Fossil Fuel Electric Power Generation
National industry · NAICS 221112
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Fossil Fuel Electric Power Generation is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 71,290 workers across 137 detailed occupations in it. A typical worker earns around $109,932 a year (Singulariki estimate, see below).
This U.S. industry comprises establishments primarily engaged in operating fossil fuel powered electric power generation facilities. These facilities use fossil fuels, such as coal, oil, or gas, in internal combustion or combustion turbine conventional steam process to 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 — 47th 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 128 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 | 62.9% of employment · 78/133 occupations have AEI task data |
| Augmentation vs. automation | 30.1% working with AI · 41.1% handed to AI |
| Most common pattern | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.5 / 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 | 16.6% |
| Use computers for various applications, such as database management or word processing. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 4.2% |
| Conduct searches to find needed information, using such sources as the Internet. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 3.9% |
| Develop or maintain internal or external company Web sites. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 2.9% |
| Enter codes and instructions to program computer-controlled machinery. | Industrial Machinery Mechanics | Directive | 2.4% |
| Estimate labor, material, or construction costs for budget preparation purposes. | Electrical Engineers | Iteration | 2.4% |
| 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 | 2.1% |
| Present investment information, such as product risks, fees, or fund performance statistics. | Managers, All Other | Learning | 1.8% |
| Document findings of study and prepare recommendations for implementation of new systems, procedures, or organizational changes. | Management Analysts | Iteration | 1.5% |
| Identify, investigate, or resolve security breaches. | Managers, All Other | Feedback loop | 1.4% |
| Compose descriptions of merchandise for posting to online storefront, auction sites, or other shopping Web sites. | Business Operations Specialists, All Other | Directive | 1.4% |
| Record and compile operational data by completing and maintaining forms, logs, or reports. | Power Plant Operators | Directive | 1.3% |
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 |
|---|---|---|---|
| Power Plant Operators | 11,860 | 16.6% | Directive |
| Electrical Engineers | 4,380 | 6.1% | Iteration |
| First-Line Supervisors of Mechanics, Installers, and Repairers | 3,010 | 4.2% | Directive |
| Industrial Machinery Mechanics | 2,260 | 3.2% | Directive |
| General and Operations Managers | 1,680 | 2.4% | Iteration |
| Customer Service Representatives | 1,390 | 1.9% | Directive |
| Electricians | 1,360 | 1.9% | Feedback loop |
| Electrical and Electronic Engineering Technologists and Technicians | 1,260 | 1.8% | Learning |
| Maintenance and Repair Workers, General | 980 | 1.4% | Learning |
| Industrial Production Managers | 940 | 1.3% | Directive |
| Production, Planning, and Expediting Clerks | 850 | 1.2% | Directive |
| Accountants and Auditors | 720 | 1.0% | 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 96.0% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.
Skills
| Skill | Employment reach | Workers |
|---|---|---|
| Active Listening | 94.7% | 67,490 |
| Critical Thinking | 92.6% | 65,990 |
| Monitoring | 92.3% | 65,780 |
| Reading Comprehension | 88.5% | 63,120 |
| Complex Problem Solving | 88.0% | 62,750 |
| Judgment and Decision Making | 86.3% | 61,510 |
| Speaking | 83.6% | 59,590 |
| Coordination | 81.8% | 58,290 |
| Time Management | 70.8% | 50,470 |
| Writing | 70.0% | 49,870 |
| Quality Control Analysis | 65.5% | 46,710 |
| Operations Monitoring | 62.1% | 44,300 |
Knowledge areas
| Knowledge area | Employment reach | Workers |
|---|---|---|
| English Language | 89.4% | 63,730 |
| Mathematics | 73.6% | 52,460 |
| Mechanical | 70.0% | 49,910 |
| Computers and Electronics | 69.1% | 49,290 |
| Customer and Personal Service | 59.0% | 42,040 |
| Administration and Management | 56.1% | 40,020 |
| Public Safety and Security | 55.6% | 39,650 |
| Engineering and Technology | 53.4% | 38,090 |
| Education and Training | 50.1% | 35,740 |
| Production and Processing | 49.9% | 35,600 |
| Design | 47.0% | 33,520 |
| Physics | 35.9% | 25,610 |
Abilities
| Abilitie | Employment reach | Workers |
|---|---|---|
| Near Vision | 96.0% | 68,460 |
| Oral Comprehension | 95.9% | 68,350 |
| Oral Expression | 95.3% | 67,920 |
| Information Ordering | 95.2% | 67,840 |
| Problem Sensitivity | 94.9% | 67,640 |
| Deductive Reasoning | 94.3% | 67,200 |
| Inductive Reasoning | 92.5% | 65,930 |
| Speech Recognition | 88.4% | 63,010 |
| Speech Clarity | 85.0% | 60,630 |
| Selective Attention | 83.7% | 59,660 |
| Written Comprehension | 83.0% | 59,150 |
| Written Expression | 76.7% | 54,710 |
Tool categories
| Tool category | Employment reach | Workers |
|---|---|---|
| Spreadsheet software | 98.8% | 70,400 |
| Office suite software | 98.4% | 70,130 |
| Electronic mail software | 97.7% | 69,640 |
| Word processing software | 96.6% | 68,880 |
| Data base user interface and query software | 82.0% | 58,490 |
| Enterprise resource planning ERP software | 80.1% | 57,120 |
| Presentation software | 77.1% | 54,940 |
| Analytical or scientific software | 68.7% | 49,010 |
| Industrial control software | 65.2% | 46,450 |
| Internet browser software | 61.4% | 43,760 |
| Computer aided design CAD software | 58.4% | 41,660 |
| Operating system software | 56.7% | 40,420 |
| Project management software | 53.3% | 38,010 |
| Development environment software | 51.8% | 36,920 |
| Document management software | 46.3% | 33,000 |
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
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).
Showing the top 40 of 137 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).
Write a report on thisheadline · factoids · citation
The Fossil Fuel Electric Power Generation workforce sits at the 47th percentile of AI task overlap — 71,290 U.S. workers
- Weighting every occupation by its real share of Fossil Fuel Electric Power Generation employment, the industry's workforce ranks in the 47th 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 71,290 U.S. workers across 137 occupations.BLS OEWS (May 2024)
- Employment-weighted typical annual pay is about $109,932.BLS OEWS (May 2024)
- Of AI use observed across this industry's occupations, 30% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
The Fossil Fuel Electric Power Generation workforce sits at the 47th percentile of AI task overlap — 71,290 U.S. workers • Weighting every occupation by its real share of Fossil Fuel Electric Power Generation employment, the industry's workforce ranks in the 47th 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 71,290 U.S. workers across 137 occupations. (BLS OEWS (May 2024)) • Employment-weighted typical annual pay is about $109,932. (BLS OEWS (May 2024)) • Of AI use observed across this industry's occupations, 30% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index) Source: Singulariki — "Fossil Fuel Electric Power Generation". https://singulariki.com/industries/221112 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.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- Census NAICS 2022 U.S. Census Bureau
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
Data compiled June 3, 2026. Figures are estimates, not advice.
Cite this page
Singulariki. "Fossil Fuel 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/221112
Singulariki. (2026). Fossil Fuel Electric Power Generation. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/221112
@misc{singulariki-221112,
title = {Fossil Fuel 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/221112}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.