What’s more, even if today’s digital technologies are holding down job creation, history suggests that it is most likely a temporary, albeit painful, shock; as workers adjust their skills and entrepreneurs create opportunities based on the new technologies, the number of jobs will rebound. That, at least, has always been the pattern. The question, then, is whether today’s computing technologies will be different, creating long-term involuntary unemployment.
Still, Katz doesn’t dismiss the notion that there is something different about today’s digital technologies—something that could affect an even broader range of work. The question, he says, is whether economic history will serve as a useful guide. Will the job disruptions caused by technology be temporary as the workforce adapts, or will we see a science-fiction scenario in which automated processes and robots with superhuman skills take over a broad swath of human tasks? Though Katz expects the historical pattern to hold, it is “genuinely a question,” he says. “If technology disrupts enough, who knows what will happen?”
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Perhaps the most damning piece of evidence, according to Brynjolfsson, is a chart that only an economist could love. In economics, productivity—the amount of economic value created for a given unit of input, such as an hour of labor—is a crucial indicator of growth and wealth creation. It is a measure of progress. On the chart Brynjolfsson likes to show, separate lines represent productivity and total employment in the United States. For years after World War II, the two lines closely tracked each other, with increases in jobs corresponding to increases in productivity. The pattern is clear: as businesses generated more value from their workers, the country as a whole became richer, which fueled more economic activity and created even more jobs. Then, beginning in 2000, the lines diverge; productivity continues to rise robustly, but employment suddenly wilts. By 2011, a significant gap appears between the two lines, showing economic growth with no parallel increase in job creation. Brynjolfsson and McAfee call it the “great decoupling.” And Brynjolfsson says he is confident that technology is behind both the healthy growth in productivity and the weak growth in jobs.
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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.
Now imagine you’re an economist back on the ground, and a panicstricken software engineer is warning that his creations are about to plow everyone straight into a world without work. Just as surely, there are a couple of statistical instruments you know to consult right away to see if this prediction checks out. If automation were, in fact, transforming the US economy, two things would be true: Aggregate productivity would be rising sharply, and jobs would be harder to come by than in the past.
IBM likes to call it cognitive computing. Essentially, Watson uses artificial-intelligence techniques, advanced natural-language processing and analytics, and massive amounts of data drawn from sources specific to a given application (in the case of health care, that means medical journals, textbooks, and information collected from the physicians or hospitals using the system). Thanks to these innovative techniques and huge amounts of computing power, it can quickly come up with “advice”—for example, the most recent and relevant information to guide a doctor’s diagnosis and treatment decisions.
An activity that requires a person’s mental or physical effort is work (as in “a day’s work”). If a person is trained for a certain type of job, they may have a profession. Typically, a job would be a subset of someone’s career. The two may differ in that one usually retires from their career, versus resignation or termination from a job.
Last year, the Japanese company SoftBank opened a cell phone store in Tokyo and staffed it entirely with sales associates named Pepper. This wasn’t as hard as it sounds, since all the Peppers were robots.
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It’s a startling assertion because it threatens the faith that many economists place in technological progress. Brynjolfsson and McAfee still believe that technology boosts productivity and makes societies wealthier, but they think that it can also have a dark side: technological progress is eliminating the need for many types of jobs and leaving the typical worker worse off than before. Brynjolfsson can point to a second chart indicating that median income is failing to rise even as the gross domestic product soars. “It’s the great paradox of our era,” he says. “Productivity is at record levels, innovation has never been faster, and yet at the same time, we have a falling median income and we have fewer jobs. People are falling behind because technology is advancing so fast and our skills and organizations aren’t keeping up.”
So if the data doesn’t show any evidence that robots are taking over, why are so many people even outside Silicon Valley convinced it’s happening? In the US, at least, it’s partly due to the coincidence of two widely observed trends. Between 2000 and 2009, 6 million US manufacturing jobs disappeared, and wage growth across the economy stagnated. In that same period, industrial robots were becoming more widespread, the internet seemed to be transforming everything, and AI became really useful for the first time. So it seemed logical to connect these phenomena: Robots had killed the good-paying manufacturing job, and they were coming for the rest of us next.
“The sustained interest in blockchain jobs is perhaps a sign that job seekers believe non-financial companies will pursue blockchain applications, even if financial companies see cryptocurrencies as a fad,” the company writes in the report.
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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.
Granted, there are much scarier forecasts out there, like that University of Oxford study. But on closer examination, those predictions tend to assume that if a job can be automated, it will be fully automated soon—which overestimates both the pace and the completeness of how automation actually gets adopted in the wild. History suggests that the process is much more uneven than that. The ATM, for example, is a textbook example of a machine that was designed to replace human labor. First introduced around 1970, ATMs hit widespread adoption in the late 1990s. Today, there are more than 400,000 ATMs in the US. But, as economist James Bessen has shown, the number of bank tellers actually rose between 2000 and 2010. That’s because even though the average number of tellers per branch fell, ATMs made it cheaper to open branches, so banks opened more of them. True, the Department of Labor does now predict that the number of tellers will decline by 8 percent over the next decade. But that’s 8 percent—not 50 percent. And it’s 45 years after the robot that was supposed to replace them made its debut. (Taking a wider view, Bessen found that of the 271 occupations listed on the 1950 census only one—elevator operator—had been rendered obsolete by automation by 2010.)
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.
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New technologies are “encroaching into human skills in a way that is completely unprecedented,” McAfee says, and many middle-class jobs are right in the bull’s-eye; even relatively high-skill work in education, medicine, and law is affected. “The middle seems to be going away,” he adds. “The top and bottom are clearly getting farther apart.” While technology might be only one factor, says McAfee, it has been an “underappreciated” one, and it is likely to become increasingly significant.
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It’s hard not to instantly like Baxter, in part because it seems so eager to please. The “eyebrows” on its display rise quizzically when it’s puzzled; its arms submissively and gently retreat when bumped. Asked about the claim that such advanced industrial robots could eliminate jobs, Brooks answers simply that he doesn’t see it that way. Robots, he says, can be to factory workers as electric drills are to construction workers: “It makes them more productive and efficient, but it doesn’t take jobs.”
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.”
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Brynjolfsson himself says he’s not ready to conclude that economic progress and employment have diverged for good. “I don’t know whether we can recover, but I hope we can,” he says. But that, he suggests, will depend on recognizing the problem and taking steps such as investing more in the training and education of workers.
Nor does the job market show signs of an incipient robopocalypse. Unemployment is below 5 percent, and employers in many states are complaining about labor shortages, not labor surpluses. And while millions of Americans dropped out of the labor force in the wake of the Great Recession, they’re now coming back—and getting jobs. Even more strikingly, wages for ordinary workers have risen as the labor market has improved. Granted, the wage increases are meager by historical standards, but they’re rising faster than inflation and faster than productivity. That’s something that wouldn’t be happening if human workers were on the fast track to obsolescence.
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It is this onslaught of digital processes, says Arthur, that primarily explains how productivity has grown without a significant increase in human labor. And, he says, “digital versions of human intelligence” are increasingly replacing even those jobs once thought to require people. “It will change every profession in ways we have barely seen yet,” he warns.
The phrase “don’t quit your day job” is a humorous response to a poor or mediocre performance not up to professional caliber. The phrase implies that the performer is not talented enough in that activity to be able to make a career out of it.
In a less anxious world, Pepper might come across as a cute technological novelty. But for many pundits and prognosticators, he’s a sign of something much more grave: the growing obsolescence of human workers. (Images of the doe-eyed Pepper have accompanied numerous articles with variations on the headline “robots are coming for your job.”)
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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If automation were truly remaking the job market, you’d also expect to see a lot of what economists call job churn as people move from company to company and industry to industry after their jobs have been destroyed. But we’re seeing the opposite of that. According to a recent paper by Robert Atkinson and John Wu of the Information Technology and Innovation Foundation, “Levels of occupational churn in the United States are now at historic lows.” The amount of churn since 2000—an era that saw the mainstreaming of the internet and the advent of AI—has been just 38 percent of the level of churn between 1950 and 2000. And this squares with the statistics on median US job tenure, which has lengthened, not shortened, since 2000. In other words, rather than a period of enormous disruption, this has been one of surprising stability for much of the American workforce. Median job tenure today is actually similar to what it was in the 1950s—the era we think of as the pinnacle of job stability.
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The contention that automation and digital technologies are partly responsible for today’s lack of jobs has obviously touched a raw nerve for many worried about their own employment. But this is only one consequence of what Brynjolfsson and McAfee see as a broader trend. The rapid acceleration of technological progress, they say, has greatly widened the gap between economic winners and losers—the income inequalities that many economists have worried about for decades. Digital technologies tend to favor “superstars,” they point out. For example, someone who creates a computer program to automate tax preparation might earn millions or billions of dollars while eliminating the need for countless accountants.