Leadership1 publisher3 min readPublished
San Jose tops Indeed's ranking of 386 US metros for generative AI exposure
Indeed Hiring Lab put San Jose first among 386 US metros for generative AI exposure, scoring it 59 against an average near 44. The score reflects each city's job postings, so multi-city employers learn more from their own roles at each site.
The Board Room · Leadership desk
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What happened
- Seattle ranked second at 57, followed by Washington, D.C., at 54, San Francisco at 53 and Austin, Texas, at 52.
- Lexington Park, Md., and Huntsville, Ala., rank near the top because their postings skew toward technical and scientific roles tied to a naval air station and aerospace research.
- Homosassa Springs, Fla., and Gettysburg, Pa., sit at the bottom near 40, in places where many postings are in manufacturing, healthcare and retail.
- Indeed built the scores by applying its September 2025 ratings of almost 2,900 workplace skills to the mix of jobs each metro advertises.
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Why it matters
- constraint A metro score describes a city's typical job posting, so an employer whose roles in that city differ from the local mix cannot take its own staff's exposure from the ranking.
- decision Hiring Lab says exposure becomes real change only as fast as employers adopt the technology, so each site's rollout schedule largely determines when its jobs start to change.
- cost Sites in low-exposure metros may be more insulated in the near term, and the authors warn those places could miss longer-term productivity gains.
Location matters in Indeed's ranking because occupations cluster in particular cities. Analysts An Nguyen and Laura Ullrich said whether AI changes a worker's job "depends on what you do and, perhaps surprisingly, where you do it." [10] Indeed's method explains most of the surprise. A metro scores high when more of the skills in its typical job posting are ones GenAI could largely handle with people checking its work, or do on its own [8]. San Jose comes first because software development is among the most exposed occupations and those jobs dominate its postings [4].
Most metros sit in the lower half of the range. With a floor near 40 and an average near 44, fewer than half of the 386 can score above the midpoint of 50 [3]. San Jose sits 15 points above the average [1], and about 19 points separate it from Homosassa Springs and Gettysburg [2]. Indeed said the top of the ranking mirrors the map of the country's tech and knowledge hubs [4].
Take an operator with offices in Seattle and Kankakee, Ill. They have a fair objection. The Seattle site might be a distribution center and the Kankakee site an engineering team, and neither metro score would describe them. Under Indeed's method the objection holds for the employer's own staff, because each score is built from a whole city's postings [9]. What the score does describe is the labor market each office hires from. Nguyen and Ullrich said postings are somewhat lower overall in highly exposed metros for now, and that this could change depending on whether AI replaces or augments labor [11]. They also said it is unclear whether exposure will prove a net positive or negative for communities [11]. Nobody yet knows which way those markets will settle.
The clearest pressure in the related research is on junior hiring. Stanford Digital Economy Lab research, using payroll data through June 2026, found that employment among workers aged 22 to 25 in highly AI-exposed occupations was 19% below where it would have been had it kept pace with less-exposed occupations [15]. The authors said the findings are descriptive and do not establish that AI caused the gap [15]. PwC's 2026 AI Jobs Barometer found the most exposed junior roles are seven times more likely than the least exposed to demand senior skills such as leadership [16]. Both studies measure occupations, not cities [15][16]. The top-ranked metros are the places where those occupations dominate postings [4].
The Hiring Lab analysis does not prescribe steps for employers, according to HRD America [14]. In my view a city-by-city plan holds up if it uses two inputs: the employer's own role mix at each site to size reskilling, and the metro score to judge the hiring market around that site. The cost of doing it this way is administrative effort. A single national reskilling budget is easier to run. It also spreads money evenly across local markets that can sit up to 19 points apart on Indeed's scale [2]. How the budget is split this quarter decides which offices have retrained staff in place when adoption reaches them.
What to watch
- Whether Indeed's next metro update shows postings in high-exposure metros falling further or recovering, which would show whether AI is replacing or augmenting labor there.
- Whether Stanford's payroll series still shows the 19% employment gap for 22-to-25-year-olds once data after June 2026 is added.