But later on, when humanity muddled through the Economic Singularity without too much turmoil, it turned out that the Boomers’ luck was eclipsed by that of the Millennials.
During the 2020s, industry after industry succumbed to automation by intelligent machines, and unemployment began to soar. Professional drivers were the first to go, but they were quickly followed by the staff in car insurance companies, call centres, fast food outlets and most other types of retail. At the same time, junior positions in the middle-class professions started thinning out so that there were no trainee jobs for accountants, lawyers, architects and journalists. By 2030 even economists were admitting that lasting widespread unemployability was a thing, although they did so using such obscure language that no-one could tell if they were apologising for having denied it for so long. (They weren’t.)
People survived thanks to increasingly generous welfare payments, which were raised by desperate governments just fast enough to ward off serious social unrest. The political left screamed for the introduction of a Universal Basic Income (UBI), but pragmatic politicians pointed out there was no point diverting much-needed funds towards the people still working, and also that no-one wanted to live forever on a “basic”, i.e. subsistence level of income.
Instead of UBI, a system of payments called HELP was introduced, which stood for Human Elective Leisure Payment. The name was chosen to avoid the stigmatism that living on welfare had often aroused in the past, and also to acknowledge the fact that many of the people who received it were giving up their jobs voluntarily so that other people, less able than themselves to find meaning outside structured employment, could carry on as employees.
HELP staved off immediate disaster, but those pragmatic politicians were increasingly concerned about its affordability. The demands on the public purse were growing fast, while the tax base of most economies was shrinking. Smart machines were making products and services more efficiently, but the gains didn’t show up in increased profits to the companies that owned the machines. Instead they generated lower and lower prices for consumers. Fortunately, as it turned out, this enabled governments to reduce the level of HELP without squeezing the living standards of their citizens.
The race downhill between the incomes of governments and the costs they needed to cover for their citizens was nerve-wracking for a few years, but by the time Lauren hit middle age it was clear the outcome would be good. Most kinds of products had now been converted into services, so cars, houses, and even clothes were almost universally rented rather than bought: Lauren didn’t know anyone who owned a car. The cost of renting a car for a journey was so close to zero that the renting companies – auto manufacturers or AI giants and often both – generally didn’t bother to collect the payment. Money was still in use, but was becoming less and less necessary.
As a result, the prices of most asset classes had crashed. Huge fortunes had been wiped out as property prices collapsed, especially in the hot-spot cities, but few people minded all that much as they could get whatever they needed so easily. Art collections had mostly been donated to public galleries – which were of course free to visit, and most of the people who had previously had the good fortune to occupy the very nicest homes had surrendered their exclusive occupation.
The populations of most countries were highly mobile, gradually migrating from one interesting place to another as the fancy took them. This weekend Lauren was “renting” a self-driving mobile home to drive her – at night, while she was asleep – to Portugal, where she would spend a couple of weeks on a walking trip with some college friends. With so much of what was important to people now being digital rather than material, no-one was bothered by the impracticality of having piles of material belongings tying them to one location. And with the universal free internet providing so much bandwidth, distance was much less of a barrier to communication and friendship than it used to be.
The means of production, and the server farms which were home to the titanic banks of AI-generating computers, were still in private ownership, as no-one had yet found a way to ensure that state ownership would avoid sliding into inefficiency and corruption. But because it was clear that the owners were not profiteering, this was not seen as a problem. The reason why the owners didn’t exploit their position was partly that they didn’t see any need to, and partly that if they did, somebody else would compete away their margins with equally efficient smart machines. Most people viewed the owners as heroes rather than villains.
There were a few voices warning that the scenario of “the gods and the useless” was still a possibility, because technological innovation was still accelerating, and the owners might have privileged access to tech that would render them qualitatively different to everyone else, and they would effectively become a different species.
But like most people, Lauren thought this was unlikely to happen before the first artificial general intelligence was created, followed soon after by the first superintelligence – an entity smarter than the smartest human. Lauren was very fond of her nephew Alex, a generation younger than her. It was widely assumed that when the first superintelligence appeared, humanity would somehow merge with it, and that Alex’s generation would be the last generation to reach middle age as “natural” humans. It was therefore fitting that they were called generation Z.
* This un-forecast is not a prediction. Predictions are almost always wrong, so we can be pretty confident that the future will not turn out exactly like this. It is intended to make the abstract notion of technological unemployment more real, and to contribute to scenario planning. Failing to plan is planning to fail: if you have a plan, you may not achieve it, but if you have no plan, you most certainly won’t. In a complex environment, scenario development is a valuable part of the planning process. Thinking through how we would respond to a sufficient number of carefully thought-out scenarios could well help us to react more quickly when we see the beginnings of what we believe to be a dangerous trend.