Medium AI Risk Slow Growth

Pipelayers

SOC Code: 47-2151

Pipelayers carries a 24% AI exposure score (Medium automation risk), with a median annual wage of $48,710 and -4.1% projected employment growth from 2024 to 2034 (BLS), affecting approximately 34,400 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
24% Medium

Proportion of tasks susceptible to AI automation (O*NET analysis)

Projected Growth
-4.1%
2024–2034 (BLS)
-1,400 jobs
Median Annual Wage
$48,710
BLS May 2024
How wage figures are sourced →

AI Exposure vs Industry Growth

Workforce demand by occupation Sanctioned bespoke signature viz (@signature-viz, KIZ-799) showing occupation-level workforce demand from BLS OEWS data. Pure SVG, no external dependencies.Projected Growth 2024-2034 (BLS)Technology+12.8%Healthcare+10.2%Professional+7.8%Education+5.8%Construction+4.5%Finance+4.6%Logistics+3.2%Government+1.2%Manufacturing-2.1%Retail-3.4%
National AI Exposure
40%
Average across all occupations
Avg Wage Growth
+3.2%
Median annual wage change
High-Risk Roles
127
Occupations with >70% AI exposure

Total occupations tracked

832

Covering all SOC major groups

Data currency

2024

BLS Employment Projections

AI exposure avg

40%

Fleet-wide median across all roles

Methodology confidence 92.0%
Industry standard

Composite score weighing O*NET task data completeness, BLS projection methodology, and cross-validation with employer risk grades.

Employment Projections

34,400
Employment 2024
32,900
Projected 2034
-4.1%
Change (%)
-1,400
Change (jobs)

Occupation Insight

Pipelayers (SOC 47-2151) carries an AI exposure score of 24%, placing it in the Medium automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.

The economic context matters alongside the risk score. BLS counted approximately 34,400 workers in this occupation in 2024, and projects a -4.1% change through 2034 — a decline that often compounds with high AI exposure to create displacement headwinds. Median annual compensation stands at $48,710, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires No formal educational credential, plus None of related experience.

For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Pipelayers. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.

Education & Entry Requirements

Typical Education
No formal educational credential
Work Experience
None
On-the-Job Training
Short-term on-the-job training

Top Tasks (O*NET)

  1. 1. Grade or level trench bases, using tamping machines or hand tools.
  2. 2. Dig trenches to desired or required depths, by hand or using trenching tools.
  3. 3. Cut pipes to required lengths.
  4. 4. Install or use instruments such as lasers, grade rods, or transit levels.
  5. 5. Cover pipes with earth or other materials.
  6. 6. Connect pipe pieces and seal joints, using welding equipment, cement, or glue.
  7. 7. Install or repair sanitary or stormwater sewer structures or pipe systems.
  8. 8. Check slopes for conformance to requirements, using levels or lasers.
  9. 9. Align and position pipes to prepare them for welding or sealing.
  10. 10. Lay out pipe routes, following written instructions or blueprints and coordinating layouts with supervisors.

Key Skills Required

  • Operation and Control
  • Active Listening
  • Speaking
  • Critical Thinking
  • Coordination
  • Operations Monitoring
  • Quality Control Analysis
  • Reading Comprehension
  • Judgment and Decision Making
  • Time Management

Knowledge Areas

  • Building and Construction
  • Public Safety and Security
  • Engineering and Technology
  • Mechanical
  • Administration and Management
  • Production and Processing
  • English Language
  • Design
  • Education and Training
  • Mathematics

Frequently Asked Questions

Will AI replace Pipelayers?

Pipelayers has an AI exposure score of 24%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.

What is the job outlook for Pipelayers?

According to BLS Employment Projections 2024-2034, Pipelayers is projected to decline by 4.1% over the decade. Current employment stands at approximately 34,400 workers.

What skills are needed for Pipelayers?

Key skills for Pipelayers include Operation and Control, Active Listening, Speaking, and others. Typical entry-level education is No formal educational credential.

How much do Pipelayers earn?

The median annual wage for Pipelayers is $48,710, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.

What education is required for Pipelayers?

The typical entry-level education for Pipelayers is No formal educational credential. Employers generally expect None of related work experience. On-the-job training typically involves Short-term on-the-job training. Requirements can vary by employer and specialization.

Which companies employ Pipelayers?

Pipelayers roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.

AI Exposure Rating

1.2
out of 5.0

Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.

Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).

Related

Data sourced from official public datasets. See our methodology for details. Retrieved and formatted by PlainWorkforce Editorial