
Luis Garicano on Spain, Europe, and the Future of Work
Luis Garicano has one of the more unusual résumés in economics: trained at the University of Chicago Booth School under Gary Becker, Kevin Murphy, and Sherwin Rosen, he left academia for a stint as a member of the European Parliament before returning to research, now at the London School of Economics for a second time. His new book, Messy Jobs, co-authored with Jin Li and Yanhui Wu, argues that AI won't cause mass unemployment for a surprising reason: it will make jobs messier, not simpler, and messiness is precisely what AI struggles with. Tyler and Luis start their conversation with Spain — housing, NIMBYism, and the productivity crisis; Spanish literature and why the Civil War still looms so large; and what Chicago taught Luis about party discipline in European politics. Then to the EU's unanimity problem, capital markets, and Denmark's flexicurity model; a round of overrated-versus-underrated on Rosalía, Penélope Cruz, and Sgt. Pepper's; and finally into Messy Jobs — why retraining programs fail, whether AI's leisure dividend has arrived, and whether Spain could ever achieve AI sovereignty. Read a transcript enhanced with helpful links, or watch the full video on YouTube. Recorded July 28th, 2026. Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Luis on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Timestamps: 00:00:00 - Intro 00:01:05 - On Spain's housing and political paralysis 00:11:08 - On Spanish literature and the Civil War 00:16:52 - On Basque and Catalan public finance 00:18:59 - On party discipline and political selection 00:22:03 - On the EU's unanimity problem 00:28:47 - On flexicurity and European labor rigidity 00:30:24 - Overrated vs. underrated 00:33:12 - On the future of Spanish language 00:35:01 - On why AI won't cause mass unemployment 00:48:38 - On what's next 00:50:11 - Outro


















