Kevin Drum

You Will Lose Your Job to a Robot—and Sooner ThanYou Think

www.motherjones.com/politics/2017/10/you-will-lose-your-job-to-a-robot-and-sooner-than-you-think/

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I want to tell you straight off what this story is about: Sometime in the next 40 years, robotsare going to take your job.

I don’t care what your job is. If you dig ditches, a robot will dig them better. If you’re amagazine writer, a robot will write your articles better. If you’re a doctor, IBM’s Watson willno longer “assist” you in finding the right diagnosis from its database of millions of casestudies and journal articles. It will just be a better doctor than you.

Until we figure out how to fairly distribute the fruits of robot labor, it will be an era of massjoblessness and mass poverty.And CEOs? Sorry. Robots will run companies better than you do. Artistic types? Robots willpaint and write and sculpt better than you. Think you have social skills that no robot canmatch? Yes, they can. Within 20 years, maybe half of you will be out of jobs. A couple ofdecades after that, most of the rest of you will be out of jobs.

In one sense, this all sounds great. Let the robots have the damn jobs! No more draggingyourself out of bed at 6 a.m. or spending long days on your feet. We’ll be free to read orwrite poetry or play video games or whatever we want to do. And a century from now, this ismost likely how things will turn out. Humanity will enter a golden age.

But what about 20 years from now? Or 30? We won’t all be out of jobs by then, but a lot ofus will—and it will be no golden age. Until we figure out how to fairly distribute the fruits ofrobot labor, it will be an era of mass joblessness and mass poverty. Working-class joblosses played a big role in the 2016 election, and if we don’t want a long succession ofdemagogues blustering their way into office because machines are taking away people’slivelihoods, this needs to change, and fast. Along with global warming, the transition to aworkless future is the biggest challenge by far that progressive politics—not to mention all ofhumanity—faces. And yet it’s barely on our radar.

That’s kind of a buzzkill, isn’t it? Luckily, it’s traditional that stories about difficult ortechnical subjects open with an entertaining or provocative anecdote. The idea is that thisallows readers to ease slowly into daunting material. So here’s one for you: Last year atChristmas, I was over at my mother’s house and mentioned that I had recently read anarticle about Google Translate. It turns out that a few weeks previously, without telling

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anyone, Google had switched over to a new machine-learning algorithm. Almost overnight,the quality of its translations skyrocketed. I had noticed some improvement myself but hadchalked it up to the usual incremental progress these kinds of things go through. I hadn’trealized it was due to a quantum leap in software.

But if Google’s translation algorithm was better, did that mean its voice recognition wasbetter too? And its ability to answer queries? Hmm. How could we test that? We decided toopen presents instead of cogitating over this.

But after that was over, the subject of erasers somehow came up. Which ones are best?Clear? Black? Traditional pink? Come to think of it, why are erasers traditionally pink? “I’llask Google!” I told everyone. So I pulled out my phone and said, “Why are erasers pink?”Half a second later, Google told me.

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Roberto Parada

Not impressed? You should be. We all know that phones can recognize voices tolerably wellthese days. And we know they can find the nearest café or the trendiest recipe for coq auvin. But what about something entirely random? And not a simple who, where, or whenquestion. This was a why question, and it wasn’t about why the singer Pink uses erasers orwhy erasers are jinxed. Google has to be smart enough to figure out in context that I saidpink and that I’m asking about the historical reason for the color of erasers, not their healthor the way they’re shaped. And it did. In less than a second. With nothing more than a cheaplittle microprocessor and a slow link to the internet.

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(In case you’re curious, Google got the answer from Design*Sponge: “The eraser wasoriginally produced by the Eberhard Faber Company…The erasers featured pumice, avolcanic ash from Italy that gave them their abrasive quality, along with their distinctive colorand smell.”)

Still not impressed? When Watson famously won a round of Jeopardy! against the two besthuman players of all time, it needed a computer the size of a bedroom to answer questionslike this. That was only seven years ago.

What do pink erasers have to do with the fact that we’re all going to be out of a job in a fewdecades? Consider: Last October, an Uber trucking subsidiary named Otto delivered 2,000cases of Budweiser 120 miles from Fort Collins, Colorado, to Colorado Springs—without adriver at the wheel. Within a few years, this technology will go from prototype to fullproduction, and that means millions of truck drivers will be out of a job.

Automated trucking doesn’t rely on newfangled machines, like the powered looms andsteam shovels that drove the Industrial Revolution of the 19th century. Instead, like Google’sability to recognize spoken words and answer questions, self-driving trucks—and cars andbuses and ships—rely primarily on software that mimics human intelligence. By noweveryone’s heard the predictions that self-driving cars could lead to 5 million jobs being lost,but few people understand that once artificial-intelligence software is good enough to drive acar, it will be good enough to do a lot of other things too. It won’t be millions of people out ofwork; it will be tens of millions.

This is what we mean when we talk about “robots.” We’re talking about cognitive abilities,not the fact that they’re made of metal instead of flesh and powered by electricity instead ofchicken nuggets.

Unfortunately, for those of us worried about robots taking away our jobs, these advancesmean that mass unemployment is a lot closer than we feared—so close, in fact, that it maybe starting already.In other words, the advances to focus on aren’t those in robotic engineering—though theyare happening, too—but the way we’re hurtling toward artificial intelligence, or AI. Whilewe’re nowhere near human-level AI yet, the progress of the past couple of decades hasbeen stunning. After many years of nothing much happening, suddenly robots can playchess better than the best grandmaster. They can play Jeopardy! better than the besthumans. They can drive cars around San Francisco—and they’re getting better at it everyyear. They can recognize faces well enough that Welsh police recently made the first-everarrest in the United Kingdom using facial recognition software. After years of ploddingprogress in voice recognition, Google announced earlier this year that it had reduced itsword error rate from 8.5 percent to 4.9 percent in 10 months.

All of this is a sign that AI is improving exponentially, a product of both better computerhardware and software. Hardware has historically followed a growth curve called Moore’s

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law, in which power and efficiency double every couple of years, and recent improvementsin software algorithms have been even more explosive. For a long time, these advancesdidn’t seem very impressive: Going from the brainpower of a bacterium to the brainpower ofa nematode might technically represent an enormous leap, but on a practical level it doesn’tget us that much closer to true artificial intelligence. However, if you keep up the doubling fora while, eventually one of those doubling cycles takes you from the brainpower of a lizard(who cares?) to the brainpower of a mouse and then a monkey (wow!). Once that happens,human-level AI is just a short step away.

This can be hard to imagine, so here’s a chart that shows what an exponential doublingcurve looks like, measured in petaflops (quadrillions of calculations per second). During thefirst 70 years of the digital era, computing power doubled every couple of years—and thatproduced steadily improving accounting software, airplane reservation systems, weatherforecasts, Spotify, and the like. But on the scale of the human brain—usually estimated at10 to 50 petaflops—it produced computing power so minuscule that you can’t see anychange at all. Around 2025 we’ll finally start to see visible progress toward artificialintelligence. A decade later we’ll be up to about one-tenth the power of a human brain, and adecade after that we’ll have full human-level AI. It will seem like it happened overnight, butit’s really the result of a century of steady—but mostly imperceptible—progress.

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Are we really this close to true AI? Here’s a yardstick to think about. Even with all thisdoubling going on, until recently computer scientists thought we were still years away frommachines being able to win at the ancient game of Go, usually regarded as the mostcomplex human game in existence. But last year, a computer beat a Korean grandmasterconsidered one of the best of all time, and earlier this year it beat the highest-ranked Goplayer in the world. Far from slowing down, progress in artificial intelligence is nowoutstripping even the wildest hopes of the most dedicated AI cheerleaders. Unfortunately,for those of us worried about robots taking away our jobs, these advances mean that massunemployment is a lot closer than we feared—so close, in fact, that it may be startingalready. But you’d never know that from the virtual silence about solutions in policy andpolitical circles.

I’m hardly alone in thinking we’re on the verge of an AI Revolution. Many who work in thesoftware industry—people like Bill Gates and Elon Musk—have been sounding the alarm foryears. But their concerns are largely ignored by policymakers and, until recently, oftenridiculed by writers tasked with interpreting technology or economics. So let’s take a look atsome of the most common doubts of the AI skeptics.

#1: We’ll never get true AI because computing power won’t keep doubling forever.We’re going to hit the limits of physics before long. There are several pretty goodreasons to dismiss this claim as a roadblock. To start, hardware designers will invent faster,more specialized chips. Google, for example, announced last spring that it had created amicrochip called a Tensor Processing Unit, which it claimed was up to 30 times faster and80 times more power efficient than an Intel processor for machine learning tasks. A hugearray of those chips are now available to researchers who use Google’s cloud services.Other chips specialized for specific aspects of AI (image recognition, neural networking,language processing, etc.) either exist already or are certain to follow.

What’s more, this raw power is increasingly being harnessed in a manner similar to the waythe human brain works. Your brain is not a single, superpowerful computing device. It’smade up of about 100 billion neurons working in parallel—i.e., all at the same time—tocreate human-level intelligence and consciousness. At the lowest level, neurons operate inparallel to create small clusters that perform semi-independent actions like responding to aspecific environmental cue. At the next level, dozens of these clusters work together in eachof about 100 “sub-brains”—distinct organs within the brain that perform specialized jobssuch as speech, visual processing, and balance. Finally, all these sub-brains operate inparallel, and the resulting overall state is monitored and managed by executive functionsthat make sense of the world and provide us with our feeling that we have conscious controlof our actions.

Modern computers also yoke lots ofmicroprocessors together. As of 2017, thefastest computer in the world uses roughly

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40,000 processors with 260 cores each.That’s more than 10 million processingcores running in parallel. Each one ofthese cores has less power than the Intelprocessor on your desktop, but the entiremachine delivers about the same power asthe human brain.

This doesn’t mean AI is here already. Farfrom it. This “massively parallel”architecture still presents enormousprogramming challenges, but as we getbetter at exploiting it we’re certain to makefrequent breakthroughs in softwareperformance. In other words, even ifMoore’s law slows down or stops, the totalpower of everything put together—moreuse of custom microchips, moreparallelism, more sophisticated software,and even the possibility of entirely newways of doing computing—will almostcertainly keep growing for many moreyears.

#2: Even if computing power keepsdoubling, it has already been doublingfor decades. You guys keep predictingfull-on AI, but it never happens. It’s truethat during the early years of computingthere was a lot of naive optimism abouthow quickly we’d be able to build intelligentmachines. But those rosy predictions diedin the ’70s, as computer scientists came torealize that even the fastest mainframes ofthe day produced only about a billionth ofthe processing power of the human brain.It was a humbling realization, and theentire field has been almost painfullyrealistic about its progress ever since.

We’ve finally built computers with roughlythe raw processing power of the humanbrain—although only at a cost of more than

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$100 million and with an internalarchitecture that may or may not work wellfor emulating the human mind. But inanother 10 years, this level of power willlikely be available for less than $1 million,and thousands of teams will be testing AIsoftware on a platform that’s actuallycapable of competing with humans.

#3: Okay, maybe we will get full AI. Butit only means that robots will act intelligent, not that they’ll really be intelligent. This isjust a tedious philosophical debating point. For the purposes of employment, we don’t reallycare if a smart computer has a soul—or if it can feel love and pain and loyalty. We only careif it can act like a human being well enough to do anything we can do. When that day comes,we’ll all be out of jobs even if the computers taking our places aren’t “really” intelligent.

#4: Fine. But waves of automation—steam engines, electricity, computers—alwayslead to predictions of mass unemployment. Instead they just make us more efficient.The AI Revolution will be no different. This is a popular argument. It’s alsocatastrophically wrong.

The Industrial Revolution was all about mechanical power: Trains were more powerful thanhorses, and mechanical looms were more efficient than human muscle. At first, this did putpeople out of work: Those loom-smashing weavers in Yorkshire—the original Luddites—really did lose their livelihoods. This caused massive social upheaval for decades until theentire economy adapted to the machine age. When that finally happened, there were asmany jobs tending the new machines as there used to be doing manual labor. The eventualresult was a huge increase in productivity: A single person could churn out a lot more cloththan she could before. In the end, not only were as many people still employed, but theywere employed at jobs tending machines that produced vastly more wealth than anyone hadthought possible 100 years before. Once labor unions began demanding a piece of this pie,everyone benefited.

They will manufacture themselves, program themselves, repair themselves, and managethemselves. If you don’t appreciate this, then you don’t appreciate what’s barreling towardus.The AI Revolution will be nothing like that. When robots become as smart and capable ashuman beings, there will be nothing left for people to do because machines will be bothstronger and smarter than humans. Even if AI creates lots of new jobs, it’s of noconsequence. No matter what job you name, robots will be able to do it. They willmanufacture themselves, program themselves, repair themselves, and manage themselves.If you don’t appreciate this, then you don’t appreciate what’s barreling toward us.

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In fact, it’s even worse. In addition to doing our jobs at least as well as we do them,intelligent robots will be cheaper, faster, and far more reliable than humans. And they canwork 168 hours a week, not just 40. No capitalist in her right mind would continue to employhumans. They’re expensive, they show up late, they complain whenever somethingchanges, and they spend half their time gossiping. Let’s face it: We humans make lousylaborers.

If you want to look at this through a utopian lens, the AI Revolution has the potential to freehumanity forever from drudgery. In the best-case scenario, a combination of intelligentrobots and green energy will provide everyone on Earth with everything they need. But justas the Industrial Revolution caused a lot of short-term pain, so will intelligent robots. Whilewe’re on the road to our Star Trek future, but before we finally get there, the rich are going toget richer—because they own the robots—and the rest of us are going to get poorerbecause we’ll be out of jobs. Unless we figure out what we’re going to do about that, themisery of workers over the next few decades will be far worse than anything the IndustrialRevolution produced.

Wait, wait, skeptics will say: If all this is happening as we speak, why aren’t people losingtheir jobs already? Several sharp observers have made this point, including JamesSurowiecki in a recent issue of Wired. “If automation were, in fact, transforming the USeconomy,” he wrote, “two things would be true: Aggregate productivity would be risingsharply, and jobs would be harder to come by than in the past.” But neither is happening.Productivity has actually stalled since 2000 and jobs have gotten steadily more plentiful eversince the Great Recession ended. Surowiecki also points out that job churn is low, averagejob tenure hasn’t changed much in decades, and wages are rising—though he admits thatwage increases are “meager by historical standards.”

Mass unemployment is closer than we feared—in fact, it may be starting already.True enough. But as I wrote four years ago, since 2000 the share of the population that’semployed has decreased; middle-class wages have flattened; corporations have stockpiledmore cash and invested less in new products and new factories; and as a result of all this,labor’s share of national income has declined. All those trends are consistent with job lossesto old-school automation, and as automation evolves into AI, they are likely to accelerate.

That said, the evidence that AI is currently affecting jobs is hard to assess, for one big andobvious reason: We don’t have AI yet, so of course we’re not losing jobs to it. For now,we’re seeing only a few glimmers of smarter automation, but nothing even close to true AI.

Remember that artificial intelligence progresses in exponential time. This means that evenas computer power doubles from a trillionth of a human brain’s power to a billionth and thena millionth, it has little effect on the level of employment. Then, in the relative blink of an eye,

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the final few doublings take place and robots go from having a thousandth of humanbrainpower to full human-level intelligence. Don’t get fooled by the fact that nothing muchhas happened yet. In another 10 years or so, it will.

So let’s talk about which jobs are in danger first. Economists generally break employmentinto cognitive versus physical jobs and routine versus nonroutine jobs. This gives us fourbasic categories of work:

Routine physical: digging ditches, driving trucks

Routine cognitive: accounts-payable clerk, telephone sales

Nonroutine physical: short-order cook, home health aide

Nonroutine cognitive: teacher, doctor, CEO

Routine tasks will be the first to go—and thanks to advances in robotics engineering, bothphysical and cognitive tasks will be affected. In a recent paper, a team from Oxford and Yalesurveyed a large number of machine-learning researchers to produce a “wisdom of crowds”estimate of when computers would be able to take over various human jobs. Two-thirds saidprogress in machine learning had accelerated in recent years, with Asian researchers evenmore optimistic than North American researchers about the advent of full AI within 40 years.

But we don’t need full AI for everything. The machine-learning researchers estimate thatspeech transcribers, translators, commercial drivers, retail sales, and similar jobs could befully automated during the 2020s. Within a decade after that, all routine jobs could be gone.

Nonroutine jobs will be next: surgeons, novelists, construction workers, police officers, andso forth. These jobs could all be fully automated during the 2040s. By 2060, AI will be

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capable of performing any task currently done by humans. This doesn’t mean that literallyevery human being on the planet will be jobless by then—in fact, the researchers suggest itcould take another century before that happens—but that’s hardly any solace. By 2060 orthereabouts, we’ll have AI that can do anything a normal human can do, which means thatnearly all normal jobs will be gone. And normal jobs are what almost all of us have.

2060 seems a long way off, but if the Oxford-Yale survey is right, we’ll face an employmentapocalypse far sooner than that: the disappearance of routine work of all kinds by the mid-2030s. That represents nearly half the US labor force. The consulting firmPricewaterhouseCoopers recently released a study saying much the same. It predicts that38 percent of all jobs in the United States are “at high risk of automation” by the early 2030s,most of them in routine occupations. In the even nearer term, the World Economic Forumpredicts that the rich world will lose 5 million jobs to robots by 2020, while a group of AIexperts, writing in Scientific American, figures that 40 percent of the 500 biggest companieswill vanish within a decade.

Not scared yet? Kai-Fu Lee, a former Microsoft and Google executive who is now aprominent investor in Chinese AI startups, thinks artificial intelligence “will probably replace50 percent of human jobs.” When? Within 10 years. Ten years! Maybe it’s time to really startthinking hard about AI.

And forget about putting the genie back in the bottle. AI is coming whether we like it or not.The rewards are just too great. Even if America did somehow stop AI research, it would onlymean that the Chinese or the French or the Brazilians would get there first. RussianPresident Vladimir Putin agrees. “Artificial intelligence is the future, not only for Russia butfor all humankind,” he announced in September. “Whoever becomes the leader in thissphere will become the ruler of the world.” There’s just no way around it: For the vastmajority of jobs, work as we know it will come steadily to an end between about 2025 and2060.

So who benefits? The answer is obvious: the owners of capital, who will control most of therobots. Who suffers? That’s obvious too: the rest of us, who currently trade work for money.No work means no money.

But things won’t actually be quite that grim. After all, fully automated farms and factories willproduce much cheaper goods, and competition will then force down prices. Basic materialcomfort will be cheap as dirt.

Still not free, though. And capitalists can only make money if they have someone to sell theirgoods to. This means that even the business class will eventually realize that ubiquitousautomation doesn’t really benefit them after all. They need customers with money if theywant to be rich themselves.

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One way or another, then, the answer to the mass unemployment of the AI Revolution has toinvolve some kind of sweeping redistribution of income that decouples it from work. Or atotal rethinking of what “work” is. Or a totalrethinking of what wealth is. Let’s considera few of the possibilities.

The welfare state writ large: This is thesimplest to think about. It’s basically whatwe have now, but more extensive.Unemployment insurance will be moregenerous and come with no time limits.National health care will be free for all. Anyone without a job will qualify for some basicamount of food and housing. Higher taxes will pay for it, but we’ll still operate under theassumption that gainful employment is expected from anyone able to work.

This is essentially the “bury our heads in the sand” option. We refuse to accept that work istruly going away, so we continue to punish people who aren’t employed. Jobless benefitsremain stingy so that people are motivated to find work—even though there aren’t enoughjobs to go around. We continue to believe that eventually the economy will find a newequilibrium.

This can’t last for too long, and millions will suffer during the years we continue to deludeourselves. But it will protect the rich for a while.

Universal basic income #1: This is a step further down the road. Everyone would qualify fora certain level of income from the state, but the level of guaranteed income would be fairlymodest because we would still want people to work. Unemployment wouldn’t be asstigmatized as it is in today’s welfare state, but neither would widespread joblessness betruly accepted as a permanent fact of life. Some European countries are moving toward awelfare state with cash assistance for everyone.

Universal basic income #2: This is UBI on steroids. It’s available to everyone, and theincome level is substantial enough to provide a satisfying standard of living. This is whatwe’ll most likely get once we accept that mass unemployment isn’t a sign of lazy workersand social decay, but the inevitable result of improving technology. Since there’s nopersonal stigma attached to joblessness and no special reason that the rich should reap allthe rewards of artificial intelligence, there’s also no reason to keep the universal incomelevel low. After all, we aren’t trying to prod people back into the workforce. In fact, the timewill probably come when we actively want to do just the opposite: provide an income largeenough to motivate people to leave the workforce and let robots do the job better.

Silicon Valley—perhaps unsurprisingly—is fast becoming a hotbed of UBI enthusiasm. Techexecutives understand what’s coming, and that their own businesses risk a backlash unlesswe take care of its victims. Uber has shown an interest in UBI. Facebook CEO Mark

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Zuckerberg supports it. Ditto for Tesla CEO Elon Musk and Slack CEO Stewart Butterfield.A startup incubator called Y Combinator is running a pilot program to find out what happensif you give people a guaranteed income.

There are even some countries that are now trying it. Switzerland rejected a UBI proposal in2016, but Finland is experimenting with a small-scale UBI that pays the unemployed about$700 per month even after they find work. UBI is also getting limited tryouts by cities in Italyand Canada. Right now these are all pilot projects aimed at learning more about how to bestrun a UBI program and how well it works. But as large-scale job losses from automation startto become real, we should expect the idea to spread rapidly.

A tax on robots: This is a notion raised by a draft report to the European Parliament andendorsed by Bill Gates, who suggests that robots should pay income tax and payroll tax justlike human workers. That would keep humans more competitive. Unfortunately, there’s aflaw here: The end result would be to artificially increase the cost of employing robots, andthus the cost of the goods they produce. Unless every country creates a similar tax, itaccomplishes nothing except to push robot labor overseas. We’d be worse off than if wesimply let the robots take our jobs in the first place. Nonetheless, a robot tax could still havevalue as a way of modestly slowing down job losses. Economist Robert Shiller suggests thatwe should consider “at least modest robot taxes during the transition to a different world ofwork.” And where would the money go? “Revenue could be targeted toward wageinsurance,” he says. In other words, a UBI.

Socialization of the robot workforce: In this scenario, which would require a radicalchange in the US political climate, private ownership of intelligent robots would be forbidden.The market economy we have today would continue to exist with one exception: Thegovernment would own all intelligent robots and would auction off their services to privateindustry. The proceeds would be divided among everybody.

Progressive taxation on a grand scale: Let the robots take all the jobs, but tax all incomeat a flat 90 percent. The rich would still have an incentive to run businesses and earn moremoney, but for the most part labor would be considered a societal good, like infrastructure,not the product of individual initiative.

Wealth tax: Intelligent robots will be able to manufacture material goods and servicescheaply, but there will still be scarcity. No matter how many robots you have, there’s only somuch beachfront property in Southern California. There are only so many originalRembrandts. There are only so many penthouse suites. These kinds of things will be theonly real wealth left, and the rich will still want them. So if robots make the rich even richer,they’ll bid up the price of these luxuries commensurately, and all that’s left is to tax them athigh rates. The rich still get their toys, while the rest of us get everything we want except fora view of the sun setting over the Pacific Ocean.

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A hundred years from now, all of this will be moot. Society will adapt in ways we can’tforesee, and we’ll all be far wealthier, safer, and more comfortable than we are today—assuming, of course, that the robots don’t kill us all, Skynet fashion.

But someone needs to be thinking hard about how to prepare for what happens in themeantime. Not many are. Last year, for example, the Obama White House released a 48-page report called “Preparing for the Future of Artificial Intelligence.” That sounds promising.But it devoted less than one page to economic impacts and concluded only that “policyquestions raised by AI-driven automation are important but they are best addressed by aseparate White House working group.”

Regrettably, the coming jobocalypse has so far remained the prophecy of a few Cassandras:mostly futurists, academics, and tech executives. For example, Eric Schmidt, chairman ofGoogle’s parent company, believes that AI is coming faster than we think, and that weshould provide jobs to everyone during the transition. “The country’s goal should be fullemployment all the time, and do whatever it takes,” he says.

Another sharp thinker about our jobless future is Martin Ford, author of Rise of the Robots.Mass joblessness, he warns, isn’t limited to low-skill workers. Nor is it something we canfight by committing to better education. AI will decimate any job that’s “predictable”—whichmeans nearly all of them. Many of us might not like to hear this, but Ford is unsentimentalabout the work we do. “Relatively few people,” he says, are paid “primarily to engage in trulycreative work or ‘blue sky’ thinking.”

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Roberto ParadaAll this is bad enough, but it’s made worse by the fact that income inequality has alreadybeen increasing for decades. “The frightening reality,” Ford says, is that “we may face theprospect of a ‘perfect storm’ where the impacts from soaring inequality, technologicalunemployment, and climate change unfold roughly in parallel, and some ways amplify andreinforce each other.” Unsurprisingly, he believes the only plausible solution is some form ofuniversal basic income.

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So how do we get these ideas into the political mainstream? One thing is certain: Themonumental task of dealing with the AI Revolution will be almost entirely up to the politicalleft. After all, when the automation of human labor begins in earnest, the big winners areinitially going to be corporations and the rich. Because of this, conservatives will bemotivated to see every labor displacement as a one-off event, just as they currently viewevery drought, every wildfire, and every hurricane as a one-off event. They refuse to seethat global warming is behind changing weather patterns because dealing with climatechange requires environmental regulations that are bad for business and bad for the rich.Likewise, dealing with an AI Revolution will require new ways of distributing wealth. In thelong run this will be good even for the rich, but in the short term it’s a pretty scary prospectfor those with money—and one they’ll fight zealously. Until they have no choice left,conservatives are simply not going to admit this is happening, let alone think about how toaddress it. It’s not in their DNA.

Other candidates are equally unlikely. The military thinks about automation all the time—butprimarily as a means of killing people more efficiently, not as an economic threat. Thebusiness community is a slave to quarterly earnings and in any case will be too divided to beof much help. Labor unions have good reason to care, but by themselves they’re too weaknowadays to have the necessary clout with policymakers.

Nor are we likely to get much help from governments, which mostly don’t even understandwhat’s happening. Google’s Schmidt puts it bluntly. “The gap between the government, interms of their understanding of software, let alone AI, is so large that it’s almost hopeless,”he said at a conference earlier this year. Certainly that’s true of the Trump administration.Asked about AI being a threat to jobs, Treasury Secretary Steven Mnuchin stunningly wavedit off as a problem that’s still 50 or 100 years in the future. “I think we’re, like, so far awayfrom that,” he said. “Not even on my radar screen.” This drew a sharp rebuke from formerTreasury Secretary Larry Summers: “I do not understand how anyone could reach theconclusion that all the action with technology is half a century away,” he said. “Artificialintelligence is transforming everything from retailing to banking to the provision of medicalcare.”

So who’s left? Like it or not, the only real choice to sound the alarm outside the geekcommunity is the Democratic Party, along with its associated constellation of labor unions,think tanks, and activists. Imperfect as it is—and its reliance on rich donors makes itconspicuously imperfect—it’s the only national organization that has both the principles andthe size to do the job.

Unfortunately, political parties are inherently short-term thinkers. Democrats today areabsorbed with fighting President Donald Trump, saving Obamacare, pushing for a $15minimum wage—and arguing about all those things. They have no time to think hard aboutthe end of work.

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Either liberals start working on an answer now, or voters will rally around a more dangerousdemagogue than Trump.Nonetheless, somebody on the left with numbers, clout, power, and organizing energy—hopefully all the above—had better start. Conventional wisdom says Trump’s victory lastyear was tipped over the edge by a backlash among working-class voters in the UpperMidwest. When blue-collar workers start losing their jobs in large numbers, we’ll see abacklash that makes 2016 look like a gentle breeze. Either liberals start working on answersnow, or we risk voters rallying around far more effective and dangerous demagogues thanTrump.

Despite the amount of media attention that both robots and AI have gotten over the past fewyears, it’s difficult to get people to take them seriously. But start to pay attention and you seethe signs: An Uber car can drive itself. A computer can write simple sports stories.SoftBank’s Pepper robot already works in more than 140 cellphone stores in Japan and isstarting to get tryouts in America too. Alexa can order replacement Pop-Tarts before youknow you need them. A Carnegie Mellon computer that seems to have figured out humanbluffing beat four different online-poker pros earlier this year. California, suffering from a lackof Mexican workers, is ground zero for the development of robotic crop pickers. Sony ispromising a robot that will form an emotional bond with its owner.

These are all harbingers, the way a dropping barometer signals a coming storm—not thepossibility of a storm, but the inexorable reality. The two most important problems facing thehuman race right now are the need for widespread deployment of renewable energy andfiguring out how to deal with the end of work. Everything else pales in comparison.Renewable energy already gets plenty of attention, even if half the country still denies thatwe really need it. It’s time for the end of work to start getting the same attention.

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