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In many countries, as a share of overall employment there are now more high-paid professionals and managers – as well as more low-paid care workers and cleaners, teaching and healthcare assistants, caretakers and gardeners, waiters and hairdressers.
just as we would not expect a carrot to talk or a mobile phone to be angry, we should not expect a machine to be ‘intelligent’ or ‘smart’.
In short, when thinking about the future of work, we should be wary not of one omnipotent fox, but an army of industrious hedgehogs.
Article 15 of the new General Data Protection Regulation has made ‘meaningful information about the logic [of] automated decision-making’ a legal right.
But it fails to recognize that machines might still be able to carry out tasks that require empathy, judgement, or creativity when done by a human being – by doing them in some entirely other fashion.
For instance, if you come across a task where it is easy to define the goal, straightforward to tell whether that goal has been achieved, and there are lots of data for the machine to learn from, then that task can probably be automated.
Take any technology that currently exists – pick up your smartphone, open your laptop – and you can be confident in saying that this is the least advanced that it is ever going to be.
One British farm plants, nurtures, and harvests barley without a person setting foot in the field at all.
McKinsey & Co. estimate that, as of 2015, 64 per cent of worker hours in all areas of manufacturing were spent on tasks that could be automated with existing technologies – never mind future ones.29 (That these activities have not yet been automated, even though it is technically feasible to do so, is an issue
team of American researchers have developed a system that can forecast the outcome of US Supreme Court decisions; it makes correct predictions about 70 per cent of the time, whereas human experts, using their legal reasoning, tend to manage only about 60 per cent.
A team of British researchers have developed a similar system that does the same for the European Court of Human Rights, with 79 per cent predictive accuracy.
In education, more people signed up for Harvard University’s online courses in a single year than had attended the actual university in its entire existence.
In journalism, the Associated Press has begun to use algorithms to compose their sports coverage and earnings reports, now producing about fifteen times as many of the latter as when they relied upon human writers alone.
They can listen to a conversation between a woman and a child and determine whether they are related, and tell from the way a person walks into a room if they are about to do something nefarious.
Ping An, a Chinese insurance company, uses a system like this to tell whether loan applicants are being dishonest: people are recorded as they answer questions about their income and repayment intentions, and a computer evaluates the video to check whether they are telling the truth.
Roboticists often talk about the ‘uncanny valley’, the observation that when robots become almost – but not perfectly – human-like in appearance, people feel a sudden discomfort about interacting with them.
a 2019 study found that 40 per cent of Europe’s AI start-ups actually ‘do not use any AI programs in their products’.
what matters is not only how productive that machine is relative to the human alternative, but also how expensive it is relative to the human alternative.
This is also why the Institute for Fiscal Studies, a leading UK think tank, expressed worries that increasing the minimum wage might increase the risk of automation.84 If low-paid workers become more expensive, a previously unaffordable machine to replace them might now make financial sense.
A big puzzle in economic history, for instance, is why the Industrial Revolution was British, rather than, say, French or German. Robert Allen, an economic historian, thinks relative costs are responsible: at the time, the wages paid to British workers were much higher than elsewhere, while British energy prices were very low. Installing new machines that saved on labour and used readily available cheap fuel thus made economic sense in Britain, whereas it did not in other countries.
It took two decades, for instance, for the stethoscope to start being routinely used by doctors after its invention in 1816. The medical men, it is said, did not want an instrument to ‘get between their healing hands and the patient’.
Today, if we look at the top 1 per cent of the most highly cited papers in mathematics and computer science, the two universities producing the greatest number of such papers are both in China, ahead of Stanford and MIT.101
As Avent points out, it is very difficult to get more than 90 per cent of people to finish secondary school, or more than 50 per cent to graduate from university.
Still, it is estimated that there are only 22,000 PhD-educated researchers in the world who are capable of working at the cutting edge of AI, and only half are based in the US – a large proportion, but still a comparatively small number of possible workers given the sector’s importance.
More broadly, a third of Americans with degrees in STEM subjects (science, technology, engineering, and maths) are now in roles that do not require those qualifications.
they prefer not to work at all rather than take up ‘pink-collar’ work – an unfortunate term intended to capture the fact that many of the roles currently out of reach of machines are disproportionately held by women, like teaching
In either case, the consequences are the same: technological change might stir up the demand for the work of human beings, but not in the particular place a person happens to be located.
about half of households change their address every five years, and the proportion of people living in a different state from the one where they were born has risen to one-third.
Similarly, in the future, we should be cautious about focusing exclusively on the unemployment rate, and keep an eye on the participation rate as well.
‘making high earners feel better in just about every part of their lives will be a major source of job growth in the future.’
It is not so easy for truckers to retrain as programmers.
When these improvements in worker productivity were passed on to consumers (through lower prices or higher-quality offerings), that helped to raise the demand for those workers’ efforts.
In the future, new technologies will no doubt continue to make some people more productive at certain tasks. But this will only continue to raise the demand for human workers if they remain better placed to do those tasks than a machine. When that ceases to be the case, improvements in worker productivity will become increasingly irrelevant: machines will simply take their place instead.
Human plus machine is stronger only as long as the machine in any partnership cannot do whatever it is that the human being brings to the table.
On average, one more robot per thousand workers meant about 5.6 fewer jobs in the entire economy, and wages that were about 0.5 per cent lower across the whole economy as well.
our challenge will be avoiding frictional technological unemployment: in all likelihood, there will be enough work for human beings to do for a while yet, and the main risk is that some people will not be able to take it up.
Prosperity has always been unevenly shared out in society, and human beings have always struggled to agree on what to do about that.
so in fact the education provided tends to be ‘one size fits none’.
When Sebastian Thrun taught his computer science class to 200 Stanford students, and then to 160,000 non-Stanford students online, the top Stanford student ranked a measly 413th. ‘My God,’ cried Thrun on seeing this, ‘for every great Stanford student, there’s 412 amazingly great, even better students in the world.’
And Singapore offers all its citizens over twenty-five a lump-sum credit worth about $370 to spend on retraining, with periodic top-ups to refresh the balance. It is a relatively modest sum, given the scale of the challenge, but distinctly better than nothing at all.20
It is one thing to incur the expense of training at the start of your life, with decades of potential earnings ahead to pay it back, but older workers may simply not have enough productive time left in the labour market to recoup it if the burden of repayment falls on them alone.
Countries began to provide unemployment insurance and industrial injury benefits, sickness insurance and old-age pensions, all in an effort to offset the reality that those who lacked a job for any reason would have no income at all.
Second, the approach makes practical sense: universal payments are easier to administer and less confusing for the recipients, removing any uncertainty about eligibility.
If everyone receives the payments, nobody can be labelled by society as a ‘scrounger’ and no individual will feel ashamed to have to claim hers.
A single Google search, for instance, requires as much processing power as the entire Apollo space programme that put Neil Armstrong and eleven other astronauts on the Moon – not simply the processing power used during the flights themselves, but all that was used during planning and execution for the seventeen launches across eleven years.14
It will deprive people not only of income, but also of significance; it will hollow out not just the labour market, but also the sense of purpose in many people’s lives.
Look at prosperous people who could afford never to work again, and you will see many still get out of bed and go into the office on a daily basis, often after a brief and unsuccessful experiment with retirement.
Work matters not just for a worker’s own sense of meaning; it has an important social dimension as well, allowing people to show others that they live a purposeful life, and offering them a chance to gain status and social esteem.
The data imply that hunter-gathers in subsistence settings tend to take about 1,000 more hours of leisure a year, on average, than working men in UK’s prosperous modern society.
He believed that meaning could only come through leisure, and that the only purpose of work is to pay for leisure time: ‘We work in order to enjoy leisure, just as we make war in order to enjoy peace.’
