Maximilian Kasy: “The Means of Prediction: How AI Really Works (and Who Benefits)”
Release Date: 12/16/2025
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Maximilian Kasy: “The Means of Prediction: How AI Really Works (and Who Benefits)”
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Recorded on December 2, 2025, this video features a talk by Maximilian Kasy, Professor of Economics at the University of Oxford, presenting his book This talk was part of a symposium series presented by the (CRELS), which trains doctoral students representing a variety of degree programs and expertise areas in the social sciences, computer science and statistics. The talk was co-sponsored by Social Science Matrix, the (BESI) Tech Cluster, the (BIDS), and the . A transcript of this recording can be found at . About the Book AI is inescapable, from its mundane uses online to its...
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Recorded on December 2, 2025, this video features a talk by Maximilian Kasy, Professor of Economics at the University of Oxford, presenting his book The Means of Prediction: How AI Really Works (and Who Benefits).
This talk was part of a symposium series presented by the UC Berkeley Computational Research for Equity in the Legal System Training Program (CRELS), which trains doctoral students representing a variety of degree programs and expertise areas in the social sciences, computer science and statistics.
The talk was co-sponsored by Social Science Matrix, the Berkeley Economy and Society Initiative (BESI) Tech Cluster, the Berkeley Institute for Data Science (BIDS), and the UC Berkeley Department of Economics.
A transcript of this recording can be found at https://matrix.berkeley.edu/research-article/max-kasy.
About the Book
AI is inescapable, from its mundane uses online to its increasingly consequential decision-making in courtrooms, job interviews, and wars. The ubiquity of AI is so great that it might produce public resignation—a sense that the technology is our shared fate.
As economist Maximilian Kasy shows in The Means of Prediction, artificial intelligence, far from being an unstoppable force, is irrevocably shaped by human decisions—choices made to date by the ownership class that steers its development and deployment. Kasy shows that the technology of AI is ultimately not that complex. It is insidious, however, in its capacity to steer results to its owners’ wants and ends.
Kasy clearly and accessibly explains the fundamental principles on which AI works, and, in doing so, reveals that the real conflict isn’t between humans and machines, but between those who control the machines and the rest of us.
The Means of Prediction offers a powerful vision of the future of AI: a future not shaped by technology, but by the technology’s owners. Amid a deluge of debates about technical details, new possibilities, and social problems, Kasy cuts to the core issue: Who controls AI’s objectives, and how is this control maintained? The answer lies in what he calls “the means of prediction,” or the essential resources required for building AI systems: data, computing power, expertise, and energy.
As Kasy shows, in a world already defined by inequality, one of humanity’s most consequential technologies has been and will be steered by those already in power. Against those stakes, Kasy offers an elegant framework both for understanding AI’s capabilities and for designing its public control. He makes a compelling case for democratic control over AI objectives as the answer to mounting concerns about AI’s risks and harms.
The Means of Prediction is a revelation, both an expert undressing of a technology that has masqueraded as more complicated and a compelling call for public oversight of this transformative technology.
About the Speaker
Maximilian Kasy received his PhD at UC Berkeley and joined Oxford after appointments at UCLA and Harvard University. His current research interests focus on social foundations for statistics and machine learning, going beyond traditional single-agent decision theory. He also works on economic inequality, job guarantee programs, and basic income. Kasy teaches a course on foundations of machine learning at the economics department at Oxford. Learn more at his website.