The AI Economist: The Data on AI and Jobs Tells a Different Story

Are jobs actually going away? In this episode of I’ve Got Questions, I sit down with economist Avi Goldfarb, professor at the Rotman School of Management at the University of Toronto, Rotman Chair in AI and Healthcare, and chief data scientist at the Creative Destruction Lab, to unpack what AI is actually doing to the job market. We explore the popular narrative of an AI job apocalypse and what the data is really showing so far. Avi explains why AI may not simply replace workers across the board, but instead reshape which tasks become more valuable, which jobs become more exposed, and who benefits from the productivity gains. We dive into how AI could transform industries like law, healthcare, marketing, and finance, what happens to entry-level workers when AI can perform junior tasks, and why college graduates may be facing a very different path into the workforce. We also explore whether AI could increase inequality or become an unexpected equalizer, depending on what gets automated and what gets augmented. And Avi shares the four skills he believes matter most for anyone trying to stay valuable in an AI-powered economy. What You’ll Learn:[00:00:00] — Opening: Andrew Yang’s viral AI jobs warning, unpacked [00:09:24] — Why the jobs most exposed to AI are in the top 20%, not the bottom 80% [00:13:38] — One person's automation is someone else's augmentation: the doctor-nurse framework [00:23:00] — The long run vs. the short run: where Avi is confident and where he's not [00:33:32] — "Jobs aren't good": decoupling work from income and meaning [00:43:10] — Software engineering as the canary in the coal mine for the knowledge economy [00:52:34] — Why automation takes much longer than the hype says: the telephone operator [01:10:00] — The O-ring effect: why AI exposure in your job might mean higher wages [01:18:00] — The "AI paradox": could automation destroy its own consumer base? [01:26:05] — Four skills that hold their value regardless of how AI evolves [01:29:25] — The PE junior who automated his own job and got promoted [01:30:34] — What policymakers need to understand about AI trade-offs --- Notable mentions Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence (Brynjolfsson, Chandar, Chen) — Stanford Digital Economy Lab paper showing early-career workers (22–25) in AI-exposed occupations have seen a 16% relative employment decline — https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/ GPTs Are GPTs (Eloundou, Manning, Mishkin, Rock) — Science paper identifying the jobs most exposed to AI, finding they cluster in the 80th–90th income percentile — https://arxiv.org/abs/2303.10130 The System of Professions (Andrew Abbott) — Sociology book on how professions compete for jurisdiction over expert knowledge — https://press.uchicago.edu/ucp/books/book/chicago/S/bo5965590.html Power and Prediction (Avi Goldfarb, Ajay Agrawal, Joshua Gans) — Their book on point solutions vs. system solutions in AI deployment — https://www.avigoldfarb.com/powerandprediction O-Ring Automation (Joshua Gans, Avi Goldfarb) — NBER working paper on why task exposure to AI can raise rather than lower wages; explains why 75% AI exposure does not equal displacement — https://www.nber.org/papers/w34639 The O-Ring Theory of Economic Development (Michael Kremer, 1993) — The foundational economic model, named after the Challenger disaster, explaining why one essential human task can protect an entire production chain — https://academic.oup.com/qje/article-abstract/108/3/551/1881767 Baumol's cost disease — https://en.wikipedia.org/wiki/Baumol_effect Some Simple Economics of AGI (Christian Catalini, Xiang Hui, Jane Wu) — MIT/WashU paper arguing that as AI execution becomes abundant, the binding economic constraint shifts to human verification bandwidth — https://arxiv.org/abs/2602.20946 Answering the Call of Automation: How the Labor Market Adjusted to Mechanizing Telephone Operation (James Feigenbaum, Daniel P. Gross) — Documents AT&T's 1920s–40s automation of telephone operators; incumbent operators were hardest hit while later cohorts found alternative middle-skill work — https://www.nber.org/papers/w28061 Alex Imas (University of Chicago Booth) — Behavioral economist researching what becomes scarce in a world of AI abundance; human presence, social connection, and provenance emerge as the new scarce goods — Substack: https://aleximas.substack.com/ | Faculty page: https://www.chicagobooth.edu/faculty/directory/i/alex-imas Betsey Stevenson (University of Michigan, Ford School of Public Policy) — Labor economist studying AI's effects on jobs, income distribution, and human flourishing; former CEA member and Chief Economist of the U.S. Department of Labor — https://betseystevenson.com/ How Do Patent Laws Influen