Scaling Theory is a podcast dedicated to the power laws behind the growth of companies, technologies, legal and living systems. The host, Dr. Thibault Schrepel, has a PhD in antitrust law and looks at the regulation of digital ecosystems through the lens of complexity theory. The podcast is hosted by the Network Law Review. It features scholarly discussions with select guests and deep dives into the academic literature.
Scaling Theory
Claim This Podcastby Thibault Schrepel
4.9(23 reviews)
31 episodes
Updated Weekly
Accepts GuestsHas SponsorsLocation 🇺🇸
47
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality37
Social0
YouTube74
Engagement71
Podcast Overview
Scaling Theory is a podcast dedicated to the power laws behind the growth of companies, technologies, legal and living systems. The host, Dr. Thibault Schrepel, has a PhD in antitrust law and looks at the regulation of digital ecosystems through the lens of complexity theory. The podcast is hosted by the Network Law Review. It features scholarly discussions with select guests and deep dives into the academic literature.
Language
🇺🇲
Publishing Since
3/16/2024
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47
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality37
Social0
YouTube74
Engagement71
6
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excellent
Episode Length
51 minutes
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Recent Episodes
June 29, 2026
Steven Pinker on Common Knowledge, From Eye Contact to the Super Bowl
<p>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.</p><p><a href="https://psychology.fas.harvard.edu/people/steven-pinker" target="_blank" rel="noopener noreferer">Steven Pinker</a>, professor of psychology at Harvard, joins Scaling Theory to discuss his latest book, “<a href="https://stevenpinker.com/publications/when-everyone-knows-everyone-knows-common-knowledge-and-mysteries-money-power-and" target="_blank" rel="noopener noreferer">When Everyone Knows That Everyone Knows</a>.” 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.</p>
May 6, 2026
#30 – Matthew O. Jackson on How Networks Quietly Shape What You Believe
<p>Welcome back to Scaling Theory. In this episode, I speak with <strong>Matthew O. Jackson</strong>, 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.</p><p>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.</p><p>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.</p><p><strong>Papers and works referenced in the conversation</strong></p><p><strong>Books</strong></p><ol><li>The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). <a href="https://web.stanford.edu/~jacksonm/books.html" target="_blank" rel="ugc noopener noreferrer">https://web.stanford.edu/~jacksonm/books.html</a></li><li>Social and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). <a href="https://web.stanford.edu/~jacksonm/books.html" target="_blank" rel="ugc noopener noreferrer">https://web.stanford.edu/~jacksonm/books.html</a></li></ol><p><strong>Part I — The scaling of human networks</strong></p><ol><li>"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. <a href="https://arxiv.org/abs/1111.0073" target="_blank" rel="ugc noopener noreferrer">https://arxiv.org/abs/1111.0073</a></li><li>"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. <a href="https://web.stanford.edu/~jacksonm/homophily.pdf" target="_blank" rel="ugc noopener noreferrer">https://web.stanford.edu/~jacksonm/homophily.pdf</a></li><li>"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. <a href="https://academic.oup.com/restud/article/86/6/2453/5345571" target="_blank" rel="ugc noopener noreferrer">https://academic.oup.com/restud/article/86/6/2453/5345571</a></li><li>"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. <a href="https://www.pnas.org/doi/10.1073/pnas.1702155114" target="_blank" rel="ugc noopener noreferrer">https://www.pnas.org/doi/10.1073/pnas.1702155114</a></li></ol><p><strong>Part II — The scaling of AI agents</strong></p><ol><li>"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). <a href="https://arxiv.org/abs/2506.13016" target="_blank" rel="ugc noopener noreferrer">https://arxiv.org/abs/2506.13016</a></li><li>"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. <a href="https://arxiv.org/abs/2509.13323" target="_blank" rel="ugc noopener noreferrer">https://arxiv.org/abs/2509.13323</a></li><li>"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. <a href="https://www.pnas.org/doi/10.1073/pnas.2313925121" target="_blank" rel="ugc noopener noreferrer">https://www.pnas.org/doi/10.1073/pnas.2313925121</a></li></ol>
April 13, 2026
#29 – Albert-Laszlo Barabasi: The Hidden Order of Networks
Host [Host Name] interviews Albert-László Barabási, Professor of Network Science, exploring the hidden order of real-world networks and their scaling properties.
31 total episodes available
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Albert-László Barabási
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Scott E Page
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Scott Page
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Cass R Sunstein
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W Brian Arthur
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Cristina Bicchieri
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Robin Hanson
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Thibault Schrepel
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Vinton G Cerf
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Melanie Moses
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Melanie Mitchell
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Paul Seabright
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