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Solar Electric Power Generation

National industry · NAICS 221114

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Solar Electric Power Generation is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 13,950 workers across 53 detailed occupations in it. A typical worker earns around $90,100 a year (Singulariki estimate, see below).

This U.S. industry comprises establishments primarily engaged in operating solar electric power generation facilities. These facilities use energy from the sun to produce electric energy. The electric energy produced in these establishments is provided to electric power transmission systems or to electric power distribution systems. Cross-References.

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 High band — 74th 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 47 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 80.8% of employment · 39/49 occupations have AEI task data
Augmentation vs. automation 45.2% working with AI · 34.6% 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 20.3%
Answer customers' questions about products, prices, availability, or credit terms. Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products Directive 8.8%
Measure and analyze system performance and operating parameters to assess operating condition of systems or equipment. Solar Photovoltaic Installers Learning 4.0%
Use computers for various applications, such as database management or word processing. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 2.1%
Conduct searches to find needed information, using such sources as the Internet. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 1.9%
Prepare sales presentations or proposals to explain product specifications or applications. Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products Iteration 1.8%
Present investment information, such as product risks, fees, or fund performance statistics. Managers, All Other Learning 1.7%
Study documentation or other information for new scientific or technical products. Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products Directive 1.7%
Participate in the work of subordinates to facilitate productivity or to overcome difficult aspects of work. First-Line Supervisors of Office and Administrative Support Workers Iteration 1.5%
Develop or maintain internal or external company Web sites. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 1.4%
Develop or analyze information to assess the current or future financial status of firms. Financial Managers Directive 1.4%
Identify, investigate, or resolve security breaches. Managers, All Other Feedback loop 1.4%

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
Solar Photovoltaic Installers 3,390 24.3% Learning
General and Operations Managers 880 6.3% Iteration
Electricians 620 4.4% Feedback loop
First-Line Supervisors of Construction Trades and Extraction Workers 570 4.1% Directive
Accountants and Auditors 500 3.6% Directive
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products 500 3.6% Directive
Electrical Engineers 380 2.7% Iteration
Construction Managers 300 2.1% Iteration
Office Clerks, General 290 2.1% Feedback loop
Financial Managers 280 2.0% Directive
Engineers, All Other 270 1.9% Iteration
Market Research Analysts and Marketing Specialists 240 1.7% 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 85.2% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.

Skills

Skill Employment reach Workers
Active Listening 85.2% 11,880
Critical Thinking 82.8% 11,550
Time Management 81.7% 11,400
Reading Comprehension 81.4% 11,350
Monitoring 81.3% 11,340
Coordination 79.8% 11,130
Judgment and Decision Making 77.8% 10,860
Complex Problem Solving 76.3% 10,640
Active Learning 73.8% 10,300
Speaking 58.9% 8,210
Writing 56.7% 7,910
Social Perceptiveness 44.2% 6,170

Knowledge areas

Knowledge area Employment reach Workers
English Language 80.4% 11,210
Customer and Personal Service 78.1% 10,900
Administration and Management 73.1% 10,200
Mathematics 73.0% 10,190
Computers and Electronics 63.2% 8,810
Design 50.1% 6,990
Mechanical 48.8% 6,810
Public Safety and Security 43.9% 6,130
Production and Processing 40.9% 5,710
Engineering and Technology 39.4% 5,500
Building and Construction 38.7% 5,400
Education and Training 37.3% 5,200

Abilities

Abilitie Employment reach Workers
Deductive Reasoning 85.2% 11,880
Information Ordering 85.2% 11,880
Near Vision 85.2% 11,880
Oral Comprehension 85.2% 11,880
Oral Expression 85.2% 11,880
Problem Sensitivity 85.2% 11,880
Inductive Reasoning 83.4% 11,630
Category Flexibility 79.3% 11,060
Selective Attention 61.1% 8,520
Speech Clarity 60.9% 8,490
Speech Recognition 60.9% 8,490
Written Comprehension 57.1% 7,960

Tool categories

Tool category Employment reach Workers
Office suite software 98.2% 13,700
Spreadsheet software 98.2% 13,700
Electronic mail software 96.8% 13,500
Word processing software 96.2% 13,420
Project management software 93.1% 12,990
Customer relationship management CRM software 78.3% 10,920
Enterprise resource planning ERP software 69.9% 9,750
Enterprise application integration software 66.3% 9,250
Data base user interface and query software 65.7% 9,160
Presentation software 65.7% 9,160
Operating system software 65.2% 9,090
Analytical or scientific software 64.5% 9,000
Document management software 59.1% 8,250
Process mapping and design software 50.2% 7,000
Accounting software 50.0% 6,970

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 38 occupations in Solar 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 Helpers--Electricians Operating Engineers and Other Construction Equipment Operators Power Plant Operators Electricians Solar Photovoltaic Installers First-Line Supervisors of Construction Trades and Extraction Workers Construction and Building Inspectors Electrical Engineers General and Operations Managers Managers, All Other Compliance Officers Office Clerks, General Production, Planning, and Expediting Clerks First-Line Supervisors of Office and Administrative Support Workers Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Bookkeeping, Accounting, and Auditing Clerks 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
Solar Photovoltaic Installers 3,390 24.3% $49,300
General and Operations Managers 880 6.3% $159,670
Project Management Specialists 870 6.2% $92,930
Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel 820 5.9% $79,870
Electricians 620 4.4% $67,060
First-Line Supervisors of Construction Trades and Extraction Workers 570 4.1% $74,160
Accountants and Auditors 500 3.6% $103,540
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products 500 3.6% $82,180
Electrical Engineers 380 2.7% $121,160
Construction Managers 300 2.2% $135,810
Office Clerks, General 290 2.1% $58,630
Financial Managers 280 2.0% $165,320
Operating Engineers and Other Construction Equipment Operators 280 2.0% $58,940
Engineers, All Other 270 1.9% $136,230
Market Research Analysts and Marketing Specialists 240 1.7% $73,570
Power Plant Operators 230 1.6% $94,570
Bookkeeping, Accounting, and Auditing Clerks 220 1.6% $62,290
Architectural and Engineering Managers 200 1.4% $204,720
Helpers--Electricians 200 1.4% $42,060
Managers, All Other 190 1.4% $152,630
Lawyers 190 1.4% $218,130
Buyers and Purchasing Agents 170 1.2% $119,390
Business Operations Specialists, All Other 150 1.1% $113,250
Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products 150 1.1% $48,610
Construction and Building Inspectors 140 1.0% $102,980
Human Resources Specialists 130 0.9% $67,320
Management Analysts 130 0.9% $88,270
Financial and Investment Analysts 130 0.9% $100,000
First-Line Supervisors of Office and Administrative Support Workers 130 0.9% $69,840
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive 130 0.9% $39,380
Customer Service Representatives 110 0.8% $43,800
Sales Managers 100 0.7% $228,470
Mechanical Engineers 100 0.7% $167,170
Software Developers 90 0.6% $120,910
Computer Occupations, All Other 90 0.6% $102,340
Logisticians 80 0.6% $80,410
Helpers, Construction Trades, All Other 80 0.6% $48,910
Compliance Officers 70 0.5% $119,120
Production, Planning, and Expediting Clerks 70 0.5% $68,070
Executive Secretaries and Executive Administrative Assistants 70 0.5% $98,790

Showing the top 40 of 53 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
Solar Photovoltaic Installers 1324.93× 3,390
Power Plant Operators 82.75× 230
Helpers--Electricians 34.3× 200
Electrical Engineers 22.25× 380
Engineers, All Other 19.8× 270
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products 18.8× 500
Construction and Building Inspectors 11.28× 140
Architectural and Engineering Managers 10.51× 200
Project Management Specialists 9.56× 870
Construction Managers 9.52× 300
Electricians 9.23× 620
First-Line Supervisors of Construction Trades and Extraction Workers 7.82× 570
Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel 7.62× 820
Operating Engineers and Other Construction Equipment Operators 6.59× 280
Financial and Investment Analysts 4.22× 130
Buyers and Purchasing Agents 3.86× 170
Mechanical Engineers 3.85× 100
Accountants and Auditors 3.82× 500
Financial Managers 3.78× 280
Managers, All Other 3.33× 190
Write a report on thisheadline · factoids · citation

The Solar Electric Power Generation workforce sits at the 74th percentile of AI task overlap — 13,950 U.S. workers

  • Weighting every occupation by its real share of Solar Electric Power Generation employment, the industry's workforce ranks in the 74th percentile (High 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 13,950 U.S. workers across 53 occupations.BLS OEWS (May 2024)
  • Employment-weighted typical annual pay is about $90,100.BLS OEWS (May 2024)
  • Of AI use observed across this industry's occupations, 45% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
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The Solar Electric Power Generation workforce sits at the 74th percentile of AI task overlap — 13,950 U.S. workers

• Weighting every occupation by its real share of Solar Electric Power Generation employment, the industry's workforce ranks in the 74th percentile (High 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 13,950 U.S. workers across 53 occupations. (BLS OEWS (May 2024))
• Employment-weighted typical annual pay is about $90,100. (BLS OEWS (May 2024))
• Of AI use observed across this industry's occupations, 45% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index)

Source: Singulariki — "Solar Electric Power Generation". https://singulariki.com/industries/221114
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. "Solar 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/221114

APA

Singulariki. (2026). Solar Electric Power Generation. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/221114

BibTeX 
@misc{singulariki-221114,
  title  = {Solar 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/221114}
}

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