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.
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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“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.”
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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.
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.
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Shawn Boyer served as Snagajob’s CEO from the website’s launch in early 2000, to 2013; leading the company from a start-up to national employment network. Boyer was named America’s Small Business Person of the year in 2008 by the Small Business Administration and Virginia Business magazine’s Person of the Year in 2009.
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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.”
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.”)
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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.
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.
“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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Overall, the interview process was very straight forward and well laid out by the recruiter. The first round was the standard get-to-know-you recruiter call. The recruiter was great and we had a wonderful conversation about more than just the job description. He dove into a couple areas I had questions about and was more than willing to take extra time to answer my questions.
According to a new report released Wednesday, cryptocurrency-related searches on the site climbed from June through mid-December of 2017, peaking at 39 searches per million for the term “bitcoin” and 46 searches per million for the term “cryptocurrency.”
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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.
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.
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.
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.
Imagine you’re the pilot of an old Cessna. You’re flying in bad weather, you can’t see the horizon, and a frantic, disoriented passenger is yelling that you’re headed straight for the ground. What do you do? No question: You trust your instruments—your altimeter, your compass, and your artificial horizon—to give you your actual bearings, and keep flying.
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