Metro Area
AI Job Risk in San Antonio-New Braunfels, TX
San Antonio-New Braunfels, TX scores 54.8/100 for AI job risk, ranking #32 out of 396 us metro areas. That puts it in the top third for AI exposure — a relatively large share of local jobs are in occupations that AI can automate or significantly change.
Ranked #32 of 396 us metro areas
Rank
#32
more exposed than most
Total Workforce
1.2 M
jobs analysed
High-Risk Jobs
50 K
4% of workforce
Out of
396
us metro areas
Most at Risk
Jobs in San Antonio-New Braunfels, TX most likely to be automated or transformed by AI
Data Entry Keyer
1.9 K employed
Telemarketer
2.1 K employed
Transcriptionist
910 employed
Customer Service Representative
34 K employed
Tax Preparer
570 employed
Bookkeeper
9.6 K employed
Copywriter
170 employed
Computer Programmer
390 employed
Translator
360 employed
Web Developer
440 employed
Safest from AI
Jobs in San Antonio-New Braunfels, TX least likely to be affected by AI
Landscaper
4 K employed
Zookeeper
2.2 K employed
Grounds Maintenance Worker
80 employed
Roofer
410 employed
Lifeguard
1.3 K employed
Concrete Finisher
1.4 K employed
Pest Control Technician
710 employed
Massage Therapist
480 employed
Janitor
15 K employed
Tile Setter
170 employed
What this means
The exposure score (54.8/100) measures how much of San Antonio-New Braunfels, TX's workforce is in jobs that AI can automate or significantly change. It's not a prediction that jobs will disappear — it's a measure of how exposed the local economy is to AI-driven change.
How it works: We score 289 occupations from 0 (AI has little impact) to 100 (AI can do most of the job) using 10 research sources. We then match these scores to real employment data for San Antonio-New Braunfels, TX, weighting by how many people actually work in each role.
High-risk jobs (score 60+) include roles like customer service, data entry, and bookkeeping where AI can already handle most tasks. Low-risk jobs (score under 30) include trades, healthcare, and social work where physical presence or human judgement is essential.
Full methodology · Data covers 35% of occupation categories in this area