Rise of the Robots: Technology and the Threat of a Jobless Future (2 page)

It is an era that will be defined by a fundamental shift in the relationship between workers and machines. That shift will ultimately challenge one of our most basic assumptions about technology: that
machines are tools
that increase the productivity of workers. Instead, machines themselves are turning into workers, and the line between the capability of labor and capital is blurring as never before.

All this progress is, of course, being driven by the relentless acceleration in computer technology. While most people are by now familiar with Moore’s Law—the well-established rule of thumb that says computing power roughly doubles every eighteen to twenty-four months—not everyone has fully assimilated the implications of this extraordinary exponential progress.

Imagine that you get in your car and begin driving at 5 miles per hour. You drive for a minute, accelerate to double your speed to 10 mph, drive for another minute, double your speed again, and so on. The really remarkable thing is not simply the fact of the doubling but the amount of ground you cover after the process has gone on for a while. In the first minute, you would travel about 440 feet. In the third minute at 20 mph, you’d cover 1,760 feet. In the fifth minute, speeding along at 80 mph, you would go well over a mile. To complete the sixth minute, you’d need a faster car—as well as a racetrack.

Now think about how fast you would be traveling—and how much progress you would make in that final minute—if you doubled your speed twenty-seven times. That’s roughly the number of times computing power has doubled since the invention of the integrated circuit in 1958. The revolution now under way is happening not just because of the acceleration itself but because
that acceleration has been going on for so long
that the amount of progress we can now expect in any given year is potentially mind-boggling.

The answer to the question about your speed in the car, by the way, is 671
million
miles per hour. In that final, twenty-eighth minute, you would travel more than 11 million miles. Five minutes or so at that speed would get you to Mars. That, in a nutshell, is where information technology stands today, relative to when the first primitive integrated circuits started plodding along in the late 1950s.

As someone who has worked in software development for more than twenty-five years, I’ve had a front-row seat when it comes to observing that extraordinary acceleration in computing power. I’ve also seen at close hand the tremendous progress made in software design, and in the tools that make programmers more productive. And, as a small business owner, I’ve watched as technology has transformed the way I run my business—in particular, how it has dramatically reduced the need to hire employees to perform many of the routine tasks that have always been essential to the operation of any business.

In 2008, as the global financial crisis unfolded, I began to give serious thought to the implications of that consistent doubling in computational power and, especially, to the likelihood that it would dramatically transform the job market and overall economy in coming years and decades. The result was my first book,
The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future,
published in 2009.

In that book, even as I wrote about the importance of accelerating technology, I underestimated just how rapidly things would in fact move forward. For example, I noted that auto manufacturers were working on collision avoidance systems to help prevent accidents, and I suggested that “over time these systems could evolve into technology capable of driving the car autonomously.” Well, it turned out that “over time” wasn’t much time at all! Within a year of the book’s publication, Google introduced a fully automated car capable of driving in traffic. And since then, three states—Nevada, California, and Florida—have passed laws allowing self-driving vehicles to share the road on a limited basis.

I also wrote about progress being made in the field of artificial intelligence. At the time, the story of IBM’s “Deep Blue” computer and how it had defeated world chess champion Garry Kasparov in 1997, was perhaps the most impressive demonstration of AI in action. Once again, I was taken by surprise when IBM introduced Deep Blue’s successor, Watson—a machine that took on a far more difficult challenge: the television game show
Jeopardy!
Chess is a game with rigidly defined rules; it is the sort of thing we might expect a computer to be good at.
Jeopardy!
is something else entirely: a game that draws on an almost limitless body of knowledge and requires a sophisticated ability to parse language, including even jokes and puns. Watson’s success at
Jeopardy!
is not only impressive, it is highly practical, and in fact, IBM is already positioning Watson to play a significant role in fields like medicine and customer service.

It’s a good bet that nearly all of us will be surprised by the progress that occurs in the coming years and decades. Those surprises won’t be confined to the nature of the technical advances themselves: the impact that accelerating progress has on the job market and the overall economy is poised to defy much of the conventional wisdom about how technology and economics intertwine.

One widely held belief that is certain to be challenged is the assumption that automation is primarily a threat to workers who have little education and lower-skill levels. That assumption emerges from the fact that such jobs tend to be routine and repetitive. Before you get too comfortable with that idea, however, consider just how fast the frontier is moving. At one time, a “routine” occupation would probably have implied standing on an assembly line. The reality today is far different. While lower-skill occupations will no doubt continue to be affected, a great many college-educated, white-collar workers are going to discover that their jobs, too, are squarely in the sights as software automation and predictive algorithms advance rapidly in capability.

The fact is that “routine” may not be the best word to describe the jobs most likely to be threatened by technology. A more accurate
term might be “predictable.” Could another person learn to do your job by studying a detailed record of everything you’ve done in the past? Or could someone become proficient by repeating the tasks you’ve already completed, in the way that a student might take practice tests to prepare for an exam? If so, then there’s a good chance that an algorithm may someday be able to learn to do much, or all, of your job. That’s made especially likely as the “big data” phenomenon continues to unfold: organizations are collecting incomprehensible amounts of information about nearly every aspect of their operations, and a great many jobs and tasks are likely to be encapsulated in that data—waiting for the day when a smart machine learning algorithm comes along and begins schooling itself by delving into the record left by its human predecessors.

The upshot of all this is that acquiring more education and skills will not necessarily offer effective protection against job automation in the future. As an example, consider radiologists, medical doctors who specialize in the interpretation of medical images. Radiologists require a tremendous amount of training, typically a minimum of thirteen years beyond high school. Yet, computers are rapidly getting better at analyzing images. It’s quite easy to imagine that someday, in the not too distant future, radiology will be a job performed almost exclusively by machines.

In general, computers are becoming very proficient at acquiring skills, especially when a large amount of training data is available. Entry-level jobs, in particular, are likely to be heavily affected, and there is evidence that this may already be occurring. Wages for new college graduates have actually been declining over the past decade, while up to 50 percent of new graduates are forced to take jobs that do not require a college degree. Indeed, as I’ll demonstrate in this book, employment for many skilled professionals—including lawyers, journalists, scientists, and pharmacists—is already being significantly eroded by advancing information technology. They are not alone: most jobs are, on some level, fundamentally routine and
predictable, with relatively few people paid primarily to engage in truly creative work or “blue-sky” thinking.

As machines take on that routine, predictable work, workers will face an unprecedented challenge as they attempt to adapt. In the past, automation technology has tended to be relatively specialized and to disrupt one employment sector at a time, with workers then switching to a new emerging industry. The situation today is quite different. Information technology is a truly general-purpose technology, and its impact will occur across the board. Virtually every industry in existence is likely to become less labor-intensive as new technology is assimilated into business models—and that transition could happen quite rapidly. At the same time, the new industries that emerge will nearly always incorporate powerful labor-saving technology right from their inception. Companies like Google and Facebook, for example, have succeeded in becoming household names and achieving massive market valuations while hiring only a tiny number of people relative to their size and influence. There’s every reason to expect that a similar scenario will play out with respect to nearly all the new industries created in the future.

All of this suggests that we are headed toward a transition that will put enormous stress on both the economy and society. Much of the conventional advice offered to workers and to students who are preparing to enter the workforce is likely to be ineffective. The unfortunate reality is that a great many people will do everything right—at least in terms of pursuing higher education and acquiring skills—and yet will still fail to find a solid foothold in the new economy.

Beyond the potentially devastating impact of long-term unemployment and underemployment on individual lives and on the fabric of society, there will also be a significant economic price. The virtuous feedback loop between productivity, rising wages, and increasing consumer spending will collapse. That positive feedback effect is already seriously diminished: we face soaring inequality not just in income but also in consumption. The top 5 percent of households
are currently responsible for nearly 40 percent of spending, and that trend toward increased concentration at the top seems almost certain to continue. Jobs remain the primary mechanism by which purchasing power gets into the hands of consumers. If that mechanism continues to erode, we will face the prospect of having too few viable consumers to continue driving economic growth in our mass-market economy.

As this book will make clear, advancing information technology is pushing us toward a tipping point that is poised to ultimately make the entire economy less labor-intensive. However, that transition won’t necessarily unfold in a uniform or predictable way. Two sectors in particular—higher education and health care—have, so far, been highly resistant to the kind of disruption that is already becoming evident in the broader economy. The irony is that the failure of technology to transform these sectors could amplify its negative consequences elsewhere, as the costs of health care and education become ever more burdensome.

Technology, of course, will not shape the future in isolation. Rather, it will intertwine with other major societal and environmental challenges such as an aging population, climate change, and resource depletion. It’s often predicted that a shortage of workers will eventually develop as the baby boom generation exits the workforce, effectively counterbalancing—or perhaps even overwhelming—any impact from automation. Rapid innovation is typically framed purely as a countervailing force with the potential to minimize, or even reverse, the stress we put on the environment. However, as we’ll see, many of these assumptions rest on uncertain foundations: the story is sure to be far more complicated. Indeed, the frightening reality is that if we don’t recognize and adapt to the implications of advancing technology, we may face the prospect of a “perfect storm” where the impacts from soaring inequality, technological unemployment, and climate change unfold roughly in parallel, and in some ways amplify and reinforce each other.

In Silicon Valley the phrase “disruptive technology” is tossed around on a casual basis. No one doubts that technology has the power to devastate entire industries and upend specific sectors of the economy and job market. The question I will ask in this book is bigger: Can accelerating technology disrupt
our entire system
to the point where a fundamental restructuring may be required if prosperity is to continue?

Chapter 1

THE AUTOMATION WAVE

A warehouse worker approaches a stack of boxes. The boxes are of varying shapes, sizes, and colors, and they are stacked in a somewhat haphazard way.

Imagine for a moment that you can see inside the brain of the worker tasked with moving the boxes, and consider the complexity of the problem that needs to be solved.

Many of the boxes are a standard brown color and are pressed tightly against each other, making the edges difficult to perceive. Where precisely does one box end and the next begin? In other cases, there are gaps and misalignments. Some boxes are rotated so that one edge juts out. At the top of the pile, a small box rests at an angle in the space between two larger boxes. Most of the boxes are plain brown or white cardboard, but some are emblazoned with company logos, and a few are full-color retail boxes intended to be displayed on store shelves.

The human brain is, of course, capable of making sense of all this complicated visual information almost instantaneously. The worker easily perceives the dimensions and orientation of each box, and
seems to know instinctively that he must begin by moving the boxes at the top of the stack and how to move the boxes in a sequence that won’t destabilize the rest of the pile.

This is exactly the type of visual perception challenge that the human brain has evolved to overcome. That the worker succeeds in moving the boxes would be completely unremarkable—were it not for the fact that, in this case, the worker is a robot. To be more precise, it is a snake-like robotic arm, its head consisting of a suction-powered gripper. The robot is slower to comprehend than a human would be. It peers at the boxes, adjusts its gaze slightly, ponders some more, and then finally lunges forward and grapples a box from the top of the pile.
*
The sluggishness, however, results almost entirely from the staggering complexity of the computation required to perform this seemingly simple task. If there is one thing the history of information technology teaches, it is that this robot is going to very soon get a major speed upgrade.