17-2051.00
Civil engineers
Lower displacement risk
Composite of 5 dimensions (higher = more displacement pressure)
AI Exposure Analysis
Civil engineering involves significant digital knowledge work, including CAD design, data analysis, and cost estimation, which are highly susceptible to AI-driven productivity gains. However, the role is anchored by the necessity for physical site inspections, real-world surveying, and the legal accountability of a Professional Engineer (PE) license, which requires human judgment and liability that AI cannot currently replace.
Dimension Breakdown
How many of this occupation's tasks can current or near-term AI systems perform? Based on GPT-scored analysis of 342 BLS occupations validated against 6 academic exposure indices.
How quickly will firms in this sector actually deploy AI? Accounts for regulatory burden, digital maturity, competitive pressure, union density, and organizational complexity.
How well can workers in this group transition to new roles? Composite of net liquid wealth (financial buffer), skill transferability, geographic job density, and age demographics.
When AI makes this sector's output cheaper, does demand expand enough to offset job losses? High elasticity means the Jevons Paradox may preserve or even grow employment.
Is AI primarily enhancing workers in this occupation or replacing them? Based on CFO survey data where available, estimated from task composition and job dimensionality otherwise. Jobs with more distinct task clusters (high dimensionality) tend toward augmentation via the O-Ring "focus effect" — automating some tasks lets workers concentrate on remaining ones, multiplying output quality.
CFO survey base: 9.5 +0.5 from dimensionality
Task Composition
How this occupation's work time is distributed across 8 task categories, based on O*NET work activity data.
Top Work Activities
Most important work activities from O*NET, ranked by importance score (1-5).
| Activity | Category | Score |
|---|---|---|
| Getting Information | Information Processing | 4.57 |
| Making Decisions and Solving Problems | Analysis & Decision | 4.45 |
| Communicating with Supervisors, Peers, or Subordinates | Communication | 4.29 |
| Evaluating Information to Determine Compliance with Standards | Analysis & Decision | 4.14 |
| Updating and Using Relevant Knowledge | Information Processing | 4.14 |
| Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment | Information Processing | 4.14 |
| Working with Computers | Information Processing | 4.10 |
| Coordinating the Work and Activities of Others | Coordination & Mgmt | 4.10 |
| Inspecting Equipment, Structures, or Materials | Technical / Specialized | 4.05 |
| Processing Information | Information Processing | 4.05 |
Methodology
This page combines Karpathy's GPT-scored technical exposure (per-occupation) with four additional dimensions inherited from the parent SOC major group: institutional adoption speed, worker adaptability, demand elasticity, and AI complementarity.
Task composition is derived from O*NET work activity data, mapped to 8 internal categories. The complementarity score is adjusted by job dimensionality (Gans & Goldfarb 2024): occupations with more distinct task clusters tend toward augmentation rather than replacement.
Net displacement risk is computed as a weighted composite: exposure (30%), adoption speed (20%), adaptability (15%), demand elasticity (15%), complementarity (20%). Pressure dimensions are normalized independently from absorption dimensions, so defensive factors can fully counterbalance exposure.