00:00How long do you think it will take before machines do your job better than you do?
00:07Automation used to mean big, stupid machines doing repetitive work in factories.
00:12Today, they can land aircraft, diagnose cancer, and trade stocks.
00:16We are entering a new age of automation, unlike anything that's come before.
00:21According to a 2013 study, almost half of all jobs in the US could potentially be automated in the next two decades.
00:28But wait, hasn't automation been around for decades?
00:31What's different this time?
00:37Things used to be simple.
00:39Innovation made human work easier and productivity rose,
00:43which means that more staff or services could be produced per hour using the same amount of human workers.
00:49This eliminated many jobs, but also created other jobs that were better,
00:54which was important because the growing population needed work.
00:57So, in a nutshell, innovation, higher productivity, fewer old jobs, and many new and often better jobs.
01:05Overall, this worked well for a majority of people and living standards improved.
01:11There's a clear progression in terms of what humans did for a living.
01:15For the longest time, we worked in agriculture.
01:18With the Industrial Revolution, this shifted to production jobs.
01:22And as automation became more widespread, humans shifted into service jobs.
01:27And then, only a few moments ago in human history, the Information Age happened.
01:32Suddenly, the rules were different.
01:35Our jobs are now being taken over by machines much faster than they were in the past.
01:40That's worrying, of course, but innovation will clearly save us, right?
01:45While new Information Age industries are booming, they are creating fewer and fewer new jobs.
01:51In 1979, General Motors employed more than 800,000 workers and made about US$11 billion.
01:58In 2012, Google made about US$14 billion while employing 58,000 people.
02:05You may not like this comparison, but Google is an example of what created new jobs in the past.
02:11Innovative new industries.
02:13Old innovative industries are running out of steam.
02:16Just look at cars.
02:18When they became a thing 100 years ago, they created huge industries.
02:23Cars transformed our way of life, our infrastructure and our cities.
02:28Millions of people found jobs, either directly or indirectly.
02:32Decades of investment kept this momentum going.
02:35Today, this process is largely complete.
02:38Innovation in the car industry does not create as many jobs as it used to.
02:43While electric cars are great and all, they won't create millions of new jobs.
02:48But wait, what about the Internet?
02:51Some technologists argue that the Internet is an innovation on a par with the introduction of electricity.
02:57If we go with this comparison, we see how our modern innovation differs from the old one.
03:02The Internet created new industries, but they're not creating enough jobs to keep up with population growth
03:08or to compensate for the industries the Internet is killing.
03:11At its peak in 2004, Blockbuster had 84,000 employees and made 6 billion US dollars in revenue.
03:18In 2016, Netflix had 4,500 employees and made 9 billion dollars in revenue.
03:25Or take us for example.
03:27With a full-time team of just 12 people, Kurzgesagt reaches millions of people.
03:32A TV station with the same amount of viewers needs way more employees.
03:37Innovation in the information age doesn't equate to the creation of enough new jobs,
03:41which would be bad enough on its own,
03:43but now a new wave of automation and a new generation of machines is slowly taking over.
03:54To understand this, we need to understand ourselves first.
03:58Human progress is based on the division of labor.
04:01As we advanced over thousands of years, our jobs became more and more specialized.
04:07While even our smartest machines are bad at doing complicated jobs,
04:11they are extremely good at doing narrowly defined and predictable tasks.
04:16This is what destroyed factory jobs.
04:19But look at a complex job long and hard enough,
04:22and you'll find that it's really just many narrowly defined and predictable tasks, one after another.
04:28Machines are on the brink of becoming so good at breaking down complex jobs into many predictable ones,
04:34that for a lot of people, there will be no further room to specialize.
04:38We are on the verge of being out-competed.
04:42Digital machines do this via machine learning,
04:45which enables them to acquire information and skills by analyzing data.
04:49This makes them become better at something through the relationships they discover.
04:53Machines teach themselves.
04:55We make this possible by giving a computer a lot of data about the thing we want it to become better at.
05:01Show a machine all the things you bought online,
05:04and it will slowly learn what to recommend to you, so you buy more things.
05:09Machine learning is now meeting more of its potential,
05:12because in recent years, humans have started to gather data about everything.
05:17Behavior, weather patterns, medical records, communication systems, travel data.
05:24And of course, data about what we do at work.
05:28What we've created by accident is a huge library machines can use to learn how humans do things and learn to do them better.
05:36These digital machines might be the biggest job killer of all.
05:39They can be replicated instantly and for free.
05:43When they improve, you don't need to invest in big metal things, you can just use the new code.
05:49And they have the ability to get better fast.
05:52How fast?
05:54If your work involves complex work on a computer today,
05:57you might be out of work even sooner than the people who still have jobs in factories.
06:02There are actual real-world examples of how this transition might be happening.
06:08A San Francisco company offers a project management software for big corporations,
06:12which is supposed to eliminate middle management positions.
06:16When it's hired for a new project, the software first decides which jobs can be automated,
06:21and precisely where it needs actual professional humans.
06:25It then helps assemble a team of freelancers over the Internet.
06:29The software then distributes tasks to the humans and controls the quality of the work,
06:33tracking individual performance until the project is complete.
06:37Okay, this doesn't sound too bad.
06:40While this machine is killing one job, it creates jobs for freelancers, right?
06:45Well, as the freelancers complete their tasks, learning algorithms track them
06:49and gather data about their work and which tasks it consists of.
06:54So what's actually happening is that the freelancers are teaching a machine how to replace them.
06:59On average, this software reduces costs by about 50% in the first year,
07:03and by another 25% in the second year.
07:07This is only one example of many.
07:10There are machines and programs getting as good or better than humans in all kinds of fields,
07:15from pharmacists to analysts, journalists to radiologists,
07:19cashiers, bank tellers, or the unskilled worker flipping burgers.
07:24All of these jobs won't disappear overnight, but fewer and fewer humans will be doing them.
07:30We'll discuss a few cases in a follow-up video.
07:33But while jobs disappearing is bad, it's only half of the story.
07:40New Jobs
07:43It's not enough to substitute old jobs with new ones.
07:46We need to be generating new jobs constantly because the world population is growing.
07:51In the past, we have solved this through innovation.
07:54But since 1973, the generation of new jobs in the US has begun to shrink.
08:00And the first decade of the 21st century was the first one
08:03where the total amount of jobs in the US did not grow for the first time.
08:07In a country that needs to create up to 150,000 new jobs per month
08:12just to keep up with population growth, this is bad news.
08:17This is also starting to affect standards of living.
08:20In the past, it was seen as obvious that with rising productivity,
08:24more and better jobs would be created.
08:27But the numbers tell a different story.
08:29In 1998, US workers worked a total of 194 billion hours.
08:36Over the course of the next 15 years, their output increased by 42%.
08:41But in 2013, the amount of hours worked by US workers was still 194 billion hours.
08:49What this means is that despite productivity growing drastically,
08:53thousands of new businesses opening up,
08:55and the US population growing by over 40 million,
08:58there was no growth at all in the number of hours worked in 15 years.
09:03At the same time, wages for new university graduates in the US
09:07have been declining for the past decade,
09:09while up to 40% of new graduates are forced to take on jobs that don't require a degree.
09:21Productivity is separating from human labor.
09:24The nature of innovation in the information age
09:26is different from everything we encountered before.
09:29This process started years ago and is already well underway,
09:33even without new disruptions like self-driving cars or robot accountants.
09:39It looks like automation is different this time.
09:42This time, the machines might really take our jobs.
09:46Our economies are based on the premise that people consume.
09:49But if fewer and fewer people have decent work,
09:52who will be doing all the consuming?
09:55Are we producing ever more cheaply only to arrive at a point
09:58where too few people can actually buy all our stuff and services?
10:03Or will the future see a tiny minority of the super-rich
10:06who own the machines dominating the rest of us?
10:09And does our future really have to be that grim?
10:13While we were fairly dark in this video,
10:15it's far from certain that things will turn out negatively.
10:18The information age and modern automation
10:20could be a huge opportunity to change human society
10:23and reduce poverty and inequality drastically.
10:26It could be a seminal moment in human history.
10:29We'll talk about this potential and possible solutions
10:32like a universal basic income in part 2 of this video series.
10:37We need to think big and fast.
10:39Because one thing's for sure,
10:41the machines are not coming.
10:43They are already here.
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