Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters
47-3015.00Median wage $42,360/yr44,330 employed (US)Rank #882 of 923 scored · top 96% by substitution
Help plumbers, pipefitters, steamfitters, or pipelayers by performing duties requiring less skill. Duties include using, supplying, or holding materials or tools, and cleaning work area and equipment.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution10
Exposure1
Augmentation20
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
15 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%1
panel mean rating 1.0/5 → substitution pressure 1/100
Technical feasibility todayw 20%1
panel mean rating 1.0/5 → substitution pressure 1/100
Cost vs. human wagew 15%1
panel mean rating 1.0/5 → substitution pressure 1/100
Adoption barriersw 20%inverted — strong barriers lower the score47
panel mean rating 3.1/5 (barrier strength) → substitution pressure 47/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (15 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Requisition tools and equipment, select type and size of pipe, and collect and transport materials and equipment to work site.
21CI 10–33 · exposure 13 · augmentation 25 · importance 3.8/5 · click for rater detail
Requisition tools and equipment, select type and size of pipe, and collect and transport materials and equipment to work site.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and plumbing trades are physically intensive, often involve small firms with low digitization, and remain laggard in AI adoption. Material requisitioning and transport are tactical field operations in sectors that invest slowly in automation relative to tech and finance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, lowest AI-adoption sectors, with physical labor tasks like this seeing negligible AI integration in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in inventory lookup and pipe selection via digital reference, but the core value lies in human judgment about site conditions, material suitability, and transport logistics. Assistance is marginal and limited to planning phases, not core task execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with inventory lookups or generating material lists/specifications digitally, but it offers little assistance for the physical selection, collection, and transport aspects of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could potentially assist in requisitioning and selecting pipe types through data analysis of specifications, the physical tasks of collecting, transporting materials, and navigating dynamic work sites remain primarily manual. Current robotics cannot reliably handle the variable, unstructured environment of material transport and site logistics, so only preparatory planning could be partially automated, far short of 50% time savings end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical selection, handling, and transport of heavy materials and tools to a job site, which current AI systems cannot perform end-to-end; it is fundamentally a physical labor and logistics task, not information processing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Physical presence on job sites, variable site conditions, and safety compliance create moderate friction against automation. No strict licensing barrier exists, but organizational practice, site-specific logistics, and liability for material errors present meaningful adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks this specific subtask, but physical presence, judgment about material specifications, and site logistics create practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital and integration cost of autonomous systems capable of material handling and site logistics currently exceeds the loaded wage of a helper performing these tasks, and error costs (wrong materials, site damage) are high. Full automation would require substantial on-site robotics investment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical material handling and transport, so any AI-plus-robotics solution would be far more costly than a helper's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI or robotic system reliably and independently requisitions tools, selects appropriate pipe inventory, and transports materials to varied job sites at production scale. While inventory management software exists, the full task chain—including real-time site adaptation and physical transport—remains primarily human-dependent in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product autonomously requisitions plumbing tools, selects pipe types/sizes, and transports materials to construction sites; this remains firmly in the human labor domain. |
Cut pipe and lift up to fitters.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Cut pipe and lift up to fitters.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and plumbing are low-digitization, highly fragmented sectors with many small firms and physical site constraints. Automation adoption in these trades remains minimal, with strong reliance on skilled manual labor and human judgment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades are among the least digitized, slowest-adopting sectors for AI/robotics automation of physical manual tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Power tools assist with cutting, but AI offers minimal augmentation for this task since it is primarily physical labor requiring strength and spatial coordination rather than cognitive assistance or decision support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers minimal direct assistance to this specific physical cutting-and-lifting task, though some power tools or measurement apps could marginally aid precision in cutting. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cutting pipe and lifting requires physical manipulation in variable on-site conditions, precise spatial positioning, and coordination with human workers. Current robotics and AI cannot reliably handle the unstructured physical environment, material variability, and real-time coordination demands of this task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring cutting pipe with tools and physically lifting/positioning it for a fitter, which current AI systems cannot perform end-to-end as they lack embodied physical capability in unstructured job-site environments. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers for automation of this task, occupational safety regulations, site-specific conditions, liability for equipment malfunction, and the need for human coordination with fitters create moderate friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically protects the helper role from automation, but physical job-site conditions, safety requirements, and variability create practical friction against non-human systems performing this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous pipe-cutting and lifting systems would require significant capital investment, specialized equipment, and integration costs that far exceed the loaded wage of a helper performing this manual labor task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic system for this task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs pipe cutting and lifting in construction/plumbing contexts at production scale. Specialized construction robots exist for narrow, controlled environments but not for the general helper task described. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs pipe cutting and physical lifting/handoff to a tradesperson on job sites; robotics for this specific unstructured task remain research-stage at best. |
Fill pipes with sand or resin to prevent distortion, and hold pipes during bending and installation.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Fill pipes with sand or resin to prevent distortion, and hold pipes during bending and installation.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and pipeline work are characterized by low digitization, on-site variability, and dispersed small teams. Adoption of automation in this sector lags far behind white-collar and manufacturing domains. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show very low AI/robotics adoption for manual pipefitting tasks, with automation efforts focused on design and scheduling rather than physical handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Minimal augmentation potential; AI could potentially optimize material selection (sand vs. resin) via decision support, but the core physical tasks of filling and holding pipes offer limited scope for AI-assisted human productivity gain. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI tools offer no meaningful assistance to a worker physically holding and filling pipes during bending and installation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of pipes, material insertion (sand/resin), and real-time coordination during bending and installation in construction environments. Current AI systems have no robotic embodiment in typical construction settings to perform these actions reliably at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical task requiring holding and manipulating pipes and filling them with material; no current AI system can perform physical manipulation like this without a robotic embodiment, which is not deployed for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical presence requirements and safety oversight are substantial; however, no explicit licensing mandate requires a human to perform this task, only practical constraints on current automation make substitution unlikely in practice. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier specifically prevents automation, but the physical, unstructured nature of construction sites and need for dexterous manipulation create practical obstacles to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a robotics system capable of filling pipes with sand/resin and holding them during installation would require significant capital investment and custom integration, far exceeding the loaded wage of a single pipelayer helper on most jobs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so any hypothetical robotic solution would require expensive specialized hardware far exceeding the cost of a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs this specific task end-to-end. While industrial robotics exist in controlled manufacturing, the on-site, manual coordination aspects of holding pipes during installation in variable conditions remain beyond current production automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs this physical pipefitting support task; it remains purely manual labor performed by human helpers on job sites. |
Clean shop, work area, and machines, using solvent and rags.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.3/5 · click for rater detail
Clean shop, work area, and machines, using solvent and rags.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing and pipelaying are physically distributed, small-to-medium firm sectors with limited digitization. Adoption of task-specific robotics for cleaning is virtually non-existent; these sectors lag in automation overall. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades support occupations show minimal AI or robotics adoption for manual physical upkeep tasks, remaining a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics offer no meaningful augmentation for manual cleaning work—there is no software or tool that enhances human performance on the core task of physically cleaning machines and work areas with solvent and rags. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for physical cleaning tasks like wiping down machines and shop areas with solvent and rags. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of tools and materials in three-dimensional space to clean machinery and work areas—capabilities current robots lack reliably without extensive custom setup. Solvent handling, rag-based cleaning of varied machine geometries, and hazard assessment are fundamentally embodied tasks beyond current AI deployment scope. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual cleaning task requiring dexterity, mobility, and adaptation to irregular shop layouts; no AI system can perform physical cleaning end-to-end.ed |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers to automation, OSHA chemical handling regulations, workplace safety protocols around solvent use, and the need for human judgment about equipment safety create modest friction. Physical presence and adaptability to varied shop layouts also slow substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical environment variability, handling of solvents, and lack of robotic infrastructure create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of this task (if they existed at scale) would require expensive robotics, solvent-safe hardware, safety systems, and integration—far exceeding the hourly wage of a helper performing manual cleaning. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution deployed for this task, so the effective AI cost is undefined or far higher than a low-wage helper's labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mainstream deployed product reliably performs this task independently in plumbing/pipelaying shop environments. While robotic arms exist in laboratory settings, none are in production use for general shop cleaning across varied machinery and layouts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical shop-cleaning with solvents and rags; this requires robotic hardware, not software AI, and no such robotic product is in production for this niche task. |
Cut or drill holes in walls or floors to accommodate the passage of pipes.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Cut or drill holes in walls or floors to accommodate the passage of pipes.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and skilled trades remain laggard sectors in digital automation; physical task automation in this domain is minimal, with work site conditions, variability, and distributed labor making adoption slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, slowest-adopting sectors for AI and robotics, with physical site work seeing minimal automation penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential exists; laser guides or hole-location software could assist planning, but the core physical task of cutting or drilling offers minimal AI-driven productivity gain while a human remains in control. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning cut locations via building plans or stud/pipe detection apps, but offers little help with the actual physical execution of cutting or drilling. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of building materials in unstructured environments with precise spatial reasoning, coordination, and judgment about structural integrity—capabilities that current AI and robotics cannot reliably perform end-to-end on job sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring cutting/drilling through building materials, which no current AI system can perform end-to-end without embodied robotics far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, building codes, structural liability, and the requirement that work be inspected and signed off by licensed tradespeople create substantial legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human for this sub-task, but practical barriers like precision requirements, safety codes, and structural damage liability create moderate friction against any automated tool. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction robotics with the necessary positioning, sensing, and safety systems are prohibitively expensive compared to the labor cost of a skilled helper or apprentice performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any theoretical automated solution would be far more costly than a human helper performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform autonomous drilling or cutting holes in walls and floors in real construction environments; this remains in the research/prototype phase for robotics. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs autonomous drilling/cutting of walls or floors in construction/plumbing settings; this remains far outside current robotics deployment in trades work. |
Fit or assist in fitting valves, couplings, or assemblies to tanks, pumps, or systems, using hand tools.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.9/5 · click for rater detail
Fit or assist in fitting valves, couplings, or assemblies to tanks, pumps, or systems, using hand tools.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing and pipefitting remain largely small-firm, on-site, physical work with low digitization. Adoption of AI or robotics in this sector is minimal; the labor market shows no measurable displacement of helpers by automated systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show minimal AI/robotics adoption for hands-on physical assembly tasks, remaining a laggard sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for this task. Computer vision might help identify components or detect misalignment, but it provides no meaningful productivity boost to a helper actively fitting valves and couplings by hand in the field. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with instructions, diagrams, or troubleshooting guidance, but offers minimal direct assistance to the physical fitting action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical dexterity, spatial reasoning, and precise hand-tool manipulation in real-world, varied environments. Current AI systems lack embodied robot manipulation capabilities reliable enough to fit small components like valves and couplings to complex systems at production quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is manual physical work requiring fitting hardware with hand tools; no current AI system can perform physical manipulation tasks like this. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: safety codes often require licensed plumbers or steamfitters to sign off on valve and coupling installations; liability for leaks or system failures creates error-cost asymmetry; and on-site conditions demand human judgment and adaptation that automation cannot legally substitute for without human oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically prevents automation, but physical dexterity, safety, and environment variability create substantial practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this work are extremely expensive to acquire, program, and maintain, far exceeding the loaded wage of a helper. The per-task cost of current automation would be orders of magnitude higher than manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative for this physical task, so AI cost comparison is moot; a human remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform this task end-to-end today. While vision systems can identify components, the actual fitting—threading, torquing, sealing, and troubleshooting misalignment—requires embodied manipulation that remains in research/prototype phases, not production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products fit valves, couplings, or assemblies using hand tools; this requires robotic embodiment far beyond current commercial systems. |
Mount brackets and hangers on walls and ceilings to hold pipes, and set sleeves or inserts to provide support for pipes.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Mount brackets and hangers on walls and ceilings to hold pipes, and set sleeves or inserts to provide support for pipes.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in construction and trades sectors characterized by low digital automation, site-specific workflows, and structural/safety requirements that favor on-site human expertise. Adoption of AI agents in these settings remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, lowest AI-adoption sectors, with physical installation tasks seeing essentially no automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist minimally with task planning or material layout visualization, but the core physical and structural-judgment components require human execution. Augmentation potential is limited given the hands-on, real-time nature of the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, layout calculations, or generating installation instructions via mobile apps, but offers minimal real-time assistance for the physical mounting work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of brackets, hangers, and pipes in varied spatial environments, precise positioning at height, and real-time judgment about load-bearing capacity and alignment. Current AI systems cannot physically execute these actions or reliably assess site-specific structural constraints without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring measurement, drilling, fastening, and precise placement in varied physical environments; no current AI system can perform this end-to-end.5% is optimistic even; it remains firmly in physical robotics territory not yet deployable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes and safety regulations typically require that structural support systems be installed by licensed or certified personnel and inspected by qualified individuals. Liability for load-bearing failures creates strong incentives for human sign-off and legal accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this installation task to certain trades beyond general plumbing/construction codes, but physical worksite variability and safety inspection requirements create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of wall/ceiling mounting are specialized, capital-intensive, and require extensive setup for each site. The amortized cost per task instance far exceeds the loaded wage of a skilled pipefitter's helper performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system performing this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously mount hardware on walls and ceilings or perform the structural assessment required. This remains a manual, hands-on construction task with no meaningful automation in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs brackets, hangers, sleeves or pipe supports in construction settings; this remains far beyond current robotic manipulation capabilities in unstructured environments. |
Disassemble and remove damaged or worn pipe.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Disassemble and remove damaged or worn pipe.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing and pipelaying are manual, site-specific trades with low digitization; they operate in fragmented, small-to-medium firms with minimal infrastructure investment in robotics or automation technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show very low AI/robotic adoption for physical manual tasks, with automation concentrated in office/digital work rather than field trades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for hands-on pipe disassembly; remote inspection cameras or diagnostic tools might support decision-making about which pipe to remove, but the core physical task remains unaided. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics (identifying which pipe sections are damaged) or work planning, but offers minimal direct help with the physical act of disassembly and removal. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Disassembling and removing damaged pipe requires physical manipulation in varied, often constrained spaces, precise hand-tool use, and real-time assessment of structural integrity—capabilities far beyond current AI and robotics in unstructured field environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of pipes in varied, often cramped or hazardous locations—current AI cannot perform physical labor tasks, only robots with narrow, non-generalizable capabilities exist. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical, on-site work on live or potentially hazardous piping systems carries liability and safety certification requirements; plumbing work often requires licensed supervision and contractor credentials that create substantial legal and regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human for this narrow task, but physical safety, plumbing codes, and liability around pipe systems create moderate practical barriers to any automated equipment doing this unsupervised. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic pipe removal systems, where they exist at all, are research-stage prototypes or highly specialized for single contexts; deployment costs vastly exceed the wage of a skilled helper performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so the AI cost is effectively infinite or non-existent compared to human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform pipe disassembly and removal in production settings; this remains entirely human-dependent work requiring dexterity, spatial reasoning, and on-site problem-solving that commercial systems cannot match. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical pipe disassembly and removal in real work environments; this remains firmly in the domain of human manual labor. |
Immerse pipe in chemical solution to remove dirt, oil, and scale.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.5/5 · click for rater detail
Immerse pipe in chemical solution to remove dirt, oil, and scale.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a low-digitization, physical, on-site task in construction and maintenance sectors that lag in automation adoption. Current adoption of AI/robotics in this context is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show low digitization and minimal AI/robotic adoption for manual physical prep tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a worker performing physical chemical immersion of pipes; the task is purely manual and procedural with no decision support or knowledge-work component that AI could enhance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this manual, physical chemical-immersion cleaning task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of pipes in a chemical bath with monitoring for proper immersion time and solution condition—capabilities far beyond current AI/robotics in unstructured industrial settings. No off-the-shelf system can reliably handle the mechanical and sensory aspects of this wet, hazardous work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring handling pipe, chemicals, and immersion equipment; no current AI system can perform physical labor of this kind end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | OSHA and EPA regulations govern chemical handling and worker safety in pipe cleaning; the task involves hazardous materials and occupational safety requirements that create licensing/regulatory barriers to automation, and worker presence for safety monitoring is often legally mandated. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, but safety regulations around chemical handling and workplace safety create some procedural friction for any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized robotic system capable of safe chemical immersion would require significant capital investment, custom integration, and continuous maintenance—far exceeding the loaded wage of a helper for this manual task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for the physical labor involved, so any AI-based approach would require expensive robotic hardware exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs this task autonomously. The combination of handling heavy pipes, managing chemical safety, and assessing cleaning results in real-time exceeds current robot dexterity and environmental perception in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical pipe cleaning; this requires robotics and physical dexterity far beyond current commercial offerings. |
Measure, cut, thread and assemble new pipe, placing the assembled pipe in hangers or other supports.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Measure, cut, thread and assemble new pipe, placing the assembled pipe in hangers or other supports.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing and pipefitting remain highly traditional, low-digitization sectors with small firms, site-specific physical work, and minimal AI tool adoption in production workflows. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, lowest AI-adoption sectors, with physical robotics adoption for pipefitting essentially nonexistent in the field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with planning (pipe sizing calculators, layout optimization) or measurement documentation, but most of the core physical assembly work resists meaningful human-in-the-loop augmentation with current technology. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, cut-list generation, or pipe layout planning via apps, but offers little help with the physical cutting, threading, and assembly work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of materials in outdoor/industrial settings with high variability in pipe types, sizes, and environmental conditions. Current AI systems cannot handle the dexterous, real-world assembly work or adapt to site-specific constraints that this work demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring measuring, cutting, threading, and installing pipe in a work environment with variable spatial constraints; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Plumbing and pipefitting work typically requires licensed tradespeople under local building codes and safety regulations, and the work often involves high-liability systems (water, gas, steam) where human certification and accountability are legally mandated. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement for helpers specifically, but physical presence, safety requirements, and coordination with skilled tradespeople on live job sites create real organizational and safety friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robotic system capable of this task would require significant capital investment, ongoing maintenance, and specialized infrastructure—all far more expensive than the labor cost of skilled pipefitters performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive custom robotics far exceeding helper wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end pipe measurement, cutting, threading, and assembly in production settings. Robotic systems exist for controlled industrial environments but cannot generalize to field conditions where plumbers work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs pipe measuring, cutting, threading, and hanger installation in real construction/plumbing settings; this remains beyond current robotics deployment. |
Assist pipe fitters in the layout, assembly, and installation of piping for air, ammonia, gas, and water systems.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Assist pipe fitters in the layout, assembly, and installation of piping for air, ammonia, gas, and water systems.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing and pipefitting remain labor-intensive, site-based trades with slow digitization. Few organizations have invested in automation for helper-level tasks; adoption remains minimal in production despite some experimental robotics research. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, lowest AI-adoption sectors, with physical labor tasks seeing negligible automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist minimally by optimizing material layout or interpreting blueprints on a tablet, but the core task—hands-on assembly and installation under supervision—offers limited scope for augmentation without removing the human from physical execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with some planning aspects like reading blueprints or scheduling, but offers minimal assistance to the core physical assembly and installation work described. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in varied, unpredictable environments (on-site installations), reading blueprints in context, and real-time problem-solving with equipment. Current AI cannot perform the hands-on assembly and installation work, and no autonomous system can reliably handle the spatial reasoning and physical dexterity demands of layout and assembly in field conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical manual labor involving handling pipes, tools, and materials on job sites; no current AI system can perform physical assembly or installation tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Plumbing and gas system installation are regulated in most jurisdictions; licensed plumbers and pipefitters must oversee or sign off on the work for code compliance and safety. Physical site presence and human judgment about real-world constraints create organizational and legal friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing typically required for helpers, but safety regulations around gas/ammonia systems and on-site coordination with skilled tradespeople create moderate organizational and safety-driven friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves specialized physical work requiring custom robotics and site-specific integration, which would be prohibitively expensive compared to the relatively low wage of a helper role in construction and installation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical helper labor, so AI cost is not comparable or cheaper; a human helper remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task autonomously. While computer vision can identify pipes and some planning systems exist in research, no production system integrates layout, assembly, and installation at scale in live piping projects. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical pipe layout and installation assistance; this requires robotics far beyond current commercial capability in unstructured environments. |
Excavate and grade ditches, and lay and join pipe for water and sewer service.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Excavate and grade ditches, and lay and join pipe for water and sewer service.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs predominantly in small construction firms, municipalities, and field settings with low digitization. Adoption of advanced automation in this sector remains minimal and primarily limited to basic equipment aids rather than autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show minimal AI/robotics adoption for physical excavation and pipefitting tasks, remaining a laggard, low-digitization field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance for the core physical and spatial aspects of this task. GPS-guided excavation and inspection drones provide marginal support, but do not substantially transform worker productivity on the main task of laying and joining pipe. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, permit documentation, or grading calculations via software, but offers minimal direct assistance to the physical excavation and pipe-joining work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Excavating, grading, and laying pipe requires physical manipulation in variable outdoor conditions, heavy machinery operation, and spatial reasoning that current AI systems cannot perform end-to-end. This is fundamentally a physical construction task with no meaningful autonomous execution capability today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical excavation and pipefitting task requiring manual labor, equipment operation, and precise physical manipulation in variable field conditions; no AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes, local regulations, and inspection requirements typically mandate that licensed plumbers and pipefitters perform or directly supervise pipe installation and joining. Liability for water/sewer infrastructure failures creates strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing typically restricts helpers, safety regulations, utility locating requirements, and inspection sign-offs create real procedural friction around excavation work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized excavation equipment and trained human laborers remain far cheaper than any robotic system capable of this work. Infrastructure automation in this domain is prohibitively expensive relative to manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical labor, so any AI cost comparison is moot—human labor with heavy equipment remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task reliably. While robotics research exists for some construction tasks, there is no production system in real organizations autonomously excavating and laying pipes at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs excavation, grading, or physical pipe-laying; this remains a purely manual/robotics-research-stage domain far from production autonomy. |
Clean and renew steam traps.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Clean and renew steam traps.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automation in traditional skilled trades and industrial maintenance remains low; most sites still rely on human technicians, with robotics adoption concentrated in narrow, controlled environments rather than field maintenance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial trades are among the slowest sectors to adopt AI/robotics for physical maintenance tasks, with minimal digitization of this specific work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostic sensing (vibration analysis, thermal imaging) to identify failing traps before maintenance, but the physical work itself remains human-dependent, and current augmentation tools are not widely integrated into this workflow. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, documentation, or diagnostic guidance for steam trap maintenance, but offers minimal direct assistance for the physical cleaning and renewal work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning and renewing steam traps requires physical manipulation in confined, high-temperature industrial environments where site-specific assessment and real-time adjustment are essential. Current AI systems lack the embodied robotics, dexterity, and environmental adaptation needed to perform this work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Cleaning and renewing steam traps requires physical disassembly, inspection, and manual manipulation of hardware in industrial settings, none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task carries high liability and regulatory barriers: steam system work often requires licensed technicians or certified oversight, safety codes mandate human inspection and sign-off, and insurance typically requires qualified personnel responsible for system integrity. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed at the helper level, safety protocols around steam systems, physical access constraints, and employer liability create meaningful friction against any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized industrial robots capable of operating in high-temperature steam environments with necessary safety controls and oversight would cost significantly more than a trained helper earning typical wages for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical labor, so the human remains the only cost-effective option for this hands-on task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs steam trap maintenance autonomously. While research into industrial robotics exists, production systems do not operate independently in live steam systems at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs this specific maintenance task in production; this remains a manual trade task requiring physical dexterity and judgment on-site. |
Perform rough-ins, repair and replace fixtures and water heaters, and locate, repair, or remove leaking or broken pipes.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Perform rough-ins, repair and replace fixtures and water heaters, and locate, repair, or remove leaking or broken pipes.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plumbing is a traditional, physical trade with low digitization, small firm dominance, and heavy reliance on skilled craft knowledge. Adoption of AI or automation in this sector remains minimal and nascent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show low AI/robotics adoption for physical manual tasks, with automation efforts still experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited opportunity for AI augmentation: leak detection visualization and pipe routing tools offer modest assistance, but the core tasks—hands-on repair, fixture installation, decision-making under constraint—remain fundamentally human-centric with little scope for AI co-agency. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics (e.g., leak detection sensors, documentation, part lookup, scheduling) but offers minimal direct assistance to the physical repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of pipes, fixtures, and water heaters in diverse spatial configurations, real-time leak detection, and judgment about pipe condition—capabilities far beyond current AI systems. No autonomous robotics solution exists that can reliably perform these varied, site-specific interventions at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade task requiring hands-on manipulation of pipes, fixtures, and tools in variable, cluttered physical environments; no current AI system can perform this end-to-end.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Plumbing work is typically licensed in most jurisdictions and must be performed or certified by licensed professionals; building codes and safety regulations mandate human expertise and sign-off. Liability and safety requirements create strong legal barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Plumbing work is often subject to licensing, code compliance, and inspection requirements, and physical dexterity in unpredictable environments creates strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of any portion of this work (robotic arms, leak detection) remain extremely expensive relative to the loaded wage of a plumber, and integration costs are very high. Human plumbers remain far more cost-effective for this labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost is effectively infinite relative to a human plumber's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can autonomously perform rough-ins, fixture replacement, or pipe repair in real buildings. Current robotics and AI lack the dexterity, environmental perception, and generalization needed for plumbing work in uncontrolled field conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs plumbing rough-ins or physical pipe repair; robotics for this remains research-stage at best. |
Install gas burners to convert furnaces from wood, coal, or oil.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Install gas burners to convert furnaces from wood, coal, or oil.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in small-scale, physically-distributed service sectors with low digitization and reliance on skilled trades; adoption patterns remain traditional and hands-on with minimal AI use. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/automation for physical tasks, with adoption largely limited to scheduling or diagnostic software rather than hands-on work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with diagnostic guidance, documentation, or safety checklists, but the core task of physical installation cannot be meaningfully augmented by current systems; the human technician remains fully responsible for execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with technical manuals, diagnostics, or planning the conversion, but offers little direct help with the physical installation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical installation of specialized equipment into existing furnaces, involving precise mechanical assembly, safety connections, and on-site customization. Current AI systems cannot perform hands-on physical work or make real-time safety-critical adjustments in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical installation task requiring manipulation of heavy equipment, precise fitting, and gas line connections; no current AI system can perform physical manual labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Gas burner installation is subject to strict safety codes, building permits, and often requires licensed plumbers or HVAC technicians to perform or sign off on the work, creating hard legal and regulatory barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas line work typically requires licensed plumbers/pipefitters and adherence to safety codes and permits, with high liability for improper installation, creating strong regulatory and safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot perform this task at all, so comparison is not applicable; a human worker is essential and no cost substitution is possible with current technology. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so AI cost is effectively infinite relative to a human helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically install gas burners into furnaces. This is fundamentally a hands-on mechanical task requiring human technicians with specialized tools and physical presence on-site. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical furnace conversion installation; robotics for this kind of variable, confined-space plumbing work does not exist in production. |
Related occupations — Construction & Extraction
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.