rise of the robots
INTRODUCTION
Sometime during the 1960s, the Nobel laureate economist Milton Friedman was consulting with the government of a developing
Asian nation. Friedman was taken to a large-scale public works project, where he was surprised to see large numbers of workers wielding
shovels, but very few bulldozers, tractors, or other heavy earth-moving
equipment. When asked about this, the government official in charge
explained that the project was intended as a “jobs program.” Friedman’s caustic reply has become famous: “So then, why not give the
workers spoons instead of shovels?”
Friedman’s remark captures the skepticism—and often outright
derision—expressed by economists confronting fears about the prospect of machines destroying jobs and creating long-term unemployment. Historically, that skepticism appears to be well-founded. In the
United States, especially during the twentieth century, advancing technology has consistently driven us toward a more prosperous society.
There have certainly been hiccups—and indeed major disruptions—
along the way. The mechanization of agriculture vaporized millions of jobs and drove crowds of unemployed farmhands into cities in search of factory work. Later, automation and globalization
pushed workers out of the manufacturing sector and into new service
jobs. Short-term unemployment was often a problem during these
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x Introduction
transitions, but it never became systemic or permanent. New jobs
were created and dispossessed workers found new opportunities.
What’s more, those new jobs were often better than earlier counterparts, requiring upgraded skills and offering better wages. At no
time was this more true than in the two and a half decades following World War II. This “golden age” of the American economy was
characterized by a seemingly perfect symbiosis between rapid technological progress and the welfare of the American workforce. As the
machines used in production improved, the productivity of the workers operating those machines likewise increased, making them more
valuable and allowing them to demand higher wages. Throughout
the postwar period, advancing technology deposited money directly
into the pockets of average workers as their wages rose in tandem
with soaring productivity. Those workers, in turn, went out and
spent their ever-increasing incomes, further driving demand for the
products and services they were producing.
As that virtuous feedback loop powered the American economy
forward, the profession of economics was enjoying its own golden
age. It was during the same period that towering figures like Paul
Samuelson worked to transform economics into a science with a
strong mathematical foundation. Economics gradually came to be
almost completely dominated by sophisticated quantitative and statistical techniques, and economists began to build the complex mathematical models that still constitute the field’s intellectual basis. As
the postwar economists did their work, it would have been natural
for them to look at the thriving economy around them and assume
that it was normal: that it was the way an economy was supposed to
work—and would always work.
In his 2005 book Collapse: How Societies Choose to Succeed
or Fail, Jared Diamond tells the story of agriculture in Australia. In
the nineteenth century, when Europeans first colonized Australia,
they found a relatively lush, green landscape. Like American economists in the 1950s, the Australian settlers assumed that what they
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Introduction xi
were seeing was normal, and that the conditions they observed would
continue indefinitely. They invested heavily in developing farms and
ranches on this seemingly fertile land.
Within a decade or two, however, reality struck. The farmers
found that the overall climate was actually far more arid than they
were initially led to believe. They had simply had the good fortune
(or perhaps misfortune) to arrive during a climactic “Goldilocks
period”—a sweet spot when everything happened to be just right for
agriculture. Today in Australia, you can find the remnants of those
ill-fated early investments: abandoned farm houses in the middle of
what is essentially a desert.
There are good reasons to believe that America’s economic Goldilocks period has likewise come to an end. That symbiotic relationship
between increasing productivity and rising wages began to dissolve
in the 1970s. As of 2013, a typical production or nonsupervisory
worker earned about 13 percent less than in 1973 (after adjusting for
inflation), even as productivity rose by 107 percent and the costs of
big-ticket items like housing, education, and health care have soared.1
On January 2, 2010, the Washington Post reported that the first
decade of the twenty-first century resulted in the creation of no new
jobs. Zero.2
This hasn’t been true of any decade since the Great Depression; indeed, there has never been a postwar decade that produced less than a 20 percent increase in the number of available jobs.
Even the 1970s, a decade associated with stagflation and an energy
crisis, generated a 27 percent increase in jobs.3
The lost decade of
the 2000s is especially astonishing when you consider that the US
economy needs to create roughly a million jobs per year just to keep
up with growth in the size of the workforce. In other words, during
those first ten years there were about 10 million missing jobs that
should have been created—but never showed up.
Income inequality has since soared to levels not seen since 1929,
and it has become clear that the productivity increases that went into
workers’ pockets back in the 1950s are now being retained almost
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xii Introduction
entirely by business owners and investors. The share of overall national income going to labor, as opposed to capital, has fallen precipitously and appears to be in continuing free fall. Our Goldilocks
period has reached its end, and the American economy is moving
into a new era.
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.
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Introduction xiii
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.
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xiv Introduction
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
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Introduction xv
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
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xvi Introduction
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
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Introduction xvii
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.
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xviii Introduction
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?
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1
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
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2 RISE OF THE ROBOTS
* A video of Industrial Perception’s box-moving robot can be seen on the company’s website at http://www.industrial-perception.com/technology.html.
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 suctionpowered 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.
Indeed, engineers at Industrial Perception, Inc., the Silicon Valley start-up company that designed and built the robot, believe the
machine will ultimately be able to move a box every second. That
compares with a human worker’s maximum rate of a box roughly
every six seconds.1
Needless to say, the robot can work continuously;
it will never get tired or suffer a back injury—and it will certainly
never file a worker’s compensation claim.
Industrial Perception’s robot is remarkable because its capability sits at the nexus of visual perception, spatial computation, and
dexterity. In other words, it is invading the final frontier of machine
automation, where it will compete for the few relatively routine, manual jobs that are still available to human workers.
Robots in factories are, of course, nothing new. They have become indispensable in virtually every sector of manufacturing, from
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The Automation Wave 3
automobiles to semiconductors. Electric-car company Tesla’s new
plant in Fremont, California, uses 160 highly flexible industrial robots
to assemble about 400 cars per week. As a new-car chassis arrives at
the next position in the assembly line, multiple robots descend on it
and operate in coordination. The machines are able to autonomously
swap the tools wielded by their robotic arms in order to complete a
variety of tasks. The same robot, for example, installs the seats, retools itself, and then applies adhesive and drops the windshield into
place.2
According to the International Federation of Robotics, global
shipments of industrial robots increased by more than 60 percent
between 2000 and 2012, with total sales of about $28 billion in 2012.
By far the fastest-growing market is China, where robot installations
grew at about 25 percent per year between 2005 and 2012.3
While industrial robots offer an unrivaled combination of speed,
precision, and brute strength, they are, for the most part, blind actors in a tightly choreographed performance. They rely primarily on
precise timing and positioning. In the minority of cases where robots
have machine vision capability, they can typically see in just two
dimensions and only in controlled lighting conditions. They might,
for example, be able to select parts from a flat surface, but an inability to perceive depth in their field of view results in a low tolerance
for environments that are to any meaningful degree unpredictable.
The result is that a number of routine factory jobs have been left for
people. Very often these are jobs that involve filling the gaps between
the machines, or they are at the end points of the production process.
Examples might include choosing parts from a bin and then feeding
them into the next machine, or loading and unloading the trucks that
move products to and from the factory.
The technology that powers the Industrial Perception robot’s
ability to see in three dimensions offers a case study in the ways that
cross-fertilization can drive bursts of innovation in unexpected areas.
It might be argued that the robot’s eyes can trace their origin to November 2006, when Nintendo introduced its Wii video game console.
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4 RISE OF THE ROBOTS
Nintendo’s machine included an entirely new type of game controller:
a wireless wand that incorporated an inexpensive device called an
accelerometer. The accelerometer was able to detect motion in three
dimensions and then output a data stream that could be interpreted
by the game console. Video games could now be controlled through
body movements and gestures. The result was a dramatically different game experience. Nintendo’s innovation smashed the stereotype
of the nerdy kid glued to a monitor and a joystick, and opened a new
frontier for games as active exercise.
It also demanded a competitive response from the other major
players in the video game industry. Sony Corporation, makers of the
PlayStation, elected to essentially copy Nintendo’s design and introduced its own motion-detecting wand. Microsoft, however, aimed to
leapfrog Nintendo and come up with something entirely new. The
Kinect add-on to the Xbox 360 game console eliminated the need
for a controller wand entirely. To accomplish this, Microsoft built
a webcam-like device that incorporates three-dimensional machine
vision capability based in part on imaging technology created at a
small Israeli company called PrimeSense. The Kinect sees in three
dimensions by using what is, in essence, sonar at the speed of light:
it shoots an infrared beam at the people and objects in a room and
then calculates their distance by measuring the time required for the
reflected light to reach its infrared sensor. Players could now interact
with the Xbox game console simply by gesturing and moving in view
of the Kinect’s camera.
The truly revolutionary thing about the Kinect was its price. Sophisticated machine vision technology—which might previously have
cost tens or even hundreds of thousands of dollars and required bulky
equipment—was now available in a compact and lightweight consumer device priced at $150. Researchers working in robotics instantly
realized the potential for the Kinect technology to transform their
field. Within weeks of the product’s introduction, both universitybased engineering teams and do-it-yourself innovators had hacked
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The Automation Wave 5
into the Kinect and posted YouTube videos of robots that were now
able to see in three dimensions.4
Industrial Perception likewise decided to base its vision system on the technology that powers the
Kinect, and the result is an affordable machine that is rapidly approaching a nearly human-level ability to perceive and interact with
its environment while dealing with the kind of uncertainty that characterizes the real world.
A Versatile Robotic Worker
Industrial Perception’s robot is a highly specialized machine focused
specifically on moving boxes with maximum efficiency. Boston-based
Rethink Robotics has taken a different track with Baxter, a lightweight humanoid manufacturing robot that can easily be trained to
perform a variety of repetitive tasks. Rethink was founded by Rodney Brooks, one of the world’s foremost robotics researchers at MIT
and a co-founder of iRobot, the company that makes the Roomba
automated vacuum cleaner as well as military robots used to defuse
bombs in Iraq and Afghanistan. Baxter, which costs significantly
less than a year’s wages for a typical US manufacturing worker, is
essentially a scaled-down industrial robot that is designed to operate
safely in close proximity to people.
In contrast to industrial robots, which require complex and expensive programming, Baxter can be trained simply by moving its
arms through the required motions. If a facility uses multiple robots,
one Baxter can be trained and then the knowledge can be propagated
to the others simply by plugging in a USB device. The robot can be
adapted to a variety of tasks, including light assembly work, transferring parts between conveyer belts, packing products into retail
packaging, or tending machines used in metal fabrication. Baxter
is particularly talented at packing finished products into shipping
boxes. K’NEX, a toy construction set manufacturer located in Hatfield, Pennsylvania, found that Baxter’s ability to pack its products
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6 RISE OF THE ROBOTS
tightly allowed the company to use 20–40 percent fewer boxes.5
Rethink’s robot also has two-dimensional machine vision capability
powered by cameras on both wrists and can pick up parts and even
perform basic quality-control inspections.
The Coming Explosion in Robotics
While Baxter and Industrial Perception’s box-moving robot are dramatically different machines, they are both built on the same fundamental software platform. ROS—or Robot Operating System—was
originally conceived at Stanford University’s Artificial Intelligence
Laboratory and then developed into a full-fledged robotics platform
by Willow Garage, Inc., a small company that designs and manufactures programmable robots that are used primarily by researchers at
universities. ROS is similar to operating systems like Microsoft Windows, Macintosh OS, or Google’s Android but is geared specifically
toward making robots easy to program and control. Because ROS
is free and also open source—meaning that software developers can
easily modify and enhance it—it is rapidly becoming the standard
software platform for robotics development.
The history of computing shows pretty clearly that once a standard operating system, together with inexpensive and easy-to-use
programming tools, becomes available, an explosion of application
software is likely to follow. This has been the case with personal
computer software and, more recently, with iPhone, iPad, and Android apps. Indeed, these platforms are now so saturated with application software that it can be genuinely difficult to conceive of an
idea that hasn’t already been implemented.
It’s a good bet that the field of robotics is poised to follow a similar path; we are, in all likelihood, at the leading edge of an explosive
wave of innovation that will ultimately produce robots geared toward
nearly every conceivable commercial, industrial, and consumer task.
That explosion will be powered by the availability of standardized
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The Automation Wave 7
software and hardware building blocks that will make it a relatively
simple matter to assemble new designs without the need to reinvent
the wheel. Just as the Kinect made machine vision affordable, other
hardware components—such as robotic arms—will see their costs
driven down as robots begin scaling up to high-volume production.
As of 2013, there were already thousands of software components
available to work with ROS, and development platforms were cheap
enough to allow nearly anyone to start designing new robotics applications. Willow Garage, for example, sells a complete mobile robot
kit called TurtleBot that includes Kinect-powered machine vision
for about $1,200. After inflation is taken into account, that’s far less
than what an inexpensive personal computer and monitor cost in
the early 1990s, when Microsoft Windows was in the early stages of
producing its own software explosion.
When I visited the RoboBusiness conference and tradeshow in
Santa Clara, California, in October 2013, it was clear that the robotics industry had already started gearing up for the coming explosion. Companies of all sizes were on hand to showcase robots
designed to perform precision manufacturing, transport medical
supplies between departments in large hospitals, or autonomously
operate heavy equipment for agriculture and mining. There was
a personal robot named “Budgee” capable of carrying up to fifty
pounds of stuff around the house or at the store. A variety of educational robots focused on everything from encouraging technical
creativity to assisting children with autism or learning disabilities. At
the Rethink Robotics booth, Baxter had received Halloween training and was grasping small boxes of candy and then dropping them
into pumpkin-shaped trick-or-treat buckets. There were also companies marketing components like motors, sensors, vision systems,
electronic controllers, and the specialized software used to construct
robots. Silicon Valley start-up Grabit Inc. demonstrated an innovative electroadhesion-powered gripper that allows robots to pick up,
carry, and place nearly anything simply by employing a controlled
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8 RISE OF THE ROBOTS
electrostatic charge. To round things out, a global law firm with a
specialized robotics practice was on hand to help employers navigate the complexities of labor, employment, and safety regulations
when robots are brought in to replace, or work in close proximity
to, people.
One of the most remarkable sights at the tradeshow was in the
aisles—which were populated by a mix of human attendees and dozens of remote-presence robots provided by Suitable Technologies,
Inc. These robots, consisting of a flat screen and camera mounted
on a mobile pedestal, allowed remote participants to visit tradeshow
booths, view demonstrations, ask questions, and otherwise interact normally with other participants. Suitable Technologies offered
remote presence at the tradeshow for a minimal fee, allowing visitors from outside the San Francisco Bay area to avoid thousands of
dollars in travel costs. After a few minutes, the robots—each with
a human face displayed on its screen—did not seem at all out of
place as they prowled between booths and engaged other attendees
in conversation.
Manufacturing Jobs and Factory Reshoring
In a September 2013 article, Stephanie Clifford of the New York
Times told the story of Parkdale Mills, a textile factory in Gaffney,
South Carolina. The Parkdale plant employs about 140 people. In
1980, the same level of production would have required more than
2,000 factory workers. Within the Parkdale plant, “only infrequently
does a person interrupt the automation, mainly because certain tasks
are still cheaper if performed by hand—like moving half-finished
yarn between machines on forklifts.”6
Completed yarn is conveyed
automatically toward packing and shipping machines along pathways
attached to the ceiling.
Nonetheless, those 140 factory jobs represent at least a partial reversal of a decades-long decline in manufacturing employment. The
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The Automation Wave 9
US textile industry was decimated in the 1990s as production moved
to low-wage countries, especially China, India, and Mexico. About
1.2 million jobs—more than three-quarters of domestic employment
in the textile sector—vanished between 1990 and 2012. The last few
years, however, have seen a dramatic rebound in production. Between 2009 and 2012, US textile and apparel exports rose by 37 percent to a total of nearly $23 billion.7
The turnaround is being driven
by automation technology so efficient that it is competitive with even
the lowest-wage offshore workers.
Within the manufacturing sector in the United States and other
developed countries, the introduction of these sophisticated laborsaving innovations is having a mixed impact on employment. While
factories like Parkdale don’t directly create large numbers of manufacturing jobs, they do drive increased employment at suppliers and
in peripheral areas like driving the trucks that move raw materials and finished products. While a robot like Baxter can certainly
eliminate the jobs of some workers who perform routine tasks, it
also helps make US manufacturing more competitive with low-wage
countries. Indeed, there is now a significant “reshoring” trend under
way, and this is being driven both by the availability of new technology and by rising offshore labor costs, especially in China where
typical factory workers saw their pay increase by nearly 20 percent
per year between 2005 and 2010. In April 2012, the Boston Consulting Group surveyed American manufacturing executives and found
that nearly half of companies with sales exceeding $10 billion were
either actively pursuing or considering bringing factories back to the
United States.8
Factory reshoring dramatically decreases transportation costs
and also provides many other advantages. Locating factories in close
proximity to both consumer markets and product design centers allows companies to cut production lead times and be far more responsive to their customers. As automation becomes ever more flexible
and sophisticated, it’s likely that manufacturers will trend toward
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10 RISE OF THE ROBOTS
offering more customizable products—perhaps, for example, allowing customers to create unique designs or specify hard-to-find clothing sizes through easy-to-use online interfaces. Domestic automated
production could then put a finished product into a customer’s hands
within days.
There is, however, one important caveat to the reshoring narrative. Even the relatively small number of new factory jobs now being
created as a result of reshoring won’t necessarily be around over the
long term; as robots continue to get more capable and dexterous and
as new technologies like 3D printing come into widespread use, it
seems likely that many factories will eventually approach full automation. Manufacturing jobs in the United States currently account
for well under 10 percent of total employment. As a result, manufacturing robots and reshoring are likely to have a fairly marginal
impact on the overall job market.
The story will be very different in developing countries like
China, where employment is far more focused in the manufacturing sector. In fact, advancing technology has already had a dramatic
impact on Chinese factory jobs; between 1995 and 2002 China lost
about 15 percent of its manufacturing workforce, or about 16 million
jobs.9
There is strong evidence to suggest that this trend is poised to
accelerate. In 2012, Foxconn—the primary contract manufacturer
of Apple devices—announced plans to eventually introduce up to a
million robots in its factories. Taiwanese company Delta Electronics,
Inc., a producer of power adapters, has recently shifted its strategy
to focus on low-cost robots for precision electronics assembly. Delta
hopes to offer a one-armed assembly robot for about $10,000—less
than half the cost of Rethink’s Baxter. European industrial robot
manufacturers like ABB Group and Kuka AG are likewise investing
heavily in the Chinese market and are currently building local factories to churn out thousands of robots per year.10
Increased automation is also likely to be driven by the fact that the
interest rates paid by large companies in China are kept artificially
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The Automation Wave 11
low as a result of government policy. Loans are often rolled over
continuously, so that the principal is never repaid. This makes capital
investment extremely attractive even when labor costs are low and
has been one of the primary reasons that investment now accounts
for nearly half of China’s GDP.11 Many analysts believe that this artificially low cost of capital has caused a great deal of mal-investment
throughout China, perhaps most famously the construction of “ghost
cities” that appear to be largely unoccupied. By the same token, low
capital costs may create a powerful incentive for big companies to
invest in expensive automation, even in those cases where it does not
necessarily make good business sense to do so.
One of the biggest challenges for a transition to robotic assembly
in the Chinese electronics industry will be designing robots that are
flexible enough to keep up with rapid product lifecycles. Foxconn,
for example, maintains massive facilities where workers live onsite in
dormitories. In order to accommodate aggressive production schedules, thousands of workers can be woken in the middle of the night
and set immediately to work. That results in an astonishing ability
to rapidly ramp up production or adjust to product design changes,
but it also puts extreme pressure on workers—as evidenced by the
near epidemic of suicides that occurred at Foxconn facilities in 2010.
Robots, of course, have the ability to work continuously, and as they
become more flexible and easier to train for new tasks, they will become an increasingly attractive alternative to human workers, even
when wages are low.
The trend toward increased factory automation in developing
countries is by no means limited to China. Clothing and shoe production, for example, continues to be one of the most labor-intensive
sectors of manufacturing, and factories have been transitioning from
China to even lower-wage countries like Vietnam and Indonesia. In
June 2013, athletic-shoe manufacturer Nike announced that rising
wages in Indonesia had negatively impacted its quarterly financial
numbers. According to the company’s chief financial officer, the
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12 RISE OF THE ROBOTS
long-term solution to that problem is going to be “engineering the
labor out of the product.”12 Increased automation is also seen as a
way to deflect criticism regarding the sweatshop-like environments
that often exist in third-world garment factories.
The Service Sector: Where the Jobs Are
In the United States and other advanced economies, the major disruption will be in the service sector—which is, after all, where the
vast majority of workers are now employed. This trend is already
evident in areas like ATMs and self-service checkout lanes, but the
next decade is likely to see an explosion of new forms of service sector automation, potentially putting millions of relatively low-wage
jobs at risk.
San Francisco start-up company Momentum Machines, Inc.,
has set out to fully automate the production of gourmet-quality
hamburgers. Whereas a fast food worker might toss a frozen patty
onto the grill, Momentum Machines’ device shapes burgers from
freshly ground meat and then grills them to order—including even
the ability to add just the right amount of char while retaining all
the juices. The machine, which is capable of producing about 360
hamburgers per hour, also toasts the bun and then slices and adds
fresh ingredients like tomatoes, onions, and pickles only after the
order is placed. Burgers arrive assembled and ready to serve on a
conveyer belt. While most robotics companies take great care to spin
a positive tale when it comes to the potential impact on employment,
Momentum Machines co-founder Alexandros Vardakostas is very
forthright about the company’s objective: “Our device isn’t meant to
make employees more efficient,” he said. “It’s meant to completely
obviate them.”13 *
The company estimates that the average fast food
* The company is not unaware of the potential impact its technology will have
on jobs and, according to its website, plans to support a program that will offer
discounted technical training to workers who are displaced.
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The Automation Wave 13
restaurant spends about $135,000 per year on wages for employees
who produce hamburgers and that the total labor cost for burger
production for the US economy is about $9 billion annually.14 Momentum Machines believes its device will pay for itself in less than a
year, and it plans to target not just restaurants but also convenience
stores, food trucks, and perhaps even vending machines. The company argues that eliminating labor costs and reducing the amount of
space required in kitchens will allow restaurants to spend more on
high-quality ingredients, enabling them to offer gourmet hamburgers
at fast food prices.
Those burgers might sound very inviting, but they would come
at a considerable cost. Millions of people hold low-wage, often parttime, jobs in the fast food and beverage industries. McDonald’s alone
employs about 1.8 million workers in 34,000 restaurants worldwide.15
Historically, low wages, few benefits, and a high turnover rate have
helped to make fast food jobs relatively easy to find, and fast food
jobs, together with other low-skill positions in retail, have provided
a kind of private sector safety net for workers with few other options: these jobs have traditionally offered an income of last resort
when no better alternatives are available. In December 2013, the US
Bureau of Labor Statistics ranked “combined food preparation and
serving workers,” a category that excludes waiters and waitresses
in full-service restaurants, as one of the top employment sectors in
terms of the number of job openings projected over the course of the
decade leading up to 2022—with nearly half a million new jobs and
another million openings to replace workers who leave the industry.16
In the wake of the Great Recession, however, the rules that used
to apply to fast food employment are changing rapidly. In 2011,
McDonald’s launched a high-profile initiative to hire 50,000 new
workers in a single day and received over a million applications—a
ratio that made landing a McJob more of a statistical long shot than
getting accepted at Harvard. While fast food employment was once
dominated by young people looking for a part-time income while
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14 RISE OF THE ROBOTS
in school, the industry now employs far more mature workers who
rely on the jobs as their primary income. Nearly 90 percent of fast
food workers are twenty or older, and the average age is thirty-five.17
Many of these older workers have to support families—a nearly impossible task at a median wage of just $8.69 per hour.
The industry’s low wages and nearly complete lack of benefits
have drawn intensive criticism. In October 2013, McDonald’s was
lambasted after an employee who called the company’s financial help
line was advised to apply for food stamps and Medicaid.18 Indeed, an
analysis by the Labor Center at the University of California, Berkeley, found that more than half of the families of fast food workers
are enrolled in some type of public assistance program and that the
resulting cost to US taxpayers is nearly $7 billion per year.19
When a spate of protests and ad hoc strikes at fast food restaurants broke out in New York and then spread to more than fifty
US cities in the fall of 2013, the Employment Policies Institute, a
conservative think tank with close ties to the restaurant and hotel
industries, placed a full-page ad in the Wall Street Journal warning
that “Robots Could Soon Replace Fast Food Workers Demanding a
Higher Minimum Wage.” While the ad was doubtless intended as
a scare tactic, the reality is that—as the Momentum Machines device demonstrates—increased automation in the fast food industry
is almost certainly inevitable. Given that companies like Foxconn
are introducing robots to perform high-precision electronic assembly
in China, there is little reason to believe that machines won’t also
eventually be serving up burgers, tacos, and lattes across the fast
food industry.*
Japan’s Kura sushi restaurant chain has already successfully pioneered an automation strategy. In the chain’s 262 restaurants, robots
* Economists categorize fast food as part of the service sector; however, from a
technical standpoint it is really closer to being a form of just-in-time manufacturing.
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The Automation Wave 15
help make the sushi while conveyor belts replace waiters. To ensure
freshness, the system keeps track of how long individual sushi plates
have been circulating and automatically removes those that reach
their expiration time. Customers order using touch panel screens, and
when they are finished dining they place the empty dishes in a slot
near their table. The system automatically tabulates the bill and then
cleans the plates and whisks them back to the kitchen. Rather than
employing store managers at each location, Kura uses centralized
facilities where managers are able to remotely monitor nearly every
aspect of restaurant operations. Kura’s automation-based business
model allows it to price sushi plates at just 100 yen (about $1), significantly undercutting its competitors.20
It’s fairly easy to envision many of the strategies that have worked
for Kura, especially automated food production and offsite management, eventually being adopted across the fast food industry. Some
significant steps have already been taken in that direction; McDonalds, for example, announced in 2011 that it would install touch
screen ordering systems at 7,000 of its European restaurants.21 Once
one of the industry’s major players begins to gain significant advantages from increased automation, the others will have little choice but
to follow suit. Automation will also offer the ability to compete on
dimensions beyond lower labor costs. Robotic production might be
viewed as more hygienic since fewer workers would come into contact with the food. Convenience, speed, and order accuracy would
increase, as would the ability to customize orders. Once a customer’s
preferences were recorded at one restaurant, automation would make
it a simple matter to consistently produce the same results at other
locations.
Given all this, I think it is quite easy to imagine that a typical
fast food restaurant may eventually be able to cut its workforce by
50 percent, or perhaps even more. At least in the United States, the
fast food market is already so saturated that it seems very unlikely
that new restaurants could make up for such a dramatic reduction in
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16 RISE OF THE ROBOTS
the number of workers required at each location. And this, of course,
would mean that a great many of the job openings forecast by the
Bureau of Labor Statistics might never materialize.
The other major concentration of low-wage service jobs is in the
general retail sector. Economists at the Bureau of Labor Statistics
rank “retail salesperson” second only to “registered nurse” as the specific occupation that will add the most jobs in the decade ending in
2020 and expect over 700,000 new jobs to be created.22 Once again,
however, technology has the potential to make the government projections seem optimistic. We can probably anticipate that three major
forces will shape employment in the retail sector going forward.
The first will be the continuing disruption of the industry by
online retailers like Amazon, eBay, and Netflix. The competitive
advantage that online suppliers have over brick and mortar stores
is already, of course, evident with the demise of major retail chains
like Circuit City, Borders, and Blockbuster. Both Amazon and eBay
are experimenting with same-day delivery in a number of US cities,
with the objective of undermining one of the last major advantages
that local retail stores still enjoy: the ability to provide immediate
gratification after a purchase.
In theory, the encroachment of online retailers should not necessarily destroy jobs but, rather, would transition them from traditional
retail settings to the warehouses and distribution centers used by the
online companies. However, the reality is that once jobs move to a
warehouse they become far easier to automate. Amazon purchased
Kiva Systems, a warehouse robotics company in 2012. Kiva’s robots,
which look a bit like huge, roving hockey pucks, are designed to move
materials within warehouses. Rather than having workers roam the
aisles selecting items, a Kiva robot simply zips under an entire pallet
or shelving unit, lifts it, and then brings it directly to the worker
packing an order. The robots navigate autonomously using a grid
laid out by barcodes attached to the floor and are used to automate
warehouse operations at a variety of major retailers in addition to
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The Automation Wave 17
Amazon, including Toys “R” Us, the Gap, Walgreens, and Staples.23
A year after the acquisition, Amazon had about 1,400 Kiva robots in
operation but had only begun the process of integrating the machines
into its massive warehouses. One Wall Street analyst estimates that
the robots will ultimately allow the company to cut its order fulfillment costs by as much as 40 percent.24
The Kroger Company, one of the largest grocery retailers in the
United States, has also introduced highly automated distribution centers. Kroger’s system is capable of receiving pallets containing large
supplies of a single product from vendors and then disassembling
them and creating new pallets containing a variety of different products that are ready to ship to stores. It is also able to organize the way
that products are stacked on the mixed pallets in order to optimize
the stocking of shelves once they arrive at stores. The automated
warehouses completely eliminate the need for human intervention,
except for loading and unloading the pallets onto trucks.25 The obvious impact that these automated systems have on jobs has not been
lost on organized labor, and the Teamsters Union has repeatedly
clashed with Kroger, as well as other grocery retailers, over their
introduction. Both the Kiva robots and Kroger’s automated system
do leave some jobs for people, and these are primarily in areas, such
as packing a mixture of items for final shipment to customers, that
require visual recognition and dexterity. Of course, these are the very
areas in which innovations like Industrial Perception’s box-moving
robots are rapidly advancing the technical frontier.
The second transformative force is likely to be the explosive
growth of the fully automated self-service retail sector—or, in other
words, intelligent vending machines and kiosks. One study projects
that the value of products and services vended in this market will
grow from about $740 billion in 2010 to more than $1.1 trillion by
2015.26 Vending machines have progressed far beyond dispensing
sodas, snacks, and lousy instant coffee, and sophisticated machines
that sell consumer electronics products like Apple’s iPod and iPad are
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18 RISE OF THE ROBOTS
now common in airports and upscale hotels. AVT, Inc., one of the
leading manufacturers of automated retail machines, claims that it
can design a custom self-service solution for virtually any product.
Vending machines make it possible to dramatically reduce three of
the most significant costs incurred in the retail business: real estate,
labor, and theft by customers and employees. In addition to providing 24-hour service, many of the machines include video screens
and are able to offer targeted point-of-sale advertising that’s geared
toward enticing customers to purchase related products in much the
same way that a human sales clerk might do. They can also collect
customer email addresses and send receipts. In essence, the machines
offer many of the advantages of online ordering, with the added benefit of instant delivery.
While the proliferation of vending machines and kiosks is certain
to eliminate traditional retail sales jobs, these machines will also, of
course, create jobs in areas like maintenance, restocking, and repair.
The number of those new jobs, however, is likely to be more limited
than you might expect. The latest-generation machines are directly
connected to the Internet and provide a continuous stream of sales
and diagnostic data; they are also specifically designed to minimize
the labor costs associated with their operation.
In 2010, David Dunning was the regional operations supervisor
responsible for overseeing the maintenance and restocking of 189
Redbox movie rental kiosks in the Chicago area.27 Redbox has over
42,000 kiosks in the United States and Canada, typically located
at convenience stores and supermarkets, and rents about 2 million
videos per day.28 Dunning managed the Chicago-area kiosks with a
staff of just seven. Restocking the machines is highly automated; in
fact, the most labor-intensive aspect of the job is swapping the translucent movie advertisements displayed on the kiosk—a process that
typically takes less than two minutes for each machine. Dunning and
his staff divide their time between the warehouse, where new movies
arrive, and their cars and homes, where they are able to access and
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The Automation Wave 19
manage the machines via the Internet. The kiosks are designed from
the ground up for remote maintenance. For example, if a machine
jams it will report this immediately, and a technician can log in with
his or her laptop computer, jiggle the mechanism, and fix the problem
without the need to visit the site. New movies are typically released
on Tuesdays, but the machines can be restocked at any time prior
to that; the kiosk will automatically make the movies available for
rental at the right time. That allows technicians to schedule restocking visits to avoid traffic.
While the jobs that Dunning and his staff have are certainly interesting and desirable, in number they are a fraction of what a traditional retail chain would create. The now-defunct Blockbuster, for
example, once had dozens of stores in greater Chicago, each employing its own sales staff.29 At its peak, Blockbuster had a total of about
9,000 stores and 60,000 employees. That works out to about seven
jobs per store—roughly the same number that Redbox employed in
the entire region serviced by Dunning’s team.
The third major force likely to disrupt employment in the retail
sector will be the introduction of increased automation and robotics
into stores as brick and mortar retailers strive to remain competitive. The same innovations that are enabling manufacturing robots
to advance the frontier in areas like physical dexterity and visual
recognition will eventually allow retail automation to begin moving
from warehouses into more challenging and varied environments
like stocking shelves in stores. In fact, as far back as 2005, Walmart
was already investigating the possibility of using robots that rove
store aisles at night and automatically scan barcodes in order to track
product inventories.30
At the same time, self-service checkout aisles and in-store information kiosks are sure to become easier to use, as well as more
common. Mobile devices will also become an ever more important
self-service tool. Future shoppers will rely more and more on their
phones as a way to shop, pay, and get help and information about
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20 RISE OF THE ROBOTS
products while in traditional retail settings. The mobile disruption
of retail is already under way. Walmart, for example, is testing an
experimental program that allows shoppers to scan barcodes and
then checkout and pay with their phones—completely avoiding long
checkout lines.31 Silvercar, a start-up rental car company, offers the
capability to reserve and pick up a car without ever having to interact
with a rental clerk; the customer simply scans a barcode to unlock the
car and then drives away.32 As natural language technology like Apple’s Siri or even more powerful systems like IBM’s Watson continue
to advance and become more affordable, it’s easy to imagine shoppers
soon being able to ask their mobile devices for assistance in much
the same way they might ask a store employee. The difference, of
course, is that the customer will never have to wait for or hunt down
the employee; the virtual assistant will always be instantly available
and will rarely, if ever, give an inaccurate answer.
While many retailers may choose to bring automation into traditional retail configurations, others may instead elect to entirely
redesign stores—perhaps, in essence, turning them into scaled-up
vending machines. Stores of this type might consist of an automated
warehouse with an attached showroom where customers could examine product samples and place orders. Orders might then be delivered directly to customers, or perhaps even loaded robotically into
vehicles. Regardless of the specific technological path ultimately followed by the retail industry, it’s difficult to imagine that the eventual
result won’t be more robots and machines—and significantly fewer
jobs for people.
Cloud Robotics
One of the most important propellants of the robot revolution may
turn out to be “cloud robotics”—or the migration of much of the
intelligence that animates mobile robots into powerful, centralized
computing hubs. Cloud robotics has been enabled by the dramatic
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The Automation Wave 21
acceleration in the rate at which data can be communicated; it is now
possible to offload much of the computation required by advanced
robotics into huge data centers while also giving individual robots
access to network-wide resources. That, of course, makes it possible to build less expensive robots, since less onboard computational
power and memory are required, and also allows for instant software
upgrades across multiple machines. If one robot employs centralized
machine intelligence to learn and adapt to its environment, then that
newly acquired knowledge could become instantly available to any
other machines accessing the system—making it easy to scale machine
learning across large numbers of robots. Google announced support
for cloud robotics in 2011 and provides an interface that allows robots
to take advantage of all the services designed for Android devices.*
The impact of cloud robotics may be most dramatic in areas like
visual recognition that require access to vast databases as well as
powerful computational capability. Consider, for example, the enormous technical challenge involved in building a robot capable of performing a variety of housekeeping chores. A robotic maid tasked
with clearing up the clutter in a room would need to be able to recognize an almost unlimited number of objects and then decide what to
do with them. Each of those items might come in a variety of styles,
be oriented in different ways, and perhaps even be somehow entangled with other objects. Compare that challenge to the one taken on
by the Industrial Perception box-moving robot we met at the beginning of this chapter. While that robot’s ability to discern and grasp
individual boxes even when they are stacked in a careless way is an
impressive achievement, it is still limited to, well, boxes. That’s obviously a very long way from being able to recognize and manipulate
virtually any object of any shape and in any configuration.
* Google’s strong interest in robotics was further demonstrated in 2013, when
the company purchased eight robotics start-up companies over a six-month period. Among the companies acquired was Industrial Perception.
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22 RISE OF THE ROBOTS
Building such comprehensive visual perception and recognition
into an affordable robot poses a daunting challenge. Yet, cloud robotics offers at least a glimpse of the path that may eventually lead
to a solution. Google introduced its “Goggles” feature for cameraequipped mobile devices in 2010 and has significantly improved
the technology since then. This feature allows you to take a photo
of things like landmark buildings, books, works of art, and commercial products and then have the system automatically recognize
and retrieve information relevant to the photo. While building the
ability to recognize nearly any object into a robot’s onboard system
would be extraordinarily difficult and expensive, it’s fairly easy to
imagine robots of the future recognizing the objects in their environment by accessing a vast centralized database of images similar
to the one used by the Goggles system. The cloud-based image
library could be updated continuously, and any robots with access
to the system would get an instant upgrade to their visual recognition capability.
Cloud robotics is sure to be a significant driver of progress in
building more capable robots, but it also raises important concerns,
especially in the area of security. Aside from its uncomfortable similarity to “Skynet,” the controlling machine intelligence in the Terminator movies starring Arnold Schwarzenegger, there is the much
more practical and immediate issue of susceptibility to hacking or
cyber attack. This will be an especially significant concern if cloud
robotics someday takes on an important role in our transportation
infrastructure. For example, if automated trucks and trains eventually move food and other critical supplies under centralized control,
such a system might create extreme vulnerabilities. There is already
great concern about the vulnerability of industrial machinery, and of
vital infrastructure like the electrical grid, to cyber attack. That vulnerability was demonstrated by the Stuxnet worm that was created
by the US and Israeli governments in 2010 to attack the centrifuges
used in Iran’s nuclear program. If, someday, important infrastructure
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The Automation Wave 23
components are dependent on centralized machine intelligence, those
concerns could be raised to an entirely new level.
Robots in Agriculture
Of all the employment sectors that make up the US economy, agriculture stands out as the one that has already undergone the most
dramatic transformation as a direct result of technological progress. Most of those new technologies were, of course, mechanical
in nature and came long before the advent of advanced information technology. In the late nineteenth century, nearly half of all US
workers were employed on farms; by 2000 that fraction had fallen
below 2 percent. For crops like wheat, corn, and cotton that can be
planted, maintained, and harvested mechanically, the human labor
required per bushel of output is now nearly negligible in advanced
countries. Many aspects of raising and managing livestock are also
mechanized. For example, robotic milking systems are in common
use on dairy farms, and in the United States, chickens are grown to
standardized sizes so as to make them compatible with automated
slaughtering and processing.
The remaining labor-intensive areas of agriculture are primarily
geared toward picking delicate, high-value fruits and vegetables, as
well as ornamental plants and flowers. As with other relatively routine, manual occupations, these jobs have so far been protected from
mechanization primarily because they are highly dependent on visual
perception and dexterity. Fruits and vegetables are easily damaged
and often need to be selected based on color or softness. For a machine, visual recognition is a significant challenge: lighting conditions
can be highly variable, and individual fruits can be in a variety of orientations and may be partly or even completely obscured by leaves.
The same innovations that are advancing the robotics frontier in factory and warehouse settings are finally making many of
these remaining agricultural jobs susceptible to automation. Vision
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24 RISE OF THE ROBOTS
Robotics, a company based in San Diego, California, is developing an
octopus-like orange harvesting machine. The robot will use threedimensional machine vision to make a computer model of an entire
orange tree and then store the location of each fruit. That information will then be passed on to the machine’s eight robotic arms,
which will rapidly harvest the oranges.33 Boston-area start-up Harvest Automation is initially focused on building robots to automate
operations in nurseries and greenhouses; the company estimates that
manual labor accounts for over 30 percent of the cost of growing
ornamental plants. In the longer run, the company believes that its
robots will be able to perform up to 40 percent of the manual agricultural labor now required in the United States and Europe.34 Experimental robots are already pruning grapevines in France using
machine vision technology combined with algorithms that decide
which stems should be cut.35 In Japan, a new machine is able to select ripe strawberries based on subtle color variations and then pick
a strawberry every eight seconds—working continuously and doing
most of the work at night.36
Advanced agricultural robots are especially attractive in countries that do not have access to low-wage, migrant labor. Australia
and Japan, for example, are both island nations with rapidly aging
workforces. Security considerations likewise make Israel a virtual
island in terms of labor mobility. Many fruits and vegetables need
to be harvested within a very small time window, so that a lack of
available workers at just the right time can easily turn out to be a
catastrophic problem.
Beyond reducing the need for labor, agricultural automation
has enormous potential to make farming more efficient and far less
resource-intensive. Computers have the ability to track and manage crops at a level of granularity that would be inconceivable for
human workers. The Australian Centre for Field Robotics (ACFR)
at the University of Sydney is focused on employing advanced agricultural robotics to help position Australia as a primary supplier of
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The Automation Wave 25
food for Asia’s exploding population—in spite of the country’s relative paucity of arable land and fresh water. ACFR envisions robots
that continuously prowl fields taking soil samples around individual
plants and then injecting just the right amount of water or fertilizer.37
Precision application of fertilizer or pesticides to individual plants,
or even to specific fruits growing on a tree, could potentially reduce
the use of these chemicals by up to 80 percent, thereby dramatically
decreasing the amount of toxic runoff that ultimately ends up fouling
rivers, streams, and other bodies of water.38 *
Agriculture in most developing countries is notoriously inefficient. The plots of land worked by families are often tiny, capital
investment is minimal, and modern technology is unavailable. Even
though farming techniques are labor-intensive, the land often has
to support more people than are really necessary to cultivate it. As
global population grows to 9 billion and beyond in the coming decades, there will be ever-increasing pressure to transition any and
all available arable land into larger and more efficient farms that
are capable of producing higher crop yields. Advancing agricultural
technology will have a significant role to play, especially in countries
where water is scarce and ecosystems have been damaged by overuse
of chemicals. Increased mechanization, however, will also mean that
the land will provide livelihoods for far fewer people. The historical norm has been for those excess workers to migrate to cities and
industrial centers in search of factory work—but as we have seen,
those factories are themselves going to be transformed by accelerating automation technology. In fact, it seems somewhat difficult to
imagine how many developing countries will succeed in navigating
these technological disruptions without running into significant unemployment crises.
* Precision agriculture—or the ability to keep track of and manage individual
plants or even fruits—is part of the “big data” phenomenon, a subject that we’ll
examine in more depth in Chapter 4.
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26 RISE OF THE ROBOTS
In the United States, agricultural robotics has the potential to
eventually throw a wrench into many of the fundamental assumptions that underlie immigration policy—an area that is already subject to intensely polarized politics. The impact is already evident in
some areas that used to employ large numbers of farmworkers. In
California, machines skirt around the daunting visual challenge of
picking individual almonds by simply grasping the entire tree and
violently shaking it. The almonds fall to the ground where they’ll
be harvested by a different machine. Many California farmers have
transitioned from delicate crops like tomatoes to more robust nuts
because they can be harvested mechanically. Overall agricultural
employment in California fell by about 11 percent in the first decade
of the twenty-first century, even as the total production of crops like
almonds, which are compatible with automated farming techniques,
has exploded.39
AS ROBOTICS AND ADVANCED self-service technologies are increasingly
deployed across nearly every sector of the economy, they will primarily threaten lower-wage jobs that require modest levels of education
and training. These jobs, however, currently make up the vast majority of the new positions being generated by the economy—and the
US economy needs to create something on the order of a million jobs
per year just to tread water in the face of population growth. Even
if we set aside the possibility of an actual reduction in the number
of these jobs as new technologies emerge, any decline in the rate at
which they are created will have dire, cumulative consequences for
employment over the long run.
Many economists and politicians might be inclined to dismiss
this as a problem. After all, routine, low-wage, low-skill jobs—at
least in advanced economies—tend to be viewed as inherently undesirable, and when economists discuss the impact of technology on
these kinds of jobs, you are very likely to encounter the phrase “freed
up”—as in, workers who lose their low-skill jobs will be freed up
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The Automation Wave 27
to pursue more training and better opportunities. The fundamental
assumption, of course, is that a dynamic economy like the United
States will always be capable of generating sufficient higher-wage,
higher-skill jobs to absorb all those newly freed up workers—given
that they succeed in acquiring the necessary training.
That assumption rests on increasingly shaky ground. In the next
two chapters we’ll look at the impact that automation has already
had on jobs and incomes in the United States and consider the characteristics that set information technology apart as a uniquely disruptive force. That discussion will provide a jumping-off point from
which to delve into an unfolding story that is poised to upend the
conventional wisdom about the types of jobs most likely to be automated and the viability of ever more education and training as a
solution: the machines are coming for the high-wage, high-skill jobs
as well.
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