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 together
• How remote work accelerated AI automation by first digitizing and codifying knowledge work
• Why you can beat AI on depth but not breadth, and how deep expertise still produces something AI doesn't have: taste and judgment
• What taste actually is, why doing the work yourself is the only way to develop it, and why taking the summary first destroys it
• Why college needs to move toward applied learning, and why that's not the same thing as vocational training
• How academia is secretly closer to entrepreneurship than most people expect

About 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/

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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

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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

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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

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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

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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 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 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

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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

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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

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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

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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

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