David A. Price serves as interlocutor in an interview: “Daron Acemoglu: On Henry Ford, making AI worker-friendly, and how democracy improves economic growth” (Econ Focus, Federal Reserve Bank of Richmond, Second Quarter 2023, pp. 22-26). The preface to the interview offers this summary: “Today, Acemoglu says hurray for economic growth — but is also concerned that choices made by policymakers and companies are channeling the gains from that growth away from workers. And as he sees things, the powerful AI technologies that have come to the fore in the past several years, embedded in products such as ChatGPT, should be regulated with the economic interests of workers in mind.” Here are a few of Acemoglu’s comments that caught my eye:
What type of AI do we want? What are the technologies of the future that would be most beneficial to society, particularly workers? I cannot imagine any technology that would be harmful to workers for a long period of time and yet would be beneficial for society. And therefore, my view is that right now we are going in the wrong direction in the AI community. We are going in the wrong direction in the tech community, because there is no regard paid to what these technologies are doing to workers’ jobs, democracy, mental health, all sorts of issues. So we really need to ask, can we redirect these technologies? …
[O]f course workers need to adapt as well. And I think workers who have skills or choose to specialize in things that one way or another are going to be done by machines are not going to do well. So I think social skills, social communication, teamwork, adaptability, and creativity are going to be rewarded by the labor market. The way that machines augment humans, humans should also augment machines.
But make no mistake, it’s not just those skills. Today, and I believe in the next 10 years, the United States economy is going to need a huge number of carpenters, electricians, plumbers, lots of people who do very valuable, very meaningful skill-requiring, expertise-requiring combinations of manual and cognitive work. It’s a mistake for us to think everything is going to be digital. And it could be very beneficial for us if we tried to make new machines, including AI, in such a way that they complement electricians, plumbers, carpenters. I think that complementarity is really critical. …
If you want to think about workers benefiting, you have to think about what new tasks they can perform. And the key thing about electrical machinery — and the Ford factory in the early 20th century is a great exemplar of this — is that it generated a whole series of new tasks.
With the introduction of electrical machinery, production became more complex. So you needed workers to attend to the machinery and then you needed a lot of supporting occupations: maintenance, design, repair, and a whole slew of engineering tasks as well as many other white-collar occupations. So what really was beneficial both from the point of view of the workers and from the point of view of productivity wasn’t the fact that those factories were substituting electrical power for some other kind of power. They were completely reorganizing work in a way that made it more complex and thus created more gainful activities for workers.
Not everything was rosy. It was hard work. Compared to today, workers were worn out. They found it very difficult to keep up with the pace. It was still much noisier than the kind of factories that we would see later. And Henry Ford himself, especially later in his career, became zealous for anti-union activity. So it’s not like saying Ford was a visionary in every dimension. But Ford exemplified a new type of industrialization, which created new tasks and thus opportunities for workers.
I am perhaps less optimistic than Acemoglu about the ability of economists and social scientists to predict the current direction and effects of new technologies, and to propose ways of redirecting these technologies. Even if such analysis can be carried out in broadly persuasive ways, I am downright skeptical of the ability of the political system to implement such policies. Moreover, while the US and perhaps a few other countries are debating about what technology might become, other countries around the world will not be waiting for the results of this contemplative process, but will be moving ahead on the cutting edge of these technologies.
That said, it’s interesting to contemplate what kinds of technologies are encouraged by present economic and institutional arrangements. Technology often chases market size. Thus, investments in health care technologies that might be desirable to consumers in high-income countries will tend to be larger than those that could save lives in low-income countries. In addition, a health care technology aimed at a new market of consumers with health insurance may be a more attractive investment than a technology which, say, cuts an existing expense by 10%. Similarly, investments in agricultural technology that affect crops and farmers in high-income countries are likely to be larger that those that would improve the situation of crops and farmers in low-income countries. As Acemoglu suggests, business executives in high-income countries may be more likely to prioritize technologies that can replace workers, rather than technologies that empower workers. Venture capitalists may be more likely to support digital companies that can start up with relatively few employees, rather than supporting companies in industries that would require building factories and hiring more workers. A common criticism is that government tends to want research projects that are pretty likely to show a positive result, and thus tends to emphasize research that offers predictable but modest gains, rather than research that offers unpredictable but sometime much higher gains. There’s a lot of useful thinking to be done about whether the underlying incentives built into the existing eco-system technological investment.
In 2019, Acemoglu and Pascual Restrepo wrote “Automation and New Tasks: How Technology Displaces and Reinstates Labor” in the Spring issue of the Journal of Economic Perspectives. Interested readers might turn there for more detail. From the abstract of that article:
We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.