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Hydroelectric Power Generation

National industry · NAICS 221111

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Hydroelectric Power Generation is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 6,840 workers across 51 detailed occupations in it. A typical worker earns around $109,830 a year (Singulariki estimate, see below).

This U.S. industry comprises establishments primarily engaged in operating hydroelectric power generation facilities. These facilities use water 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 — 38th 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 48 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 68.3% of employment · 31/49 occupations have AEI task data
Augmentation vs. automation 26.4% working with AI · 44.1% handed to AI
Most common pattern Directive · AI does it; you give the instruction
Typical AI autonomy 3.4 / 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 26.9%
Use computers for various applications, such as database management or word processing. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 4.9%
Conduct searches to find needed information, using such sources as the Internet. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 4.5%
Enter codes and instructions to program computer-controlled machinery. Industrial Machinery Mechanics Directive 3.5%
Develop or maintain internal or external company Web sites. Secretaries and Administrative Assistants, Except Legal, Medical, and Executive Directive 3.4%
Present investment information, such as product risks, fees, or fund performance statistics. Managers, All Other Learning 2.4%
Estimate labor, material, or construction costs for budget preparation purposes. Electrical Engineers Iteration 2.1%
Identify, investigate, or resolve security breaches. Managers, All Other Feedback loop 1.9%
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 1.7%
Record and compile operational data by completing and maintaining forms, logs, or reports. Power Plant Operators Directive 1.6%
Compose descriptions of merchandise for posting to online storefront, auction sites, or other shopping Web sites. Business Operations Specialists, All Other Directive 1.6%
Document findings of study and prepare recommendations for implementation of new systems, procedures, or organizational changes. Management Analysts Iteration 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
Power Plant Operators 1,620 23.7% Directive
Electrical Engineers 440 6.4% Iteration
Industrial Machinery Mechanics 370 5.4% Directive
First-Line Supervisors of Mechanics, Installers, and Repairers 270 4.0% Directive
Maintenance and Repair Workers, General 220 3.2% Learning
General and Operations Managers 210 3.1% Iteration
Compliance Officers 130 1.9% Directive
Electricians 110 1.6% Feedback loop
Office Clerks, General 100 1.5% Feedback loop
Business Operations Specialists, All Other 90 1.3% Directive
Environmental Scientists and Specialists, Including Health 90 1.3% Iteration
Accountants and Auditors 80 1.2% 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 94.3% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.

Skills

Skill Employment reach Workers
Active Listening 93.7% 6,410
Critical Thinking 92.8% 6,350
Monitoring 92.8% 6,350
Complex Problem Solving 89.0% 6,090
Judgment and Decision Making 89.0% 6,090
Reading Comprehension 87.0% 5,950
Coordination 86.8% 5,940
Speaking 83.5% 5,710
Writing 69.9% 4,780
Quality Control Analysis 68.6% 4,690
Time Management 67.3% 4,600
Operations Monitoring 64.8% 4,430

Knowledge areas

Knowledge area Employment reach Workers
English Language 90.5% 6,190
Mathematics 75.1% 5,140
Mechanical 72.5% 4,960
Computers and Electronics 68.4% 4,680
Administration and Management 59.9% 4,100
Production and Processing 56.7% 3,880
Public Safety and Security 55.4% 3,790
Engineering and Technology 55.0% 3,760
Education and Training 53.8% 3,680
Customer and Personal Service 51.6% 3,530
Design 48.1% 3,290
Physics 37.4% 2,560

Abilities

Abilitie Employment reach Workers
Deductive Reasoning 94.3% 6,450
Near Vision 94.3% 6,450
Oral Comprehension 94.3% 6,450
Problem Sensitivity 94.3% 6,450
Inductive Reasoning 93.7% 6,410
Information Ordering 93.7% 6,410
Oral Expression 93.7% 6,410
Speech Recognition 91.1% 6,230
Speech Clarity 85.7% 5,860
Selective Attention 83.5% 5,710
Written Comprehension 83.5% 5,710
Written Expression 77.0% 5,270

Tool categories

Tool category Employment reach Workers
Electronic mail software 99.0% 6,770
Office suite software 99.0% 6,770
Spreadsheet software 99.0% 6,770
Word processing software 97.5% 6,670
Data base user interface and query software 85.7% 5,860
Presentation software 82.9% 5,670
Enterprise resource planning ERP software 81.0% 5,540
Analytical or scientific software 70.5% 4,820
Internet browser software 66.4% 4,540
Industrial control software 64.8% 4,430
Development environment software 57.0% 3,900
Computer aided design CAD software 53.1% 3,630
Operating system software 51.3% 3,510
Project management software 49.9% 3,410
Facilities management software 45.2% 3,090

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 Hydroelectric 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 Electrical Power-Line Installers and Repairers Plumbers, Pipefitters, and Steamfitters Water and Wastewater Treatment Plant and System Operators Maintenance and Repair Workers, General Forest and Conservation Technicians Power Plant Operators Control and Valve Installers and Repairers, Except Mechanical Door Foresters Occupational Health and Safety Specialists Electrical and Electronic Engineering Technologists and Technicians Industrial Production Managers First-Line Supervisors of Production and Operating Workers Electrical Engineers General and Operations Managers Computer and Information Systems Managers Hydrologic Technicians Office Clerks, General Environmental Scientists and Specialists, Including Health 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
Power Plant Operators 1,620 23.7% $95,150
Electrical Power-Line Installers and Repairers 640 9.4% $116,540
Electrical Engineers 440 6.4% $124,350
Industrial Machinery Mechanics 370 5.4% $102,140
First-Line Supervisors of Mechanics, Installers, and Repairers 270 3.9% $144,910
First-Line Supervisors of Production and Operating Workers 260 3.8% $127,420
Maintenance and Repair Workers, General 220 3.2% $85,620
General and Operations Managers 210 3.1% $149,870
Project Management Specialists 190 2.8% $120,570
Electrical and Electronics Repairers, Powerhouse, Substation, and Relay 180 2.6% $102,850
Compliance Officers 130 1.9% $120,010
Hydrologic Technicians 130 1.9% $78,170
Electricians 110 1.6% $129,580
Office Clerks, General 100 1.5% $95,050
Business Operations Specialists, All Other 90 1.3% $103,730
Software Developers 90 1.3% $152,300
Environmental Scientists and Specialists, Including Health 90 1.3% $102,230
Water and Wastewater Treatment Plant and System Operators 90 1.3% $107,310
Accountants and Auditors 80 1.2% $95,350
Electrical and Electronic Engineering Technologists and Technicians 80 1.2% $94,200
Occupational Health and Safety Specialists 80 1.2% $113,870
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive 80 1.2% $58,920
Control and Valve Installers and Repairers, Except Mechanical Door 80 1.2% $100,420
Industrial Production Managers 70 1.0% $160,540
Managers, All Other 70 1.0% $162,820
Buyers and Purchasing Agents 70 1.0% $79,890
Electrical and Electronics Repairers, Commercial and Industrial Equipment 70 1.0% $135,330
Management Analysts 60 0.9% $106,530
Operating Engineers and Other Construction Equipment Operators 60 0.9% $119,160
Computer and Information Systems Managers 50 0.7% $181,700
Human Resources Specialists 50 0.7% $92,170
Logisticians 50 0.7% $86,290
Mechanical Engineers 50 0.7% $73,860
Forest and Conservation Technicians 50 0.7% $84,200
Bookkeeping, Accounting, and Auditing Clerks 50 0.7% $57,740
Production, Planning, and Expediting Clerks 50 0.7% $95,270
Financial Managers 40 0.6% $163,280
Foresters 40 0.6% $101,520
First-Line Supervisors of Office and Administrative Support Workers 40 0.6% $121,410
Plumbers, Pipefitters, and Steamfitters 40 0.6% $121,320

Showing the top 40 of 51 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
Power Plant Operators 1188.73× 1,620
Hydrologic Technicians 996.75× 130
Electrical and Electronics Repairers, Powerhouse, Substation, and Relay 176.11× 180
Electrical Power-Line Installers and Repairers 116.65× 640
Electrical Engineers 52.54× 440
Industrial Machinery Mechanics 19.77× 370
First-Line Supervisors of Mechanics, Installers, and Repairers 10.13× 270
First-Line Supervisors of Production and Operating Workers 8.55× 260
Compliance Officers 7.37× 130
Project Management Specialists 4.26× 190
Electricians 3.34× 110
Maintenance and Repair Workers, General 3.24× 220
General and Operations Managers 1.32× 210
Office Clerks, General 0.9× 100
Write a report on thisheadline · factoids · citation

The Hydroelectric Power Generation workforce sits at the 38th percentile of AI task overlap — 6,840 U.S. workers

  • Weighting every occupation by its real share of Hydroelectric Power Generation employment, the industry's workforce ranks in the 38th 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 6,840 U.S. workers across 51 occupations.BLS OEWS (May 2024)
  • Employment-weighted typical annual pay is about $109,830.BLS OEWS (May 2024)
  • Of AI use observed across this industry's occupations, 26% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
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The Hydroelectric Power Generation workforce sits at the 38th percentile of AI task overlap — 6,840 U.S. workers

• Weighting every occupation by its real share of Hydroelectric Power Generation employment, the industry's workforce ranks in the 38th 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 6,840 U.S. workers across 51 occupations. (BLS OEWS (May 2024))
• Employment-weighted typical annual pay is about $109,830. (BLS OEWS (May 2024))
• Of AI use observed across this industry's occupations, 26% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index)

Source: Singulariki — "Hydroelectric Power Generation". https://singulariki.com/industries/221111
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. "Hydroelectric 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/221111

APA

Singulariki. (2026). Hydroelectric Power Generation. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/221111

BibTeX 
@misc{singulariki-221111,
  title  = {Hydroelectric 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/221111}
}

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