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The postwar 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 work Why using staffing firms drives wages down The McKinsey statistic that says the quiet part out loud: 95% of a firm's value comes from 5% of its employees Why "at will" employment is a spectrum, not a switch, and why disposable workers sit at a different end of it entirely The hidden cost firms pay: contractors and marginal workers report substantially less organizational commitment and willingness to put in extra effort What Walmart, Google, and Activision show about the levers that have moved firm behavior when political pressure became real Check out Paul's new book, Disposable Workers: https://a.co/d/05hPXELl About 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_uO8C 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 biggest questions in development economics keep running into the same wall: we don't have good enough data on how people actually work across most of the world. Charles Gottlieb has spent a decade trying to build it. Topics covered: Why the binding constraint in global labor data has shifted from availability to comparability Land tenure regimes in sub-Saharan Africa: why "use it or lose it" land rights trap workers in agriculture and slow urbanization Why gender gaps in labor force participation don't close with economic growth, and why the drivers are largely non-economic How time use data could answer questions that labor force surveys can never reach, including what people do with the hours they stop working Why studying the same distortions in isolation keeps the development literature from understanding how they compound and interact What AI means for the unglamorous work of data harmonization, and why it may be the field's biggest near-term accelerant About Charles Gottlieb: Charles Gottlieb is a professor of economics at the University of Geneva. His research focuses on global labor markets, structural transformation, time use, and gender, with a particular emphasis on low and middle income country contexts. He is building a unified global labor market data infrastructure to enable systematic cross-country research. 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 secret to better schools isn't always more money, smaller classes, or better curriculum. It's often better teachers. The problem is the American education system is almost perfectly designed to prevent that from happening. In this episode, Ben sits down with Rick Hanushek, senior fellow at Stanford's Hoover Institution, to dig into what the evidence says about how schools work, why they often don't, and what AI is about to do to all of it. Topics covered: Why the core value of education isn't knowledge but adaptability, and why that makes it more important as labor markets shift faster Why cognitive skills measured by tests predict long-run economic growth better than years of schooling Teacher quality as the only input that reliably moves student outcomes, and why the US pay system is built to ignore it entirely The Washington D.C., Dallas, and Denver experiments with performance-based pay, what they showed, and why they haven't spread Why AI will complement high-skill workers and substitute for low-skill ones, making the stakes of getting education right higher than ever Why homework is dying and what that means for the gap between students willing to use AI as a learning tool and those who use it as a shortcut About Rick Hanushek: Rick Hanushek is a senior fellow at the Hoover Institution at Stanford University and one of the founders of the economics of education as a field. His research on teacher quality, cognitive skills, and school accountability has shaped education policy debates around the world for more than five decades. 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 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 else Why monopsony's wage effect is probably 5 to 10 percent below competitive wages, not the 30 to 50 percent implied by some elasticity estimates Why the more important story may be rent attribution: who captures firm profits when competition declines, and why that share has shifted toward capital What 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 is Sectoral 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-negotiation Why 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 puzzle About 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/annastansbury 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
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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 first The unbundling productivity trade-off: why breaking jobs apart unlocks more growth but also drives wages down as labor supply floods a narrower set of tasks Why technology without business process reconfiguration produces almost no productivity gains The junior professional problem in the current landscape Why reduced job mobility might inadvertently push firms back toward German-style on-the-job training Europe's stagnation trap About 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/0hvVSmnS Check out Luis’s substack: https://substack.com/@luisgaricano 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 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 more Why AI is better understood as a continuation of the internet than a rupture What AI does to the signaling value of elite degrees AI as tutor: the most powerful educational tool ever built, and why using it as a substitute for your own thinking is problematic Supply chains as an early proof of concept: why volatile, complex environments are where AI shows its clearest value Why the models economists built for a stable world need to be rebuilt About 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 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 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 actually 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 planning Why you can't afford to be late to recursive self-improvement even when it feels far away What OpenAI's own labor data is showing The four-category job framework: which roles are at risk, which will be reorganized, which will expand as prices fall, and which are largely insulated The 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 another Extensive vs. intensive margin: why 2025 was about getting firms to adopt AI at all, and why 2026 is about measuring how deeply they're actually using it AI and entrepreneurship: whether the falling cost of starting a business is producing more solo firms, thicker startup markets, or just more noise Whether AI favors small firms that can now punch above their weight, or large firms with the data, governance, and training capacity to go deeper B2B Signals: what OpenAI's enterprise data set reveals about the gap between power users and median users The globalization of AI: which countries are growing fastest in AI usage About 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/signals 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 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 collapsed Designed data vs. organic data Why statistical agencies aren't slow out of ignorance but because the cost of a measurement error is catastrophic Why CO2 emissions are almost certainly being underestimated by a huge margin Why globalization turned workers into widgets The monopoly problem: why the US has quietly stopped having real markets in sector after sector, and what that has to do with political polarization Why authentic human collective action is what Roberto believes AI will never fully replicate About 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 Project 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
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 it Why the problem isn't just low wages but also hours, conditions, scheduling, and the terms of work that workers can't individually negotiate Why unions and competition aren't simply substitutes How to define a labor market The sleepy monopolist hypothesis: whether product market concentration reduces firms' incentive to hire the best people Non-compete agreements Job search and online platforms: how the structure of matching markets shapes who finds work, at what wage, and how quickly Why AI in public policy may be one of the most important and underrated frontiers About 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 website 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