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.
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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?”
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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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.
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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.

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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Getting a first job is an important rite of passage in many cultures. The youth may start by doing household work, odd jobs, or working for a family business. In many countries, school children get summer jobs during the longer summer vacation. Students enrolled in higher education can apply for internships or coops to further enhance the probability of securing an entry level job upon graduation.
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.
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.
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.
A job, or occupation, is a person’s role in society. More specifically, a job is an activity, often regular and often performed in exchange for payment (“for a living”). Many people have multiple jobs (e.g., parent, homemaker, and employee). A person can begin a job by becoming an employee, volunteering, starting a business, or becoming a parent. The duration of a job may range from temporary (e.g., hourly odd jobs) to a lifetime (e.g., judges).
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.”
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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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)
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.
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.
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.
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.
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.
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