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The Means of Prediction: How AI Really Works
An eye-opening examination of how power—not technology—will define life with AI.
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 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 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 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.
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 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 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 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.
- GenresComputer Science
228 pages, Kindle Edition
Published November 4, 2025
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122 people want to read
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Displaying 1 - 4 of 4 reviews
July 12, 2025
Book Review: The Means of Prediction: How AI Really Works (and Who Benefits) by Maximilian Kasy
Rating: 4.7/5
Maximilian Kasy’s The Means of Prediction is a bracing, brilliantly clear-eyed dismantling of AI’s mythos—one that left me equal parts enlightened and unsettled. Stripping away the veneer of technological inevitability, Kasy reframes AI not as an autonomous force but as a tool of power, meticulously designed to serve its owners’ interests. As someone who’s oscillated between AI optimism and dystopian anxiety, I found his focus on control rather than capability revelatory.
Kasy’s greatest strength is his ability to demystify complexity without oversimplifying. His chapters on supervised learning and deep learning (Part II) are masterclasses in accessibility, using crisp analogies to explain how AI “fits” patterns to data—and why that process inherently reproduces existing inequalities. The concept of the means of prediction (data, compute, expertise, energy) crystallizes the book’s central thesis: AI’s dangers aren’t technical glitches but structural choices. When Kasy details how algorithmic “fairness” often masks corporate self-interest (Chapter 17), I found myself audibly groaning in recognition.
Yet the book’s real power lies in its vision for democratic resistance. Kasy’s proposal for public oversight (Chapter 20) is pragmatic yet radical, grounded in historical precedents like utility regulation. I wished for more concrete examples of grassroots movements challenging AI power—stories to balance the abstract institutional solutions—but his critique of ideological obfuscation (Chapter 13) is worth the price alone.
By the end, I felt both urgency and agency. This isn’t just another AI ethics primer; it’s a call to reclaim technology as a collective good.
Summary Takeaways:
-The Das Kapital of AI: Kasy exposes how prediction algorithms entrench power—and how to fight back.
-Forget rogue robots—the real AI threat is shareholder profit. Kasy’s book is the wake-up call we need.
-A revelation: Turns out AI isn’t magic—it’s math, money, and monopoly. Read this before the next ‘AI revolution’ headline.
-If you’ve ever felt AI was ‘out of control,’ Kasy proves it’s precisely under control—just not yours.
-The most important book on AI since Weapons of Math Destruction—with a roadmap for democratic hope.
Thank you to the University of Chicago Press and Edelweiss for the advance copy. The Means of Prediction is essential reading for fans of [Atlas of AI] or [Race After Technology]—a rare blend of scholarly rigor and polemical fire.
Rating: 4.7/5
Maximilian Kasy’s The Means of Prediction is a bracing, brilliantly clear-eyed dismantling of AI’s mythos—one that left me equal parts enlightened and unsettled. Stripping away the veneer of technological inevitability, Kasy reframes AI not as an autonomous force but as a tool of power, meticulously designed to serve its owners’ interests. As someone who’s oscillated between AI optimism and dystopian anxiety, I found his focus on control rather than capability revelatory.
Kasy’s greatest strength is his ability to demystify complexity without oversimplifying. His chapters on supervised learning and deep learning (Part II) are masterclasses in accessibility, using crisp analogies to explain how AI “fits” patterns to data—and why that process inherently reproduces existing inequalities. The concept of the means of prediction (data, compute, expertise, energy) crystallizes the book’s central thesis: AI’s dangers aren’t technical glitches but structural choices. When Kasy details how algorithmic “fairness” often masks corporate self-interest (Chapter 17), I found myself audibly groaning in recognition.
Yet the book’s real power lies in its vision for democratic resistance. Kasy’s proposal for public oversight (Chapter 20) is pragmatic yet radical, grounded in historical precedents like utility regulation. I wished for more concrete examples of grassroots movements challenging AI power—stories to balance the abstract institutional solutions—but his critique of ideological obfuscation (Chapter 13) is worth the price alone.
By the end, I felt both urgency and agency. This isn’t just another AI ethics primer; it’s a call to reclaim technology as a collective good.
Summary Takeaways:
-The Das Kapital of AI: Kasy exposes how prediction algorithms entrench power—and how to fight back.
-Forget rogue robots—the real AI threat is shareholder profit. Kasy’s book is the wake-up call we need.
-A revelation: Turns out AI isn’t magic—it’s math, money, and monopoly. Read this before the next ‘AI revolution’ headline.
-If you’ve ever felt AI was ‘out of control,’ Kasy proves it’s precisely under control—just not yours.
-The most important book on AI since Weapons of Math Destruction—with a roadmap for democratic hope.
Thank you to the University of Chicago Press and Edelweiss for the advance copy. The Means of Prediction is essential reading for fans of [Atlas of AI] or [Race After Technology]—a rare blend of scholarly rigor and polemical fire.
November 18, 2025
A timely book to read given the era we are living in. It goes into the whos and whys of AI models, explaining in simple language who profits, who loses, and what we might possibly do to correct this.
It gives clear and accurate descriptions of how various machine learning and AI models are built and work, without going into any of the details required to build one. This makes it a really good book for anyone without an AI background to get acquainted with the field more closely than just knowing the buzzwords of the week. I work in AI and still found the explanations accurate and helpful.
The book explains this better than I can, but if you know what someone’s aim is when building a model, the underlying method isn’t really that important unless you’re building them yourself, so the surface level explanations are more then enough for the rest of the content of the book to build on.
The framing the author takes with regards to who the different parties involved with AI are is a powerful way to think about the issue, and there are many insights sprinkled through the book that I found extremely interesting and well thought out. The argument that GDPR (while good for individual privacy) does little to nothing to protect individuals from the kind of general prediction algorithms that are so prevalent in today’s society due to little incentive for most users to withhold their data was particularly interesting to me.
In a world where so much of our daily lives is shaped by hidden algorithms we cannot see, I think this is an invaluable book to read.
It gives clear and accurate descriptions of how various machine learning and AI models are built and work, without going into any of the details required to build one. This makes it a really good book for anyone without an AI background to get acquainted with the field more closely than just knowing the buzzwords of the week. I work in AI and still found the explanations accurate and helpful.
The book explains this better than I can, but if you know what someone’s aim is when building a model, the underlying method isn’t really that important unless you’re building them yourself, so the surface level explanations are more then enough for the rest of the content of the book to build on.
The framing the author takes with regards to who the different parties involved with AI are is a powerful way to think about the issue, and there are many insights sprinkled through the book that I found extremely interesting and well thought out. The argument that GDPR (while good for individual privacy) does little to nothing to protect individuals from the kind of general prediction algorithms that are so prevalent in today’s society due to little incentive for most users to withhold their data was particularly interesting to me.
In a world where so much of our daily lives is shaped by hidden algorithms we cannot see, I think this is an invaluable book to read.
November 10, 2025
This book, proves once again, with any technology, it’s not about the mechanics. It’s about the people: who benefits, who suffers, and how we move forward through change.
Rather than a sweeping polemic on robot overlords or the inevitable singularity: it examines who gets to decide what problems artificial intelligence solves.
His expertise and clarity is his strength. I’ll admit the inner workings of this tech went over my head, yet his explanations managed to help me grasp the concept. What a feat!
The case for democratic oversight is a strong and well-argued one. If you care about who, rather than what, shapes our technological future, start here.
I received an early copy courtesy of the publishers via Netgalley. All opinions are mine alone.
Rather than a sweeping polemic on robot overlords or the inevitable singularity: it examines who gets to decide what problems artificial intelligence solves.
His expertise and clarity is his strength. I’ll admit the inner workings of this tech went over my head, yet his explanations managed to help me grasp the concept. What a feat!
The case for democratic oversight is a strong and well-argued one. If you care about who, rather than what, shapes our technological future, start here.
I received an early copy courtesy of the publishers via Netgalley. All opinions are mine alone.
October 28, 2025
This is a really well done overview of what AI actually is, how it works, and what it is all of these AI companies are actually selling with their products. As of the time of my writing this, the bubble implosion appears to be starting, and honestly, it can't come soon enough.
Displaying 1 - 4 of 4 reviews