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Emerging role · No. 4 fastest-growing

Wind Turbine Service Technicians: job outlook 2025–35

BLS projects wind turbine service technicians employment to grow 29.5% from 2025 to 2035 (No. 4 fastest), with 1,500 openings a year and a $64,120 median wage.

Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026

Projected growth, 2025–35

+29.5%

Typical entry education: Postsecondary nondegree award.

BLS Employment Projections 2025–35

Job outlook

U.S. projections, BLS Employment Projections 2025–35

+29.5%

Projected employment change, 2025–35

From 11,800 jobs in 2025 to 15,300 in 2035. All occupations: +3.5%.

BLS Employment Projections 2025–35

1,500

Openings per year, 2025–35 average

Includes openings from growth and from workers who retire or change occupations.

BLS Employment Projections 2025–35

$64,120

Median annual wage, 2025

BLS Employment Projections 2025–35

Postsecondary nondegree award

Typical education for entry

BLS Employment Projections 2025–35

Primary profiles: BLS Occupational Outlook Handbook · O*NET OnLine

Core tasks

Highest-importance task statements, O*NET 31.0 Database

  1. Troubleshoot or repair mechanical, hydraulic, or electrical malfunctions related to variable pitch systems, variable speed control systems, converter systems, or related components.
  2. Perform routine maintenance on wind turbine equipment, underground transmission systems, wind fields substations, or fiber optic sensing and control systems.
  3. Diagnose problems involving wind turbine generators or control systems.
  4. Test electrical components of wind systems with devices, such as voltage testers, multimeters, oscilloscopes, infrared testers, or fiber optic equipment.
  5. Start or restart wind turbine generator systems to ensure proper operations.

Most important skills

Importance on a 1–5 scale, O*NET 31.0 Database

  • Equipment Maintenance4.00
  • Operations Monitoring4.00
  • Repairing4.00
  • Troubleshooting4.00
  • Critical Thinking3.50
  • Complex Problem Solving3.25

AI exposure

How much of the work large language models touch

4%

Low exposure (β, human-rated)

Share of the occupation's tasks an LLM could speed up by at least half at equal quality, with software-dependent tasks at half weight.

Eloundou et al., Science 2024

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.

  1. Emerging roles are the 30 occupations in the BLS fastest-growing occupations table (Table 1.3, 2025–35), ranked by projected percent change.
  2. Growth, openings, wage, and entry education are the BLS figures as published; openings include growth plus replacement needs.
  3. Tasks are O*NET 31.0 core task statements ranked by importance; skills are O*NET importance ratings (1–5).
  4. 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'.
  5. 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.

  • 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.

  • O*NET 31.0 Database

    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.

  • Eloundou et al., Science 2024

    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 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.

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