It is an invisible force that goes by many names. Computerization. Automation. Artificial intelligence. Technology. Innovation. And, everyone's favorite, ROBOTS.
Whatever name you prefer, some form of it has been stoking progress and killing jobs—from seamstresses to paralegals—for centuries. But this time is different: Nearly half of American jobs today could be automated in "a decade or two," according to a new paperby Carl Benedikt Frey and Michael A. Osborne, discussed recently in The Economist. The question is: Which half?
Another way of posing the same question is: Where do machines work better than people? Tractors are more powerful than farmers. Robotic arms are stronger and more tireless than assembly-line workers. But in the past 30 years, software and robots have thrived at replacing a particular kind of occupation: the average-wage, middle-skill, routine-heavy worker, especially in manufacturing and office admin.
Indeed, Frey and Osborne project that the next wave of computer progress will continue to shred human work where it already has: manufacturing, administrative support, retail, and transportation. Most remaining factory jobs are "likely to diminish over the next decades," they write. Cashiers, counter clerks, and telemarketers are similarly endangered. On the far right side of this graph, you can see the industry breakdown of the 47 percent of jobs they consider at "high risk."
And, for the nitty-gritty breakdown, here's a chart of the ten jobs with a 99-percentlikelihood of being replaced by machines and software. They are mostly routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers, and insurance underwriters). At the bottom, I've also listed the dozen jobs they consider least likely to be automated. Health care workers, people entrusted with our safety, and management positions dominate the list.
If you wanted to use this graph as a guide to the future of automation, your upshot would be: Machines are better at rules and routines; people are better at directing and diagnosing. But it doesn't have to stay that way.
Predicting the future typically means extrapolating the past. It often fails to anticipate breakthroughs. But it's precisely those unpredictable breakthroughs in computing that could have the biggest impact on the workforce.
For example, imagine somebody in 2004 forecasting the next ten years in mobile technology. In 2004, three years before the introduction of the iPhone, the best-selling mobile device, the Nokia 2600, looked like this:
Many extrapolations of phones from the early 2000s were just "the same thing, but smaller." It hasn't turned out that way at all: Smartphones are hardly phones, and they're bigger than the Nokia 2600. If you think wearable technology or the "Internet of Things" seem kind of stupid today, well, fine. But remember that ten years ago, the future of mobile appeared to be a minuscule cordless landline phone with Tetris, and now smartphones sales are about to overtake computers. Breakthroughs can be fast.
We might be on the edge of a breakthrough moment in robotics and artificial intelligence. Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats. Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they're bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do. This is why some people think managers are, for the moment, one of the largest categories immune to the rushing wave of AI.
Meanwhile, lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can't answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.
But perhaps we've hit an inflection point. As Erik Brynjolfsson and Andrew McAfee pointed out in their book Race Against the Machine (and in their new book The Second Machine Age), robots are finally crossing these moats by moving and thinking like people. Amazon has bought robots to work its warehouses. Narrative Science can write earnings summaries that are indistinguishable from wire reports. We can say to our phones I'm lost, help and our phones can tell us how to get home.
Computers that can drive cars, in particular, were never supposed to happen. Even ten years ago, many engineers said it was impossible. Navigating a crowded street isn't mindlessly routine. It needs a deft combination of spacial awareness, soft focus, and constant anticipation--skills that are quintessentially human. But I don't need to tell you about Google's self-driving cars, because they're one of the most over-covered stories in tech today.
And that's the most remarkable thing: In a decade, the idea of computers driving cars went from impossible to boring.
In the 19th century, new manufacturing technology replaced what was then skilled labor. Somebody writing about the future of innovation then might have said skilled labor is doomed. In the second half of the 20th century, however, software technology took the place of median-salaried office work, which economists like David Autor have called the "hollowing out" of the middle-skilled workforce.
The first wave showed that machines are better at assembling things. The second showed that machines are better at organization things. Now data analytics and self-driving cars suggest they might be better at pattern-recognition and driving. So what are we better at?
If you go back to the two graphs in this piece to locate the safest industries and jobs, they're dominated by managers, health-care workers, and a super-category that encompasses education, media, and community service. One conclusion to draw from this is that humans are, and will always be, superior at working with, and caring for, other humans. In this light, automation doesn't make the world worse. Far from it: It creates new opportunities for human ingenuity.
But robots are already creeping into diagnostics and surgeries. Schools are already experimenting with software that replaces teaching hours. The fact that some industries have been safe from automation for the last three decades doesn't guarantee that they'll be safe for the next one. As Frey and Osborne write in their conclusion:
While computerization has been historically confined to routine tasks involving explicit rule-based activities, algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labour in a wide range of non-routine cognitive tasks. In addition, advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks. This is likely to change the nature of work across industries and occupations.
It would be anxious enough if we knew exactly which jobs are next in line for automation. The truth is scarier. We don't really have a clue.
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