Spotify, Machine Learning, and the Business of Recommendation Engines

You’re surely familiar—though you may not know it by name—with the Paradox of Choice; we’re surrounded by it: 175 salad dressing choices, 80,000 possible Starbucks beverages, 50 different mutual funds for your retirement account. “All of this choice,” psychologists say, “starts to be not only unproductive, but counterproductive—a source of pain, regret, worry about missed opportunities, and unrealistically high expectations.” And yet, we have more choices than ever— 32,000 hours to watch on Netflix, 10 million e-books on our Kindles, 5000 different car makes and models, not counting color and dozens of options.

It’s too much. We need help. And that help is available in the form of recommendation engines. In fact, they may be helping us a bit too much, according to my guest today.

Michael Schrage is a research fellow at the MIT Sloan School's Initiative on the Digital Economy. He advises corporations— including Procter & Gamble, Google, Intel, and Siemens—on innovation and investment, and he’s the author of several books including 2014’s The Innovator’s Hypothesis, and the 2020 book Recommendation Engines, newly published by MIT Press.