The Economics of Work with Ben Zweig

Work is changing, and the forces shaping it are endlessly fascinating. In The Economics of Work , Ben Zweig sits down with leading economists, researchers, and thinkers to explore the ideas that define how we work, why we work, and what the future of work could look like. Each conversation dives deep into the literature, economic theory, and the philosophical questions that underlie the world of labor.

The Economics of Work with Ben Zweig is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts The Economics of Work with Ben Zweig, a business show with 0 episodes published.

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  Episode #15

  Deena Mousa - What AI Means for the Developing World

  Aug 19, 202637 minS1

  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

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  Episode #14

  Luis Garicano - Why the Most Valuable Work Is the Hardest to Automate

  Aug 12, 202650 minS1

  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

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  Episode #13

  Ronnie Chatterji - How The Labor Market Looks From Inside Open AI

  Aug 5, 202644 minS1

  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 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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  Episode #12

  Bill Easterly - What We Get Wrong About Foreign Aid

  Jul 30, 202636 minS1

  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

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  Episode #11

  Isabella Loaiza - Beyond Exposure

  Jul 23, 202642 minS1

  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

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