Global warming in and of itself isn’t a problem. After all, life on earth has survived numerous cycles of cooling and heating. The real problem with global warming is how quickly it happens. If there isn’t enough time for living things (including us) to adapt, rapid changes in climate, not to mention more volatile weather patterns, can sow havoc. The consequences of catastrophic climate change can reverberate for centuries as species suffer horrific losses of their habitat, leading to mass extinctions.
The impact of technological change on our labor markets works the same way. As long as change is gradual, the markets can respond. Too fast, and it’s chaos. And as with my particular environmental preferences, it creates winners and losers.
The likely accelerating effect of recent advances in artificial intelligence on technological change is going to roil our labor markets in two fundamental ways. The first is the simple truth that most automation replaces workers, so it eliminates jobs. That means fewer places for people to work. This threat is easy to see and measure— employers roll in a robot and walk a worker to the door. But sometimes change is less visible. Each new workstation may eliminate the need for one-fifth of a salesperson, or free Skype calls may allow you to work more productively at home one day a week, deferring the need for that new hire until next quarter.
If this happens slowly, the resulting improvements in productivity and reduced cost eventually create wealth, stimulating job growth that compensates for the losses. The growth may be directly in the newly improved enterprise, as lower prices and better quality increase sales, creating a need to hire more workers. Or it may be in distant parts of the economy where the customers who no longer need to pay as much for some product or service decide to spend the money they saved. If new drilling technologies cause natural gas prices to drop, there’s more left over from your paycheck to save for that sailboat you’ve got your eye on.
But the second threat is much more subtle and difficult to predict. Many technological advances change the rules of the game by permitting businesses to reorganize and reengineer the way they operate. These organizational and process improvements often make obsolete not only jobs but skills. A teller may get laid off when a bank installs ATMs; the improved service creates a need to hire network engineers but not tellers. Even if the bank ultimately expands its total workforce, the tellers remain out of luck. Weavers can eventually learn to operate looms, gardeners to service lawnmowers, and doctors to use computers to select the right antibiotics—once they accept that synthetic intellects are superior to their own professional judgment. But learning the new skills doesn’t happen overnight, and sometimes the redundant workers simply aren’t capable of adapting—that will have to wait for a new generation of workers. For an example of labor market transformation that we have weathered successfully, consider agriculture. As recently as the early 1800s, farms employed a remarkable 80 percent of U.S. workers. Consider what this means. Producing food was by far the dominant thing people did for a living, and no doubt this pattern had been typical since the invention of agriculture about five thousand years ago. But by 1900, that figure had dropped in half, to 40 percent, and today it’s only 1.5 percent, including unpaid family and undocumented workers. Basically, we managed to automate nearly everyone out of a job, but instead of causing widespread unemployment, we freed people up for a host of other productive and wealth-producing activities. So over the last two centuries the U.S. economy was able to absorb on average about 1/2 percent loss of agricultural job opportunities each year without any obvious dislocations.
Now imagine that this had happened in two decades instead of two centuries. Your father worked on a farm, and his father before him, as far back as anyone could remember. Then a Henry Ford of farming revolutionized the entire industry in what seemed like a flash. The ground shook with the rumble of shiny new plows, threshers, and harvesters; the air was thick with the smell of diesel. Food prices plummeted, and corporations bought up farmland everywhere with the backing of deep-pocketed Wall Street financiers. Within a few years, your family’s farm was lost to foreclosure, along with every possession except the family Bible.
You and your five brothers and sisters, with an average third-grade education, found your skills of shoeing horses, plowing straight furrows, and baling hay utterly useless, as did all of your neighbors. But you still had to eat. You knew someone who knew someone who operated one of the new machines twelve hours a day in return for three squares, who supposedly got the job in Topeka, so you moved to one of the vast tent cities ringing the major Midwestern cities in the hope of finding work—any kind of work. Before long, you got word that your parents sold the Bible to buy medicine for your youngest sister, but she died of dysentery anyway. Eventually you lost track of the rest of your other siblings.
The 1 percent who still had jobs lived in tiny tract houses and barely got by, but they were nonetheless the envy of the rest—at least they had a solid roof over their heads. Each day, you waited in line outside their gated communities hoping for a chance to wash their clothes or deliver their bag lunches. Rumors spread that the daughters of the storied entrepreneur who changed the world had used his vast fortune to build a fabulous art museum made of crystal in a small town in Arkansas. But all this was before the revolution. After that, things got really bad.
I’m going to argue that a similarly tectonic shift looms ahead, though doubtlessly less dramatic and more humane. Forged laborers will displace the need for most skilled labor; synthetic intellects will largely supplant the skilled trades of the educated. When initially deployed, many new technologies will substitute directly for workers, getting the job done pretty much the same way. But other innovations will not only idle the workers; they will eliminate the types of jobs that they perform.
For example, consider the way Amazon constantly adapts the stock patterns in its warehouses. If a person were to do the warehouse planning (as in many more traditional companies), products might be organized in a logical and comprehensible way—identical items would be stored next to each other, for example, so when you needed to pick one, you knew where it was. But a synthetic intellect of the sort Amazon has built isn’t subject to this constraint. Like items can be located next to others that are frequently shipped with them, or on any shelf where they fit more compactly. To the human eye, it looks like chaos—products of different sizes and shapes are stacked randomly everywhere—which is why this type of warehouse organization is known as chaotic storage. But a synthetic intellect can keep track of everything and direct a worker to exactly the right place to fulfill an order far more efficiently than a human organizer could.
A side effect of introducing this innovation is that it reduces the training and knowledge required of warehouse workers, making them more susceptible to replacement by forged laborers. These employees no longer have to be familiar with the location of products on the shelves; indeed, it would be near impossible to do so in such a haphazard and evolving environment. Having first simplified the skills required to get the job done, Amazon can now replace the workers that roam the warehouse floor picking those orders. This is likely why the company bought the robotics company Kiva Systems, reportedly for $775 million, in 2012.
This is a single example of a profound shift that synthetic intellects will cause in our world. The need to impose order—not only for warehouses but for just about everything—is driven by the limitations of the human mind. Synthetic intellects suffer no such constraint, and their impact will turn tidiness to turmoil in many aspects of our lives. Our efforts to tame our intellectual and physical domains into manicured gardens will give way to tangled thickets, impenetrable by us.
When most people think about automation, they usually have in mind only the simple replacement of labor or improving workers’ speed or productivity, not the more extensive disruption caused by process reengineering. That’s why some jobs that you might least expect to succumb to automation may nonetheless disappear.
For instance, studies often cite jobs that require good people skills or powers of persuasion as examples of ones unlikely to be automated in the foreseeable future. But this isn’t necessarily the case.
The ability to convince you that you look terrific in a particular outfit is certainly the hallmark of a successful salesperson. But why do you need that person when you can ask hundreds of real people? Imagine a clothing store where you are photographed in several different outfits, and the images are immediately (and anonymously, by obscuring your face) posted to a special website where visitors can offer their opinion as to which one makes you look slimmer. Within seconds, you get objective, statistically reliable feedback from impartial strangers, who earn points if you complete a purchase. (This concept is called “crowdsourcing.”) Why put your faith in a salesperson motivated by commission when you can find out for sure?
Reflecting these two different effects of automation on labor (replacing workers and rendering skills obsolete), economists have two different names for the resulting unemployment. The first is “cyclical,” meaning that people are cycling in and out of jobs. In bad times, the pool of people who are between jobs may grow, leading to higher unemployment. But historically, as soon as the economy picks up, the idled workers find new jobs. Fewer people are unemployed and for shorter periods of time. This works just like the housing market: in a slow market, there are more houses available and the ones that are take longer to sell. But when the market turns around this excess inventory is quickly absorbed.
I was surprised to learn just how much turnover there is in the U.S. labor market. In 2013, a fairly typical year, 40 percent of workers changed jobs. That’s a very fluid market. By contrast, less than 4 percent of homes are sold each year. So when we talk about 8 percent unemployment, it doesn’t take long for small changes in the rates of job creation and destruction to soak that up, or conversely to spill more people out of work.
The other type of unemployment is called “structural,” which means that some group of unemployed simply can’t find suitable employment at all. They can send out résumés all day long, but no one wants to hire them, because their skills are a poor match for the available jobs. The equivalent in the housing market would be if the types of houses available weren’t suitable for the available buyers. Suddenly couples start having triplets instead of single kids and so need more bedrooms, or people start commuting to work in flying cars that can take off only from flat rooftops, while most houses have pitched roofs.
As you can see from my fanciful examples, the factors that change the desirability of housing don’t usually change very fast, so builders and remodelers have plenty of time to adapt. But this isn’t true for automation because the pace of invention and the rate of adoption can change quickly and unpredictably, shifting the character of whole labor market segments far more rapidly than people can learn new skills—if they can be retrained at all. We’re buffeted about by these fickle winds precisely because they are hard to anticipate and virtually impossible to measure.
Economists and academics who study labor markets have a natural bias toward the quantifiable. This is understandable, because to credibly sound the alarm, they must have the hard data to back it up. Their work must stand up to objective, independent peer review, which basically means it must be reduced to numbers. But as I learned in business, spreadsheets and financial statements can capture only certain things, while trends that resist reduction to measurement often dominate the outcome. (Indeed, there’s an argument to be made that the troublesome and unpredictable business cycles plaguing our economy are largely driven by the fact that returns are easily quantified, but risks are not.) I can’t count the number of meticulously detailed yet bogus sales projections I’ve seen bamboozle management teams. At work I sometimes felt my most important contribution as a manager was anticipating that which had yet to manifest itself in quantifiable form.
But talking about the overall labor market, unemployment statistics, or the aggregate rate of change obscures the reality of the situation because the landscape of useful skills shifts erratically. The complexity of this web of disappearing labor habitats and evolving job ecosystems resists analysis by traditional mathematical tools, which is why attempts to quantify this whole process tend to bog down in reams of charts and tables or devolve into hand-waving.
Luckily I’m not bound by these same professional constraints, so fasten your seat belt for a quick tour of the future. My approach will be to look at some specific examples, then attempt to reason by analogy to get a broader picture. Let’s start with retail—the largest commercial job market, as determined by the U.S. Bureau of Labor Statistics (BLS).
The BLS reports that about 10 percent of all U.S. workers are employed in retailing, or approximately 4.5 million people. To analyze trends, let’s use salespersons as a proxy for the whole group. The BLS projects that this labor force, which stood at 4.4 million in 2012, will grow by 10 percent to 4.9 million over the next ten years. But this is based on current demographic trends, not a qualitative analysis of what’s actually going on in the industry.
To get a sense of what’s really going to happen, consider the effect on employment of the transition from bricks-and-mortar stores to online retailers. A useful way to analyze this is to use a statistic called revenue per employee. You take the total annual revenue of a company and divide it by the number of employees. It’s a standard measure of how efficient a company is, or at least how labor-efficient.
Average revenue per employee for Amazon (the largest online retailer) over the past five years is around $855,000. Compare that to Walmart (the largest bricks-and-mortar retailer), whose revenue per employee is around $213,000—one of the highest of any retailer. This means that for each $1 million in sales, Walmart employs about five people. But for the same amount of sales, Amazon employs slightly more than one person. So for every $1 million in sales that shift from Walmart to Amazon, four jobs are potentially lost.
Now, both companies sell pretty much the same stuff. And Walmart does a good portion of its sales online as well, so the job loss implied by the shift to online sales is understated. And neither company is standing still; both are likely to grow more efficient in the future.
Excerpted from "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" by Jerry Kaplan, published by Yale University Press. Copyright c 2015 by Jerry Kaplan. Reprinted by permission of the publisher. All rights reserved.