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Prediction Machines
Avi Goldfarb, Joshua Gans & Ajay Agrawal
Artificial intelligence is not magic — it is simply cheap prediction. This microbook breaks down how falling prediction costs reshape decisions, jobs, and business strategy, using real cases from Amazon, Google, and others to show what gets more valuable when machines handle the forecasting and what that means for the humans still in the loop.
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Who it is for
For anyone trying to separate AI hype from what it actually changes at work — especially those who sense the shift is real but aren't sure where to focus their energy or how to rethink their decisions and workflows.
About the Author
He is a professor of marketing at the University of Toronto's Rotman School of Management, the Chief Data Scientist at the Creative Destruction Lab, and a Research Associate at the National Bureau of Economic Research. Goldfarb is widely recognized for his contributions to understanding the economics of digital technology and AI. He co-authored the bestselling books “Prediction Machines: The Simple Economics of Artificial Intelligence” and “Power and Prediction: The Disruptive Economics of Artificial Intelligence.”
View author profileLessons
- Understand that AI reduces the cost of prediction, not the need for human judgment.
- Deconstruct your workflows into micro-tasks to find exactly where prediction is the bottleneck.
- Learn to engineer your reward functions before delegating any decision to an algorithm.
Key Takeaways
- When prediction becomes cheap, the real value shifts to the judgment that makes those predictions actionable.
- Every decision can be broken into input, prediction, judgment, action, outcome, and feedback — and AI only transforms one of those parts.
- The companies that win the AI era will be those who define the right payoffs, not just those who build the best models.
More knowledge in less time
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