What’s it about?

A World Without Work (2020) explores how synthetic intelligence will convey unemployment to such a lot of industries – and why that doesn’t need to be a horrific thing. The writer outlines the records of technological development and explains how new skills will permit remarkable productivity. Yes, many roles turn irrelevant, but, as a society, we can make sure that everyone may be higher off in this new world.

About the author:

Daniel Susskind is a Fellow in Economics at Balliol College, Oxford, and the co-author of The Future of Professions, named as one of the best books of the year by the Financial Times, New Scientist, and the Times Literary Supplement.


What can I get? Learn more about the benefits and challenges we face in an increasingly automated world. 

There is no doubt that you have heard all kinds of strange predictions about how technology will change the world. Our society will never be the same again! We will all become superfluous! An army of robots will do our job! 

Both optimists and pessimists agree that a change of direction is necessary, but how should it be? 

Blinking will penetrate the noise, allowing you to understand better what automation is, what impact it will have on human society, and most importantly, how we can use it to create a better world. 

You will use history and economics courses to learn the history of technological change. You will also see a new way forward and enter a world where everyone can live a happy and fulfilling life without work.


Robots will replace some jobs, but they will also supplement other jobs. 

The machine takes over. You may have heard of this before. And it is not difficult to see the source of this argument: each year brings new technological innovations. When computers and robots become more innovative and intelligent, will people become redundant? 

Of course, not everything is so simple in real life, so there is nothing to worry about! The machine will never take away all our work. The impact on the labor market is more diverse. 

Fear of technological change is nothing new. Centuries ago, at the beginning of the British Industrial Revolution, weavers destroyed the first machine. These people, they are called Luddites, worry about their work. They have reasons to do so. Worry; rapid technological change in your industry has caused significant changes. 

But is this change so bad? 

Now, although some workers suffer, others benefit. If a low-skilled person learns how to use the new machine, his output will increase significantly, and eventually, his profit will increase. 

New technologies often complement each other. Although it replaces some workers, it makes others more efficient. As? Help them complete some more complex tasks. 

For example, algorithms that can process legal documents do not replace lawyers but instead allow them to do more creative work, such as writing, solving problems, and meeting clients in person. 

The increase in output brings the second advantage of automation. Think of the economy of a country as a pie that everyone can share. Of course, the machine will change the arrangement of the cakes. But they also made a lot of cakes themselves. More importantly. 

Don’t you believe it? Well, an automated teller machine (ATM) is a good example. When these machines were first introduced, there was concern that they would completely replace bank employees. 

But let us see what happened: in the past 30 years, the number of ATMs in the United States has quadrupled. At the same time, the number of bank employees increased by approximately 20%. ATMs have replaced cashiers in distributing cash, but they have also liberated people’s ability to provide financial advice and personal assistance. 

Economic growth has increased the overall demand for banking and financial consulting. Overall, in the past few decades, the average number of cashiers in banks has decreased by about a third. But the number of banks where cashiers can find jobs has increased. As high as 43%.


All jobs are at risk of technological change. 

Whose job is the machine? Do these people work on the assembly line? Or a supermarket cashier? Or should neurosurgeons worry that one-day robots will replace them? 

Technological progress will affect everyone, but recent trends provide some clues as to which sectors of the economy are most likely to be automated. 

In the past few decades, highly skilled workers with formal education have benefited more from technology than their low-skilled neighbors. Why? The answer is computers. From 1950 to 2000, their capacity increased by 10 billion times. 

Highly qualified workers who can use new machines are needed. As demand increases, so does supply, and more and more people are learning to use computers. Unsurprisingly, wages have fallen. But then something interesting happened: demand continued to rise, and the wages of highly skilled workers began to rise. By 2008, economists documented an unprecedented income gap between American college graduates and those who just graduated from high school. 

Does this mean that technology will always benefit people with better education? Not really. It used to be the opposite. Do you remember the Luddites? In England in the 18th century, weaving was a highly skilled thing. In this scene, you no longer need to hone your skills to make suitable fabrics. Low-skilled workers benefit from it. 

So who will benefit from automation in the future? Economists believe that technology creates low-skilled and high-skilled jobs; the middle class suffers. There are more cleaners and lawyers, but fewer secretaries and sales staff. 

Three economists from the Massachusetts Institute of Technology explained this. His theory is that “routine” tasks are more accessible to automate than “non-standard” tasks due to creativity, judgment, interpersonal skills, or complex manual work. 

Simple and common skills can interpret, decompose, and transform into algorithms. The computer has almost no problems in this regard, but the machine lacks non-standard functions that are difficult to explain. 

For a long time, people believed that non-standard work could avoid automation because scientists could not teach how to do it. 


When computers no longer think like humans, artificial intelligence research has made a breakthrough. 

The ancient Greek poet Homer is famous for “Iliad” and “Odyssey,” but did you know that he wrote more than heroes and battles? He also described what we now call artificial intelligence or AI. The “unmanned” three-legged stool fell into their hands at the request of the owner, a bit reminiscent of modern self-driving cars. 

Homer may not have thought of self-driving cars when telling this story, but he talked about an important point: People have long dreamed of self-driving vehicles, and recent developments make these dreams a reality. 

To understand the capabilities of artificial intelligence, let’s start with their stories. The first attempt to create artificial intelligence can be traced back to the mid-20th century when computers became a reality. Early artificial intelligence researchers tried to reconstruct the human mind. 

For example, when developing chess software, they asked the masters to explain their views on the game, and then the engineers tried to teach the computer the same process. 

However, in the late 1980s, this approach stagnated. Whether it is chess, translation, or object recognition, early artificial intelligence could not defeat humans by thinking like humans. So what is the solution? Scientists realize that they must change direction: so far, imitating human thought can only lead to the appearance of computers. 

The next wave of artificial intelligence research takes a more pragmatic approach: Scientists assign machines to tasks and tell the software to complete them in any possible way, even if it doesn’t make sense to humans. Or translation strategy, the new artificial intelligence program has taken hundreds of millions of data points and scanned them for patterns. 

It means that artificial intelligence research has made great strides. In 1997, IBM’s Deep Blue defeated the world chess champion, Gary Kasparov. Artificial intelligence is not only flourishing in chess; for example, modern image recognition programs often outperform people in the competition. 

These advancements are critical to understanding how automation affects future work. Economists once believed that computers would never work without human guidance. Still, now machines can find inhumane solutions to problems and problems—the ability to master non-standard skills that were previously thought to be beyond the scope of their ability.


Machines will become better in all areas of activity, but technological advancements will vary from place to place. 

Science fiction writer William Gibson once said: “The future is here. It’s just uneven distribution.” This line is proper when we talk about automation. When it comes to what computers can do, they have a long future. I can outperform people in more and more tasks, from finding cheaters to making prostheses. 

However, the leap from “AI can do” to “AI will do” is vast, and the gap between countries is also different. 

With the continuous growth of technological capabilities, automation is changing all industries. Just look at agriculture! Modern farmers have uncrewed tractors, facial recognition systems for cattle, and automatic sprinklers. In Japan, 90% of spraying plants is done with drones. Tasks that previously required fine motor skills have been automated: robots can pick oranges by shaking them from a tree. 

What about industries that require more complex thinking? The software has appeared in law, finance, and medicine and can analyze more information than humans. AI is excellent for finding trend patterns and past cases. 

The diagnostic system jointly developed by Chinese technology companies Tencent and Guangzhou Hospital uses more than 300 million medical records to evaluate patients. 

Machines can now perform tasks that require feelings and emotions. In terms of whether the smile is genuine, some facial recognition systems are already superior to humans. 

“Social robots” are so named because they can sense and respond to human emotions and are expected to develop into a $67 billion industry. They are becoming more common in the healthcare field; a humanoid machine called Pepper is placed in some hospitals in Belgium. Your job is to welcome patients and guide them through the maze of corridors and buildings. 

But just because you can automate more tasks doesn’t mean you will. Automation requirements and costs vary from region to region, so countries may not develop at the same rate. 

For example, in Japan, there are many older adults but insufficient nurses. Hence, hospitals have a solid motivation to automate nursing work, which is different from countries with a young population and many people willing to accept jobs. Low-income people are less motivated to automate healthcare. We can even use political pressure to prevent the emergence of robotic medicine.


More and more productive machines will lead to a lot of unemployment. 

Finding a job is not so fun. Now imagine that you are unemployed because your occupation is automated. How do you find a job when you are replaced by a car? This problem is likely to affect millions of people. 

We know from previous experience that automation will increase the economic pie. Machines will create new jobs to offset unemployment, but will people fill these new jobs? Well, the answer is far from too many obstacles, such as skill mismatch. 

Assume that most new jobs are highly skilled, such as artificial intelligence management experts. It does not help unskilled factory workers. 

Or consider geographical differences. Do you want to drive a few hundred kilometers to work? The Internet helps to make telecommuting more accessible, but the geographic location is still important. Think about Silicon Valley. It attracts so many technology companies. Many talented programmers in the region mean that startup founders often move to Silicon Valley to find talent. And establish new contacts. 

Economists refer to these obstacles as frictions. Scientists believe that they are all short-term and will weaken in the long run, but some things have nowhere to go: structural changes in the labor market. 

We found that technology can increase productivity and overall output. However, as technology advances, it will reach the point where humans no longer need humans at all. Take the car as an example. The driver can choose a more efficient route. However, driverless vehicles are now ready to replace them entirely. Even if the demand for taxis increases, it will not create more job opportunities for people; the company will produce more self-driving cars. 

This change will not happen overnight. As Silicon Valley researcher Roy Amara once said, “We tend to overestimate the short-term impact of technology and underestimate the long-term impact.” 

But how long is that? These effects will show up in decades rather than centuries and will only intensify as artificial intelligence becomes smarter. The trend is clear: as production continues to grow, people’s jobs will decrease.


Automation has increased the income gap between jobs, thereby exacerbating inequality. 

For most of human history, we have been fighting for survival. This term was coined by the famous economist John Maynard Keynes and means that the output of human society is not enough for everyone to live. 

Thanks to the advancement of modern technology, we produce enough products to enable everyone to live a comfortable life. The economic pie has become more extensive, but how do you distribute it? 

After looking at the economic data, one thing becomes apparent: the share of cakes has become more unequal in recent years. 

Let us think about what we have: from an economic point of view, our capital. We can divide our capital into two categories: traditional capital and human capital. Traditional capital is something that we can purchase, such as land, equipment, or intellectual property. It is a broader concept that covers all your skills and abilities. 

If everyone has enough traditional capital, automation doesn’t make much sense. However, most people have little control over regular money. Instead, they use human means to accumulate wealth. Lose this human capital. Do you see the problem? 

The data shows the severity of the problem. Until 1980, the income of all Americans maintained steady growth. However, from 1980 to 2014, everything changed. The payment of the population who has earned more has increased dramatically. 

This pattern has been repeated in rich countries worldwide: for all workers with lower skills, human capital is unequal. 2% of the country’s wealth, while the wealthiest people own 40%. 

Here are some essential points. The most obvious is that future changes in the world of work will lead to alarming inequalities. Another question: How will society function when people no longer need to work?


If automation disrupts the labor market, the “big powers” must be responsible for distributing wealth. 

Traditionally, most of us have to work hard to get our economic pie, but what if automation will lay off employees? How should society support people being replaced by machines? 

If the labor market is unsuccessful, another institution must register. Only one organization has the right to do this: the state. 

Most developed countries already have welfare states. This concept in its current form dates back to the beginning of the 20th century. 

But the welfare state must also change. It was initially designed to supplement the job market. The basic principle is that people who work support people who don’t. This work is exciting. 

But in the automated world, this will no longer be the case. The welfare state is being replaced by what the author calls a “great country,” and this institution understands that it will not provide enough jobs for everyone. 

A large country must achieve two main goals: taxing those who benefit from automation and redistributing income to those affected. 

Large countries can levy taxes on workers such as software developers or managers of technology companies. They can also impose tariffs on owners of traditional capital (including land, machinery, or property rights). Finally, the state can tax companies, especially those that generate additional profits through automation. 

After the big countries have collected all this money, how should they distribute it? Some economists have proposed ​​providing universal basic income (UBI) paid in cash for all. 

But the author thinks that he can modify this idea further. Provide conditional basic income or CBI. This money will support specific communities. 

CBI avoids one of the shortcomings of universal basic income: its perception of inequality. If everyone takes money from the state, some people will feel unfair; we will face the risk of community division and even conflict. 

In contrast, the author’s proposed CBI will only target people who meet specific criteria. The system allows winners to share their wealth with those they want to help. 

In this way, a better and more stable society has been created. In this society, people have fewer jobs, but they can still feel the support of the surrounding strong communities.