Scaling Theory

My guest is Yann LeCun, a pioneering French-American computer scientist, known for his groundbreaking work in machine learning, computer vision, and neural networks. Yann is the Silver Professor at the Courant Institute of Mathematical Sciences at New York University and serves as the Vice President and Chief AI Scientist at Meta. Yann is one of the world’s most influential computer scientists. He has accumulated over 350,000 citations on Google Scholar, he is one of the founding figures in the field of deep learning thanks to its contribution to convolutional neural networks and backpropagation algorithms, and he is a vocal proponent of open source. In recognition of his significant contributions to artificial intelligence, he was awarded the Turing Award in 2018, often referred to as the “Nobel Prize of Computing.” Our conversation is structured into three distinct parts. We begin by discussing the overarching dynamics in the AI space, then narrow our focus to the firm level, and finally, we conclude with an exploration of the challenges that lie ahead. By the end of this discussion, you will learn whether open source has a chance to make it in AI, the key factors for scaling an AI foundation model, the role ecosystems play in market dynamics, Meta long term strategy in the space, how concentration among chip manufacturers impacts AI companies, the current effect of the European AI Act on AI companies, what Yann would like to see regulators doing, and more. I hope you enjoy the conversation.

J. Doyne Farmer is the Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, a Professor at the Mathematical Institute of Oxford University, and an External Professor at the Santa Fe Institute. In this episode, we explore Doyne’s latest book, “Making Sense of Chaos.” We focus on the relationship between chaos and scaling theory, and more specifically, how chaos can be factored into scaling theory. By the end of this conversation, you will learn why it might be easier to predict the long distant future than predicting tomorrow, how Moore’s Law conflicts with other scaling laws that underpin technological progress, how agent-based modeling can help all scientists and policymakers, how to dominate the world with your theories (...), and even how to trick casinos. I hope you enjoy the conversation. Find me on X at @⁠⁠ProfSchrepel⁠⁠. Also, be sure to subscribe to the Scaling Theory podcast; it helps its growth. ***

Thomas Wolf is the co-founder and Chief Science Officer of Hugging Face, the company at the center of the open-source AI ecosystem. He has a Ph.D. in statistical & quantum physics. In this episode, we explore why open-source (“OS”) AI may be preferable to closed-source, whether OS has a real chance to take over the space, the challenges OS developers must overcome to scale their foundational models, the role of data and infrastructure in scaling dynamics, how big tech companies are positioning themselves in the ecosystem and responding to other companies' strategies in a true complexity science fashion, and overall, the power laws that undermine the growth of the ecosystem. I hope you enjoy the conversation. I hope you enjoy the conversation. Find me on X at @⁠ProfSchrepel⁠. Also, be sure to subscribe to the Scaling Theory podcast; it helps its growth. ***

Geoffrey West is a physicist, former president and distinguished professor of the Santa Fe Institute. His book, “Scale: The Universal Laws of Life, Growth, and Death in Organisms, Cities, and Businesses” (2017), is a masterpiece. In this episode, we talk about the power laws behind living organisms, cities, businesses, and technologies. By the end of this episode, you will know more about the power law behind the heartbeat of all mammals, the number of patents and crime in big cities compared to small cities, innovation, the way technology scales, and more. I hope you enjoy the conversation. Find me on X at @ProfSchrepel. Also, be sure to subscribe to the Scaling Theory podcast; it helps its growth. *** References Geoffrey West, ⁠Scale: The Universal Laws of Life, Growth, and Death in Organisms, Cities, and Businesses⁠ (Penguin Books, 2017) George J. Stigler, “The Economies of Scale”, The Journal of Law & Economics 1 (1958): 54–71 Michael HR Stanley, et al. “⁠Scaling Behaviour in the Growth of Companies”, Nature 379.6568 (1996): 804-806.APA W. Brian. Arthur, “⁠Competing Technologies, Increasing Returns, and Lock-In by Historical Events”, The Economic Journal 99.394 (1989): 116-131. Madeleine IG Daepp, et al. “⁠⁠⁠⁠⁠⁠⁠⁠⁠The Mortality of Companies⁠”, Journal of the Royal Society Interface 12.106 (2015): 20150120.APA Jiang Zhang, et al. “⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scaling Laws and a General Theory for the Growth of Public Companies⁠⁠”, arXiv preprint arXiv:2109.10379 (2021)

In this first episode, Dr. Thibault Schrepel (@⁠ProfSchrepel⁠) introduces “Scaling Theory”, a podcast dedicated to the power laws behind the growth of companies, technologies, legal and living systems. *** References: ➝ Charles Darwin, On the Origin of Species (1859) ➝ Melanie Mitchell, Complexity: A Guided Tour (2011) ➝ Mitchell Waldrop, Complexity The Emerging Science at the Edge of Order and Chaos (2019) ➝ John H. Miller & Scott Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (2007) ➝ W. Brian Arthur, Complexity and the Economy (2014) ➝ Geoffrey West, Scale: The Universal Laws of Life and Death in Organisms, Cities and Companies (2017)