Cities, firms, market crashes and CEO pay all follow the same statistical shape. Xavier Gabaix has spent his career working out why.

He is the Pershing Square Professor of Economics and Finance at Harvard, a mathematician by training from the École Normale Supérieure, and a winner of the Fischer Black Prize.

We talk about what that shared shape means for anyone who treats big companies as a problem to be corrected. We get into how a few very large firms can move a whole national economy, and why the standard objection, that individual shocks cancel out, fails. We finish on the cost of thinking, which he has measured, and what becomes of that cost once the thinking can be handed to a machine.

**

Mentioned in this episode
Xavier Gabaix, “Zipf’s Law for Cities: An Explanation” (QJE, 1999): https://doi.org/10.1162/003355399556133
Xavier Gabaix, “Power Laws in Economics and Finance” (Annual Review of Economics, 2009): https://doi.org/10.1146/annurev.economics.050708.142940
Xavier Gabaix, “Power Laws in Economics: An Introduction” (JEP, 2016): https://doi.org/10.1257/jep.30.1.185
Xavier Gabaix and Augustin Landier, “Why Has CEO Pay Increased So Much?” (QJE, 2008): https://doi.org/10.1162/qjec.2008.123.1.49
Xavier Gabaix, “The Granular Origins of Aggregate Fluctuations” (Econometrica, 2011): https://doi.org/10.3982/ECTA8769
Xavier Gabaix and Ralph Koijen, “Granular Instrumental Variables” (JPE, 2024): https://www.journals.uchicago.edu/doi/10.1086/728743
Xavier Gabaix and Thomas Graeber, “The Complexity of Economic Decisions” (NBER, 2024): https://www.nber.org/papers/w33109
Xavier Gabaix, “A Theory of Complexity Aversion” (working paper, 2025): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5185671
Xavier Gabaix’s publications: https://xgabaix.scholars.harvard.edu/publications

Scaling Theory is a podcast by Thibault Schrepel on the laws that govern how things grow, from companies and cities to AI and law.
Spotify: https://open.spotify.com/show/7o5GqRfTOzyCRABkvs6RpM
Apple Podcasts: https://podcasts.apple.com/us/podcast/scaling-theory/id1736309658

John H. Cochrane is the Rose-Marie and Jack Anderson Senior Fellow at the Hoover Institution. He spent thirty years as a professor of finance at the University of Chicago Booth School of Business. He writes The Grumpy Economist (over 34,000 subscribers).

Cochrane is a pilot, and the empty skies are his example of what permission costs when it compounds for fifty years.

We cover three things. Why compound growth dwarfs everything else on the policy agenda and still moves almost no voters. What happens to the scaling of an economy when the default answer is that you must ask permission. And what has become of universities and peer review, where Cochrane says nobody reads the papers and the profession rewards where you published rather than whether you convinced anyone. I hope you enjoy the conversation.

**

Mentioned in this episode

• John Cochrane, "Economic Growth" (2016), in John Norton Moore (ed.), The Presidential Debates, Carolina Academic Press, pp. 65-90. A shorter version appeared as "The Election's Most Important Issue": https://www.hoover.org/research/elections-most-important-issue
• John Cochrane, "Finance: Function Matters, Not Size," Journal of Economic Perspectives 27(2), 2013: https://www.aeaweb.org/articles?id=10.1257/jep.27.2.29
• John Cochrane, "The Rule of Law in the Regulatory State" (2015), prepared for the Hoover Institution Magna Carta conference: https://www.johnhcochrane.com/news-op-eds-all/rule-of-law-regulation
• John Cochrane, "Refine," The Grumpy Economist: https://www.grumpy-economist.com/p/refine
• John Cochrane, "Writing Tips for PhD Students": https://www.johnhcochrane.com/writing-group
• John Cochrane, "Conti on the Future of Universities," The Grumpy Economist: https://www.grumpy-economist.com/p/conti-on-the-future-of-universities
• Gregory Conti, "The Rise of the Sectarian University," Compact: https://www.compactmag.com/article/the-rise-of-the-sectarian-university/
• The Grumpy Economist, John Cochrane's Substack: https://www.grumpy-economist.com/
• Thibault Schrepel, "The Matthew Effect at Scale: Attention Scarcity and the AI Output Explosion," Network Law Review: https://www.networklawreview.org/matthew-effect/
• Robert E. Lucas Jr., "Expectations and the Neutrality of Money," Journal of Economic Theory 4(2), 1972, pp. 103-124.

Peter Howitt won the 2025 Nobel Prize in Economic Sciences. The award was for the theory of sustained growth through creative destruction. He and Philippe Aghion gave that theory a mathematical form in 1992.

Competition and innovation do not move together in a straight line. What decides the direction is where a firm sits relative to the technological frontier. Peter also draws the line between a merger that buys innovation and one that removes it, and he describes the route by which an incumbent turns market power into political power. We then turn to agent-based modeling. He is candid about why a method he still defends took thirty years to find readers.

Follow me on X (https://x.com/profschrepel) and BlueSky (@ProfSchrepel).

***

References:

• "A Model of Growth Through Creative Destruction" (with Aghion), Econometrica 60(2), March 1992, 323–351. https://www.jstor.org/stable/2951599.
• Endogenous Growth Theory (with Aghion), MIT Press, 1998. https://mitpress.mit.edu/9780262528467/endogenous-growth-theory/
• "Competition and Innovation: An Inverted-U Relationship" (with Aghion, Bloom, Blundell, Griffith), Quarterly Journal of Economics 120(2), May 2005, 701–728. https://academic.oup.com/qje/article-abstract/120/2/701/1933966.
• "The Effects of Entry on Incumbent Innovation and Productivity" (with Aghion, Blundell, Griffith, Prantl), Review of Economics and Statistics 91(1), February 2009, 20–32. https://direct.mit.edu/rest/article/91/1/20/57753/The-Effects-of-Entry-on-Incumbent-Innovation-and.
• "The Emergence of Economic Organization" (with Robert Clower), Journal of Economic Behavior & Organization 41(1), January 2000, 55–84. https://www.sciencedirect.com/science/article/abs/pii/S0167268199000876.
• "Banks, Market Organization, and Macroeconomic Performance: An Agent-Based Computational Analysis" (with Ashraf and Gershman), JEBO 135, March 2017, 143–180. DOI 10.1016/j.jebo.2016.12.023.
• "How Inflation Affects Macroeconomic Performance: An Agent-Based Computational Investigation" (with Ashraf and Gershman), Macroeconomic Dynamics 20(2), March 2016, 558–581. https://www.cambridge.org/core/journals/macroeconomic-dynamics/article/abs/how-inflation-affects-macroeconomic-performance-an-agentbased-computational-investigation/DC3AF3CA750F82FDE20164B36935FA1F.
• "Getting at Systemic Risk via an Agent-Based Model of the Housing Market" (with Geanakoplos, Axtell, Farmer and others), American Economic Review 102(3), May 2012, 53–58. https://www.aeaweb.org/articles?id=10.1257/aer.102.3.53.

Some things change the world not because they are new, but because everyone learns them at once. That is the difference between mutual knowledge, where each of us knows something, and common knowledge, where each of us knows that the other knows, without end. It is the hidden machinery behind money, language, authority, and revolution.

Steven Pinker, professor of psychology at Harvard, joins Scaling Theory to discuss his latest book, “When Everyone Knows That Everyone Knows.” We move from a folk tale to game theory, from the evolution of altruism to the future of artificial agents. I read the book as a theory of scaling. A single mind can hold only a few layers of who knows what about whom, yet we coordinate in the millions. How we bridge that gap, and what happens to it in an age of fragmented media and machines that can model one another, is what I wanted to understand.

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.

We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.

By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.

Papers and works referenced in the conversation

Books

1. The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.html
2. Social and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.html

Part I — The scaling of human networks

1. "Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073
2. "How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf
3. "Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571
4. "Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114

Part II — The scaling of AI agents

1. "Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016
2. "AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323
3. "A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

Welcome back to Scaling Theory. My guest today is Albert-László Barabási, Professor of Network Science at Northeastern University and one of the most cited scientists alive with over 320 000 citations. His books include Linked, The Formula, and Network Science.

In 1999, Albert-László Barabási published a paper that changed how we understand networks. The finding was this: real-world networks are not random. They are dominated by hubs. A few nodes collect most of the links, and they do so because they already have them. In this episode, he explains the details of what he actually found. We then move to the scaling of networks, and the temptation to control them. We conclude with a discussion about art, ballet dancers, architecture, and what mapping careers across disciplines reveals about how networks really work.

You can follow me on X (@ProfSchrepel) and BlueSky (@ProfSchrepel).

References:

➝ Papers

Barabási, A.-L. & Albert, R. "Emergence of Scaling in Random Networks." Science 286, no. 5439 (1999): 509–512. https://doi.org/10.1126/science.286.5439.509

Albert, R., Jeong, H. & Barabási, A.-L. "Diameter of the World-Wide Web." Nature 401 (1999): 130–131. https://doi.org/10.1038/43601

Watts, D.J. & Strogatz, S.H. "Collective Dynamics of 'Small-World' Networks." Nature 393 (1998): 440–442. https://doi.org/10.1038/30918

Erdős, P. & Rényi, A. "On Random Graphs." Publicationes Mathematicae 6 (1959): 290–297. https://snap.stanford.edu/class/cs224w-readings/erdos59random.pdf

➝ Books

Barabási, A.-L. Linked: The New Science of Networks. Cambridge, MA: Perseus Publishing, 2002. https://en.wikipedia.org/wiki/Linked:_The_New_Science_of_Networks

Barabási, A.-L. Network Science. Cambridge: Cambridge University Press, 2016. https://networksciencebook.com (open access)

Barabási, A.-L. The Formula: The Universal Laws of Success. New York: Little, Brown and Company, 2018. https://www.hachettebookgroup.com/titles/albert-laszlo-barabasi/the-formula/9780316505499

Welcome back to scaling theory. My guest today is ⁠Scott E. Page⁠, Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, and an external faculty member at the Santa Fe Institute. He is an elected member of the National Academy of Sciences and the American Academy of Arts and Sciences, and a recipient of the Guggenheim Fellowship. His books include The Difference, Diversity and Complexity, The Diversity Bonus, and The Model Thinker.

In this episode of Scaling Theory, Scott walks us through what complexity actually is. He unpacks the difference between complicated and genuinely complex systems, explains why cognitively diverse teams systematically outperform homogeneous ones on complex tasks, and what that means for how organizations scale. We also take up path dependence, the spillover effects of overlapping games across platform ecosystems, and where complexity tools have changed real decisions in practice. We close on the single open problem whose resolution would most reshape our understanding of social systems. As you will hear, Scott’s thinking is exceptionally clear. It is always a pleasure to talk with him and to listen to his insights. I hope you enjoy our discussion.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

**

Books

• Page, S.E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press.
• Page, S.E. (2011). Diversity and Complexity. Princeton University Press (Primers in Complex Systems).
• Page, S.E. (2018). The Model Thinker: What You Need to Know to Make Data Work for You. Basic Books.
• Miller, J.H. and Page, S.E. (2007). Complex Adaptive Social Systems: An Introduction to Computational Models of Social Life. Princeton University Press.

Peer-reviewed articles

• Hong, L. and Page, S.E. (2004). "Groups of diverse problem solvers can outperform groups of high-ability problem solvers." Proceedings of the National Academy of Sciences, 101(46): 16385–16389.
• Page, S.E. (2006). "Path Dependence." Quarterly Journal of Political Science, 1(1): 87–115.
• Page, S.E. (2007). "Type Interactions and the Rule of Six." Economic Theory, 30(2): 223–241.
• Bednar, J. and Page, S.E. (2007). "Can Game(s) Theory Explain Culture? The Emergence of Cultural Behavior Within Multiple Games." Rationality and Society, 19(1): 65–97.
• Bednar, J., Bramson, A., Jones-Rooy, A. and Page, S.E. (2010). "Emergent Cultural Signatures and Persistent Diversity: A Model of Conformity and Consistency." Rationality and Society, 22(4): 407–444.

My guest today is Cass R. Sunstein, University Professor at Harvard and one of the most influential legal and political thinkers of our time. A prolific author of dozens of books and hundreds of academic articles, Cass has shaped debates in constitutional law, administrative law, behavioral economics, and public policy. He is regularly ranked amongst the very top of the most cited legal scholars alive. Cass also served as Administrator of the White House Office of Information and Regulatory Affairs under President Obama. He has advised governments and international organizations around the world, and was awarded the Holberg Prize, the equivalent of a Nobel in law and the humanities.

His latest book, On Liberalism: In Defense of Freedom, is a systematic defense of the liberal tradition at a moment when it is, as he shows, under unprecedented pressure. Our conversation is centered around his book. We begin with the urgency at the heart of the book: how liberalism confronts critiques from moral conservatives and egalitarian progressives alike, what it means to defend the liberal framework in an era of fragmentation, etc. We then turn to questions of scaling: does liberalism have internal patterns or institutional mechanisms that allow it to scale across diverse societies. We grapple with how the liberal tradition’s “big tent” of thinkers (from Mill and Hayek to Roosevelt’s Second Bill of Rights) impact liberalism ability to scale. We also explore how liberalism navigates technological change, expertise versus public accountability, and the pretence of knowledge. I hope you enjoy our discussion.

You can follow me on X (@⁠⁠ProfSchrepel⁠⁠) and BlueSky (@⁠⁠ProfSchrepel⁠⁠).

**

References:

• On Liberalism (MIT Press, 2025) https://mitpress.mit.edu/9780262049771/on-liberalism/

In the very first episode of Scaling Theory, I mentioned a few scientists who have shaped my understanding of the world. At the very top of that list is today’s guest: W. Brian Arthur.

Brian was born and raised in Belfast, Northern Ireland, and went on to become one of the most important figures of complexity science. Today, he is widely known as the father of complexity economics, a field that has transformed how we think about the evolution of modern economies.

His influence is remarkable. Brian’s work has been cited more than 58,000 times according to Google Scholar. He received numerous awards and recognition, such as being the inaugural laureate of the Lagrange Prize in Complexity Science, an award that many have described as complexity’s equivalent of the Nobel Prize. Brian has been, at age 37, the youngest endowed chair holder at Stanford University. He went on to work for my institutions, including the Santa Fe Institute, as we will talk about. On a personal note, I consider Brian a friend.

Now, what makes me especially happy to have Brian on the podcast is the unique perspective he brings on how economies form and evolve. His understanding of technology, how it emerges and scales, offers a lens that none others have developed. It is a way of seeing economic life as something alive. Be ready to be blown away.

You can follow me on X (@⁠ProfSchrepel⁠) and BlueSky (@⁠ProfSchrepel⁠).

**

References:

• W. Brian Arthur, Competing Technologies, Increasing Returns, and Lock-In by Historical Events (1989) https://www.rochelleterman.com/ir/sites/default/files/arthur 1989.pdf
• W. Brian Arthur, Foundations of Complexity Economics (2021) https://pmc.ncbi.nlm.nih.gov/articles/PMC7844781/pdf/42254_2020_Article_273.pdf
• W. Brian Arthur, The Nature of Technology: What It Is and How It Evolves (2009)
• W. Brian Arthur, Economics in Nouns and Verbs (2023) https://www.sciencedirect.com/science/article/pii/S0167268122003936
• Thibault Schrepel, The Evolution of Economies, Technologies, and Other Institutions: Exploring W. Brian Arthur's Insights (2024) https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8809341E2E94D76B8CCAB4A4DDACBC4C/S1744137424000067a.pdf/evolution_of_economies_technologies_and_other_institutions_exploring_w_brian_arthurs_insights.pdf

Welcome back to Scaling Theory. My guest today is Cristina Bicchieri, Professor of Social Thought and Comparative Ethics at the University of Pennsylvania, Director of the Center for Social Norms and Behavioral Dynamics, and one of the most influential scholars working on norm formation and collective behaviour. Her work is widely cited and, as we will talk about, has led to many field experiments and changes across the world.

In our conversation, Cristina and I talk about how norms emerge, scale, and sometimes collapse. We look at tipping signals, self-reinforcing equilibria, and why some norms spread through a population while others fail beyond small groups.

We then move to applied dimensions. Cristina takes us through the field experiments she has conducted and the patterns she has observed. Her work shows how norms and legal rules evolve together, which offers a fresh perspective on the forces that regulate and constrain much of what we do. We also talk about the challenges that appear when behaviours on digital platforms and AI ecosystems evolve faster than regulation. Finally, Cristina offers concrete guidance for policymakers and firms that want to design interventions grounded in norm theory.

I hope you will enjoy the conversation. Keep scaling, keep skating on thin ice.

You can follow me on X (@⁠⁠⁠ProfSchrepel⁠⁠⁠) and BlueSky (@⁠⁠⁠ProfSchrepel⁠⁠⁠).