Durham-Chapel Hill

๐Ÿ“ North CarolinaยทPop. 728,787ยท342,530 employedยทRanked #392 of 393 metros
55

Elevated

AI Risk Score

โš ๏ธ

55/100

#392 of 393 ยท -15 vs avg

Workers Vulnerable

๐ŸŽฏ

78,110

22.8% of workforce

Average Wage

๐Ÿ’ฐ

$78K

+$18K vs national

Tech Employment

๐Ÿ’ป

7.1%

National avg: 2.0%

Service Employment

๐Ÿช

25.3%

National avg: 31.3%

WARN Notices (2025)

๐Ÿ“‹

0

Layoff filings

๐Ÿ’ก Durham-Chapel Hill has an AI risk score of 55/100 with 22.8% of workers in vulnerable roles โ€” led by Retail. Average wages of $78K are above the national metro average. See North Carolina overview โ†’

AI Risk Analysis

The Durham-Chapel Hill metropolitan area receives an AI displacement risk score of 55 out of 100, placing it at rank #392 among 393 US metros. This is 15 points below the national metro average of 70, suggesting the area has meaningful structural resilience against AI disruption. An estimated 78,110 workers โ€” 22.8% of the workforce โ€” hold positions in occupations highly susceptible to automation.

The primary driver of risk in Durham-Chapel Hill is the concentration of employment in Retail, 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 Cashiers, Retail Salespersons, Medical Secretaries and Administrative Assistants, and Customer Service Representatives 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 (7.1% vs 2.0% nationally) creates a dual dynamic: while tech workers build AI tools, many adjacent roles face displacement.

Higher-than-average wages ($78K 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

78,110

workers at risk (22.8%)

0%22.8%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: Retail

Tech Sector7.1%

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

Service Sector25.3%

National avg: 31.3% โœ… Below average exposure

โš ๏ธ Retail Trade โ€” Highest Risk Industry

National risk score: 56/100 ยท 26,703,360 employed nationally ยท Projected -2.1% job decline ยท Advanced AI adoption stage

Comparison to National Average

Risk Score

-15

vs 70 national avg

Average Wage

+$18K

vs $60K national avg

Vulnerable Workers

-6.7%

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.