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Take productivity, which is a measure of how much the economy puts out per hour of human labor. Since automation allows companies to produce more with fewer people, a great wave of automation should drive higher productivity growth. Yet, in reality, productivity gains over the past decade have been, by historical standards, dismally low. Back in the heyday of the US economy, from 1947 to 1973, labor productivity grew at an average pace of nearly 3 percent a year. Since 2007, it has grown at a rate of around 1.2 percent, the slowest pace in any period since World War II. And over the past two years, productivity has grown at a mere 0.6 percent—the very years when anxiety about automation has spiked. That’s simply not what you’d see if efficient robots were replacing inefficient humans en masse. As McAfee puts it, “Low productivity growth does slide in the face of the story we tell about amazing technological progress.”
In the tony northern suburbs of New York City, IBM Research is pushing super-smart computing into the realms of such professions as medicine, finance, and customer service. IBM’s efforts have resulted in Watson, a computer system best known for beating human champions on the game show Jeopardy! in 2011. That version of Watson now sits in a corner of a large data center at the research facility in Yorktown Heights, marked with a glowing plaque commemorating its glory days. Meanwhile, researchers there are already testing new generations of Watson in medicine, where the technology could help physicians diagnose diseases like cancer, evaluate patients, and prescribe treatments.
Jump up ^ John Reid Blackwell (October 18, 2011). “Snagajob snags top place to work award – Richmond Times Dispatch: Metro-Richmond’s Latest Business & Economic News”. .timesdispatch.com. Retrieved June 4, 2013.
Despite the system’s remarkable ability to make sense of all that data, it’s still early days for Dr. Watson. While it has rudimentary abilities to “learn” from specific patterns and evaluate different possibilities, it is far from having the type of judgment and intuition a physician often needs. But IBM has also announced it will begin selling Watson’s services to customer-support call centers, which rarely require human judgment that’s quite so sophisticated. IBM says companies will rent an updated version of Watson for use as a “customer service agent” that responds to questions from consumers; it has already signed on several banks. Automation is nothing new in call centers, of course, but Watson’s improved capacity for natural-language processing and its ability to tap into a large amount of data suggest that this system could speak plainly with callers, offering them specific advice on even technical and complex questions. It’s easy to see it replacing many human holdouts in its new field.
Though advances like these suggest how some aspects of work could be subject to automation, they also illustrate that humans still excel at certain tasks—for example, packaging various items together. Many of the traditional problems in robotics—such as how to teach a machine to recognize an object as, say, a chair—remain largely intractable and are especially difficult to solve when the robots are free to move about a relatively unstructured environment like a factory or office.
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David Autor, an economist at MIT who has extensively studied the connections between jobs and technology, also doubts that technology could account for such an abrupt change in total employment. “There was a great sag in employment beginning in 2000. Something did change,” he says. “But no one knows the cause.” Moreover, he doubts that productivity has, in fact, risen robustly in the United States in the past decade (economists can disagree about that statistic because there are different ways of measuring and weighing economic inputs and outputs). If he’s right, it raises the possibility that poor job growth could be simply a result of a sluggish economy. The sudden slowdown in job creation “is a big puzzle,” he says, “but there’s not a lot of evidence it’s linked to computers.”
Of course, if automation is happening much faster today than it did in the past, then historical statistics about simple machines like the ATM would be of limited use in predicting the future. Ray Kurzweil’s book The Singularity Is Near (which, by the way, came out 12 years ago) describes the moment when a technological society hits the “knee” of an exponential growth curve, setting off an explosion of mutually reinforcing new advances. Conventional wisdom in the tech industry says that’s where we are now—that, as futurist Peter Nowak puts it, “the pace of innovation is accelerating exponentially.” Here again, though, the economic evidence tells a different story. In fact, as a recent paper by Lawrence Mishel and Josh Bivens of the Economic Policy Institute puts it, “automation, broadly defined, has actually been slower over the last 10 years or so.” And lately, the pace of microchip advancement has started to lag behind the schedule dictated by Moore’s law.

A warehouse equipped with Kiva robots can handle up to four times as many orders as a similar unautomated warehouse, where workers might spend as much as 70 percent of their time walking about to retrieve goods. (Coincidentally or not, Amazon bought Kiva soon after a press report revealed that workers at one of the retailer’s giant warehouses often walked more than 10 miles a day.)
The talking bot can supposedly identify joy, sadness, anger, and surprise and determine whether a person is in a good or bad mood—abilities that Pepper’s engineers figured would make “him” an ideal personal assistant or salesperson. And sure enough, there are more than 10,000 Peppers now at work in SoftBank stores, Pizza Huts, cruise ships, homes, and elsewhere.
Shawn Boyer served as Snagajob’s CEO from the website’s launch in early 2000, to 2013; leading the company from a start-up to national employment network. Boyer was named America’s Small Business Person of the year in 2008 by the Small Business Administration[4] and Virginia Business magazine’s Person of the Year in 2009[5].
Noma Bar (Illustration); Data from Bureau of Labor Statistics (Productivity, Output, GDP Per Capita); International Federation of Robotics; CIA World Factbook (GDP by Sector); Bureau of Labor Statistics (Job Growth, Manufacturing Employment); D. Autor and D. Dorn, U.S. Census, American Community Survey, and Department of Labor (Change in Employment and Wages by Skill, Routine Jobs); Bureau of Labor Statistics (Productivity, Output, GDP Per Capita); International Federation of Robotics; CIA World Factbook (GDP by Sector)
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While such changes can be painful for workers whose skills no longer match the needs of employers, Lawrence Katz, a Harvard economist, says that no historical pattern shows these shifts leading to a net decrease in jobs over an extended period. Katz has done extensive research on how technological advances have affected jobs over the last few centuries—describing, for example, how highly skilled artisans in the mid-19th century were displaced by lower-skilled workers in factories. While it can take decades for workers to acquire the expertise needed for new types of employment, he says, “we never have run out of jobs. There is no long-term trend of eliminating work for people. Over the long term, employment rates are fairly stable. People have always been able to create new jobs. People come up with new things to do.”
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Over the past few years, it has become conventional wisdom that dramatic advances in robotics and artificial intelligence have put us on the path to a jobless future. We are living in the midst of a “second machine age,” to quote the title of the influential book by MIT researchers Erik Brynjolfsson and Andrew McAfee, in which routine work of all kinds—in manufacturing, sales, bookkeeping, food prep—is being automated at a steady clip, and even complex analytical jobs will be superseded before long. A widely cited 2013 study by researchers at the University of Oxford, for instance, found that nearly half of all jobs in the US were at risk of being fully automated over the next 20 years. The endgame, we’re told, is inevitable: The robots are on the march, and human labor is in retreat.
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McAfee points to both retail and transportation as areas where automation is likely to have a major impact. Yet even in those industries, the job-loss numbers are less scary than many headlines suggest. Goldman Sachs just released a report predicting that autonomous cars could ultimately eat away 300,000 driving jobs a year. But that won’t happen, the firm argues, for another 25 years, which is more than enough time for the economy to adapt. A recent study by the Organization for Economic Cooperation and Development, meanwhile, predicts that 9 percent of jobs across 21 different countries are under serious threat from automation. That’s a significant number, but not an apocalyptic one.
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Alas, the future this study envisions seems to be very far off. To be sure, the fact that fears about automation have been proved false in the past doesn’t mean they will continue to be so in the future, and all of those long-foretold positive feedback loops exponential growth may abruptly kick in someday. But it isn’t easy to see how we’ll get there from here anytime soon, given how little companies are investing in new technology and how slowly the economy is growing. In that sense, the problem we’re facing isn’t that the robots are coming. It’s that they aren’t.
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To be sure, Autor says, computer technologies are changing the types of jobs available, and those changes “are not always for the good.” At least since the 1980s, he says, computers have increasingly taken over such tasks as bookkeeping, clerical work, and repetitive production jobs in manufacturing—all of which typically provided middle-class pay. At the same time, higher-paying jobs requiring creativity and problem-solving skills, often aided by computers, have proliferated. So have low-skill jobs: demand has increased for restaurant workers, janitors, home health aides, and others doing service work that is nearly impossible to automate. The result, says Autor, has been a “polarization” of the workforce and a “hollowing out” of the middle class—something that has been happening in numerous industrialized countries for the last several decades. But “that is very different from saying technology is affecting the total number of jobs,” he adds. “Jobs can change a lot without there being huge changes in employment rates.”
To get some insight into Katz’s question, it is worth looking at how today’s most advanced technologies are being deployed in industry. Though these technologies have undoubtedly taken over some human jobs, finding evidence of workers being displaced by machines on a large scale is not all that easy. One reason it is difficult to pinpoint the net impact on jobs is that automation is often used to make human workers more efficient, not necessarily to replace them. Rising productivity means businesses can do the same work with fewer employees, but it can also enable the businesses to expand production with their existing workers, and even to enter new markets.
For that reason, Leonard says, it is easier to see how robots could work with humans than on their own in many applications. “People and robots working together can happen much more quickly than robots simply replacing humans,” he says. “That’s not going to happen in my lifetime at a massive scale. The semiautonomous taxi will still have a driver.”
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Despite the labor-saving potential of the robots, Mick Mountz, Kiva’s founder and CEO, says he doubts the machines have put many people out of work or will do so in the future. For one thing, he says, most of Kiva’s customers are e-commerce retailers, some of them growing so rapidly they can’t hire people fast enough. By making distribution operations cheaper and more efficient, the robotic technology has helped many of these retailers survive and even expand. Before founding Kiva, Mountz worked at Webvan, an online grocery delivery company that was one of the 1990s dot-com era’s most infamous flameouts. He likes to show the numbers demonstrating that Webvan was doomed from the start; a $100 order cost the company $120 to ship. Mountz’s point is clear: something as mundane as the cost of materials handling can consign a new business to an early death. Automation can solve that problem.
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