The talking bot can supposedly identify joy, sadness, anger, and surprise and determine whether a person is in a good or bad mood—abilities that Pepper’s engineers figured would make “him” an ideal personal assistant or salesperson. And sure enough, there are more than 10,000 Peppers now at work in SoftBank stores, Pizza Huts, cruise ships, homes, and elsewhere.
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?”
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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.
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).
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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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Summary Creates a welcome environment for Customers. Sells soft drinks, packaged and/or bulk candies, popcorn, hot dogs, ice cream, coffee, and other food items to theatre patrons. Operates and cleans concession and/or restaurant equipment. Cleans, maintains, and stocks the concession stand and/or restaurant. The Concession or Restaurant Worker may also be asked to double as the Box Office Cashier or Usher, as staffing needs require, and, as a result, such an Employee must also be able to perform the essential job functions of those positions. Some locations are equipped for alcohol sales….
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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)
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If automation were truly remaking the job market, you’d also expect to see a lot of what economists call job churn as people move from company to company and industry to industry after their jobs have been destroyed. But we’re seeing the opposite of that. According to a recent paper by Robert Atkinson and John Wu of the Information Technology and Innovation Foundation, “Levels of occupational churn in the United States are now at historic lows.” The amount of churn since 2000—an era that saw the mainstreaming of the internet and the advent of AI—has been just 38 percent of the level of churn between 1950 and 2000. And this squares with the statistics on median US job tenure, which has lengthened, not shortened, since 2000. In other words, rather than a period of enormous disruption, this has been one of surprising stability for much of the American workforce. Median job tenure today is actually similar to what it was in the 1950s—the era we think of as the pinnacle of job stability.
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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.