Automation risk assessment
Civil Engineers: AI exposure and automation risk
Civil Engineers score 38% on the Eloundou et al. LLM exposure measure (moderate exposure). 11 of 17 rated tasks are exposed. BLS projects +6.4% employment, 2025–35.
Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026
LLM exposure (β)
38%
Moderate exposure
BLS projects +6.4% employment for civil engineers from 2025 to 2035.
Eloundou et al., Science 2024
Exposure scores
How much of this occupation's task list large language models could speed up
- 38%
Moderate exposure (β, human-rated)
Share of this occupation's tasks that annotators judged an LLM could do at least 50% faster at the same quality, counting tasks that need extra software at half weight.
Eloundou et al., Science 2024
- 45%
Same measure, rated by GPT-4
The study's model-rated β for comparison. Directly exposed tasks alone (α) score 14%; all exposed tasks (ζ) score 61%.
Eloundou et al., Science 2024
- 1%
Observed exposure in real AI usage
How much of the theoretically exposed work actually shows up as automated, work-related Claude usage.
Anthropic observed exposure (Mar 2026)
Exposed and unexposed tasks
Human ratings of 17 O*NET task statements, Eloundou et al., Science 2024
Of 17 rated tasks, 2 are directly exposed (E1), 9 are exposed once complementary software is built (E2), and 6 are not exposed (E0).
Exposed tasks
Direct engineering activities, ensuring compliance with environmental, safety, or other governmental regulations.
E2 · Exposed with added software
Plan and design transportation or hydraulic systems or structures, using computer-assisted design or drawing tools.
E2 · Exposed with added software
Analyze survey reports, maps, drawings, blueprints, aerial photography, or other topographical or geologic data.
E1 · Directly exposed
Estimate quantities and cost of materials, equipment, or labor to determine project feasibility.
E2 · Exposed with added software
Prepare or present public reports on topics such as bid proposals, deeds, environmental impact statements, or property and right-of-way descriptions.
E1 · Directly exposed
Tasks not exposed
Manage and direct the construction, operations, or maintenance activities at project site.
E0 · Not exposed
Inspect project sites to monitor progress and ensure conformance to design specifications and safety or sanitation standards.
E0 · Not exposed
Compute load and grade requirements, water flow rates, or material stress factors to determine design specifications.
E0 · Not exposed
Provide technical advice to industrial or managerial personnel regarding design, construction, program modifications, or structural repairs.
E0 · Not exposed
Direct or participate in surveying to lay out installations or establish reference points, grades, or elevations to guide construction.
E0 · Not exposed
Job outlook
U.S. projections, BLS Employment Projections 2025–35
- +6.4%
Projected employment change, 2025–35
From 380,600 jobs in 2025 to 405,100 in 2035. All occupations: +3.5%.
BLS Employment Projections 2025–35
- 22,700
Openings per year, 2025–35 average
Includes openings from growth and from workers who retire or change occupations.
BLS Employment Projections 2025–35
- $100,840
Median annual wage, 2025
BLS Employment Projections 2025–35
- Bachelor's degree
Typical education for entry
BLS Employment Projections 2025–35
Primary profiles: BLS Occupational Outlook Handbook · O*NET OnLine
How AI is used on this work
Claude.ai conversations on this occupation's tasks, Anthropic Economic Index (May 2026)
Augmentation and automation are shares of conversations with a classified collaboration pattern, excluding unclassified conversations. The individual pattern shares include them. 73.1% of all conversations on these tasks were for work. Low-volume occupation: 0.04% of all conversations (under 0.05%), so treat these shares as indicative. See the full collaboration breakdown
Live openings
Current listings in Metaintro's jobs index
Browse current civil engineer openings in Metaintro's jobs index.
Sources & methodology
Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.
- Exposure is the human-annotator β score from Eloundou et al.: the share of an occupation's O*NET tasks that a large language model could do at least 50% faster at equal quality, counting tasks that need extra software (E2) at half weight. We label β under 0.25 low, 0.25–0.5 moderate, and 0.5 or more high; these bands are ours, not the authors'.
- Task labels (E0 not exposed, E1 directly exposed, E2 exposed with complementary software) are the study's aggregated human ratings of each O*NET task statement for the occupation. We show up to five tasks per group.
- Observed exposure (Anthropic, March 2026) combines that theoretical feasibility with real Claude usage on the occupation's tasks, weighting automated and work-related use more heavily than augmentative use, then averaging by time spent on each task. Values run from 0 to 1.
- Employment, growth, openings, wage, and education are the BLS 2025–35 projections as published; BLS utilization notes are quoted verbatim.
- Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
- BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.
- Exposure measures what AI could help do, not a forecast of job loss.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).
Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.
Anthropic observed exposure (Mar 2026)
Massenkoff, M., & McCrory, P. (2026). "Labor market impacts of AI: A new measure and early evidence." Anthropic, March 5, 2026. Occupation-level observed exposure (Anthropic Economic Index, labor_market_impacts/job_exposure.csv).
Vintage: Published March 5, 2026. License: CC BY (data). Retrieved 2026-10-09.
BLS Employment Projections 2025–35
U.S. Bureau of Labor Statistics, Employment Projections program. Occupational projections, 2025–35, and worker characteristics, 2025 (Table 1.2); fastest growing occupations (Table 1.3); factors affecting occupational utilization (Table 1.12).
Vintage: 2025–35 projections, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.
Anthropic Economic Index (May 2026)
Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.
Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.
This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. Metaintro has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.
Vintage: O*NET 31.0, August 2026 release. License: CC BY 4.0. Retrieved 2026-10-09.
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