Research Essay (minimum 750 words)You may agree or disagree with the author’s opinion on the topic, or you may agree with parts of his/herargument and disagree with other parts. You must, of course,
IMAGINE THAT 7 out of 10 working A mer ica ns got f ired tom orrow . W hat w ould they all do?
It’ s hard to believe you’d have an economy at all if you gave pink slips to more than half the
labor force. But that—in slow motion—is what the industrial revolution did to the workforce of
the early 19th century . Two hundred years ago, 70 percent of American workers lived on the
farm. T oday automation has eliminated all but 1 percent of their jobs, replacing them (and their
work animals) with machines. But the displaced workers did not sit idle. Instead, automation
created hundreds of millions of jobs in entirely new fields. Those who once farmed were now
manning the legions of factories that churned out farm equipment, cars, and other industrial
products. Since then, wave upon wave of new occupations have arrived—appliance repairman,
offset printer , food chemist, photographer , web designer—each building on previous automation.
Today , the vast majority of us are doing jobs that no farmer from the 1800s could have imagined
It may be hard to believe, but before the end of this century , 70 percent of today’s occupations
will likewise be replaced by automation. Y es, dear reader, even you will have your job taken
away by machines. In other words, robot replacement is just a matter of time. This upheaval is
being led by a second wave of automation, one that is centered on artificial cognition, cheap
sensors, machine learning, and distributed smarts. This deep automation will touch all jobs, from
manual labor to knowledge work.
First, machines will consolidate their gains in alreadyautomated industries. After robots finish
replacing assembly line workers, they will replace the workers in warehouses. Speedy bots able
to lift 150 pounds all day long will retrieve boxes, sort them, and load them onto trucks. Fruit
and vegetable picking will continue to be robotized until no humans pick outside of specialty
farms. Pharmacies will feature a single pilldispensing robot in the back while the pharmacists
focus on patient consulting. Next, the more dexterous chores of cleaning in of fices and schools
will be taken over by latenight robots, starting with easytodo floors and windows and
eventually getting to toilets. The highway legs of longhaul trucking routes will be driven by
robots embedded in truck cabs.
All the while, robots will continue their migration into whitecollar work. We already have
artificial intelligence in many of our machines; we just don’t call it that. Witness one piece of
software by Narrative Science (profiled in issue 20.05) that can write newspaper stories about
sports games directly from the games’ stats or generate a synopsis of a company’ s stock
performance each day from bits of text around the web. Any job dealing with reams of
paperwork will be taken over by bots, including much of medicine. Even those areas of medicine
not defined by paperwork, such as surgery, are becoming increasingly robotic. The rote tasks of
any informationintensive job can be automated. It doesn’ t matter if you are a doctor, lawyer,
architect, reporter , or even programmer: The robot takeover will be epic. And it has already begun. Baxter is an early example of a new class of industrial robots created
to work alongside humans.
Here’s why we’re at the inflection point: Machines are acquiring smarts.
We have preconceptions about how an intelligent robot should look and act, and these can blind
us to what is already happening around us. T o demand that artificial intelligence be humanlike is
the same flawed logic as demanding that artificial flying be birdlike, with flapping wings. Robots
will think different. To see how far artificial intelligence has penetrated our lives, we need to
shed the idea that they will be humanlike.
Consider Baxter , a revolutionary new workbot from Rethink Robotics. Designed by Rodney
Brooks, the former MIT professor who invented the bestselling Roomba vacuum cleaner and its
descendants, Baxter is an early example of a new class of industrial robots created to work
alongside humans. Baxter does not look impressive. It’ s got big strong arms and a flatscreen
display like many industrial bots. And Baxter’s hands perform repetitive manual tasks, just as
factory robots do. But it’ s different in three significant ways.
First, it can look around and indicate where it is looking by shifting the cartoon eyes on its head.
It can perceive humans working near it and avoid injuring them. And workers can see whether it
sees them. Previous industrial robots couldn’ t do this, which means that working robots have to
be physically segregated from humans. The typical factory robot is imprisoned within a
chainlink fence or caged in a glass case. They are simply too dangerous to be around, because
they are oblivious to others. This isolation prevents such robots from working in a small shop,
where isolation is not practical. Optimally, workers should be able to get materials to and from
the robot or to tweak its controls by hand throughout the workday; isolation makes that dif ficult.
Baxter, however , is aware. Using forcefeedback technology to feel if it is colliding with a person
or another bot, it is courteous. Y ou can plug it into a wall socket in your garage and easily work
right next to it.
Second, anyone can train Baxter . It is not as fast, strong, or precise as other industrial robots, but
it is smarter. To train the bot you simply grab its arms and guide them in the correct motions and
sequence. It’ s a kind of “watch me do this” routine. Baxter learns the procedure and then repeats
it. Any worker is capable of this showandtell; you don’ t even have to be literate. Previous
workbots required highly educated engineers and crack programmers to write thousands of lines
of code (and then debug them) in order to instruct the robot in the simplest change of task. The
code has to be loaded in batch mode, i.e., in large, infrequent batches, because the robot cannot
be reprogrammed while it is being used. Turns out the real cost of the typical industrial robot is
not its hardware but its operation. Industrial robots cost $100,000plus to purchase but can require four times that amount over a lifespan to program, train, and maintain. The costs pile up
until the average lifetime bill for an industrial robot is half a million dollars or more.
The third difference, then, is that Baxter is cheap. Priced at $22,000, it’ s in a different league
compared with the $500,000 total bill of its predecessors. It is as if those established robots, with
their batchmode programming, are the mainframe computers of the robot world, and Baxter is
the first PC robot. It is likely to be dismissed as a hobbyist toy , missing key features like
submillimeter precision, and not serious enough. But as with the PC, and unlike the mainframe,
the user can interact with it directly, immediately, without waiting for experts to mediate—and
use it for nonserious, even frivolous things. It’ s cheap enough that smalltime manufacturers can
afford one to package up their wares or custom paint their product or run their 3D printing
machine. Or you could staf f up a factory that makes iPhones.
Baxter was invented in a centuryold brick building near the Charles River in Boston. In 1895
the building was a manufacturing marvel in the very center of the new manufacturing world. It
even generated its own electricity . For a hundred years the factories inside its walls changed the
world around us. Now the capabilities of Baxter and the approaching cascade of superior robot
workers spur Brooks to speculate on how these robots will shift manufacturing in a disruption
greater than the last revolution. Looking out his of fice window at the former industrial
neighborhood, he says, “Right now we think of manufacturing as happening in China. But as
manufacturing costs sink because of robots, the costs of transportation become a far greater
factor than the cost of production. Nearby will be cheap. So we’ll get this network of locally
franchised factories, where most things will be made within 5 miles of where they are needed.”
That may be true of making stuff, but a lot of jobs left in the world for humans are service jobs. I
ask Brooks to walk with me through a local McDonald’ s and point out the jobs that his kind of
robots can replace. He demurs and suggests it might be 30 years before robots will cook for us.
“In a fast food place you’re not doing the same task very long. Y ou’re always changing things on
the fly, so you need special solutions. W e are not trying to sell a specific solution. W e are
building a generalpurpose machine that other workers can set up themselves and work
alongside.” And once we can cowork with robots right next to us, it’ s inevitable that our tasks
will bleed together, and soon our old work will become theirs—and our new work will become
something we can hardly imagine.
To understand how robot replacement will happen, it’ s useful to break down our relationship
with robots into four categories, as summed up in this chart:
The rows indicate whether robots will take over existing jobs or make new ones, and the
columns indicate whether these jobs seem (at first) like jobs for humans or for machines.
Let’s begin with quadrant A: jobs humans can do but robots can do even better . Humans can
weave cotton cloth with great effort, but automated looms make perfect cloth, by the mile, for a
few cents. The only reason to buy handmade cloth today is because you want the imperfections
humans introduce. We no longer value irregularities while traveling 70 miles per hour ,
though—so the fewer humans who touch our car as it is being made, the better .
And yet for more complicated chores, we still tend to believe computers and robots can’ t be
trusted. That’s why we’ve been slow to acknowledge how they’ve mastered some conceptual
routines, in some cases even surpassing their mastery of physical routines. A computerized brain
known as the autopilot can fly a 787 jet unaided, but irrationally we place human pilots in the
cockpit to babysit the autopilot “just in case.” In the 1990s, computerized mortgage appraisals
replaced human appraisers wholesale. Much tax preparation has gone to computers, as well as
routine xray analysis and pretrial evidencegathering—all once done by highly paid smart
people. We’ve accepted utter reliability in robot manufacturing; soon we’ll accept it in robotic
intelligence and service.
Next is quadrant B: jobs that humans can’ t do but robots can. A trivial example: Humans have
trouble making a single brass screw unassisted, but automation can produce a thousand exact
ones per hour. Without automation, we could not make a single computer chip—a job that
requires degrees of precision, control, and unwavering attention that our animal bodies don’ t
possess. Likewise no human, indeed no group of humans, no matter their education, can quickly
search through all the web pages in the world to uncover the one page revealing the price of eggs in Katmandu yesterday. Every time you click on the search button you are employing a robot to
do something we as a species are unable to do alone.
While the displacement of formerly human jobs gets all the headlines, the greatest benefits
bestowed by robots and automation come from their occupation of jobs we are unable to do. W e
don’t have the attention span to inspect every square millimeter of every CA T scan looking for
cancer cells. We don’t have the millisecond reflexes needed to inflate molten glass into the shape
of a bottle. W e don’t have an infallible memory to keep track of every pitch in Major League
Baseball and calculate the probability of the next pitch in real time.
We aren’ t giving “good jobs” to robots. Most of the time we are giving them jobs we could never
do. Without them, these jobs would remain undone.
Now let’ s consider quadrant C, the new jobs created by automation—including the jobs that we
did not know we wanted done. This is the greatest genius of the robot takeover: W ith the
assistance of robots and computerized intelligence, we already can do things we never imagined
doing 150 years ago. We can remove a tumor in our gut through our navel, make a
talkingpicture video of our wedding, drive a cart on Mars, print a pattern on fabric that a friend
mailed to us through the air . We are doing, and are sometimes paid for doing, a million new
activities that would have dazzled and shocked the farmers of 1850. These new accomplishments
are not merely chores that were dif ficult before. Rather they are dreams that are created chiefly
by the capabilities of the machines that can do them. They are jobs the machines make up.
Before we invented automobiles, air conditioning, flatscreen video displays, and animated
cartoons, no one living in ancient Rome wished they could watch cartoons while riding to Athens
in climatecontrolled comfort. Two hundred years ago not a single citizen of Shanghai would
have told you that they would buy a tiny slab that allowed them to talk to faraway friends before
they would buy indoor plumbing. Crafty AIs embedded in firstpersonshooter games have given
millions of teenage boys the urge, the need, to become professional game designers—a dream
that no boy in Victorian times ever had. In a very real way our inventions assign us our jobs.
Each successful bit of automation generates new occupations—occupations we would not have
fantasized about without the prompting of the automation.
To reiterate, the bulk of new tasks created by automation are tasks only other automation can
handle. Now that we have search engines like Google, we set the servant upon a thousand new
errands. Google, can you tell me where my phone is? Google, can you match the people
suf fering depression with the doctors selling pills? Google, can you predict when the next viral
epidemic will erupt? T echnology is indiscriminate this way , piling up possibilities and options
for both humans and machines. It is a safe bet that the highestearning professions in the year 2050 will depend on automations
and machines that have not been invented yet. That is, we can’t see these jobs from here, because
we can’t yet see the machines and technologies that will make them possible. Robots create jobs
that we did not even know we wanted done.
Finally, that leaves us with quadrant D, the jobs that only humans can do—at first. The one thing
humans can do that robots can’ t (at least for a long while) is to decide what it is that humans
want to do. This is not a trivial trick; our desires are inspired by our previous inventions, making
this a circular question.
When robots and automation do our most basic work, making it relatively easy for us to be fed,
clothed, and sheltered, then we are free to ask, “What are humans for?” Industrialization did
more than just extend the average human lifespan. It led a greater percentage of the population to
decide that humans were meant to be ballerinas, fulltime musicians, mathematicians, athletes,
fashion designers, yoga masters, fanfiction authors, and folks with oneofa kind titles on their
business cards. With the help of our machines, we could take up these roles; but of course, over
time, the machines will do these as well. W e’ll then be empowered to dream up yet more answers
to the question “What should we do?” It will be many generations before a robot can answer that.
This postindustrial economy will keep expanding, even though most of the work is done by bots,
because part of your task tomorrow will be to find, make, and complete new things to do, new
things that will later become repetitive jobs for the robots. In the coming years robotdriven cars
and trucks will become ubiquitous; this automation will spawn the new human occupation of trip
optimizer, a person who tweaks the traf fic system for optimal energy and time usage. Routine
robosurgery will necessitate the new skills of keeping machines sterile. When automatic
selftracking of all your activities becomes the normal thing to do, a new breed of professional
analysts will arise to help you make sense of the data. And of course we will need a whole army
of robot nannies, dedicated to keeping your personal bots up and running. Each of these new
vocations will in turn be taken over by robots later .
The real revolution erupts when everyone has personal workbots, the descendants of Baxter , at
their beck and call. Imagine you run a small organic farm. Your fleet of worker bots do all the
weeding, pest control, and harvesting of produce, as directed by an overseer bot, embodied by a
mesh of probes in the soil. One day your task might be to research which variety of heirloom
tomato to plant; the next day it might be to update your custom labels. The bots perform
everything else that can be measured.
Right now it seems unthinkable: W e can’t imagine a bot that can assemble a stack of ingredients
into a gift or manufacture spare parts for our lawn mower or fabricate materials for our new
kitchen. W e can’t imagine our nephews and nieces running a dozen workbots in their garage, churning out inverters for their friend’s electricvehicle startup. We can’t imagine our children
becoming appliance designers, making custom batches of liquidnitrogen dessert machines to sell
to the millionaires in China. But that’ s what personal robot automation will enable.
Everyone will have access to a personal robot, but simply owning one will not guarantee success.
Rather, success will go to those who innovate in the or ganization, optimization, and
customization of the process of getting work done with bots and machines. Geographical clusters
of production will matter, not for any differential in labor costs but because of the dif ferential in
human expertise. It’s humanrobot symbiosis. Our human assignment will be to keep making
jobs for robots—and that is a task that will never be finished. So we will always have at least that
one “job.”
In the coming years our relationships with robots will become ever more complex. But already a
recurring pattern is emer ging. No matter what your current job or your salary , you will progress
through these Seven Stages of Robot Replacement, again and again:
● 1. A robot/computer cannot possibly do the tasks I do.
● [Later.]
● 2. OK, it can do a lot of them, but it can’ t do everything I do.
● [Later.]
● 3. OK, it can do everything I do, except it needs me when it breaks down, which is
often.
● [Later.]
● 4. OK, it operates flawlessly on routine stuf f, but I need to train it for new tasks.
● [Later.]
● 5. OK, it can have my old boring job, because it’ s obvious that was not a job that
humans were meant to do.
● [Later.]
● 6. Wow, now that robots are doing my old job, my new job is much more fun and pays
more!
● [Later .]
● 7. I am so glad a robot/computer cannot possibly do what I do now .
● This is not a race against the machines. If we race against them, we lose. This is a race
with the machines. You’ll be paid in the future based on how well you work with
robots. Ninety percent of your coworkers will be unseen machines. Most of what you
do will not be possible without them. And there will be a blurry line between what you
do and what they do. Y ou might no longer think of it as a job, at least at first, because
anything that seems like drudgery will be done by robots.
● We need to let robots take over . They will do jobs we have been doing, and do them
much better than we can. They will do jobs we can’ t do at all. They will do jobs we never imagined even needed to be done. And they will help us discover new jobs for
ourselves, new tasks that expand who we are. They will let us focus on becoming
more human than we were.
● Let the robots take the jobs, and let them help us dream up new work that matters.
● Kevin Kelly ( kk.org ) is senior maverick of W ired and the author, most recently, of
What Technology W ants.
