Experts disagree about the future. That might not seem extraordinary, but it’s the conclusion of a new survey on robots from Pew, and it’s more significant than it sounds.
For all the talk of “robots stealing jobs,” 2,551 experts surveyed were deeply divided over the following question: “Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?”
Forty-eight percent agreed with this pessimistic take, while a thin majority was more optimistic.
To those in fear of being replaced by automation, the fact that experts are divided may seem like consolation – unfortunately, it’s anything but.
Historically, fears of technology-driven unemployment have failed to materialize both because demand for goods and services continued to rise, and because workers learned new skills and found new work. We might need fewer workers to produce food than we once did, but we’ve developed appetites for bigger houses, faster cars and more elaborate entertainment that more than make up for the difference. Farmworkers eventually find employment producing those things, and society moves on.
In their recent book, The Second Machine Age, MIT’s Erik Brynjolfsson and Andrew McAfee challenge the assumption that this pattern will repeat itself, arguing that the sheer pace of today’s digital change threatens to leave many workers behind. Much of the book is dedicated to making the case that technical change is accelerating, due to Moore’s Law, the observation that computing power roughly doubles every 18 months.
Several Pew respondents – experts from a wide range of technology-related fields – echoed this line of thinking. As technology consultant and futurist Bryan Alexander put it: “The education system is not well-positioned to transform itself to help shape graduates who can ‘race against the machines.’ Not in time, and not at scale. Autodidacts will do well, as they always have done, but the broad masses of people are being prepared for the wrong economy.”
Also, in their book, Brynjolfsson and McAfee highlight how predictions made in 2004 on the basis of comparative advantage failed to predict even today’s division of labor between people and machines.
Economists Frank Levy and Richard Murnane theorized that computers would handle arithmetic and rule-based work, while humans would be required for pattern recognition – like driving – as well as communication. Today, self-driving cars are well on their way to adoption and speech recognition is embedded in every smartphone. The list of things that machines can do better than humans continues to grow, confounding our predictions. The jobs we think are safe may not be, and the ones we fear we’ll lose may be safer than we think.
(Walter Frick is an associate editor at the Harvard Business Review)
© 2014 Harvard Business School Publishing Corp. Distributed by The New York Times Syndicate