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occupation level. This current absence of any visible aggregate effects of AI could lower our sense
of urgency to understand its impact on work, even when such effects appear likely in the future.
b) What New Jobs and Tasks Will Emerge from AI?
In capturing AI’s benefits, an important lever for policymakers is that AI not only automates but
also augments work. History is full of examples of jobs that were predicted to be doomed by
automation but that instead flourished and were transformed. The introduction of the first ATMs
around 1970 was predicted to end the job of traditional bank tellers, but the US today instead has
many more bank tellers, at many more bank branches, doing different tasks than before because
ATMs are poorly suited to such as relationship banking (Bessen 2015). If the set of tasks were
fixed, then advancing automation would crowd workers into an ever-narrowing subset of tasks,
perhaps finally making human labor altogether obsolete, if AI would evolve into a state of AGI.
However, it is possible that even AGI will create many new jobs for workers.
While AI’s potential to automate jobs has received relatively little attention, even less is known
about AI’s potential to create new jobs for workers. However, it is possible to learn from the larger
literature that asks how many new jobs does technological progress create? To answer this
question, Autor et al. (2022) exploit the emergence of new job titles in the US Census Bureau’s
occupational descriptions that survey respondents supply on their Census forms. Their analyses
show that, irrespective of whether a new job is created because of technological progress or some
other reason, new work is quantitatively important. They estimate that more than 60 percent of US
employment in 2018 was found in job titles that did not exist in 1940. Examples of new titles are
“fingernail technician,” which was added in 2000, and “solar photovoltaic electrician,” which was
added in 2018. Interestingly, “artificial intelligence specialist” first appeared in 2000.
Turning to the nature of new work, they find that between 1940 and 1980, most new work that
employed non-college workers was found in middle-skilled occupations. After 1980, however, the
locus of new work creation for non-college workers shifted away from these middle-tier
occupations and toward traditionally lower-paid personal services. Conversely, new work creation
employing college-educated workers became increasingly concentrated in professional, technical,
and managerial occupations. In combination, these patterns indicate that new work creation has
polarized after 1980, mirroring (and in part driving) aggregate job polarization.
To further explain the creation of new job titles, and the role of technological progress, Autor et
al. (2022) follow a procedure like Webb (2020) by examining patent data using NLP. Different
from Webb (2020), however, is that they also instructed their NLP algorithm to look for text that
indicates augmentation instead of automation of worker tasks. For example, in 1999, the U.S.
Patent and Trademark Office granted a patent for a “method of strengthening and repairing
fingernails.” Their algorithm links this patent to the occupational title of “Technician, fingernail,”
which was added by Census Bureau in 2000. Similarly, their algorithm links the 2014 patent