What is the future of work? Will gigs replace salaried employment and will robots eventually leave humans with nothing to do? I see reason for skepticism, but also for concern.
Technology is already making independent work a lot easier. It puts workers into contact with customers and helps them run a back office. More importantly, it allows them to build and promote their reputations at low cost.
Customers used to rely on a taxi company’s reputation or choose a washing machine by the manufacturer’s brand. Now, each worker has a brand: On Uber, customers can reject drivers based on their personal ratings.
A firm’s collective reputation, with the concomitant control of its employees’ behavior, is losing importance.
That said, technology can also favor salaried employment.
Economists George Baker and Thomas Hubbard have said onboard computers could change US trucking. By monitoring behavior, they would solve a moral hazard problem: Drivers have little incentive to be as careful with company trucks as they would with their own.
As a result, more drivers could become employees of companies that buy and maintain fleets, rather than going it alone. They would not have to invest in their own vehicles, which makes them vulnerable to recessions by putting their savings in the same sector as their labor; and they would not be out of pocket and out of work when their trucks break down.
More generally, conventional jobs have a lot of advantages.
First, a single worker or group of workers might lack the capital needed to set up a business or prefer to avoid the stress and risk of running one — consider doctors or dentists who choose to be employees of a medical clinic.
Second, business owners might not want employees to have other bosses, particularly if the work involves confidential information or team projects that require undivided time and attention.
Third, reputations based on ratings might not be reliable: Economist Diane Coyle has shown that the quality of individual consultants can be hard to monitor, at least immediately, whereas traditional consultancies might be more efficient at “guaranteeing” quality.
In short, I believe that salaried employment will not disappear, although it might become less prevalent over time.
What about artificial intelligence? Not a day passes without the media fretting about the mass unemployment that will ensue as robots take over for humans. Many jobs involving routine — and thus codifiable — tasks have been eliminated: Banking transactions are digitized, checks are processed by optical readers, callcenters use software to shorten the length of conversations, or even replace humans with bots.
These changes have global repercussions. They threaten the low-salary, outsourced jobs that emerging nations have counted on to escape poverty.
In developed countries, as economist David Autor and his coauthors have demonstrated, they tend to benefit those employees whose skills complement the new digital tools. This “hollows out” the distribution of jobs into either high-paying skilled positions or low-paying basic service positions.
In the US, the difference in salary between university and high-school graduates has grown enormously in the past 30 years.
However, it is still not clear which human tasks computers will be able to replace and what the effects will be. Deductive problems, in which the particular is deduced from the general rule in a logical way, are the easiest.
An automated teller machine (ATM) verifies a card number, the PIN code and the bank account balance before issuing money and debiting the account. Nonetheless, total employment in banking rose even as the ATM network spread, because demand grew and teller jobs were replaced by new tasks.
Computers have also made great advances in induction, which starts with specific facts and works toward a general law.
For example, algorithms are capable of predicting the US Supreme Court’s decisions about patents as well as any legal experts. Similar techniques are enabling automated facial recognition, voice recognition, medical diagnosis and other tasks that previously only humans could perform.
The most difficult tasks for computers involve unforeseen problems that do not match any programmed routine. Rare events cannot be analyzed inductively to generate an empirical law.
Although machine learning might be able to solve this problem, it illustrates the obstacles that computers still encounter.
Humans and computers face different challenges. Computers are much faster and more reliable when processing logical and predictable tasks. Thanks to machine learning, they can increasingly cope with unforeseen situations, provided they have enough data to recognize the structure of the problem.
On the other hand, the human brain is more flexible: A five-year-old child can handle some problems better than any computer. So the people best equipped to succeed in the new world will be those who have acquired abstract knowledge that helps them adapt to their environment, while those with only simple knowledge preparing them for routine tasks are most in danger of being replaced.
This is why education is crucial. If we do not have a system that gives everyone a chance to gain the necessary skills, differences in education and family background will lead to even greater inequality.
This article is adapted from Economics for the Common Good, to be published next month by Princeton University Press.
Jean Tirole, winner of the 2014 Nobel Prize in Economics, is chairman of the Toulouse School of Economics and of the Institute for Advanced Study in Toulouse.
This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.
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