At least since the Industrial Revolution began in the 1700s, improvements in technology have changed the nature of work and destroyed some types of jobs in the process. In 1900, 41 percent of Americans worked in agriculture; by 2000, it was only 2 percent. Likewise, the proportion of Americans employed in manufacturing has dropped from 30 percent in the post–World War II years to around 10 percent today—partly because of increasing automation, especially during the 1980s.
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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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.
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.
Manual work seems to shorten one’s lifespan.[2] High rank[3] (a higher position at the pecking order) has a positive effect. Professions that cause anxiety have a direct negative impact on health and lifespan.[4] Some data is more complex to interpret due to the various reasons of long life expectancy; thus skilled professionals, employees with secure jobs and low anxiety occupants may live a long life for variant reasons.[5] The more positive characteristics one’s job is, the more likely he or she will have a longer lifespan.[6][7] Gender, country,[8] and actual (what statistics reveal, not what people believe) danger are also notable parameters.[9][10]
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.
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.
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.
Anecdotal evidence that digital technologies threaten jobs is, of course, everywhere. Robots and advanced automation have been common in many types of manufacturing for decades. In the United States and China, the world’s manufacturing powerhouses, fewer people work in manufacturing today than in 1997, thanks at least in part to automation. Modern automotive plants, many of which were transformed by industrial robotics in the 1980s, routinely use machines that autonomously weld and paint body parts—tasks that were once handled by humans. Most recently, industrial robots like Rethink Robotics’ Baxter (see “The Blue-Collar Robot,” May/June 2013), more flexible and far cheaper than their predecessors, have been introduced to perform simple jobs for small manufacturers in a variety of sectors. The website of a Silicon Valley startup called Industrial Perception features a video of the robot it has designed for use in warehouses picking up and throwing boxes like a bored elephant. And such sensations as Google’s driverless car suggest what automation might be able to accomplish someday soon.
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).
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.
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.
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Meanwhile, Kiva itself is hiring. Orange balloons—the same color as the robots—hover over multiple cubicles in its sprawling office, signaling that the occupants arrived within the last month. Most of these new employees are software engineers: while the robots are the company’s poster boys, its lesser-known innovations lie in the complex algorithms that guide the robots’ movements and determine where in the warehouse products are stored. These algorithms help make the system adaptable. It can learn, for example, that a certain product is seldom ordered, so it should be stored in a remote area.
“Over the last year interest in cryptocurrency jobs on Indeed has risen strongly. However, in recent months the prices of bitcoin and other cryptocurrencies have been volatile and (in some cases) declining,” the company said. “Job seeker interest on Indeed for bitcoin and cryptocurrency jobs has fallen, too.”
McAfee, associate director of the MIT Center for Digital Business at the Sloan School of Management, speaks rapidly and with a certain awe as he describes advances such as Google’s driverless car. Still, despite his obvious enthusiasm for the technologies, he doesn’t see the recently vanished jobs coming back. The pressure on employment and the resulting inequality will only get worse, he suggests, as digital technologies—fueled with “enough computing power, data, and geeks”—continue their exponential advances over the next several decades. “I would like to be wrong,” he says, “but when all these science-fiction technologies are deployed, what will we need all the people for?”
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.
Given his calm and reasoned academic demeanor, it is easy to miss just how provocative Erik Brynjolfsson’s contention really is. ­Brynjolfsson, a professor at the MIT Sloan School of Management, and his collaborator and coauthor Andrew McAfee have been arguing for the last year and a half that impressive advances in computer technology—from improved industrial robotics to automated translation services—are largely behind the sluggish employment growth of the last 10 to 15 years. Even more ominous for workers, the MIT academics foresee dismal prospects for many types of jobs as these powerful new technologies are increasingly adopted not only in manufacturing, clerical, and retail work but in professions such as law, financial services, education, and medicine.
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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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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.
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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.)

Brynjolfsson and McAfee are not Luddites. Indeed, they are sometimes accused of being too optimistic about the extent and speed of recent digital advances. Brynjolfsson says they began writing Race Against the Machine, the 2011 book in which they laid out much of their argument, because they wanted to explain the economic benefits of these new technologies (Brynjolfsson spent much of the 1990s sniffing out evidence that information technology was boosting rates of productivity). But it became clear to them that the same technologies making many jobs safer, easier, and more productive were also reducing the demand for many types of human workers.
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“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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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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