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.
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.”)
That robots, automation, and software can replace people might seem obvious to anyone who’s worked in automotive manufacturing or as a travel agent. But Brynjolfsson and McAfee’s claim is more troubling and controversial. They believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. And, they suspect, something similar is happening in other technologically advanced countries.
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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.”
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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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Techniques using vast amounts of computational power have gone a long way toward helping robots understand their surroundings, but John Leonard, a professor of engineering at MIT and a member of its Computer Science and Artificial Intelligence Laboratory (CSAIL), says many familiar difficulties remain. “Part of me sees accelerating progress; the other part of me sees the same old problems,” he says. “I see how hard it is to do anything with robots. The big challenge is uncertainty.” In other words, people are still far better at dealing with changes in their environment and reacting to unexpected events.
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A less dramatic change, but one with a potentially far larger impact on employment, is taking place in clerical work and professional services. Technologies like the Web, artificial intelligence, big data, and improved analytics—all made possible by the ever increasing availability of cheap computing power and storage capacity—are automating many routine tasks. Countless traditional white-collar jobs, such as many in the post office and in customer service, have disappeared. W. Brian Arthur, a visiting researcher at the Xerox Palo Alto Research Center’s intelligence systems lab and a former economics professor at Stanford University, calls it the “autonomous economy.” It’s far more subtle than the idea of robots and automation doing human jobs, he says: it involves “digital processes talking to other digital processes and creating new processes,” enabling us to do many things with fewer people and making yet other human jobs obsolete.
But something else happened in the global economy right around 2000 as well: China entered the World Trade Organization and massively ramped up production. And it was this, not automation, that really devastated American manufacturing. A recent paper by the economists Daron Acemoglu and Pascual Restrepo—titled, fittingly, “Robots and Jobs”—got a lot of attention for its claim that industrial automation has been responsible for the loss of up to 670,000 jobs since 1990. But just in the period between 1999 and 2011, trade with China was responsible for the loss of 2.4 million jobs: almost four times as many. “If you want to know what happened to manufacturing after 2000, the answer is very clearly not automation, it’s China,” Dean Baker says. “We’ve been running massive trade deficits, driven mainly by manufacturing, and we’ve seen a precipitous plunge in the number of manufacturing jobs. To say those two things aren’t correlated is nuts.” (In other words, Donald Trump isn’t entirely wrong about what’s happened to American factory jobs.)
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.
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The irony of our anxiety about automation is that if the predictions about a robot-dominated future were to come true, a lot of our other economic concerns would vanish. A recent study by Accenture, for instance, suggests that the implementation of AI, broadly defined, could lift annual GDP growth in the US by two points (to 4.6 percent). A growth rate like that would make it easy to deal with the cost of things like Social Security and Medicare and the rising price of health care. It would lead to broader wage growth. And while it would complicate the issue of how to divide the economic pie, it’s always easier to divide a growing pie than a shrinking one.
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Humanoid robots, to be more precise, which SoftBank describes as “kindly, endearing, and surprising.” Each Pepper is equipped with three multidirectional wheels, an anticollision system, multiple sensors, a pair of arms, and a chest-mounted tablet that allows customers to enter information. Pepper can “express his own emotions” and use a 3-D camera and two HD cameras “to identify movements and recognize the emotions on the faces of his interlocutors.”
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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.
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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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.”
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.”
Workers often talk of “getting a job”, or “having a job”. This conceptual metaphor of a “job” as a possession has led to its use in slogans such as “money for jobs, not bombs”. Similar conceptions are that of “land” as a possession (real estate) or intellectual rights as a possession (intellectual property).
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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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.
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.
Now, it’s possible that some of the productivity slowdown is the result of humans shifting out of factories into service jobs (which have historically been less productive than factory jobs). But even productivity growth in manufacturing, where automation and robotics have been well-established for decades, has been especially paltry of late. “I’m sure there are factories here and there where automation is making a difference,” says Dean Baker, an economist at the Center for Economic and Policy Research. “But you can’t see it in the aggregate numbers.”
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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.
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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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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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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.
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The expression day job is often used for a job one works in order to make ends meet while performing low-paying (or non-paying) work in their preferred vocation. Archetypal examples of this are the woman who works as a waitress (her day job) while she tries to become an actress, and the professional athlete who works as a laborer in the off season because he is currently only able to make the roster of a semi-professional team.
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.