Prediction machines
by Ajay Agrawal
Agrawal reframes AI as cheap prediction - an economic lens that transforms how we understand artificial intelligence's impact
"Prediction is the process of filling in missing information".
Editorial Summary
Three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution. Ajay Agrawal is Professor of Strategic Management and Peter Munk Professor of Entrepreneurship at the University of Toronto's Rotman School of Management, co-authoring this work with Joshua Gans and Avi Goldfarb. The book demonstrates how prediction is at the heart of making decisions under uncertainty and explores companies like Google, Tesla, Amazon, and Mastercard that leverage machine learning for everything from translation to fraud detection. Rather than getting lost in technical jargon, the authors apply standard economic principles to show how cheaper prediction creates new business opportunities and transforms entire industries.
Perspective
"Reading this feels like finally having someone translate the AI revolution into language that makes business sense—you'll understand why self-driving cars are really just prediction problems wrapped in metal and glass. The book's distinctive contribution is recasting the rise of AI as a drop in the cost of prediction, cutting through both hype and fear to provide a practical economic framework for understanding artificial intelligence. Business leaders and policymakers who need to make strategic decisions about AI in their organizations will find a clear roadmap for thinking about when and where prediction technologies create competitive advantage."
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