generality reflects specific beliefs about human limitations and who gets to build the futureThe "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 alarmingToday'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 XFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Leah Boustan - What Immigration Tells Us About Economic Opportunity 09.07.2026 37λ

  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 mobilityThe Norway brothers study: why migrants earned double what their brothers who stayed earned, and why it had little to do with changing occupationsWhat immigrants bring back when they return home and how that shapes sending countriesHow firms fit into immigrant assimilationLocation and mobility: why immigrants have historically moved to high-productivity cities, whether that's changing, and what remote work means for this patternThe gap between public sentiment toward immigration and what economists know about its effectsAbout 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 GoldFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  John Boudreau - Are Business Leaders Listening to HR Theory? 02.07.2026 46λ

  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 instinctThe bottleneck problem: why investing equally in every role is as irrational as improving every stage of a production line simultaneouslyWhy economists were studying tasks long before HR wasWhy AI pilots aren't experiments: the difference between watching what happens and actually measuring itWhat it would take to create generally accepted principles for people decisions, and why codified principles matter more than codified measuresWhy the word "job" may be the single biggest obstacle to clear thinking about the future of workAbout 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 LinkedInFollow us on LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Dan Hamermesh - Time, Beauty, and What Economics Gets Wrong About Work 25.06.2026 40λ

  Keynes predicted we'd be working 15-hour weeks by now. So what went wrong? Does it even matter?In this episode, Ben sits down with Dan Hamermesh, one of the most prolific and wide-ranging labor economists of the past half century, to explore questions the field rarely asks: Why do Americans work more than anyone else in the rich world? What do we actually do with leisure when we get it?Topics covered:Why cutting work hours wouldn't make us poor, and why policy, not technology, might be the only realistic path to changeThe "greedy jobs" debate, and what's driving long hours at the topWhat people do with extra leisure timeThe coordination failure at the heart of overworkAbout Dan Hamermesh: Dan Hamermesh is an economist whose career has spanned more than five decades, with foundational contributions to labor economics, the study of time use, and the economics of beauty and discrimination. He is the author of Beauty Pays and Spending Time, among other books, and has been a professor at the University of Texas at Austin, Barnard, and elsewhere.Check out Dan's books Beauty Pays and Spending TimeFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Rachel Lipson - Bridging Business, Education, and Policy to Build a Better Workforce 18.06.2026 43λ

  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 togetherThe CHIPS Act as a workforce policy outlier: how it flipped the government's default from antipoverty lens to competitiveness and innovationWhy 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 driverWhy subsidizing firm-specific training isn't a corporate giveawayWhat community colleges can offer that on-the-job hiring can'tHow to think about preparing the next generation for jobs that don't exist yetAbout 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 LinkedInFollow Ben on LinkedInFollow us on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Daniel Rock - What Automation Means for How We Organize Jobs 11.06.2026 42λ

  What 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" measuresWhy tasks are "assemblages," not atoms, and what that means for how we think about job changeThe task chaining paper: why the sequence in which tasks are automated matters as much as which tasks get automatedWhy the handoff costs of breaking work into steps also have handoff benefits and when human checkpoints create value rather than frictionJobs 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 attemptWhat academics can learn from entrepreneurs and vice versaWhy the green shoots of AI's impact on science and medicine point toward something much bigger than productivity gains at workAbout 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 LinkedInPapers discussed:Weak Bundle, Strong BundleTask ChainingFollow us on LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Alexis Fink - The Science of Making Work Not Suck 04.06.2026 43λ

  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 isWhy meetings are almost always done wrong and what a genuinely good one looks likeWhy AI adoption metrics are measuring the wrong metricsThe 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, returnsWhy strategic workforce planning is one of the most underrated practices in business, and why so few companies actually do it wellAbout 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 LinkedInFollow us on LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Nick Bloom - The New Geography of Work 28.05.2026 36λ

  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 peakWhy culture, not technology, explains why some countries went back to the office while others didn'tThe "personal trainer effect": why being in the same room still matters for concentration and collaborationWhy forcing people back five days a week drives up attrition by a third and costs firms more than they saveThe U-shaped relationship between age and office preference, and what it means for how companies should design their policiesWhat good management actually looks like, and why so much bad management persists even when better practices are well understoodThe "donut effect": how hybrid work is reshaping cities and suburbsAbout 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 XFollow us on LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]
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  Al Roth - Moral Economics and Repugnant Transactions 21.05.2026 46λ

  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 transactionsSurrogacy, gene editing, and AI companions: where the line between protection and paternalism blursThe coercion vs. exploitation distinction: is banning a market for poor people's benefit sometimes just denying them an opportunity?How public opinion and legislation divergeWhy labor markets are fundamentally different from commodity marketsHow the internet (and now AI) has flooded job markets with applications and destroyed the information value of applyingWhat the economics job market's "signaling" system can teach LinkedIn, dating apps, and corporate hiring alikeAl 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 LinkedInFollow Ben on LinkedInSign up for our NewsletterVisit our website for more informationGet in touch with us at [email protected]