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 5 discusses the use of data with prediction machines. Three types of data used are training data, input data, and feedback data. Data collection can be expensive; however, if the cost is balanced with the benefits of enhanced prediction accuracy, it becomes a profitable investment. Nevertheless, statistical and economic aspects need to be considered to determine if having more data creates more value in each situation.