AI Automation Index

Will AI Replace Pipelayers?

Task-level automation exposure for Pipelayers: 14 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

12% weighted task index

14 of 14 tasks scored

Median Salary

$49,000 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Medium

33,050 people hold this job — size, not growth

Projected 2025–35

-3.1% employment change

2,100 openings a year · BLS line item

Pay by state

BLS OEWS publishes 45 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$25,620 – $86,870

Highest medians

Wisconsin $86,870

Washington $82,450

Minnesota $81,980

New Jersey $78,830

California $76,180

Lowest medians

Puerto Rico $25,620

Arkansas $37,650

West Virginia $40,440

Pennsylvania $41,600

Mississippi $41,900

State figures come from the 47-2151 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 12%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 80%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 7%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 0%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 14/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

14 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 0%

0 tasks

Medium exposure (≥ 35%) 7%

1 tasks

Low exposure (below 35%) 93%

13 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#11536 · Core · 7% automatable

Grade or level trench bases, using tamping machines or hand tools.

physical manipulation — Operating compaction equipment by hand on site

Compact materials to create level bases.

#11539 · Core · 3% automatable

Dig trenches to desired or required depths, by hand or using trenching tools.

physical manipulation — Manual digging with tools in ground

Dig holes or trenches.

#11533 · Core · 6% automatable

Cut pipes to required lengths.

physical manipulation — Metal cutting with tools, hands-on

Cut metal components for installation.

#11535 · Core · 8% automatable

Install or use instruments such as lasers, grade rods, or transit levels.

physical manipulation — Positioning hand tools requires manual dexterity

Position hand tools.

#11531 · Core · 3% automatable

Cover pipes with earth or other materials.

physical manipulation — Hands-on spreading of loose materials

Spread sand, dirt or other loose materials onto surfaces.

#11532 · Core · 9% automatable

Connect pipe pieces and seal joints, using welding equipment, cement, or glue.

physical manipulation — Welding is skilled hands-on metalwork

Apply adhesives to construction materials. Weld metal components.

#11534 · Core · 7% automatable

Install or repair sanitary or stormwater sewer structures or pipe systems.

physical manipulation — Plumbing repair is manual dexterity in situ

Install plumbing or piping. Maintain plumbing structures or fixtures.

#11530 · Core · 40% automatable

Check slopes for conformance to requirements, using levels or lasers.

decision support — AI can review specs/docs; physical project check is human

Evaluate projects to determine compliance with technical specifications.

#11538 · Core · 4% automatable

Align and position pipes to prepare them for welding or sealing.

physical manipulation — Hands-on plumbing in structures

Install plumbing or piping.

#11537 · Core · 25% automatable

Lay out pipe routes, following written instructions or blueprints and coordinating layouts with supervisors.

interpersonal — On-site construction discussion is contextual, physical

Mark reference points on construction materials. Communicate with other construction or extraction personnel to discuss project details.

#11542 · Core · 12% automatable

Tap and drill holes into pipes to introduce auxiliary lines or devices.

physical manipulation — Drilling construction materials is hands-on tool work

Drill holes in construction materials.

#11540 · Core · 9% automatable

Operate mechanized equipment, such as pickup trucks, rollers, tandem dump trucks, front-end loaders, or backhoes.

situated mobility — Truck driving needs real-world navigation and control

Operate equipment or vehicles to clear construction sites or move materials. Drive trucks or truck-mounted equipment.

#11541 · Core · 24% automatable

Train or supervise others in laying pipe.

physical manipulation — Construction training requires hands-on demonstration and safety

Train construction or extraction personnel. Direct construction or extraction personnel.

#11543 · Core · 22% automatable

Locate existing pipes needing repair or replacement, using magnetic or radio indicators.

visual inspection — Physical locating of worn parts needs hands-on presence

Locate equipment or materials in need of repair or replacement.

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Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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