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Wind Electric Power Generation

National industry · NAICS 221115

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Wind Electric Power Generation is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 9,930 workers across 47 detailed occupations in it. A typical worker earns around $92,333 a year (Singulariki estimate, see below).

This U.S. industry comprises establishments primarily engaged in operating wind electric power generation facilities. These facilities use wind power to drive a turbine and produce electric energy. The electric energy produced in these establishments is provided to electric power transmission systems or to electric power distribution systems.

Employment is national May 2024 OEWS. "Typical pay" is Singulariki's own figure — the employment-weighted average of each occupation's national median wage — a rough center of the industry, not an official BLS number.

How exposed this industry is to AI

Weighting every occupation in this industry by its employment and its unified AI-exposure index (the OpenAI "GPTs are GPTs" human-rated task overlap folded with the Felten/Raj/Seamans AIOE index), this industry sits in the Moderate band — 40th percentile across all industries.

Exposure measures how much of the work overlaps with what today's AI can do, not a prediction of automation; high-exposure industries are where AI is most likely to reshape tasks. Employment-weighted across 46 occupations that carry an exposure score. Compare every industry on the AI exposure hub.

How AI is actually used in this industry

Among measured Claude.ai (Free and Pro) conversations mapped to O*NET task statements (Anthropic Economic Index, 2026-01-15), these patterns are most associated with the occupations in this industry, weighted by its employment mix. They are shares of observed AI conversations — not of worker time, revenue, or what could be automated — and reflect one AI assistant's consumer sample, not all AI.

Signal coverage 46.7% of employment · 39/47 occupations have AEI task data
Augmentation vs. automation 40.8% working with AI · 37.8% handed to AI
Most common pattern Directive · AI does it; you give the instruction
Typical AI autonomy 3.6 / 5 · higher = AI acts more independently

Tasks driving the signal

The task families that account for the most AI activity across this industry's occupations (employment × observed usage), each attributed to the occupation it comes from.

Task Occupation How Share of signal
Troubleshoot problems involving office equipment, such as computer hardware and software. Office Clerks, General Feedback loop 12.7%
Present investment information, such as product risks, fees, or fund performance statistics. Managers, All Other Learning 7.6%
Identify, investigate, or resolve security breaches. Managers, All Other Feedback loop 6.0%
Use computers for various applications, such as database management or word processing. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 3.2%
Conduct searches to find needed information, using such sources as the Internet. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 3.0%
Compose descriptions of merchandise for posting to online storefront, auction sites, or other shopping Web sites. Business Operations Specialists, All Other Directive 2.4%
Develop or maintain internal or external company Web sites. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 2.2%
Present and explain proposals, reports, or findings to clients. Architectural and Engineering Managers Iteration 1.5%
Compose images of products, using video or still cameras, lighting equipment, props, or photo or video editing software. Business Operations Specialists, All Other Iteration 1.5%
Develop or analyze information to assess the current or future financial status of firms. Financial Managers Directive 1.3%
Review financial statements, sales or activity reports, or other performance data to measure productivity or goal achievement or to identify areas needing cost reduction or program improvement. General and Operations Managers Directive 1.2%
Monitor financial or operational performance of individual investments to ensure portfolios meet risk goals. Managers, All Other Directive 1.2%

Occupations behind the signal

The occupations whose AI-touched tasks contribute most to this industry's signal, by employment here.

Occupation Workers Share How they use AI
General and Operations Managers 490 4.9% Iteration
First-Line Supervisors of Mechanics, Installers, and Repairers 430 4.3% Directive
Power Plant Operators 410 4.1% Directive
Managers, All Other 370 3.7% Directive
Business Operations Specialists, All Other 230 2.3% Directive
Electrical Engineers 200 2.0% Iteration
Solar Photovoltaic Installers 200 2.0% Learning
Architectural and Engineering Managers 170 1.7% Iteration
Accountants and Auditors 160 1.6% Directive
Construction Managers 130 1.3% Iteration
Electrical and Electronic Engineering Technologists and Technicians 120 1.2% Learning
Financial Managers 110 1.1% Directive

This rollup is only as complete as the occupation-task matches available for the industry; the coverage figure above is shown so sparse industries do not look falsely precise. AI exposure is not the same as replacement.

Skill & tool metabolism

What this industry's work actually runs on. Each figure is the share of the industry's workers in occupations that significantly rely on a skill, knowledge area, or ability (O*NET importance ≥ 3 of 5), or that use a tool category — its employment reach. This is a measure of how widespread a requirement is across the workforce, not how intensively any one worker uses it. Shares are independent and need not add to 100%.

Based on 95.2% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.

Skills

Skill Employment reach Workers
Active Listening 95.2% 9,450
Critical Thinking 95.2% 9,450
Monitoring 94.4% 9,370
Reading Comprehension 94.4% 9,370
Speaking 93.2% 9,250
Coordination 93.1% 9,240
Judgment and Decision Making 92.7% 9,210
Complex Problem Solving 92.2% 9,160
Time Management 89.8% 8,920
Active Learning 86.5% 8,590
Systems Analysis 81.1% 8,050
Systems Evaluation 79.2% 7,860

Knowledge areas

Knowledge area Employment reach Workers
English Language 95.2% 9,450
Administration and Management 87.5% 8,690
Mathematics 85.6% 8,500
Computers and Electronics 82.9% 8,230
Mechanical 69.3% 6,880
Engineering and Technology 66.5% 6,600
Public Safety and Security 66.4% 6,590
Education and Training 64.2% 6,380
Physics 59.5% 5,910
Building and Construction 52.1% 5,170
Customer and Personal Service 40.2% 3,990
Administrative 25.8% 2,560

Abilities

Abilitie Employment reach Workers
Deductive Reasoning 95.2% 9,450
Information Ordering 95.2% 9,450
Near Vision 95.2% 9,450
Oral Comprehension 95.2% 9,450
Oral Expression 95.2% 9,450
Problem Sensitivity 95.2% 9,450
Inductive Reasoning 94.7% 9,400
Written Comprehension 93.2% 9,250
Speech Recognition 92.4% 9,180
Written Expression 91.8% 9,120
Speech Clarity 91.6% 9,100
Category Flexibility 90.4% 8,980

Tool categories

Tool category Employment reach Workers
Electronic mail software 99.0% 9,830
Office suite software 99.0% 9,830
Spreadsheet software 99.0% 9,830
Word processing software 99.0% 9,830
Enterprise resource planning ERP software 96.3% 9,560
Presentation software 96.3% 9,560
Data base user interface and query software 96.0% 9,530
Project management software 92.1% 9,150
Analytical or scientific software 88.6% 8,800
Internet browser software 88.5% 8,790
Industrial control software 77.5% 7,700
Facilities management software 67.6% 6,710
Document management software 42.1% 4,180
Operating system software 40.0% 3,970
Development environment software 35.0% 3,480

Reach = share of industry employment in occupations where the requirement is significant; it is not a per-worker usage or proficiency measure. Skill, knowledge, and ability importance is from O*NET; tool use is reported presence of a technology category.

Largest occupations

Exposure quadrant: AI task-overlap percentile vs Median pay AI task-overlap (horizontal) versus median pay (vertical), each as a percentile across all scored occupations, for 39 occupations in Wind Electric Power Generation. Overlap measures shared tasks with AI, not automation. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Wind Turbine Service Technicians Power Plant Operators Solar Photovoltaic Installers Inspectors, Testers, Sorters, Samplers, and Weighers Occupational Health and Safety Specialists First-Line Supervisors of Mechanics, Installers, and Repairers Industrial Production Managers First-Line Supervisors of Production and Operating Workers Electrical Engineers General and Operations Managers Architectural and Engineering Managers Office Clerks, General Civil Engineers Production, Planning, and Expediting Clerks First-Line Supervisors of Office and Administrative Support Workers Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Dispatchers, Except Police, Fire, and Ambulance AI task-overlap percentile → ↑ Median pay
The largest occupations in this industry with both an AI task-overlap score and a wage, plotted by task-overlap percentile (horizontal) and median-pay percentile (vertical). Overlap measures shared tasks with AI, not automation.

The occupations that employ the most people in this industry, with their share of the industry's workforce and national median pay for the occupation (not industry-specific pay).

Occupation Workers Share National median pay
Wind Turbine Service Technicians 4,620 46.5% $64,170
General and Operations Managers 490 4.9% $169,600
First-Line Supervisors of Mechanics, Installers, and Repairers 430 4.3% $105,900
Power Plant Operators 410 4.1% $75,600
Managers, All Other 370 3.7% $165,550
Project Management Specialists 270 2.7% $110,470
Business Operations Specialists, All Other 230 2.3% $96,970
Electrical Engineers 200 2.0% $129,540
Solar Photovoltaic Installers 200 2.0% $69,340
Architectural and Engineering Managers 170 1.7% $188,800
Accountants and Auditors 160 1.6% $93,120
Construction Managers 130 1.3% $139,730
Electrical and Electronic Engineering Technologists and Technicians 120 1.2% $95,490
Financial Managers 110 1.1% $176,020
Training and Development Specialists 110 1.1% $72,010
Engineers, All Other 110 1.1% $124,050
Buyers and Purchasing Agents 100 1.0% $102,910
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive 90 0.9% $60,330
Industrial Production Managers 80 0.8% $137,320
Financial and Investment Analysts 80 0.8% $116,220
Civil Engineers 80 0.8% $118,730
Office Clerks, General 80 0.8% $61,970
Industrial Machinery Mechanics 80 0.8% $82,040
First-Line Supervisors of Production and Operating Workers 80 0.8% $102,980
Computer and Information Systems Managers 70 0.7% $177,890
Occupational Health and Safety Specialists 70 0.7% $108,890
Lawyers 70 0.7% $193,600
Production, Planning, and Expediting Clerks 70 0.7% $92,230
Electrical and Electronics Repairers, Powerhouse, Substation, and Relay 70 0.7% $126,460
Sales Managers 60 0.6% $111,700
Compliance Officers 60 0.6% $125,330
Human Resources Specialists 60 0.6% $102,270
Mechanical Engineers 60 0.6% $106,790
Customer Service Representatives 60 0.6% $35,480
Computer Occupations, All Other 50 0.5% $102,990
Inspectors, Testers, Sorters, Samplers, and Weighers 50 0.5% $64,880
Purchasing Managers 40 0.4% $127,500
Market Research Analysts and Marketing Specialists 40 0.4% $103,820
First-Line Supervisors of Office and Administrative Support Workers 40 0.4% $80,520
Dispatchers, Except Police, Fire, and Ambulance 40 0.4% $60,400

Showing the top 40 of 47 occupations by employment.

Most distinctive occupations

The occupations most unusually concentrated in this industry compared with the economy as a whole. The location quotient is how many times more common an occupation is here versus its economy-wide share (a value of 5 means five times as concentrated).

Occupation Concentration Workers
Wind Turbine Service Technicians 6393.6× 4,620
Power Plant Operators 207.23× 410
Solar Photovoltaic Installers 109.81× 200
Electrical and Electronic Engineering Technologists and Technicians 20.1× 120
Electrical Engineers 16.45× 200
Architectural and Engineering Managers 12.55× 170
Engineers, All Other 11.33× 110
First-Line Supervisors of Mechanics, Installers, and Repairers 11.12× 430
Managers, All Other 9.11× 370
Construction Managers 5.79× 130
Project Management Specialists 4.17× 270
Training and Development Specialists 3.91× 110
Buyers and Purchasing Agents 3.19× 100
Business Operations Specialists, All Other 3.17× 230
General and Operations Managers 2.12× 490
Financial Managers 2.09× 110
Accountants and Auditors 1.72× 160
Write a report on thisheadline · factoids · citation

The Wind Electric Power Generation workforce sits at the 40th percentile of AI task overlap — 9,930 U.S. workers

  • Weighting every occupation by its real share of Wind Electric Power Generation employment, the industry's workforce ranks in the 40th percentile (Moderate band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk.Eloundou et al. + Felten AIOE, weighted by BLS OEWS
  • The industry employs about 9,930 U.S. workers across 47 occupations.BLS OEWS (May 2024)
  • Employment-weighted typical annual pay is about $92,333.BLS OEWS (May 2024)
  • Of AI use observed across this industry's occupations, 41% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
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The Wind Electric Power Generation workforce sits at the 40th percentile of AI task overlap — 9,930 U.S. workers

• Weighting every occupation by its real share of Wind Electric Power Generation employment, the industry's workforce ranks in the 40th percentile (Moderate band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk. (Eloundou et al. + Felten AIOE, weighted by BLS OEWS)
• The industry employs about 9,930 U.S. workers across 47 occupations. (BLS OEWS (May 2024))
• Employment-weighted typical annual pay is about $92,333. (BLS OEWS (May 2024))
• Of AI use observed across this industry's occupations, 41% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index)

Source: Singulariki — "Wind Electric Power Generation". https://singulariki.com/industries/221115
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 3, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Wind Electric Power Generation." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026. https://singulariki.com/industries/221115

APA

Singulariki. (2026). Wind Electric Power Generation. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/221115

BibTeX 
@misc{singulariki-221115,
  title  = {Wind Electric Power Generation},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
  url    = {https://singulariki.com/industries/221115}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.