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  • How long do you think it will take

  • before machines do your job better than you do?

  • Automation used to mean big stupid machines doing repetitive work in factories.

  • Today they can land aircraft, diagnose cancer and trade stocks.

  • We are entering a new age of automation unlike anything that's come before.

  • According to a 2013 study, almost half of all jobs in the

  • US could potentially be automated in the next two decades.

  • But wait; Hasn't automation been around for decades?

  • What's different this time?

  • Things used to be simple.

  • Innovation made human work easier and productivity rose.

  • Which means that more staff or services could be produced

  • per hour using the same amount of human workers.

  • This eliminated many jobs, but also created other jobs that were better

  • which was important because the growing population needed work.

  • So, in a nutshell, innovation, higher productivity,

  • fewer old jobs, and many new and often better jobs.

  • Overall, this worked well for a majority of people and living standards improved.

  • There's a clear progression in terms of what humans did for

  • a living. For the longest time, we worked in agriculture.

  • With the Industrial Revolution, this shift into production jobs and as

  • automation became more widespread, humans shifted into service jobs.

  • And then only a few moments ago in human history, the Information Age happened.

  • Suddenly, the rules were different. Our jobs are now being

  • taken over by machines much faster than they were in the past.

  • That's worrying of course... but innovation will clearly save us, right?

  • While new information age industries are booming,

  • they are creating fewer and fewer new jobs.

  • In 1979, General Motors employed more than 800,000

  • workers and made about $11 billion US dollars.

  • In 2012, Google made about $14 billion US dollars while employing 58,000 people.

  • You may not like this comparison, but Google is

  • an example of what created new jobs in the past:

  • Innovative new industries.

  • Old innovative industries are running out of steam. Just look at cars.

  • When they became a thing 100 years ago, they created huge industries.

  • Cars transformed our way of life, our infrastructure, and our cities.

  • Millions of people found jobs either directly or indirectly.

  • Decades of investment kept this momentum going.

  • Today, this process is largely complete. Innovation in the

  • car industry does not create as many jobs as it used to.

  • While electric cars are great and all, they won't create millions of new jobs.

  • But wait; what about the internet?

  • Some technologists argue that the Internet is an

  • innovation on a par of the introduction of electricity.

  • If we go with this comparison, we see how our

  • modern innovation differs from the old one.

  • The Internet created new industries,

  • but they're not creating enough jobs to keep up

  • with population growth or to compensate for the industries the Internet is killing.

  • At its peak in 2004,

  • Blockbuster had 84,000 employees and made $6 billion US dollars in revenue.

  • In 2016, Netflix had 4,500 employees and made $9 billion dollars in revenue.

  • Or take us, for example.

  • With a full-time team of just 12 people, Kurzgesagt reaches millions of people.

  • A TV station with the same amount of viewers needs way more employees.

  • Innovation in the Information Age doesn't equate to

  • the creation of enough new jobs, which would be bad

  • enough on its own but now, a new wave of automation and

  • a new generation of machines is slowly taking over.

  • To understand this, we need to understand ourselves first.

  • Human progress is based on the division of labor.

  • As we advanced over thousands of years, our jobs became more and more specialized.

  • While even our smartest machines are bad at doing complicated jobs,

  • they are extremely good at doing narrowly defined and predictable tasks.

  • This is what destroyed factory jobs.

  • But look at a complex job long and hard enough,

  • and you'll find that it's really just many narrowly

  • defined and predictable tasks one after another.

  • Machines are on the brink of becoming so good at

  • breaking down complex jobs into many predictable ones,

  • that for a lot of people, there will be no further room to specialize.

  • We are on the verge of being outcompeted.

  • Digital machines do this via machine learning,

  • which enables them to acquire information and skills by analyzing data.

  • This makes them become better at something through the relationships they discover.

  • Machines teach themselves.

  • We make this possible by giving a computer a lot of

  • data about the thing we wanted to become better at.

  • Show a machine all the things you bought online,

  • and it will slowly learn what to recommend to you, so you buy more things.

  • Machine learning is now meeting more of its potential because in recent years,

  • humans have started to gather data about everything.

  • Behavior, weather patterns, medical records, communication systems,

  • travel data, and of course, data about what we do at work.

  • What we've created by accident is a huge library machines can

  • use to learn how humans do things and learn to do them better.

  • These digital machines might be the biggest job killer of all.

  • They can be replicated instantly and for free.

  • When they improve, you don't need to invest in

  • big metal things; you can just use the new code.

  • And they have the ability to get better fast. How fast?

  • If your work involves complex work on a computer today, you might be out

  • of work even sooner than the people who still have jobs in factories.

  • There are actual real-world examples of how this transition might be happening.

  • A San Francisco company offers a project management software for big

  • corporations, which is supposed to eliminate middle management positions.

  • When it's hired for a new project, the software first decides which jobs

  • can be automated and precisely where it needs actual professional humans.

  • It then helps assemble a team of freelancers over the Internet.

  • The software then distributes tasks to the humans, and controls the quality

  • of the work, tracking individual performance until the project is complete.

  • Okay. This doesn't sound too bad.

  • While this machine is killing one job, it creates jobs for freelancers, right?

  • Well, as the freelancers complete their tasks,

  • learning algorithms track them, and gather data

  • about their work, and which tasks it consists of.

  • So what's actually happening, is that

  • the freelancers are teaching a machine how to replace them.

  • On average, this software reduces costs by about 50%

  • in the first year, and by another 25% in the second year.

  • This is only one example of many.

  • There are machines and programs getting as good

  • or better than humans in all kinds of fields.

  • From pharmacists to analysts, journalists to radiologists,

  • cashiers to bank tellers, or the unskilled worker flipping burgers.

  • All of these jobs won't disappear overnight,

  • but fewer and fewer humans will be doing them.

  • We'll discuss a few cases in a follow-up video.

  • But while jobs disappearing is bad, it's only half of the story.

  • It's not enough to substitute old jobs with new ones.

  • We need to be generating new jobs constantly

  • because the world population is growing.

  • In the past we have solved this through innovation.

  • But, since 1973, the generation of new jobs in the US has begun to shrink.

  • And the first decade of the 21st century, was the first one, where

  • the total amount of jobs in the US, did not grow for the first time.

  • In a country that needs to create up to 150,000 new jobs per

  • month, just to keep up with population growth, this is bad news.

  • This is also starting to affect standards of living.

  • In the past, it was seen as obvious that with rising

  • productivity, more and better jobs would be created.

  • But the numbers tell a different story.

  • In 1998, US workers worked a total of 194 billion hours.

  • Over the course of the next 15 years, their output increased by 42 percent.

  • But in 2013, the amount of hours worked by US workers was still 194 billion hours.

  • What this means, is that despite productivity growing

  • drastically, thousands of new businesses opening up, and the

  • US population growing by over 40 million, there was no

  • growth at all in the number of hours worked in 15 years.

  • At the same time, wages for new university graduates

  • in the US, have been declining for the past decade,

  • while up to 40 percent of new graduates, are forced

  • to take on jobs that don't require a degree.

  • Productivity is separating from human labor.

  • The nature of innovation in the Information Age is

  • different from everything we've encountered before.

  • This process started years ago and is already well underway.

  • Even without new disruptions like self-driving cars, or robot accountants.

  • It looks like automation is different this time.

  • This time, the machines might really take our jobs.

  • Our economies are based on the premise that people consume.

  • But if fewer and fewer people have decent work, who will be doing all the consuming?

  • Are we producing ever more cheaply only to arrive at a point where

  • too few people can actually buy all our stuff and services?

  • Or, will the future see a tiny minority of the super rich who own the machines...

  • dominating the rest of us?

  • And does our future really have to be that grim?

  • While we were fairly dark in this video, it's far

  • from certain that things will turn out negatively.

  • The Information Age and modern automation, could be a huge opportunity

  • to change human society, and reduce poverty and inequality drastically.

  • It could be a seminal moment in human history.

  • We'll talk about this potential, and possible solutions like

  • a universal basic income, in part 2 of this video series.

  • We need to think big, and fast.

  • Because one thing's for sure, the machines are not coming;

  • They are already here.

  • This video took us about 900 hours to make,

  • and we've been working on it for over nine months.

  • Projects like this one would not be possible

  • without your support on patreon.com.

  • If you want to help us out and get a personal

  • Kurzgesagt bird in return, that would be really useful.

  • We based much of this video on two very good books:

  • and

  • You can find links to both of them in the video description; highly recommended!

  • Also, we made a little robot poster.

  • You can buy it and a lot of other stuff in our DFTBA shop.

  • This video is part of a larger series about how technology

  • is already changing and will change human life forever.

  • If you want to continue watching, we have a few playlists.

How long do you think it will take

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