Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

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Three economists from the University of Toronto's Rotman School of Management reframe AI as a technology that dramatically reduces the cost of prediction, then apply standard microeconomic theory to trace its cascading effects on decision-making, business strategy, and industry structure. By decomposing tasks into prediction, judgement, data, and action components, they provide a practical framework for managers to identify where AI will create value and where human judgement remains essential.

Published:: 2018
Pages:: 272

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In the Conversation

In this collection, Prediction Machines: The Simple Economics of Artificial Intelligence references 4 other books.

It draws on The Second Machine Age, The Innovator's Dilemma and Thinking, Fast and Slow.

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What This Book Draws On

The books Goldfarb references and why each one mattered to the argument.

Agrawal, Gans, and Goldfarb build directly on Brynjolfsson and McAfee's The Second Machine Age analysis of digital technologies as general-purpose technologies, reframing AI specifically as a prediction technology rather than a general automation technology

References

The Second Machine Age

by Erik Brynjolfsson

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Apply Christensen's The Innovator's Dilemma framework to AI adoption, arguing that cheap prediction will disrupt established industries by enabling new entrants to unbundle prediction from judgement in ways incumbents resist

References

The Innovator's Dilemma

by Clayton Christensen

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Draw on Kahneman's Thinking, Fast and Slow distinction between System 1 and System 2 to clarify the division of labor between AI prediction (pattern matching) and human judgement (weighing tradeoffs under uncertainty)

References

Thinking, Fast and Slow

by Daniel Kahneman

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Extend Davenport and Harris's Competing on Analytics thesis to the AI era, arguing that the competitive advantage has shifted from having better analytics to having cheaper and more pervasive prediction embedded in every business process

References

Competing on Analytics: The New Science of Winning

by Thomas H. Davenport and Jeanne G. Harris

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What Other Authors Say About It

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Intellectual Lineage

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Builds on (2 layers deep)

Directly cites

The Second Machine Age

The Innovator's Dilemma

Thinking, Fast and Slow

Competing on Analytics: The ...

2 steps back

Good to Great

Built to Last

The Black Swan

Stumbling on Happiness

The Wisdom of Crowds

Sources of Power

Unexpected Connections

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