Editor’s Note: This story originally appeared on (un)Common Logic.
Technological advances have long been changing the way people work, but the current pace of change seems unprecedented.
From the invention of personal computers in the 1970s to the introduction of the World Wide Web in the 1990s, followed by the emergence of smartphones, social media, and cloud computing in the 2000s, recent decades have seen significant changes in the way we work. And now, in the 2020s, the latest transformative force appears to be artificial intelligence (AI).
With the spread of AI-related research and new technologies enabled by AI, computers now have the potential to disrupt many industries and jobs that were previously believed to be automated.
Due to the distribution of industries and jobs across the country, AI-driven job displacement is likely to impact some areas more than others.
The following is a breakdown of AI-related risks for the displacement of large metropolitan areas.
This analysis was conducted by (un)Common Logic, a data-driven digital marketing agency, using data from the US Bureau of Labor Statistics and other sources. For more information, see the methodology section at the end.
1. Tampa-St. Petersburg-Clearwater, FL
- Share of workers at risk of AI-related automation: 11.5%
- Total workforce at risk of AI-related automation: 161,827
- The share of workers at risk of any computer automation: 45.0%
- The total number of workers at risk of any computer automation: 635,836
- Average annual salary: $46,420
2. Miami-Fort Lauderdale-West Palm Beach, FL
- Share of workers at risk of AI-related automation: 10.8%
- Total workforce at risk of AI-related automation: 294,157
- The share of workers at risk of any computer automation: 46.3%
- The total number of workers at risk of any computer automation: 1,260,383
- Average annual salary: $46,510
3. Birmingham-Hoover, AL
- Share of workers at risk of AI-related automation: 10.8%
- Total workforce at risk of AI-related automation: 54,915
- The share of workers at risk of any computer automation: 46.6%
- The total number of workers at risk of any computer automation: 237,476
- Average annual salary: $44,830
4. Jacksonville, FL
- Share of workers at risk of AI-related automation: 10.7%
- Total workforce at risk of AI-related automation: 79,026
- The share of workers at risk of any computer automation: 46.0%
- The total number of workers at risk of any computer automation: 340,637
- Average annual salary: $45,420
5. Buffalo-Cheektowaga-Niagara Falls, NY
- Share of workers at risk of AI-related automation: 10.4%
- Total workforce at risk of AI-related automation: 54,745
- The share of workers at risk of any computer automation: 44.1%
- The total number of workers at risk of any computer automation: 232,621
- Average annual salary: $48,560
6. Austin-Round Rock, TX
- Share of workers at risk of AI-related automation: 10.3%
- Total workforce at risk of AI-related automation: 126,753
- The share of workers at risk of any computer automation: 40.2%
- The total number of workers at risk of any computer automation: 496,209
- Average annual salary: $50,070
7. Kansas City, MO-KS
- Share of workers at risk of AI-related automation: 10.2%
- Total workforce at risk of AI-related automation: 110,027
- The share of workers at risk of any computer automation: 45.6%
- The total number of workers at risk of any computer automation: 491,448
- Average annual salary: $48,040
8. Phoenix-Mesa-Scottsdale, AZ
- Share of workers at risk of AI-related automation: 10.2%
- Total workforce at risk of AI-related automation: 233,338
- The share of workers at risk of any computer automation: 44.2%
- The total number of workers at risk of any computer automation: 1,012,755
- Average annual salary: $48,600
9. Denver-Aurora-Lakewood, CO
- Share of workers at risk of AI-related automation: 10.1%
- Total workforce at risk of AI-related automation: 161,291
- The share of workers at risk of any computer automation: 40.4%
- The total number of workers at risk of any computer automation: 643,137
- Average annual salary: $58,490
10. New York-Newark-Jersey City, NY-NJ-PA
- Share of workers at risk of AI-related automation: 10.1%
- Total workforce at risk of AI-related automation: 962,697
- The share of workers at risk of any computer automation: 38.8%
- The total number of workers at risk of any computer automation: 3,680,763
- Average annual salary: $59,390
11. Baltimore-Columbia-Towson, MD
- Share of workers at risk of AI-related automation: 10.1%
- Total workforce at risk of AI-related automation: 131,647
- The share of workers at risk of any computer automation: 39.3%
- The total number of workers at risk of any computer automation: 513,065
- Average annual salary: $54,140
12. Richmond, VA
- Share of workers at risk of AI-related automation: 9.9%
- Total workforce at risk of AI-related automation: 64,301
- The share of workers at risk of any computer automation: 42.7%
- The total number of workers at risk of any computer automation: 276,743
- Average annual salary: $48,470
13. Nashville-Davidson-Murfreesboro-Franklin, TN
- Share of workers at risk of AI-related automation: 9.7%
- Total workforce at risk of AI-related automation: 104,407
- The share of workers at risk of any computer automation: 47.3%
- The total number of workers at risk of any computer automation: 508,631
- Average annual salary: $46,950
14. Raleigh, NC
- Share of workers at risk of AI-related automation: 9.7%
- Total workforce at risk of AI-related automation: 68,125
- The share of workers at risk of any computer automation: 42.6%
- The total number of workers at risk of any computer automation: 299,004
- Average annual salary: $48,800
15. Orlando-Kissimmee-Sanford, FL
- Share of workers at risk of AI-related automation: 9.7%
- Total workforce at risk of AI-related automation: 132,506
- The share of workers at risk of any computer automation: 47.8%
- The total number of workers at risk of any computer automation: 652,685
- Average annual salary: $43,120
How to do it
Data sources include the US Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (2023), Frey and Osborne’s Probability of Computerization by Occupation (2013) and Felten et al.’s AI Occupational Exposure (2021).
To determine the areas with the most workers at risk of AI-related layoffs, the researchers calculated the percentage of workers in jobs with high exposure to AI and high likelihood of using a computer.
For the purpose of this analysis, high AI exposure was defined as at least one standard deviation above the mean and high probability of computerization was defined as 70% or more.
The researchers also calculated the percentage of workers at risk of any computer automation, which is simply the share of workers in jobs with a high likelihood of computerization, regardless of their exposure to AI.
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