As of August 2026, Civil Engineering Technologists and Technicians has an AI-exposure score of 70/100 (High exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Civil Engineering Technologists and Technicians

70/100
High exposure
LowModerateElevatedHighVery High

More exposed than 90% of the roles we track. Median pay ~US$64,950. About 5,500 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Civil Engineering Technologists and Technicians?

No exposure score can predict whether AI will replace this role. The 70/100 score means our current model estimates high task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role is in the High exposure band, but its assessed task mix is not automation-heavy. The study found the decline concentrated where AI was more likely to automate rather than augment work, so the headline figure should not be applied directly.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Apply theory and principles of civil engineering in planning, designing, and overseeing construction and maintenance of structures and facilities under the direction of engineering staff or physical scientists.

Common titles
Civil Engineering TechnicianEngineer TechnicianEngineering AssistantEngineering TechnicianCivil DesignerCivil Engineering Assistant
O*NET job-zone preparation
Job Zone 3 · Medium Preparation Needed Most occupations in this zone require training in vocational schools, related on-the-job experience, or an associate's degree. Previous work-related skill, knowledge, or experience is required for these occupations.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
Critical ThinkingReading ComprehensionMathematicsSpeakingMonitoringWriting
Work context
Frequent contact with othersIndoor controlled settingDecision latitudeRepeating tasks

Source: O*NET 29.1 exact occupation - Civil Engineering Technologists and Technicians, SOC 17-3022.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Civil Engineering Technologists and Technicians tasks, by AI exposure

Automatable
  • Calculate dimensions, square footage, profile and component specifications, and material quantities, using calculator or computer.
  • Respond to public suggestions and complaints.
  • Plan and conduct field surveys to locate new sites and analyze details of project sites.
Augmentable
  • Read and review project blueprints and structural specifications to determine dimensions of structure or system and material requirements.
  • Prepare reports and document project activities and data.
6 more augmentable tasks locked in the report.
Durable
  • Confer with supervisor to determine project details such as plan preparation, acceptance testing, and evaluation of field conditions.
  • Report maintenance problems occurring at project site to supervisor and negotiate changes to resolve system conflicts.
1 more durable task locked in the report.

We analyzed all 14 Civil Engineering Technologists and Technicians tasks - 3 automatable, 8 augmentable and 3 durable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.

Mechanical Engineering Technologists and Technicians
72% skills overlap; 12 points lower - lower exposure; Elevated band; ~US$74,510
View path
58
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Calculate dimensions, square footage, profile and component specifications, and material quantities, using calculator or computer.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

AI was the most-cited reason for U.S. layoffs through mid-2026 - the workers who adapt earliest fare best. - Challenger, Gray & Christmas, 2026The upside: Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Civil Engineering Technologists and Technicians - median pay by US state (BLS OEWS, USD)

California: US$83,490New York: US$73,790Florida: US$63,410Texas: US$59,390

Median annual wage, in USD. US national: US$64,950. More states are being added.

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