San Jose-Sunnyvale-Santa Clara

๐Ÿ“ CaliforniaยทPop. 2,414,170ยท1,134,660 employedยทRanked #376 of 393 metros
59

Elevated

AI Risk Score

โš ๏ธ

59/100

#376 of 393 ยท -11 vs avg

Workers Vulnerable

๐ŸŽฏ

240,010

21.2% of workforce

Average Wage

๐Ÿ’ฐ

$121K

+$61K vs national

Tech Employment

๐Ÿ’ป

13.8%

National avg: 2.0%

Service Employment

๐Ÿช

24.1%

National avg: 31.3%

WARN Notices (2025)

๐Ÿ“‹

0

Layoff filings

๐Ÿ’ก San Jose-Sunnyvale-Santa Clara has an AI risk score of 59/100 with 21.2% of workers in vulnerable roles โ€” led by Food Service & Hospitality. Average wages of $121K are above the national metro average. See California overview โ†’

AI Risk Analysis

The San Jose-Sunnyvale-Santa Clara metropolitan area receives an AI displacement risk score of 59 out of 100, placing it at rank #376 among 393 US metros. This is 11 points below the national metro average of 70, suggesting the area has meaningful structural resilience against AI disruption. An estimated 240,010 workers โ€” 21.2% of the workforce โ€” hold positions in occupations highly susceptible to automation.

The primary driver of risk in San Jose-Sunnyvale-Santa Clara is the concentration of employment in Food Service & Hospitality, an industry where routine tasks, data processing, and customer interactions are increasingly being handled by AI systems. Among the most at-risk occupations in the area are Fast Food and Counter Workers, Cashiers, Retail Salespersons, and Office Clerks, General โ€” roles where advances in natural language processing, computer vision, and robotic process automation are already reducing demand. The area's above-average tech employment (13.8% vs 2.0% nationally) creates a dual dynamic: while tech workers build AI tools, many adjacent roles face displacement.

Higher-than-average wages ($121K vs $60K nationally) may provide workers more resources to invest in reskilling, but also create stronger economic incentives for employers to automate. Workers in this metro should consider developing complementary AI skills, exploring transition paths to lower-risk occupations, and leveraging local workforce development resources.

Automation Vulnerability

240,010

workers at risk (21.2%)

0%21.2%33%+

Top At-Risk Occupations

* Estimated local employment based on metro's share of national workforce. Actual distribution may vary.

Industry Breakdown

Top at-risk industry: Food Service & Hospitality

Tech Sector13.8%

National avg: 2.0% โฌ†๏ธ Above average

Service Sector24.1%

National avg: 31.3% โœ… Below average exposure

Comparison to National Average

Risk Score

-11

vs 70 national avg

Average Wage

+$61K

vs $60K national avg

Vulnerable Workers

-8.3%

vs 29.5% national avg

National Economic Context

Latest national labor market indicators from FRED (Federal Reserve Economic Data)

Unemployment Rate

4.4%

2026-02

Labor Participation

62%

2026-02

Weekly UI Claims

214,000,000

2026-02

Job Openings

6.9M

2026-01

๐Ÿ“Š Methodology

Metro area AI risk scores are calculated using a composite model that weighs multiple factors: occupational automation probability (based on Frey & Osborne methodology and updated GenAI exposure scores), industry concentration risk, local employment mix, wage levels, and historical WARN Act layoff notices.

Scores range from 0 (lowest risk) to 100 (highest risk) and represent relative vulnerability compared to other US metro areas. Individual occupation risk scores within the metro are estimated by applying the metro's employment share to national occupation-level data. Data sources include BLS Occupational Employment and Wage Statistics, Census Bureau population estimates, and state WARN Act filings.