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Prediction Machines [Summary & Takeaways]

by The Quick Book Summary Team
4 minutes read

Main Topic

“Prediction Machines” explores the economic implications of artificial intelligence (AI) and how it transforms business strategies and decision-making by focusing on the central role of prediction.

Key Ideas or Arguments

  • The authors highlight the idea that AI, particularly machine learning, is primarily about improving the accuracy of predictions. They emphasize that this shift from human judgment to machine predictions has far-reaching implications.
  • The book explains the “AI as a commodity” concept, where AI predictions become cost-effective and readily available, affecting business strategies and competition.
  • It introduces the concept of “complementarity,” suggesting that AI should be used to enhance human decision-making rather than replace it.
  • The authors discuss how to address issues like privacy, ethics, and the impact of AI on the job market.

Chapter Titles or Main Sections

  1. Introduction: The authors set the stage for the book, explaining the central role of prediction in AI and its economic significance.
  2. The Simple Economics of Machine Intelligence: This chapter delves into the fundamental economics of AI and how it relates to traditional decision-making processes.
  3. Complements, Not Substitutes: The concept of AI complementarity is introduced, emphasizing how AI can enhance human decision-making rather than replacing it entirely.
  4. The Data Trap: Discusses the importance of data in AI and how companies can avoid falling into the “data trap” while harnessing the power of AI.
  5. Unpacking Machine Learning: Provides insights into the technical aspects of machine learning and its implications for businesses.
  6. The Second Half of the Chessboard: This chapter explores the exponential growth of AI and its impact on various industries.
  7. The Organizational Challenge: Discusses how organizations can effectively integrate AI into their operations and decision-making processes.
  8. The Policy Challenge: Examines the policy and ethical considerations surrounding AI, including privacy, accountability, and workforce implications.
  9. Conclusion: Summarizes key takeaways and provides a framework for understanding the economics of AI.

Prediction Machines, Updated and Expanded

by Ajay Agrawal, Joshua Gans, and Avi Goldfarb


Key Takeaways

  • AI is primarily about improving prediction accuracy.
  • AI predictions become a commodity, impacting business strategies and competition.
  • Complementarity between AI and human decision-making is essential.
  • Data is a critical resource in AI applications.
  • The book addresses organizational and policy challenges related to AI adoption.

Author’s Background and Qualifications

Ajay Agrawal, Joshua Gans, and Avi Goldfarb are professors at the Rotman School of Management in Toronto. They are experts in economics and innovation and have conducted extensive research in the field of AI economics.

Comparison to Other Books

“Prediction Machines” stands out for its focus on the economics of AI and the simplicity with which it conveys complex concepts. It complements other AI-related books that may delve more into technical details.

Target Audience

This book is suitable for business professionals, policymakers, and anyone interested in understanding the economic implications of AI. It is accessible to a broad audience, regardless of technical background.

Reception or Critical Response

The book received positive reviews for its clear and practical insights into AI economics. It has been praised for making complex concepts accessible to a wide readership.

Publisher and First Published Date

Published by Harvard Business Review Press, “Prediction Machines” was first published in April 2018.


  • “AI Superpowers” by Kai-Fu Lee.
  • “The Age of Em” by Robin Hanson.

To Sum Up:

The book’s biggest takeaway is that AI is fundamentally about improving prediction accuracy, and as AI predictions become cost-effective and widely available, businesses must embrace complementarity between AI and human decision-making to thrive in the AI-driven economy.

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