Excerpts from an interesting piece on the jobs displacement effect of AI from The Economist (16 January 2021 edition):
The robots are indeed coming, they reckon—just a bit more slowly and stealthily than you might have expected. If new technologies largely assist current workers or boost productivity by enough to spark expansion, then more AI might well go hand-in-hand with more employment. This does not appear to be happening.
[...]Take work by Daron Acemoglu and David Autor of the Massachusetts Institute of Technology, Jonathon Hazell of Princeton University and Pascual Restrepo of Boston University, which was presented at the recent meeting of the American Economic Association (AEA). The authors use rich data provided by Burning Glass Technologies, a software company that maintains and analyses fine-grained job information gleaned from 40,000 firms. They identify tasks and jobs in the dataset that could be done by AI today (and are therefore vulnerable to displacement). Unsurprisingly, the researchers find that businesses that are well-suited to the adoption of AI are indeed hiring people with AI expertise. Since 2010 there has been substantial growth in the number of AI-related job vacancies advertised by firms with lots of AI-vulnerable jobs. At the same time, there has been a sharp decline in these firms’ demand for capabilities that compete with those of existing AI.
An AI-induced change in the mix of jobs need not translate into less hiring overall. If new technologies largely assist current workers or boost productivity by enough to spark expansion, then more AI might well go hand-in-hand with more employment. This does not appear to be happening. Instead the authors find that firms with more AI-vulnerable jobs have done much less hiring on net; that was especially the case in 2014-18, when AI-related vacancies in the database surged. But the relationship between greater use of AI and reduced hiring that is present at the firm level does not show up in aggregate data, the authors note. Machines are not yet depressing labour demand across the economy as a whole. As machines become cleverer, however, that could change.
Evidence that AI affects labour markets primarily by taking over human tasks is at odds with some earlier studies of how firms use the technology. A paper from 2019 by Timothy Bresnahan of Stanford University argues that the most valuable applications of AI have nothing to do with displacing humans. Rather, they are examples of “capital deepening”, or the accumulation of more and better capital per worker, in very specific contexts, such as the matching algorithms used by Amazon and Google to offer better product recommendations and ads to users. To the extent that AI leads to disruption, it is at a “system level”, says Mr Bresnahan—as Amazon’s sales displace those of other firms, say.
[...] Does more automation mean a surge in productivity is just over the horizon? Not necessarily. Speaking at the (virtual) aea meeting, Mr Acemoglu mused that automation comes in different sorts, with different economic consequences. “Good” automation generates large productivity increases, and its transformative nature leads to the creation of many new tasks (and therefore jobs) for humans. Advanced robotics, for example, eliminates production jobs while creating work for robot technicians and programmers. “So-so” automation, by contrast, displaces workers but generates only meagre benefits. Mr Acemoglu cites automated check-out kiosks as an example; though they save some time and money, their deployment is hardly revolutionary. From 1947 to 1987, the displacement effect of new technologies was generally offset by a “reinstatement” effect, he reckons, through which new tasks occupied displaced workers. The rate of reinstatement has since fallen, though, while displacement has not, suggesting an increase in so-so automation relative to the good kind.
Noha Smith has a techno-optimism roundup here, arguing that the COVID vaccine was a turning point. Meanwhile, Eli Douardo argues that it is not easy to unleash total factor productivity growth as easily as tech adoption.
TFP only budges when new technologies are adopted at scale, and generally this means products, not just science. Science lays critical groundwork for new technology, but after all the science is done, much work remains. Someone must shepherd the breakthrough to the product stage, where it can actually affect TFP. This means building businesses, surmounting regulatory obstacles, and scaling production.
