In "Prediction Machines," economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explore the advancement and growing use of artificial intelligence (AI). The key to AI is not actually intelligence but prediction. This text looks at the value of prediction and data, the importance of trade-offs, and the impact of AI in the workplace. Beneficial to business leaders, financial analysts, policy makers, and students, "Prediction Machines" offers insights, tools, and strategies on how to adapt businesses to the world's ever-growing use of AI. Part 1-consisting of chapters 3 through 6-discusses prediction itself, including how prediction machines work, why many call prediction machines artificial intelligence, the need for data with prediction, and how prediction machines affect the division of labor within organizations. Chapter 6 begins with an exploration of humans' typically poor predictions and how people usually do not consider statistical properties. Humans and machines each have strengths and weaknesses when it comes to prediction, and a company's division of labor between people and machines should align with these strengths and weaknesses and should be adjusted as prediction machines improve. The idea of prediction by exception is also discussed.