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This anxiety about automation is understandable in light of the hair-raising progress that tech companies have made lately in robotics and artificial intelligence, which is now capable of, among other things, defeating Go masters, outbluffing champs in Texas Hold’em, and safely driving a car. And the notion that we’re on the verge of a radical leap forward in the scale and scope of automation certainly jibes with the pervasive feeling in Silicon Valley that we’re living in a time of unprecedented, accelerating innovation. Some tech leaders, including Y Combinator’s Sam Altman and Tesla’s Elon Musk, are so sure this jobless future is imminent—and, perhaps, so wary of torches and pitchforks—that they’re busy contemplating how to build a social safety net for a world with less work. Hence the sudden enthusiasm in Silicon Valley for a so-called universal basic income, a stipend that would be paid automatically to every citizen, so that people can have something to live on after their jobs are gone.
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.”
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sales, bookkeeping, food prep—is being automated at a steady clip, and even complex analytical jobs will be superseded before long.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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“We were lucky and steadily rising productivity raised all boats for much of the 20th century,” he says. “Many people, especially economists, jumped to the conclusion that was just the way the world worked. I used to say that if we took care of productivity, everything else would take care of itself; it was the single most important economic statistic. But that’s no longer true.” He adds, “It’s one of the dirty secrets of economics: technology progress does grow the economy and create wealth, but there is no economic law that says everyone will benefit.” In other words, in the race against the machine, some are likely to win while many others lose.
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Even our fears about automation and computerization aren’t new; they closely echo the anxieties of the late 1950s and early 1960s. Observers then too were convinced that automation would lead to permanent unemployment. The Ad Hoc Committee on the Triple Revolution—a group of scientists and thinkers concerned about the impact of what was then called cybernation—argued that “the capability of machines is rising more rapidly than the capacity of many human beings to keep pace.” Cybernation “has broken the link between jobs and income, exiling from the economy an ever-­widening pool of men and women,” wrote W. H. Ferry, of the Center for the Study of Democratic Institutions, in 1965. Change “cybernation” to “automation” or “AI,” and all that could have been written today.
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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.
In December of last year, Indeed also provided CoinDesk with data regarding blockchain jobs posted. The report indicated that the number of blockchain jobs posted in the U.S. had increased by 207 percent since 2016, and by 631 percent since November 2015.
One of the friendlier, more flexible robots meant to work with humans is Rethink’s Baxter. The creation of Rodney Brooks, the company’s founder, Baxter needs minimal training to perform simple tasks like picking up objects and moving them to a box. It’s meant for use in relatively small manufacturing facilities where conventional industrial robots would cost too much and pose too much danger to workers. The idea, says Brooks, is to have the robots take care of dull, repetitive jobs that no one wants to do.
Not everyone agrees with Brynjolfsson and McAfee’s conclusions—particularly the contention that the impact of recent technological change could be different from anything seen before. But it’s hard to ignore their warning that technology is widening the income gap between the tech-savvy and everyone else. And even if the economy is only going through a transition similar to those it’s endured before, it is an extremely painful one for many workers, and that will have to be addressed somehow. Harvard’s Katz has shown that the United States prospered in the early 1900s in part because secondary education became accessible to many people at a time when employment in agriculture was drying up. The result, at least through the 1980s, was an increase in educated workers who found jobs in the industrial sectors, boosting incomes and reducing inequality. Katz’s lesson: painful long-term consequences for the labor force do not follow inevitably from technological changes.
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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.
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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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Moonlighting is the practice of holding an additional job or jobs, often at night, in addition to one’s main job, usually to earn extra income. A person who moonlights may have little time left for sleep or leisure activities.
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Corporate America, for its part, certainly doesn’t seem to believe in the jobless future. If the rewards of automation were as immense as predicted, companies would be pouring money into new technology. But they’re not. Investments in software and IT grew more slowly over the past decade than the previous one. And capital investment, according to Mishel and Bivens, has grown more slowly since 2002 than in any other postwar period. That’s exactly the opposite of what you’d expect in a rapidly automating world. As for gadgets like Pepper, total spending on all robotics in the US was just $11.3 billion last year. That’s about a sixth of what Americans spend every year on their pets.
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Nevertheless, automation will indeed destroy many current jobs in the coming decades. As McAfee says, “When it comes to things like AI, machine learning, and self-driving cars and trucks, it’s still early. Their real impact won’t be felt for years yet.” What’s not obvious, though, is whether the impact of these innovations on the job market will be much bigger than the massive impact of technological improvements in the past. The outsourcing of work to machines is not, after all, new—it’s the dominant motif of the past 200 years of economic history, from the cotton gin to the washing machine to the car. Over and over again, as vast numbers of jobs have been destroyed, others have been created. And over and over, we’ve been terrible at envisioning what kinds of new jobs people would end up doing.
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.
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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