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  • Every human used to have to hunt or gather to survive. But humans are smart-ly lazy so

  • we made tools to make our work easier. From sticks, to plows to tractors weve gone

  • from everyone needing to make food to, modern agriculture with almost no one needing to

  • make foodand yet we still have abundance.

  • Of course, it’s not just farming, it’s everything. Weve spent the last several

  • thousand years building tools to reduce physical labor of all kinds. These are mechanical muscles

  • stronger, more reliable, and more tireless than human muscles could ever be.

  • And that's a good thing. Replacing human labor with mechanical muscles frees people to specialize

  • and that leaves everyone better off even though still doing physical labor. This is how economies

  • grow and standards of living rise.

  • Some people have specialized to be programmers and engineers whose job is to build mechanical

  • minds. Just as mechanical muscles made human labor less in demand so are mechanical minds

  • making human brain labor less in demand.

  • This is an economic revolution. You may think we've been here before, but we haven't.

  • This time is different.

  • ## Physical Labor

  • When you think of automation, you probably think of this: giant, custom-built, expensive,

  • efficient but really dumb robots blind to the world and their own work. There were a

  • scary kind of automation but they haven't taken over the world because they're only

  • cost effective in narrow situations.

  • But they are the old kind of automation, this is the new kind.

  • Meet Baxter.

  • Unlike these things which require skilled operators and technicians and millions of

  • dollars, Baxter has vision and can learn what you want him to do by watching you do it.

  • And he costs less than the average annual salary of a human worker. Unlike his older

  • brothers he isn't pre-programmed for one specific job, he can do whatever work is within the

  • reach of his arms. Baxter is what might be thought of as a general purpose robot and

  • general purpose is a big deal.

  • Think computers, they too started out as highly custom and highly expensive, but when cheap-ish

  • general-purpose computers appeared they quickly became vital to everything.

  • A general-purpose computer can just as easily calculate change or assign seats on an airplane

  • or play a game or do anything by just swapping its software. And this huge demand for computers

  • of all kinds is what makes them both more powerful and cheaper every year.

  • Baxter today is the computer in the 1980s. He’s not the apex but the beginning. Even

  • if Baxter is slow his hourly cost is pennies worth of electricity while his meat-based

  • competition costs minimum wage. A tenth the speed is still cost effective when it's a

  • hundred times cheaper. And while Baxtor isn't as smart as some of the other things we will

  • talk about, he's smart enough to take over many low-skill jobs.

  • And we've already seen how dumber robots than Baxter can replace jobs. In new supermarkets

  • what used to be 30 humans is now one human overseeing 30 cashier robots.

  • Or the hundreds of thousand baristas employed world-wide? There’s a barista robot coming

  • for them. Sure maybe your guy makes your double-mocha-whatever just perfect and you’d never trust anyone

  • else -- but millions of people don’t care and just want a decent cup of coffee. Oh and

  • by the way this robot is actually a giant network of robots that remembers who you are

  • and how you like your coffee no matter where you are. Pretty convenient.

  • We think of technological change as the fancy new expensive stuff, but the real change comes

  • from last decade's stuff getting cheaper and faster. That's what's happening to robots

  • now. And because their mechanical minds are capable of decision making they are out-competing

  • humans for jobs in a way no pure mechanical muscle ever could.

  • ## Luddite Horses

  • Imagine a pair of horses in the early 1900s talking about technology. One worries all

  • these new mechanical muscles will make horses unnecessary.

  • The other reminds him that everything so far has made their lives easier -- remember all

  • that farm work? Remember running coast-to-coast delivering mail? Remember riding into battle?

  • All terrible. These city jobs are pretty cushy -- and with so many humans in the cities there

  • are more jobs for horses than ever.

  • Even if this car thingy takes off you might say, there will be new jobs for horses we

  • can't imagine.

  • But you, dear viewer, from beyond 2000 know what happened -- there are still working horses,

  • but nothing like before. The horse population peaked in 1915 -- from that point on it was

  • nothing but down.

  • There isn’t a rule of economics that says better technology makes more, better jobs

  • for horses. It sounds shockingly dumb to even say that out loud, but swap horses for humans

  • and suddenly people think it sounds about right.

  • As mechanical muscles pushed horses out of the economy, mechanical minds will do the

  • same to humans. Not immediately, not everywhere, but in large enough numbers and soon enough

  • that it's going to be a huge problem if we are not prepared. And we are not prepared.

  • You, like the second horse, may look at the state of technology now and think it can’t

  • possibly replace your job. But technology gets better, cheaper, and faster at a rate

  • biology can’t match.

  • Just as the car was the beginning of the end for the horse so now does the car show us

  • the shape of things to come.

  • ## The Shape Of Things to Come

  • Self-driving cars aren't the future: they're here and they work. Self-driving cars have

  • traveled hundreds of thousands of miles up and down the California coast and through

  • cities -- all without human intervention.

  • The question is not if they'll replaces cars, but how quickly. They don’t need to be perfect,

  • they just need to be better than us. Humans drivers, by the way, kill 40,000 people a

  • year with cars just in the United States. Given that self-driving cars don’t blink,

  • don’t text while driving, don’t get sleepy or stupid, it easy to see them being better

  • than humans because they already are.

  • Now to describe self-driving cars as cars at all is like calling the first cars mechanical

  • horses. Cars in all their forms are so much more than horses that using the name limits

  • your thinking about what they can even do. Lets call self-driving cars what they really

  • are:

  • Autos: the solution to the transport-objects-from-point-A-to-point-B problem. Traditional cars happen to be human

  • sized to transport humans but tiny autos can work in wear houses and gigantic autos can

  • work in pit mines. Moving stuff around is who knows how many jobs but the transportation

  • industry in the United States employs about three million people. Extrapolating world-wide

  • that’s something like 70 million jobs at a minimum.

  • These jobs are over.

  • The usual argument is that unions will prevent it. But history is filled with workers who

  • fought technology that would replace them and the workers always loose. Economics always

  • wins and there are huge incentives across wildly diverse industries to adopt autos.

  • For many transportation companies, the humans are about a third of their total costs. That's

  • just the straight salary costs. Humans sleeping in their long haul trucks costs time and money.

  • Accidents cost money. Carelessness costs money. If you think insurance companies will be against

  • it, guess what? Their perfect driver is one who pays their small premium but never gets

  • into an accident.

  • The autos are coming and they're the first place where most people will really see the

  • robots changing society. But there are many other places in the economy where the same

  • thing is happening, just less visibly.

  • So it goes with autos, so it goes for everything.

  • ## Intellectual Labor

  • ### White Collar Work

  • It's easy to look at Autos and Baxters and think: technology has always gotten rid of

  • low-skill jobs we don't want people doing anyway. They'll get more skilled and do better

  • educated jobs -- like they've always done.

  • Even ignoring the problem of pushing a hundred-million additional people through higher education,

  • white-collar work is no safe haven either. If your job is sitting in front of a screen

  • and typing and clicking -- like maybe you're supposed to be doing right now -- the bots

  • are coming for you too, buddy.

  • Software bots are both intangible and way faster and cheaper than physical robots. Given

  • that white collar workers are, from a companies perspective, both more expensive and more

  • numerous -- the incentive to automate their work is greater than low skilled work.

  • And that's just what automation engineers are for. These are skilled programmers whose

  • entire job is to replace your job with a software bot.

  • You may think even the world's smartest automation engineer could never make a bot to do your

  • job -- and you may be right -- but the cutting edge of programming isn't super-smart programmers

  • writing bots it's super-smart programmers writing bots that teach themselves how to

  • do things the programmer could never teach them to do.

  • How that works is well beyond the scope of this video, but the bottom line is there are

  • limited ways to show a bot a bunch of stuff to do, show the bot a bunch of correctly done

  • stuff, and it can figure out how to do the job to be done.

  • Even with just a goal and no example of how to do it the bots can still learn. Take the

  • stock market which, in many ways, is no longer a human endeavor. It's mostly bots that taught

  • themselves to trade stocks, trading stocks with other bots that taught themselves.

  • Again: it's not bots that are executing orders based on what their human controllers want,

  • it's bots making the decisions of what to buy and sell on their own.

  • As a result the floor of the New York Stock exchange isn't filled with traders doing their

  • day jobs anymore, it's largely a TV set.

  • So bots have learned the market and bots have learned to write. If you've picked up a newspaper

  • lately you've probably already read a story written by a bot. There are companies that

  • are teaching bots to write anything: Sports stories, TPS reports, even say, those quarterly

  • reports that you write at work.

  • Paper work, decision making, writing -- a lot of human work falls into that category

  • and the demand for human metal labor is these areas is on the way down. But surely the professions

  • are safe from bots? Yes?

  • ## Professions

  • When you think 'lawyer' it's easy to think of trials. But the bulk of lawyering is actually

  • drafting legal documents predicting the likely outcome and impact of lawsuits, and something

  • called 'discovery' which is where boxes of paperwork gets dumped on the lawyers and they

  • need to find the pattern or the one out-of-place transaction among it all.

  • This can all be bot work. Discovery, in particular, is already not a human job in many firms.

  • Not because there isn't paperwork to go through, there's more of it than ever, but because

  • clever research bots sift through millions of emails and memos and accounts in hours

  • not weeks -- crushing human researchers in terms of not just cost and time but, most

  • importantly, accuracy. Bots don't get sleeping reading through a million emails.

  • But that's the simple stuff: IBM has a bot named Watson: you may have seen him on TV

  • destroy humans at Jeopardybut that was just a fun side project for him.

  • Watson's day-job is to be the best doctor in the world: to understand what people say

  • in their own words and give back accurate diagnoses. And he's already doing that at

  • Slone-Kettering, giving guidance on lung cancer treatments.

  • Just as Auto don’t need to be perfect -- they just need to make fewer mistakes than humans,

  • -- the same goes for doctor bots.

  • Human doctors are by no means perfect -- the frequency and severity of misdiagnosis are

  • terrifying -- and human doctors are severely limited in dealing with a human's complicated

  • medical history. Understanding every drug and every drug's interaction with every other

  • drug is beyond the scope of human knowability.

  • Especially when there are research robots whose whole job it is to test 1,000s of new

  • drugs at a time.

  • Human doctors can only improve through their own experiences. Doctor bots can learn from