Making the future work for us
What does the future of work hold in store, and how should we prepare for it? The debate so far has focused on developed countries, but it is a question that will affect the entire world.
To pessimists, the introduction of these so-called general-purpose technologies—including 3D printing, Artificial Intelligence, and the Internet of Things (IoT)—threatens the demand for labour; without new forms of social solidarity, such as a universal basic income, the future will be one of widespread destitution. To optimists, the latest technological developments, like others that have propelled humanity forward, promise to deliver unprecedented levels of prosperity.
It is probably impossible to say which side is right. For a complex system such as the world economy, understanding the past is already hard enough. What is more easily ascertained are the causal links that might determine the outcome.
Rapid displacement of massive amounts of human labour is not a new occurrence. The early-19th century Luddites revolted against mechanical looms that were supplanting artisanal textile production. Almost 60 years later, agricultural employment in the US peaked at 53% of total employment. Today, it is less than 3%. In fact, since as recently as 1980, most countries have experienced large declines in agricultural employment. And it’s not just agriculture. According to the World Bank, the share of manufacturing in gross domestic product (GDP) fell in 100 of the 124 countries reporting data since 1990.
But if large shifts in the composition of employment have been the norm, what makes today’s technology-driven shifts so scary? Fundamentally, technology is a way to transform “the world as I found it” into “the world as I want it to be”—from pastures to milk, from silicon to smartphones. And it depends on three forms of knowledge: embedded knowledge in tools; codified knowledge in recipes, manuals, and protocols; and tacit knowledge, or know-how, in brains.
Most of the time, these three forms of knowledge complement one another: like coffee and sugar. But technological progress occasionally substitutes one for another, as with coffee and tea. It is these substitutions that make us fearful.
But while each new technology displaces one form of know-how, it creates others. The first industrial revolution so reduced the cost of textiles that it led to a boom in demand, production, and employment. Likewise, as David Autor of the Massachusetts Institute of Technology has pointed out, the automated teller machine (ATM) displaced human bank tellers, but so reduced the cost of branches that their number rose, fuelling an increase in employees focused on customer relationship management (for which ATMs are less than ideal).
But while it is clear which jobs new technologies displace, it is harder to anticipate how the new possibilities will be exploited. Back in 2001, many thought the internet’s fibre-optic backbone had been overbuilt, given low demand for bandwidth. But then along came iTunes, YouTube, Facebook, Twitter, Skype and Netflix. Similarly, today we are trying to predict the nature of future work before the jobs of the future have been invented.
The most important uncertain aspect of the new technologies is their diffusion capacity. If they do not diffuse worldwide, they will widen the income divide between countries and regions. Landline telephone service and electricity have diffused far less than guns and cellphones.
One determinant of a technology’s “diffusability” is its know-how intensity. Tools and codes are easy to ship; moving the know-how needed to use them is a different matter. Guns require just a little training to operate, whereas an electrical utility requires a large team of people with varied expertise to run the generators, install and service the transmission lines and sub-stations, limit theft, and compel customers to pay their bills on time. Technologies that require more diverse know-how diffuse much more slowly or not at all.
A new technology’s diffusion is also affected by its dependence on the previous diffusion of other technologies. Uber depends on the previous diffusion of cellphones, cars and credit cards. If implementing a technology requires less know-how and fewer other technologies, it is likely to diffuse even faster than the technologies it replaces.
This is technological leapfrogging. As was the case with computer-aided design and manufacturing, it is easier to run a 3D printer than to master all the steps needed to make the same part the traditional way.
Artificial Intelligence may make technology less reliant on know-how and consequently easier to diffuse. By contrast, the IoT will probably require prior diffusion of many other technologies. In 66 countries, electricity penetration is less than 60%; in 26 countries, it is less than 30%.
Finally, diffusion depends on whether countries can afford to purchase the new technology. And that, in turn, depends on whether it facilitates or complicates their search for goods and services that they can sell internationally. The globalization of value chains has made it easier for more countries to participate in international trade, because each country needs to assemble less complex teams; but it has been bad for places where fully integrated industries used to cluster.
In the end, predicting the future is beside the point. Most countries’ future is more likely to be bright if they focus on ensuring that they master every new technology and exploit every new opportunity that comes their way.
Ricardo Hausmann is a professor of economics at the Harvard Kennedy School and a former minister of planning of Venezuela.
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