فصل 2

2

WORK

When you grow up, you might not have a job

We have no idea what the job market will look like in 2050. It is generally agreed that machine learning and robotics will change almost every line of work – from producing yoghurt to teaching yoga. However, there are conflicting views about the nature of the change and its imminence. Some believe that within a mere decade or two, billions of people will become economically redundant. Others maintain that even in the long run automation will keep generating new jobs and greater prosperity for all.

So are we on a verge of a terrifying upheaval, or are such forecasts yet another example of ill-founded Luddite hysteria? It is hard to say. Fears that automation will create massive unemployment go back to the nineteenth century, and so far they have never materialised. Since the beginning of the Industrial Revolution, for every job lost to a machine at least one new job was created, and the average standard of living has increased dramatically.1 Yet there are good reasons to think that this time it is different, and that machine learning will be a real game changer.

Humans have two types of abilities – physical and cognitive. In the past, machines competed with humans mainly in raw physical abilities, while humans retained an immense edge over machines in cognition. Hence as manual jobs in agriculture and industry were automated, new service jobs emerged that required the kind of cognitive skills only humans possessed: learning, analysing, communicating and above all understanding human emotions. However, AI is now beginning to outperform humans in more and more of these skills, including in the understanding of human emotions.2 We don’t know of any third field of activity – beyond the physical and the cognitive – where humans will always retain a secure edge.

It is crucial to realise that the AI revolution is not just about computers getting faster and smarter. It is fuelled by breakthroughs in the life sciences and the social sciences as well. The better we understand the biochemical mechanisms that underpin human emotions, desires and choices, the better computers can become in analysing human behaviour, predicting human decisions, and replacing human drivers, bankers and lawyers.

In the last few decades research in areas such as neuroscience and behavioural economics allowed scientists to hack humans, and in particular to gain a much better understanding of how humans make decisions. It turned out that our choices of everything from food to mates result not from some mysterious free will, but rather from billions of neurons calculating probabilities within a split second. Vaunted ‘human intuition’ is in reality ‘pattern recognition’.3 Good drivers, bankers and lawyers don’t have magical intuitions about traffic, investment or negotiation – rather, by recognising recurring patterns, they spot and try to avoid careless pedestrians, inept borrowers and dishonest crooks. It also turned out that the biochemical algorithms of the human brain are far from perfect. They rely on heuristics, shortcuts and outdated circuits adapted to the African savannah rather than to the urban jungle. No wonder that even good drivers, bankers and lawyers sometimes make stupid mistakes.

This means that AI can outperform humans even in tasks that supposedly demand ‘intuition’. If you think AI needs to compete against the human soul in terms of mystical hunches – that sounds impossible. But if AI really needs to compete against neural networks in calculating probabilities and recognising patterns – that sounds far less daunting.

In particular, AI can be better at jobs that demand intuitions about other people. Many lines of work – such as driving a vehicle in a street full of pedestrians, lending money to strangers, and negotiating a business deal – require the ability to correctly assess the emotions and desires of other people. Is that kid about to jump onto the road? Does the man in the suit intend to take my money and disappear? Will that lawyer act on his threats, or is he just bluffing? As long as it was thought that such emotions and desires were generated by an immaterial spirit, it seemed obvious that computers will never be able to replace human drivers, bankers and lawyers. For how can a computer understand the divinely created human spirit? Yet if these emotions and desires are in fact no more than biochemical algorithms, there is no reason why computers cannot decipher these algorithms – and do so far better than any Homo sapiens.

A driver predicting the intentions of a pedestrian, a banker assessing the credibility of a potential borrower, and a lawyer gauging the mood at the negotiation table don’t rely on witchcraft. Rather, unbeknownst to them, their brains are recognising biochemical patterns by analysing facial expressions, tones of voice, hand movements, and even body odours. An AI equipped with the right sensors could do all that far more accurately and reliably than a human.

Hence the threat of job losses does not result merely from the rise of infotech. It results from the confluence of infotech with biotech. The way from the fMRI scanner to the labour market is long and tortuous, but it can still be covered within a few decades. What brain scientists are learning today about the amygdala and the cerebellum might make it possible for computers to outperform human psychiatrists and bodyguards in 2050.

AI not only stands poised to hack humans and outperform them in what were hitherto uniquely human skills. It also enjoys uniquely non-human abilities, which make the difference between an AI and a human worker one of kind rather than merely of degree. Two particularly important non-human abilities that AI possesses are connectivity and updateability.

Since humans are individuals, it is difficult to connect them to one another and to make sure that they are all up to date. In contrast, computers aren’t individuals, and it is easy to integrate them into a single flexible network. Hence what we are facing is not the replacement of millions of individual human workers by millions of individual robots and computers. Rather, individual humans are likely to be replaced by an integrated network. When considering automation it is therefore wrong to compare the abilities of a single human driver to that of a single self-driving car, or of a single human doctor to that of a single AI doctor. Rather, we should compare the abilities of a collection of human individuals to the abilities of an integrated network.

For example, many drivers are unfamiliar with all the changing traffic regulations, and they often violate them. In addition, since every vehicle is an autonomous entity, when two vehicles approach the same junction at the same time, the drivers might miscommunicate their intentions and collide. Self-driving cars, in contrast, can all be connected to one another. When two such vehicles approach the same junction, they are not really two separate entities – they are part of a single algorithm. The chances that they might miscommunicate and collide are therefore far smaller. And if the Ministry of Transport decides to change some traffic regulation, all self-driving vehicles can be easily updated at exactly the same moment, and barring some bug in the program, they will all follow the new regulation to the letter.4 Similarly, if the World Health Organization identifies a new disease, or if a laboratory produces a new medicine, it is almost impossible to update all the human doctors in the world about these developments. In contrast, even if you have 10 billion AI doctors in the world – each monitoring the health of a single human being – you can still update all of them within a split second, and they can all communicate to each other their feedback on the new disease or medicine. These potential advantages of connectivity and updateability are so huge that at least in some lines of work it might make sense to replace all humans with computers, even if individually some humans still do a better job than the machines.

You might object that by switching from individual humans to a computer network we will lose the advantages of individuality. For example, if one human doctor makes a wrong judgement, he does not kill all the patients in the world, and he does not block the development of all new medications. In contrast, if all doctors are really just a single system, and that system makes a mistake, the results might be catastrophic. In truth, however, an integrated computer system can maximise the advantages of connectivity without losing the benefits of individuality. You can run many alternative algorithms on the same network, so that a patient in a remote jungle village can access through her smartphone not just a single authoritative doctor, but actually a hundred different AI doctors, whose relative performance is constantly being compared. You don’t like what the IBM doctor told you? No problem. Even if you are stranded somewhere on the slopes of Kilimanjaro, you can easily contact the Baidu doctor for a second opinion.

The benefits for human society are likely to be immense. AI doctors could provide far better and cheaper healthcare for billions of people, particularly for those who currently receive no healthcare at all. Thanks to learning algorithms and biometric sensors, a poor villager in an underdeveloped country might come to enjoy far better healthcare via her smartphone than the richest person in the world gets today from the most advanced urban hospital.5 Similarly, self-driving vehicles could provide people with much better transport services, and in particular reduce mortality from traffic accidents. Today close to 1.25 million people are killed annually in traffic accidents (twice the number killed by war, crime and terrorism combined).6 More than 90 per cent of these accidents are caused by very human errors: somebody drinking alcohol and driving, somebody texting a message while driving, somebody falling asleep at the wheel, somebody daydreaming instead of paying attention to the road. The US National Highway Traffic Safety Administration estimated in 2012 that 31 per cent of fatal crashes in the USA involved alcohol abuse, 30 per cent involved speeding, and 21 per cent involved distracted drivers.7 Self-driving vehicles will never do any of these things. Though they suffer from their own problems and limitations, and though some accidents are inevitable, replacing all human drivers by computers is expected to reduce deaths and injuries on the road by about 90 per cent.8 In other words, switching to autonomous vehicles is likely to save the lives of a million people every year.

Hence it would be madness to block automation in fields such as transport and healthcare just in order to protect human jobs. After all, what we ultimately ought to protect is humans – not jobs. Redundant drivers and doctors will just have to find something else to do.

The Mozart in the machine

At least in the short term, AI and robotics are unlikely to completely eliminate entire industries. Jobs that require specialisation in a narrow range of routinised activities will be automated. But it will be much more difficult to replace humans with machines in less routine jobs that demand the simultaneous use of a wide range of skills, and that involve dealing with unforeseen scenarios. Take healthcare, for example. Many doctors focus almost exclusively on processing information: they absorb medical data, analyse it, and produce a diagnosis. Nurses, in contrast, also need good motor and emotional skills in order to give a painful injection, replace a bandage, or restrain a violent patient. Hence we will probably have an AI family doctor on our smartphone decades before we have a reliable nurse robot.9 The human care industry – which takes care of the sick, the young and the elderly – is likely to remain a human bastion for a long time. Indeed, as people live longer and have fewer children, care of the elderly will probably be one of the fastest-growing sectors in the human labour market.

Alongside care, creativity too poses particularly difficult hurdles for automation. We don’t need humans to sell us music any more – we can download it directly from the iTunes store – but the composers, musicians, singers and DJs are still flesh and blood. We rely on their creativity not just to produce completely new music, but also to choose among a mind-boggling range of available possibilities.

Nevertheless, in the long run no job will remain absolutely safe from automation. Even artists should be put on notice. In the modern world art is usually associated with human emotions. We tend to think that artists are channelling internal psychological forces, and that the whole purpose of art is to connect us with our emotions or to inspire in us some new feeling. Consequently, when we come to evaluate art, we tend to judge it by its emotional impact on the audience. Yet if art is defined by human emotions, what might happen once external algorithms are able to understand and manipulate human emotions better than Shakespeare, Frida Kahlo or Beyoncé?

After all, emotions are not some mystical phenomenon – they are the result of a biochemical process. Hence, in the not too distant future a machine-learning algorithm could analyse the biometric data streaming from sensors on and inside your body, determine your personality type and your changing moods, and calculate the emotional impact that a particular song – even a particular musical key – is likely to have on you.10 Of all forms of art, music is probably the most susceptible to Big Data analysis, because both inputs and outputs lend themselves to precise mathematical depiction. The inputs are the mathematical patterns of sound waves, and the outputs are the electrochemical patterns of neural storms. Within a few decades, an algorithm that goes over millions of musical experiences might learn to predict how particular inputs result in particular outputs.11 Suppose you just had a nasty fight with your boyfriend. The algorithm in charge of your sound system will immediately discern your inner emotional turmoil, and based on what it knows about you personally and about human psychology in general, it will play songs tailored to resonate with your gloom and echo your distress. These particular songs might not work well with other people, but are just perfect for your personality type. After helping you get in touch with the depths of your sadness, the algorithm would then play the one song in the world that is likely to cheer you up – perhaps because your subconscious connects it with a happy childhood memory that even you are not aware of. No human DJ could ever hope to match the skills of such an AI.

You might object that the AI would thereby kill serendipity and lock us inside a narrow musical cocoon, woven by our previous likes and dislikes. What about exploring new musical tastes and styles? No problem. You could easily adjust the algorithm to make 5 per cent of its choices completely at random, unexpectedly throwing at you a recording of an Indonesian Gamelan ensemble, a Rossini opera, or the latest K-pop hit. Over time, by monitoring your reactions, the AI could even determine the ideal level of randomness that will optimise exploration while avoiding annoyance, perhaps lowering its serendipity level to 3 per cent or raising it to 8 per cent.

Another possible objection is that it is unclear how the algorithm could establish its emotional goal. If you just fought with your boyfriend, should the algorithm aim to make you sad or joyful? Would it blindly follow a rigid scale of ‘good’ emotions and ‘bad’ emotions? Maybe there are times in life when it is good to feel sad? The same question, of course, could be directed at human musicians and DJs. Yet with an algorithm, there are many interesting solutions to this puzzle.

One option is to just leave it to the customer. You can evaluate your emotions whichever way you like, and the algorithm will follow your dictates. Whether you want to wallow in self-pity or jump for joy, the algorithm will slavishly follow your lead. Indeed, the algorithm may learn to recognise your wishes even without you being explicitly aware of them.

Alternatively, if you don’t trust yourself, you can instruct the algorithm to follow the recommendation of whichever eminent psychologist you do trust. If your boyfriend eventually dumps you, the algorithm may walk you through the official five stages of grief, first helping you deny what happened by playing Bobby McFerrin’s ‘Don’t Worry, Be Happy’, then whipping up your anger with Alanis Morissette’s ‘You Oughta Know’, encouraging you to bargain with Jacques Brel’s ‘Ne me quitte pas’ and Paul Young’s ‘Come Back and Stay’, dropping you into the pit of depression with Adele’s ‘Someone Like You’ and ‘Hello’, and finally aiding you to accept the situation with Gloria Gaynor’s ‘I Will Survive’.

The next step is for the algorithm to start tinkering with the songs and melodies themselves, changing them ever so slightly to fit your quirks. Perhaps you dislike a particular bit in an otherwise excellent song. The algorithm knows it because your heart skips a beat and your oxytocin levels drop slightly whenever you hear that annoying part. The algorithm could rewrite or edit out the offending notes.

In the long run, algorithms may learn how to compose entire tunes, playing on human emotions as if they were a piano keyboard. Using your biometric data the algorithms could even produce personalised melodies, which you alone in the entire universe would appreciate.

It is often said that people connect with art because they find themselves in it. This may lead to surprising and somewhat sinister results if and when, say, Facebook begins creating personalised art based on everything it knows about you. If your boyfriend leaves you, Facebook will treat you to an individualised song about that particular bastard rather than about the unknown person who broke the heart of Adele or Alanis Morissette. The song will even remind you of real incidents from your relationship, which nobody else in the world knows about.

Of course, personalised art might never catch on, because people will continue to prefer common hits that everybody likes. How can you dance or sing together to a tune nobody besides you knows? But algorithms could prove even more adept at producing global hits than personalised rarities. By using massive biometric databases garnered from millions of people, the algorithm could know which biochemical buttons to press in order to produce a global hit which would set everybody swinging like crazy on the dance floors. If art is really about inspiring (or manipulating) human emotions, few if any human musicians will have a chance of competing with such an algorithm, because they cannot match it in understanding the chief instrument they are playing on: the human biochemical system.

Will all this result in great art? That depends on the definition of art. If beauty is indeed in the ears of the listener, and if the customer is always right, then biometric algorithms stand a chance of producing the best art in history. If art is about something deeper than human emotions, and should express a truth beyond our biochemical vibrations, biometric algorithms might not make very good artists. But nor do most humans. In order to enter the art market and displace many human composers and performers, algorithms won’t have to begin by straightaway surpassing Tchaikovsky. It will be enough if they outperform Britney Spears.

New jobs?

The loss of many traditional jobs in everything from art to healthcare will partly be offset by the creation of new human jobs. GPs who focus on diagnosing known diseases and administering familiar treatments will probably be replaced by AI doctors. But precisely because of that, there will be much more money to pay human doctors and lab assistants to do groundbreaking research and develop new medicines or surgical procedures.12 AI might help create new human jobs in another way. Instead of humans competing with AI, they could focus on servicing and leveraging AI. For example, the replacement of human pilots by drones has eliminated some jobs but created many new opportunities in maintenance, remote control, data analysis and cyber security. The US armed forces need thirty people to operate every unmanned Predator or Reaper drone flying over Syria, while analysing the resulting harvest of information occupies at least eighty people more. In 2015 the US Air Force lacked sufficient trained humans to fill all these positions, and therefore faced an ironic crisis in manning its unmanned aircraft.13 If so, the job market of 2050 might well be characterised by human–AI cooperation rather than competition. In fields ranging from policing to banking, teams of humans-plus-AIs could outperform both humans and computers. After IBM’s chess program Deep Blue beat Garry Kasparov in 1997, humans did not stop playing chess. Rather, thanks to AI trainers human chess masters became better than ever, and at least for a while human–AI teams known as ‘centaurs’ outperformed both humans and computers in chess. AI might similarly help groom the best detectives, bankers and soldiers in history.14 The problem with all such new jobs, however, is that they will probably demand high levels of expertise, and will therefore not solve the problems of unemployed unskilled labourers. Creating new human jobs might prove easier than retraining humans to actually fill these jobs. During previous waves of automation, people could usually switch from one routine low-skill job to another. In 1920 a farm worker laid off due to the mechanisation of agriculture could find a new job in a factory producing tractors. In 1980 an unemployed factory worker could start working as a cashier in a supermarket. Such occupational changes were feasible, because the move from the farm to the factory and from the factory to the supermarket required only limited retraining.

But in 2050, a cashier or textile worker losing their job to a robot will hardly be able to start working as a cancer researcher, as a drone operator, or as part of a human–AI banking team. They will not have the necessary skills. In the First World War it made sense to send millions of raw conscripts to charge machine guns and die in their thousands. Their individual skills mattered little. Today, despite the shortage of drone operators and data analysts, the US Air Force is unwilling to fill the gaps with Walmart dropouts. You wouldn’t like an inexperienced recruit to mistake an Afghan wedding party for a high-level Taliban conference.

Consequently, despite the appearance of many new human jobs, we might nevertheless witness the rise of a new ‘useless’ class. We might actually get the worst of both worlds, suffering simultaneously from high unemployment and a shortage of skilled labour. Many people might share the fate not of nineteenth-century wagon drivers – who switched to driving taxis – but of nineteenth-century horses, who were increasingly pushed out of the job market altogether.15 In addition, no remaining human job will ever be safe from the threat of future automation, because machine learning and robotics will continue to improve. A forty-year-old unemployed Walmart cashier who by dint of superhuman efforts manages to reinvent herself as a drone pilot might have to reinvent herself again ten years later, because by then the flying of drones may also have been automated. This volatility will also make it more difficult to organise unions or secure labour rights. Already today, many new jobs in advanced economies involve unprotected temporary work, freelancing and one-time gigs.16 How do you unionise a profession that mushrooms and disappears within a decade?

Similarly, human–computer centaur teams are likely to be characterised by a constant tug of war between the humans and the computers, instead of settling down to a lifelong partnership. Teams made exclusively of humans – such as Sherlock Holmes and Dr Watson – usually develop permanent hierarchies and routines that last decades. But a human detective who teams up with IBM’s Watson computer system (which became famous after winning the US TV quiz show Jeopardy! in 2011) will find that every routine is an invitation for disruption, and every hierarchy an invitation for revolution. Yesterday’s sidekick might morph into tomorrow’s superintendent, and all protocols and manuals will have to be rewritten every year.17 A closer look at the world of chess might indicate where things are heading in the long run. It is true that for several years after Deep Blue defeated Kasparov, human–computer cooperation flourished in chess. Yet in recent years computers have become so good at playing chess that their human collaborators lost their value, and might soon become utterly irrelevant.

On 7 December 2017 a critical milestone was reached, not when a computer defeated a human at chess – that’s old news – but when Google’s AlphaZero program defeated the Stockfish 8 program. Stockfish 8 was the world’s computer chess champion for 2016. It had access to centuries of accumulated human experience in chess, as well as to decades of computer experience. It was able to calculate 70 million chess positions per second. In contrast, AlphaZero performed only 80,000 such calculations per second, and its human creators never taught it any chess strategies – not even standard openings. Rather, AlphaZero used the latest machine-learning principles to self-learn chess by playing against itself. Nevertheless, out of a hundred games the novice AlphaZero played against Stockfish, AlphaZero won twenty-eight and tied seventy-two. It didn’t lose even once. Since AlphaZero learned nothing from any human, many of its winning moves and strategies seemed unconventional to human eyes. They may well be considered creative, if not downright genius.

Can you guess how long it took AlphaZero to learn chess from scratch, prepare for the match against Stockfish, and develop its genius instincts? Four hours. That’s not a typo. For centuries, chess was considered one of the crowning glories of human intelligence. AlphaZero went from utter ignorance to creative mastery in four hours, without the help of any human guide.18

AlphaZero is not the only imaginative software out there. Many programs now routinely outperform human chess players not just in brute calculation, but even in ‘creativity’. In human-only chess tournaments, judges are constantly on the lookout for players who try to cheat by secretly getting help from computers. One of the ways to catch cheats is to monitor the level of originality players display. If they play an exceptionally creative move, the judges will often suspect that this cannot possibly be a human move – it must be a computer move. At least in chess, creativity is already the trademark of computers rather than humans! Hence if chess is our coal-mine canary, we are duly warned that the canary is dying. What is happening today to human–AI chess teams might happen down the road to human–AI teams in policing, medicine and banking too.19 Consequently, creating new jobs and retraining people to fill them will not be a one-off effort. The AI revolution won’t be a single watershed event after which the job market will just settle into a new equilibrium. Rather, it will be a cascade of ever-bigger disruptions. Already today few employees expect to work in the same job for their entire life.20 By 2050, not just the idea of ‘a job for life’, but even the idea of ‘a profession for life’ might seem antediluvian.

Even if we could constantly invent new jobs and retrain the workforce, we may wonder whether the average human will have the emotional stamina necessary for a life of such endless upheavals. Change is always stressful, and the hectic world of the early twenty-first century has produced a global epidemic of stress.21 As the volatility of the job market and of individual careers increases, would people be able to cope? We would probably need far more effective stress-reduction techniques – ranging from drugs through neuro-feedback to meditation – to prevent the Sapiens mind from snapping. By 2050 a ‘useless’ class might emerge not merely because of an absolute lack of jobs or lack of relevant education, but also because of insufficient mental stamina.

Obviously, most of this is just speculation. At the time of writing – early 2018 – automation has disrupted many industries but it has not resulted in massive unemployment. In fact, in many countries, such as the USA, unemployment is at a historical low. Nobody can know for sure what sort of impact machine learning and automation will have on different professions in the future, and it is extremely difficult to estimate the timetable of relevant developments, especially as they depend on political decisions and cultural traditions as much as on purely technological breakthroughs. Thus even after self-driving vehicles prove themselves safer and cheaper than human drivers, politicians and consumers might nevertheless block the change for years, perhaps decades.

However, we cannot allow ourselves to be complacent. It is dangerous just to assume that enough new jobs will appear to compensate for any losses. The fact that this has happened during previous waves of automation is absolutely no guarantee that it will happen again under the very different conditions of the twenty-first century. The potential social and political disruptions are so alarming that even if the probability of systemic mass unemployment is low, we should take it very seriously.

In the nineteenth century the Industrial Revolution created new conditions and problems that none of the existing social, economic and political models could cope with. Feudalism, monarchism and traditional religions were not adapted to managing industrial metropolises, millions of uprooted workers, or the constantly changing nature of the modern economy. Consequently humankind had to develop completely new models – liberal democracies, communist dictatorships and fascist regimes – and it took more than a century of terrible wars and revolutions to experiment with these models, separate the wheat from the chaff, and implement the best solutions. Child labour in Dickensian coal mines, the First World War and the Great Ukrainian Famine of 1932–3 constituted just a small part of the tuition fees humankind paid.

The challenge posed to humankind in the twenty-first century by infotech and biotech is arguably much bigger than the challenge posed in the previous era by steam engines, railroads and electricity. And given the immense destructive power of our civilisation, we just cannot afford more failed models, world wars and bloody revolutions. This time around, the failed models might result in nuclear wars, genetically engineered monstrosities, and a complete breakdown of the biosphere. Consequently, we have to do better than we did in confronting the Industrial Revolution.

From exploitation to irrelevance

Potential solutions fall into three main categories: what to do in order to prevent jobs from being lost; what to do in order to create enough new jobs; and what to do if, despite our best efforts, job losses significantly outstrip job creation.

Preventing job losses altogether is an unattractive and probably untenable strategy, because it means giving up the immense positive potential of AI and robotics. Nevertheless, governments might decide to deliberately slow down the pace of automation, in order to lessen the resulting shocks and allow time for readjustments. Technology is never deterministic, and the fact that something can be done does not mean it must be done. Government regulation can successfully block new technologies even if they are commercially viable and economically lucrative. For example, for many decades we have had the technology to create a marketplace for human organs, complete with human ‘body farms’ in underdeveloped countries and an almost insatiable demand from desperate affluent buyers. Such body farms could well be worth hundreds of billions of dollars. Yet regulations have prevented free trade in human body parts, and though there is a black market in organs, it is far smaller and more circumscribed than what one could have expected.22 Slowing down the pace of change may give us time to create enough new jobs to replace most of the losses. Yet as noted earlier, economic entrepreneurship will have to be accompanied by a revolution in education and psychology. Assuming that the new jobs won’t be just government sinecures, they will probably demand high levels of expertise, and as AI continues to improve, human employees will need to repeatedly learn new skills and change their profession. Governments will have to step in, both by subsidising a lifelong education sector, and by providing a safety net for the inevitable periods of transition. If a forty-year-old ex-drone pilot takes three years to reinvent herself as a designer of virtual worlds, she may well need a lot of government help to sustain herself and her family during that time. (This kind of scheme is currently being pioneered in Scandinavia, where governments follow the motto ‘protect workers, not jobs’.) Yet even if enough government help is forthcoming, it is far from clear whether billions of people could repeatedly reinvent themselves without losing their mental balance. Hence, if despite all our efforts a significant percentage of humankind is pushed out of the job market, we would have to explore new models for post-work societies, post-work economies, and post-work politics. The first step is to honestly acknowledge that the social, economic and political models we have inherited from the past are inadequate for dealing with such a challenge.

Take, for example, communism. As automation threatens to shake the capitalist system to its foundation, one might suppose that communism could make a comeback. But communism was not built to exploit that kind of crisis. Twentieth-century communism assumed that the working class was vital for the economy, and communist thinkers tried to teach the proletariat how to translate its immense economic power into political clout. The communist political plan called for a working-class revolution. How relevant will these teachings be if the masses lose their economic value, and therefore need to struggle against irrelevance rather than against exploitation? How do you start a working-class revolution without a working class?

Some may argue that humans could never become economically irrelevant, because even if they cannot compete with AI in the workplace, they will always be needed as consumers. However, it is far from certain that the future economy will need us even as consumers. Machines and computers could do that too. Theoretically, you can have an economy in which a mining corporation produces and sells iron to a robotics corporation, the robotics corporation produces and sells robots to the mining corporation, which mines more iron, which is used to produce more robots, and so on. These corporations can grow and expand to the far reaches of the galaxy, and all they need are robots and computers – they don’t need humans even to buy their products.

Indeed, already today computers and algorithms are beginning to function as clients in addition to producers. In the stock exchange, for example, algorithms are becoming the most important buyers of bonds, shares and commodities. Similarly in the advertisement business, the most important customer of all is an algorithm: the Google search algorithm. When people design Web pages, they often cater to the taste of the Google search algorithm rather than to the taste of any human being.

Algorithms obviously have no consciousness, so unlike human consumers, they cannot enjoy what they buy, and their decisions are not shaped by sensations and emotions. The Google search algorithm cannot taste ice cream. However, algorithms select things based on their internal calculations and built-in preferences, and these preferences increasingly shape our world. The Google search algorithm has a very sophisticated taste when it comes to ranking the Web pages of ice-cream vendors, and the most successful ice-cream vendors in the world are those that the Google algorithm ranks first – not those that produce the tastiest ice cream.

I know this from personal experience. When I publish a book, the publishers ask me to write a short description that they use for publicity online. But they have a special expert, who adapts what I write to the taste of the Google algorithm. The expert goes over my text, and says ‘Don’t use this word – use that word instead. Then we will get more attention from the Google algorithm.’ We know that if we can just catch the eye of the algorithm, we can take the humans for granted.

So if humans are needed neither as producers nor as consumers, what will safeguard their physical survival and their psychological well-being? We cannot wait for the crisis to erupt in full force before we start looking for answers. By then it will be too late. In order to cope with the unprecedented technological and economic disruptions of the twenty-first century, we need to develop new social and economic models as soon as possible. These models should be guided by the principle of protecting humans rather than jobs. Many jobs are uninspiring drudgery, not worth saving. Nobody’s life-dream is to be a cashier. What we should focus on is providing for people’s basic needs and protecting their social status and self-worth.

One new model, which is gaining increasing attention, is universal basic income. UBI proposes that governments tax the billionaires and corporations controlling the algorithms and robots, and use the money to provide every person with a generous stipend covering his or her basic needs. This will cushion the poor against job loss and economic dislocation, while protecting the rich from populist rage.23 A related idea proposes to widen the range of human activities that are considered to be ‘jobs’. At present, billions of parents take care of children, neighbours look after one another, and citizens organise communities, without any of these valuable activities being recognised as jobs. Maybe we need to turn a switch in our minds, and realise that taking care of a child is arguably the most important and challenging job in the world. If so, there won’t be a shortage of work even if computers and robots replace all the drivers, bankers and lawyers. The question is, of course, who would evaluate and pay for these newly recognised jobs? Assuming that six-month-old babies will not pay a salary to their mums, the government will probably have to take this upon itself. Assuming, too, that we will like these salaries to cover all of a family’s basic needs, the end result will be something that is not very different from universal basic income.

Alternatively, governments could subsidise universal basic services rather than income. Instead of giving money to people, who then shop around for whatever they want, the government might subsidise free education, free healthcare, free transport and so forth. This is in fact the utopian vision of communism. Though the communist plan to start a working-class revolution might well become outdated, maybe we should still aim to realise the communist goal by other means?

It is debatable whether it is better to provide people with universal basic income (the capitalist paradise) or universal basic services (the communist paradise). Both options have advantages and drawbacks. But no matter which paradise you choose, the real problem is in defining what ‘universal’ and ‘basic’ actually mean.

What is universal?

When people speak about universal basic support – whether in the shape of income or services – they usually mean national basic support. Hitherto, all UBI initiatives have been strictly national or municipal. In January 2017, Finland began a two-year experiment, providing 2,000 unemployed Finns with 560 euros a month, irrespective of whether they find work or not. Similar experiments are under way in the Canadian province of Ontario, in the Italian city of Livorno, and in several Dutch cities.24 (In 2016 Switzerland held a referendum on instituting a national basic income scheme, but voters rejected the idea.25) The problem with such national and municipal schemes, however, is that the main victims of automation may not live in Finland, Ontario, Livorno or Amsterdam. Globalisation has made people in one country utterly dependent on markets in other countries, but automation might unravel large parts of this global trade network with disastrous consequences for the weakest links. In the twentieth century, developing countries lacking natural resources made economic progress mainly by selling the cheap labour of their unskilled workers. Today millions of Bangladeshis make a living by producing shirts and selling them to customers in the United States, while people in Bangalore earn their keep in call centres dealing with the complaints of American customers.26 Yet with the rise of AI, robots and 3-D printers, cheap unskilled labour would become far less important. Instead of manufacturing a shirt in Dhaka and shipping it all the way to the US, you could buy the shirt’s code online from Amazon, and print it in New York. The Zara and Prada stores on Fifth Avenue could be replaced by 3-D printing centres in Brooklyn, and some people might even have a printer at home. Simultaneously, instead of calling customer services in Bangalore to complain about your printer, you could talk with an AI representative in the Google cloud (whose accent and tone of voice are tailored to your preferences). The newly unemployed workers and call-centre operators in Dhaka and Bangalore don’t have the education necessary to switch to designing fashionable shirts or writing computer code – so how will they survive?

If AI and 3-D printers indeed take over from the Bangladeshis and Bangalorians, the revenues that previously flowed to South Asia will now fill the coffers of a few tech-giants in California. Instead of economic growth improving conditions all over the world, we might see immense new wealth created in hi-tech hubs such as Silicon Valley, while many developing countries collapse.

Of course, some emerging economies – including India and Bangladesh – might advance fast enough to join the winning team. Given enough time, the children or grandchildren of textile workers and call-centre operators might well become the engineers and entrepreneurs who build and own the computers and 3-D printers. But the time to make such a transition is running out. In the past, cheap unskilled labour has served as a secure bridge across the global economic divide, and even if a country advanced slowly, it could expect to reach safety eventually. Taking the right steps was more important than making speedy progress. Yet now the bridge is shaking, and soon it might collapse. Those who have already crossed it – graduating from cheap labour to high-skill industries – will probably be OK. But those lagging behind might find themselves stuck on the wrong side of the chasm, without any means of crossing over. What do you do when nobody needs your cheap unskilled labourers, and you don’t have the resources to build a good education system and teach them new skills?27 What then will be the fate of the stragglers? American voters might conceivably agree that taxes paid by Amazon and Google for their US business could be used to give stipends or free services to unemployed miners in Pennsylvania and jobless taxi-drivers in New York. However, would American voters also agree that these taxes should be sent to support unemployed people in places defined by President Trump as ‘shithole countries’?28 If you believe that, you might just as well believe that Santa Claus and the Easter Bunny will solve the problem.

What is basic?

Universal basic support is meant to take care of basic human needs, but there is no accepted definition for that. From a purely biological perspective, a Sapiens needs just 1,500–2,500 calories per day in order to survive. Anything more is a luxury. Yet over and above this biological poverty line, every culture in history defined additional needs as ‘basic’. In medieval Europe, access to church services was seen as even more important than food, because it took care of your eternal soul rather than of your ephemeral body. In today’s Europe, decent education and healthcare services are considered basic human needs, and some argue that even access to the Internet is now essential for every man, woman and child. If in 2050 the United World Government agrees to tax Google, Amazon, Baidu and Tencent in order to provide basic support for every human being on earth – in Dhaka as well as in Detroit – how will they define ‘basic’?

For example, what does basic education include: just reading and writing, or also composing computer code and playing the violin? Just six years of elementary school, or everything up to a PhD? And what about healthcare? If by 2050 medical advances make it possible to slow down ageing processes and significantly extend human lifespans, will the new treatments be available to all 10 billion humans on the planet, or just to a few billionaires? If biotechnology enables parents to upgrade their children, would this be considered a basic human need, or would we see humankind splitting into different biological castes, with rich superhumans enjoying abilities that far surpass those of poor Homo sapiens?

Whichever way you choose to define ‘basic human needs’, once you provide them to everyone free of charge, they will be taken for granted, and then fierce social competitions and political struggles will focus on non-basic luxuries – be they fancy self-driving cars, access to virtual-reality parks, or enhanced bioengineered bodies. Yet if the unemployed masses command no economic assets, it is hard to see how they could ever hope to obtain such luxuries. Consequently the gap between the rich (Tencent managers and Google shareholders) and the poor (those dependent on universal basic income) might become not merely bigger, but actually unbridgeable.

Hence even if some universal support scheme provides poor people in 2050 with much better healthcare and education than today, they might still be extremely angry about global inequality and the lack of social mobility. People will feel that the system is rigged against them, that the government serves only the super-rich, and that the future will be even worse for them and their children.29 Homo sapiens is just not built for satisfaction. Human happiness depends less on objective conditions and more on our own expectations. Expectations, however, tend to adapt to conditions, including to the condition of other people. When things improve, expectations balloon, and consequently even dramatic improvements in conditions might leave us as dissatisfied as before. If universal basic support is aimed at improving the objective conditions of the average person in 2050, it has a fair chance of succeeding. But if it is aimed at making people subjectively more satisfied with their lot and preventing social discontent, it is likely to fail.

To really achieve its goals, universal basic support will have to be supplemented by some meaningful pursuits, ranging from sports to religion. Perhaps the most successful experiment so far in how to live a contented life in a post-work world has been conducted in Israel. There, about 50% of ultra-Orthodox Jewish men never work. They dedicate their lives to studying holy scriptures and performing religious rituals. They and their families don’t starve partly because the wives often work, and partly because the government provides them with generous subsidies and free services, making sure that they don’t lack the basic necessities of life. That’s universal basic support avant la lettre.30 Although they are poor and unemployed, in survey after survey these ultra-Orthodox Jewish men report higher levels of life satisfaction than any other section of Israeli society. This is due to the strength of their community bonds, as well as to the deep meaning they find in studying scriptures and performing rituals. A small room full of Jewish men discussing the Talmud might well generate more joy, engagement and insight than a huge textile sweatshop full of hard-working factory hands. In global surveys of life satisfaction, Israel is usually somewhere near the top, thanks in part to the contribution of these jobless poor people.31 Secular Israelis often complain bitterly that the ultra-Orthodox don’t contribute enough to society, and live off other people’s hard work. Secular Israelis also tend to argue that the ultra-Orthodox way of life is unsustainable, especially as ultra-Orthodox families have seven children on average.32 Sooner or later, the state will not be able to support so many unemployed people, and the ultra-Orthodox will have to go to work. Yet it might be just the reverse. As robots and AI push humans out of the job market, the ultra-Orthodox Jews may come to be seen as the model of the future rather than as a fossil from the past. Not that everyone will become Orthodox Jews and go to the yeshivas to study the Talmud. But in the lives of all people, the quest for meaning and for community might eclipse the quest for a job.

If we manage to combine a universal economic safety net with strong communities and meaningful pursuits, losing our jobs to the algorithms might actually turn out to be a blessing. Losing control over our lives, however, is a much scarier scenario. Notwithstanding the danger of mass unemployment, what we should worry about even more is the shift in authority from humans to algorithms, which might destroy any remaining faith in the liberal story and open the way to the rise of digital dictatorships.