Still, the authors estimate that almost all large American metropolitan areas may lose more than 55 percent of their current jobs because of automation in the next two decades. “We felt it was really stunning, since we are underestimating the probability of automation,” said Johannes Moenius, the director of the Institute for Spatial Economic Analysis at the University of Redlands, which prepared the report.

Which Regional Economies Are Most Susceptible to Automation?

Moenius and colleagues used a widely cited 2013 study from Oxford University predicting which of roughly 700 common jobs are most susceptible to automation, and then mapped out which metropolitan areas have a high share of those jobs. That study, by the economists Carl Benedikt Frey and Michael A. Osborne, suggested that 47 percent of total U.S. employment is at risk of automation over the next decade or two; they found that telemarketers, insurance underwriters and appraisers, tax preparers, and cashiers were some of the most likely to see their jobs threatened by automation, while the livelihoods of mental-health and substance-abuse social workers, oral surgeons, choreographers, and physicians were more protected.

Frey and Osborne’s estimates cover about 138 million Americans’ jobs. Moenius and his colleagues found that Las Vegas, Riverside, and El Paso all had high numbers of office and administrative-support jobs, food-preparation and -serving jobs, and sales jobs, and thus had the most vulnerability to automation. Moenius estimates that 65.2 percent of jobs in Las Vegas, 63.9 percent in El Paso, and 62.6 percent of jobs in Riverside are susceptible to automation in the next two decades. The automation of transportation and material-moving jobs also contributed to the potential job loss in these places, as well as in Greensboro, North Carolina, where 62.5 percent of jobs are susceptible to automation.

The jobs that the Redlands analysis places new focus on are slightly different from the types of jobs academics once thought would be easily automatable. That’s because before the Frey and Osborne study, scholars had predicted that routine jobs were the most likely to be automated, but Frey and Osborne suggested that advances in computerization have made it likely that non-routine jobs will be automated, too. The power of machine learning means that programmers with large data sets can use them to make machines smarter, allowing them to do non-routine tasks; for example, oncologists are using data from medical journals and patient records to automatically create treatment plans for cancer patients. “It is largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition,” the authors write.

Of course, the Rust Belt will not be immune to automation in coming decades. Metropolitan areas like Detroit, Indianapolis, Cleveland, and Pittsburgh could still see more than half of their jobs computerized, the study suggests. But because so many manufacturing jobs centered in the Midwest have already been automated, those regions are not at the top of the list of the places that currently stand to lose the highest share of jobs. Instead, the brunt of the next automation wave will come in cities with a different type of low-skill job.