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.

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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.

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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.

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