Boilermakers
47-2011.00Median wage $76,410/yr10,190 employed (US)Rank #881 of 923 scored · top 95% by substitution
Construct, assemble, maintain, and repair stationary steam boilers and boiler house auxiliaries. Align structures or plate sections to assemble boiler frame tanks or vats, following blueprints. Work involves use of hand and power tools, plumb bobs, levels, wedges, dogs, or turnbuckles. Assist in testing assembled vessels. Direct cleaning of boilers and boiler furnaces. Inspect and repair boiler fittings, such as safety valves, regulators, automatic-control mechanisms, water columns, and auxiliary machines.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution10
Exposure7
Augmentation31
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
18 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%7
panel mean rating 1.3/5 → substitution pressure 7/100
Technical feasibility todayw 20%7
panel mean rating 1.3/5 → substitution pressure 7/100
Cost vs. human wagew 15%7
panel mean rating 1.3/5 → substitution pressure 7/100
Adoption barriersw 20%inverted — strong barriers lower the score24
panel mean rating 4.1/5 (barrier strength) → substitution pressure 24/100
Sector adoption velocityw 10%5
panel mean rating 1.2/5 → substitution pressure 5/100
Task breakdown (18 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.
Study blueprints to determine locations, relationships, or dimensions of parts.
26CI 23–30 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail
Study blueprints to determine locations, relationships, or dimensions of parts.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking is a traditional trade with low digitization and slow AI adoption. Most work remains small-team, job-site based, and relies on proven manual processes. Adoption of AI-driven blueprint analysis in this sector is still pilot-stage rather than production-widespread. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking and skilled trades manufacturing are physical, low-digitization sectors with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered blueprint annotation, dimension highlighting, and relationship diagramming could usefully assist boilermakers in faster comprehension and error-checking. However, current tools are not yet deeply integrated into shop-floor workflows, limiting real-world augmentation impact. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help highlight dimensions, flag inconsistencies, or convert 2D blueprints into digital annotations, assisting boilermakers but not replacing careful manual interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and identify geometric information from blueprints through computer vision and OCR, real-world blueprints often contain ambiguities, handwritten annotations, and domain-specific symbology that requires human interpretation. Determining relationships and spatial context typically still needs skilled judgment rather than fully autonomous performance. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision models can extract some information from blueprints but reliably interpreting complex mechanical drawings to determine precise spatial relationships and dimensions for physical assembly is still unreliable and requires human verification for real fabrication work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Boilermakers are a licensed trade in many jurisdictions; workers must have verified competency to interpret structural specifications. Safety-critical consequences of misreading dimensions or relationships create liability barriers, and industry standards often require sign-off by certified personnel, limiting substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement to read blueprints, but liability for fabrication errors and quality/safety inspection requirements create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI document analysis and blueprint interpretation services are still relatively expensive per task, and integration with boilermaking workflows adds overhead. The cost is likely comparable to or exceeds a skilled worker spending focused time on blueprint study for typical jobs. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Setting up AI blueprint analysis with sufficient accuracy and integration into fabrication workflows requires significant engineering investment, and residual human verification costs keep the ratio close to the human baseline. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for blueprint digitization and basic feature extraction (e.g., CAD conversion tools, blueprint scanning software), but they require significant manual verification and are unreliable on complex or non-standard drawings. No mature product reliably interprets all relationships and dimensions without human review in production boilermaking contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD-integrated tools and vision-language models can read technical drawings, but no deployed product reliably performs full blueprint interpretation for boilermaking-grade precision work in production settings. |
Inspect assembled vessels or individual components, such as tubes, fittings, valves, controls, or auxiliary mechanisms, to locate any defects.
24CI 23–25 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail
Inspect assembled vessels or individual components, such as tubes, fittings, valves, controls, or auxiliary mechanisms, to locate any defects.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking is a capital-equipment, union-skilled trade with strong licensing and safety culture; adoption of AI automation is slow, concentrated in large fabricators, and resisted by workforce and regulatory conservatism. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking is a physical, low-digitization trade with minimal AI adoption in the field; this sector shows slow uptake of automation technologies compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted imaging and anomaly flagging can help boilermakers prioritize visual inspection areas and document findings, but the human inspector remains essential for judgment, hands-on verification, and certification—offering useful but not transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered image analysis, thermal imaging processing, or predictive maintenance software can help flag potential defect areas for a human inspector to verify, offering meaningful but partial productivity assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect visible surface defects (cracks, corrosion, misalignment), boilermaker inspection requires judgment about structural integrity, fit tolerances, and internal/hidden defects that demand hands-on tactile assessment and contextual knowledge—far from the 50% time-saving threshold for autonomous performance. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical inspection of boiler vessels and components requires manual access, handling, and often specialized NDT techniques; while some visual defect detection can be aided by computer vision, most of the physical inspection process cannot be automated end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical inspection in pressurized vessels is heavily regulated (ASME, BPVC codes); liability for missed defects is severe, and inspectors must often be certified/licensed, creating strong regulatory and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel inspections are often subject to safety codes and certification requirements (e.g., ASME) mandating qualified personnel sign-off, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision systems and integration overhead are non-trivial, while boilermaker inspectors earn moderate wages but bring irreplaceable judgment; the all-in AI cost per complex vessel inspection remains comparable to or higher than the human alternative. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor rigs, robotic crawlers, or drone-based inspection systems require significant capital investment and integration, making the all-in cost comparable to or higher than skilled labor for many boiler inspection scenarios. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision inspection products exist for surface-level defect detection in controlled environments, but deployed systems lack the spatial reasoning, multi-modal sensing (vibration, pressure, acoustic), and domain expertise needed for comprehensive boilermaker vessel inspection in production today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted visual inspection tools exist for industrial equipment, but deployed products for boilermaker-specific vessel/component inspection covering tubes, fittings, valves, and controls are narrow and not widely used in production for this exact trade task. |
Lay out plate, sheet steel, or other heavy metal and locate and mark bending and cutting lines, using protractors, compasses, and drawing instruments or templates.
20CI 10–30 · exposure 13 · augmentation 38 · importance 3.7/5 · click for rater detail
Lay out plate, sheet steel, or other heavy metal and locate and mark bending and cutting lines, using protractors, compasses, and drawing instruments or templates.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking is a traditional, small-to-medium-sector industry with lower digitization than information or finance. Adoption of automated marking systems exists but remains piecemeal and concentrated in larger fabrication shops; most small boilermakers still rely on manual layout methods. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking and heavy metal fabrication are physical, low-digitization trades with minimal AI agent adoption in production environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD-based design tools and digital measurement aids can assist layout workers by auto-generating mark positions and reducing arithmetic errors, but the core task of physical marking and orientation remains human-dominated. AI augmentation is useful for design acceleration but does not dramatically transform on-floor productivity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM and nesting software can assist in planning cut layouts and reducing waste, offering some productivity gains, but the hands-on marking and measuring on physical stock sees little AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Marking and layout tasks involve measuring and precise line placement, which can be partially automated with CAD and CNC marking systems, but the initial physical assessment of material condition, interpretation of bending needs in context, and handling of variability in sheet orientation require skilled human judgment. Current AI cannot reliably execute the full task end-to-end at 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical layout task on heavy metal stock requiring manual measurement, marking, and manipulation of large workpieces; no off-the-shelf AI system performs this physical work end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Physical boilermaking plants are mature, cost-conscious industries with some resistance to capital investment. There are no hard regulatory barriers to automation, but organizational friction (operator familiarity, line downtime, equipment integration) and the specialized nature of heavy-plate work introduce moderate friction to adoption. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically for layout marking, but quality/safety tolerances in structural and pressure vessel fabrication create strong organizational and liability-driven preference for skilled human tradespeople. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated marking equipment (lasers, CNC systems) has substantial capital and integration costs; labor for skilled layout workers remains relatively inexpensive compared to equipment ownership and maintenance. The all-in cost of automation currently exceeds the loaded wage for manual layout in most boilermaking settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Given no viable AI substitute for the physical layout task, deploying AI would add cost without replacing the human labor, making AI more expensive or simply inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While CAD software and automated marking systems exist in production (CNC and laser marking), they typically handle design-to-mark workflows for standardized inputs. However, fully autonomous physical marking of heavy metal sheets on a factory floor—assessing material condition, orienting sheets, and executing precise marks in real conditions—lacks reliable deployed solutions at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI products that physically lay out and mark plate/sheet steel in shop settings; some CAD/CAM software can generate cut paths but the physical marking and layout remains manual or CNC-driven, not AI-driven. |
Bell, bead with power hammers, or weld pressure vessel tube ends to ensure leakproof joints.
15CI 5–25 · exposure 13 · augmentation 38 · importance 3.7/5 · click for rater detail
Bell, bead with power hammers, or weld pressure vessel tube ends to ensure leakproof joints.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking is a skilled trades sector with moderate digitization, largely concentrated in heavy industry with slower capital deployment cycles. Adoption of welding automation remains pilot-stage or limited to high-volume, standardized production rather than widespread. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Skilled trades and heavy industrial fabrication are among the slowest sectors to adopt AI/robotics due to physical variability, safety requirements, and low digitization. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Robotic welding assists by handling repetitive joint passes and reducing fatigue, but the human operator must still set up, inspect, and validate critical pressure-bearing welds, providing moderate productivity uplift without removing human responsibility. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with weld procedure documentation, defect detection via inspection imaging, or training simulations, but offers minimal direct assistance during the hands-on belling/beading/welding process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Welding pressure vessel tube ends requires precise spatial reasoning, real-time adaptability to material variations, and quality judgment under safety-critical conditions. Current AI lacks reliable full end-to-end automation of this task, though robotic welding assists human operators in controlled industrial settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical trade task requiring manipulation of power hammers and welding equipment on heavy pressure vessel components, well beyond current robotic manipulation and perception capabilities in unstructured industrial settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pressure vessel welds are heavily regulated (ASME, PED standards) and typically require certified welders to perform or inspect the work; liability for leaks is severe, creating strong legal and insurance barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel work is governed by codes (e.g., ASME) requiring certified welders and inspections, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic welding equipment and integration costs are substantial (hundreds of thousands upfront), and pressure vessel quality requirements demand expensive inspection and oversight. For many boilermakers, human wages are still competitive when amortized over diverse, low-volume jobs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific task, so any hypothetical automation would require expensive custom robotic welding cells far exceeding the cost of a skilled boilermaker for this work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic welding systems exist in production but are typically programmed for repetitive, geometrically consistent joints; pressure vessel tube ends often vary in geometry, material composition, and positioning, requiring human oversight and rework. Fully autonomous systems for this specific task are not reliably deployed at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs autonomous belling, beading, or pressure-critical welding of tube ends; robotic welding exists mainly in fixed, repetitive factory contexts, not this variable field/shop task requiring leakproof precision. |
Shape seams, joints, or irregular edges of pressure vessel sections or structural parts to attain specified fit of parts, using cutting torches, hammers, files, or metalworking machines.
15CI 5–25 · exposure 13 · augmentation 38 · importance 3.4/5 · click for rater detail
Shape seams, joints, or irregular edges of pressure vessel sections or structural parts to attain specified fit of parts, using cutting torches, hammers, files, or metalworking machines.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking remains a skilled trade concentrated in small-to-medium shops, construction, and maintenance—sectors with low digitization and slow adoption of AI agents. While large shipyards or manufacturers use some CNC, widespread production adoption of end-to-end AI shaping is not evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking and heavy metal fabrication are physical, low-digitization trades with minimal AI/robotic adoption for this kind of custom fitting work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Computer vision assisted measurement and CNC-guided cuts can help a boilermaker position work and reduce manual tool setup, but the human remains essential for final inspection, fit judgment, and quality control. Modest productivity gain, not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI has limited direct application here beyond possibly CAD/design assistance or measurement tools; the hands-on shaping and fitting itself sees little productivity augmentation from current AI. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While cutting torches and some metalworking machines can be automated, the core task requires real-time judgment to assess fit, detect irregular edges, and adjust technique. Current AI vision systems cannot reliably guide complex 3D shaping with the precision and adaptability needed for pressure vessel work, especially when specifications are context-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical metalworking task requiring manual dexterity, torch handling, and fine motor judgment in irregular, unstructured environments; no AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pressure vessels are heavily regulated under ASME codes and federal safety standards; the final fit and quality sign-off typically requires a certified boilermaker or inspector. Liability for failures is severe, creating strong legal and organizational barriers to full automation without human certification. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel work is often subject to welding/fabrication codes (e.g., ASME) requiring certified tradespeople and inspection sign-off, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC and robotic setups carry high capital and integration costs, plus ongoing programming labor. For one-off or low-volume pressure vessel work with custom specifications, the all-in AI cost exceeds paying a skilled boilermaker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic substitute exists for this manual fabrication work, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated cutting systems exist (CNC plasma, laser), but they require pre-programmed geometry and struggle with irregular, hand-fitted seams. No deployed system reliably handles the full scope: assessing fit quality, detecting deviations, and adjusting cuts in real-time without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI or robotic products that reliably shape pressure vessel seams and irregular metal edges to precision fit in production settings. |
Examine boilers, pressure vessels, tanks, or vats to locate defects, such as leaks, weak spots, or defective sections, so that they can be repaired.
13CI 0–25 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail
Examine boilers, pressure vessels, tanks, or vats to locate defects, such as leaks, weak spots, or defective sections, so that they can be repaired.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boiler inspection remains a hands-on, localized trade with slow digital transformation; while some operators use remote cameras or sensors, the sector is fragmented, risk-averse, and dominated by small/medium service providers with limited AI integration in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking is a physical, on-site trade with low digitization and minimal AI agent deployment in real-world inspection workflows to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered visual inspection tools and defect-detection models can assist boilermakers by highlighting suspicious regions or logging observations, speeding up documentation and pattern recognition, but the human inspector remains essential for tactile assessment, judgment, and regulatory sign-off. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled sensors, thermal imaging analysis, and predictive maintenance software can help flag likely defect locations or analyze inspection data, assisting but not replacing the physical inspection process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect some visual defects (corrosion, obvious cracks) in images, boilermaker inspection requires hands-on tactile assessment (tapping, ultrasonic testing, thermal imaging interpretation in context) and judgment about structural safety that current AI cannot reliably perform end-to-end without substantial human oversight, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection of large industrial vessels, often involving climbing, confined-space entry, and tactile/visual assessment of metal integrity that current AI cannot perform end-to-end without a human physically present and manipulating tools. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Boiler inspection is heavily regulated (ASME codes, state/local licensing) and often legally requires a certified boilermaker or inspector to certify findings; liability and safety asymmetry are extreme because missed defects cause catastrophic failure, creating strong regulatory and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Pressure vessel inspections are typically governed by strict safety codes (e.g., ASME) requiring certified inspectors or qualified tradespeople to sign off, creating hard regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI-assisted inspection tools (cameras, software) require expensive equipment and integration, plus mandatory human expert review, so the all-in cost per inspection remains comparable to or exceeds a skilled boilermaker's loaded wage given the liability and re-inspection overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical inspection requires specialized robotics, sensors, and human oversight for safety-critical judgment calls, making any AI-assisted approach currently more expensive than a trained boilermaker performing the inspection. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision models exist for defect detection in industrial imagery, but deployed inspection systems still require human boilermakers to confirm findings, interpret results in context, and make critical safety decisions; no production AI system performs independent boiler inspection at the reliability level required for safety-critical equipment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously conducts physical boiler/pressure vessel inspections; some AI-assisted sensor analysis exists in research or narrow NDT contexts but not as a full replacement in production. |
Bolt or arc weld pressure vessel structures and parts together, using wrenches or welding equipment.
13CI 0–25 · exposure 13 · augmentation 25 · importance 3.4/5 · click for rater detail
Bolt or arc weld pressure vessel structures and parts together, using wrenches or welding equipment.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Boilermaking is a traditional, physically-rooted trade in construction and manufacturing with moderate digitization. While some large fabrication shops have adopted automated welding for high-volume runs, widespread AI-driven adoption in the field remains limited and slow due to site variability and regulatory constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy industrial fabrication sectors have very low AI/robotics adoption for on-site structural welding due to variability, safety, and certification requirements. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with design verification or weld-quality inspection feedback, but the hands-on nature of positioning, bolting, and executing welds means AI augmentation is limited. Human judgment, spatial problem-solving, and real-time adjustments remain central to the task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, weld procedure specifications, or defect detection via imaging, but it offers minimal direct assistance to the hands-on bolting/welding execution itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Bolting and arc welding involve complex spatial reasoning, material property judgment, and real-time sensorimotor control in hazardous environments. While robotic systems can perform welding in highly controlled settings, the varied geometries, positions, and quality control requirements of pressure vessel work remain beyond current off-the-shelf AI capabilities to achieve at equal quality with 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity, mobility in confined industrial spaces, and manual manipulation of heavy pressure vessel parts using wrenches or welding equipment—no AI system today can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pressure vessel work is heavily regulated by codes (ASME, API) that require certified, licensed welders to perform and sign off on critical joints; liability is high if failures occur. Legal and safety requirements effectively mandate human certification and responsibility, creating a hard barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Pressure vessel welding is subject to strict certification (ASME codes, welder qualification tests) and safety/liability regulations requiring certified human welders to perform and stamp this work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized welding robotics are capital-intensive and require extensive programming and fixturing for each job variant. The integration and maintenance costs, combined with the need for human oversight and rework, currently exceed the loaded wage of a skilled boilermaker in most real deployments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any comparison favors the human tradesperson; robotic welding rigs for this application would be far more costly than a boilermaker's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial robots can perform welding in constrained, repetitive scenarios (e.g., automotive assembly), but deployed products lack the adaptability for the varied, site-specific demands of pressure vessel fabrication. Bolting and positioning work require human-level dexterity and error detection that current deployed systems do not reliably achieve in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs field welding or bolting of pressure vessel structures autonomously; robotic welding exists only for fixed, repetitive factory setups, not this variable structural work. |
Straighten or reshape bent pressure vessel plates or structure parts, using hammers, jacks, or torches.
9CI 5–14 · exposure 8 · augmentation 25 · importance 3.4/5 · click for rater detail
Straighten or reshape bent pressure vessel plates or structure parts, using hammers, jacks, or torches.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional skilled trade in small to mid-sized shops with low digital infrastructure. Adoption of AI-driven automation is minimal; the sector remains labor-intensive and geographically dispersed, inhibiting rapid deployment of advanced systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking is a low-digitization, physical trade with minimal AI or robotic adoption in the field; heavy manual fabrication sectors adopt automation slowly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision systems could assist in defect detection and measurement of deformations, helping a boilermaker plan their approach. However, the core physical task of reshaping offers limited augmentation potential because the work is already heavily tool-dependent and requires direct human judgment in real time. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, stress analysis, or documentation, but offers little direct assistance to the hands-on process of hammering, jacking, or torch-reshaping metal. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reshaping bent metal plates requires real-time spatial judgment, dexterity, and adaptive force application. While vision systems can detect deformations, current AI lacks the integrated sensorimotor control and real-world material feedback to handle variable metal properties and achieve the precision required end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, force-intensive manual task requiring hammers, jacks, and torches to reshape heavy metal parts; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pressure vessels are safety-critical infrastructure subject to ASME codes and regulations; certification, liability, and the requirement for qualified personnel sign-off on reshaping work create strong legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel work is often governed by welding/boiler codes and certification requirements (e.g., ASME), and safety-critical structural repairs typically require certified tradespeople to sign off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized equipment (hydraulic jacks, torches, robotic arms capable of precise metalworking) combined with integration and safety oversight would exceed the loaded wage of a skilled boilermaker for years of operation on variable workpieces. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing this physical task, so any automation would require expensive custom robotics far exceeding human labor costs for this niche task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform the full task of straightening or reshaping pressure vessel plates autonomously. Robotic systems for metalworking exist but are purpose-built for specific geometries and require extensive programming; general reshaping of arbitrary plates remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs manual plate-straightening with heat and mechanical force; industrial robotics for this bespoke, judgment-heavy task remain research-stage or absent. |
Locate and mark reference points for columns or plates on boiler foundations, following blueprints and using straightedges, squares, transits, or measuring instruments.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Locate and mark reference points for columns or plates on boiler foundations, following blueprints and using straightedges, squares, transits, or measuring instruments.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional skilled trade with low digital infrastructure penetration and strong union/licensing requirements. Adoption of autonomous systems in this sector is negligible; the work remains predominantly manual and site-dependent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking and heavy industrial construction are low-digitization, physically intensive sectors with minimal AI/robotics adoption for field layout tasks currently in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While digital blueprints and measurement software could assist interpretation, current AI offers minimal assistance in the core physical and spatial task of marking points on foundations. Handheld measurement tools remain the primary augmentation available. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital layout tools, laser measuring devices, and blueprint-reading software can somewhat assist in planning and verification, but core physical marking and instrument placement remain manual with limited AI-driven productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical site work (locating and marking points on foundations) combined with blueprint interpretation and precise spatial measurement. Current AI systems cannot physically manipulate measuring instruments or mark reference points on real boiler foundations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence at a job site to place instruments, take field measurements, and mark reference points on physical foundations—current AI systems cannot perform physical layout work end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves licensed boilermakers working with critical infrastructure (boiler foundations); there are implicit safety, liability, and regulatory requirements that a human skilled tradesperson must satisfy. Physical work on industrial construction sites carries inherent human-contact and site-safety requirements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like an electrician, precision layout work has real liability consequences (misaligned foundations affecting structural integrity) and is typically embedded in skilled trade union practices with quality sign-off requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotics, computer vision systems, and integration required to perform marking tasks on construction sites would far exceed the loaded hourly wage of a skilled boilermaker performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based approach would require expensive robotics and sensing infrastructure vastly exceeding the cost of a skilled tradesperson. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can autonomously perform this task. Robotic systems capable of precise outdoor marking exist only in specialized research or prototype contexts, not in production use across boilermaking operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical field layout marking with transits and measuring instruments on boiler foundations; this remains purely a research or robotics-frontier concept, not a production capability. |
Clean pressure vessel equipment, using scrapers, wire brushes, and cleaning solvents.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Clean pressure vessel equipment, using scrapers, wire brushes, and cleaning solvents.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional skilled trade in laggard sectors (manufacturing, utilities, chemical plants) with limited digital infrastructure and slow capital equipment adoption. Manual cleaning methods remain entrenched. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Skilled trades like boilermaking involve physical, hands-on work in industrial settings with very low AI/robotic adoption rates. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI/robotic systems might assist by mapping vessel interiors or guiding tool selection, but the core scraping and brushing task is fundamentally manual and offers limited augmentation opportunity for human productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of scraping and cleaning vessel interiors with hand tools and solvents. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning pressure vessel equipment requires physical manipulation of tools in unstructured, confined spaces with variable geometry and fragile surfaces. Current robotics and AI cannot reliably perform this task end-to-end without human oversight and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual cleaning task requiring dexterity, mobility in confined spaces, and physical tool manipulation that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pressure vessel cleaning is safety-critical work subject to ASME standards, inspection codes, and regulatory requirements. Human technicians must certify cleaning adequacy and vessel integrity, creating legal and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed per se, pressure vessel work often falls under safety codes and confined-space entry regulations requiring trained personnel, creating moderate procedural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized cleaning equipment, robotic systems capable of this work, and required integration would exceed the cost of manual labor by human boilermakers, particularly given the low hourly volume and customization per vessel. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any comparison favors the human worker entirely; robotic solutions for this niche task would be far more expensive than a boilermaker's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous cleaning of pressure vessel interiors with the dexterity and adaptability required. Specialized industrial robots exist but are not production-deployed for this specific task at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs pressure vessel cleaning with scrapers and solvents in production settings; this remains firmly manual labor. |
Assemble large vessels in an on-site fabrication shop prior to installation to ensure proper fit.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Assemble large vessels in an on-site fabrication shop prior to installation to ensure proper fit.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The boilermaking and heavy equipment assembly sector has lagged in automation adoption due to small-scale job-shop production, on-site variability, skilled labor availability, and capital-intensive barriers. Adoption remains minimal in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy fabrication trades show minimal AI/robotic adoption for this type of task; the sector is physical, low-digitization, and slow to change. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers limited assistance on this task; CAD simulation and digital planning tools provide some upstream design support, but real-time assembly execution remains fundamentally manual. Assistance is marginal compared to the human skill required. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, design review, or scheduling around this task, but offers little direct assistance to the hands-on physical assembly and fitting work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Assembling large vessels on-site requires physical manipulation, spatial reasoning, and real-time adaptation to on-site conditions that current AI cannot perform end-to-end. The task involves heavy manual labor, precise positioning, and integration with existing infrastructure—domains where robotics remain domain-specific and limited. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical assembly of large heavy vessel components requiring welding, fitting, and manual manipulation—well outside current AI capabilities which lack the robotic dexterity and mobility for such large-scale physical fabrication. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: boilermakers typically require licensing and union membership; liability for vessel integrity failures is severe; safety codes mandate human oversight and sign-off; and the inherent physical, on-site nature of the work creates hard constraints on substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel fabrication is subject to strict codes (e.g., ASME) requiring certified welders and inspections, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems for vessel assembly would require substantial capital investment, programming, and on-site customization—far exceeding the labor cost saved relative to skilled boilermakers' wages over the task duration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Automating this would require expensive specialized robotics and heavy machinery investment far exceeding skilled boilermaker labor costs for one-off or low-volume fabrication work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs full on-site assembly of large vessels at production scale. While some specialized welding robots exist, they operate in controlled factory settings, not the variable on-site conditions required here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs on-site heavy vessel assembly; robotic welding exists in controlled factory settings but not for bespoke, large-scale field fabrication requiring fit verification. |
Shape or fabricate parts, such as stacks, uptakes, or chutes, to adapt pressure vessels, heat exchangers, or piping to premises, using heavy-metalworking machines such as brakes, rolls, or drill presses.
5CI 0–10 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Shape or fabricate parts, such as stacks, uptakes, or chutes, to adapt pressure vessels, heat exchangers, or piping to premises, using heavy-metalworking machines such as brakes, rolls, or drill presses.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional skilled trade in construction and manufacturing—sectors characterized by low automation adoption, reliance on on-site physical work, and limited digital transformation; adoption of AI agents in this domain remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy industrial trades are among the slowest sectors to adopt AI/robotics for physical fabrication tasks, with minimal production deployment of automation for custom on-site metalworking. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with design optimization or measurement documentation, but the core task of shape-fabrication and machine operation is physical and real-time, leaving little room for meaningful AI augmentation of the human worker's core capability. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specifications, CAD modeling, or cut-list generation prior to fabrication, but offers little direct assistance during the hands-on shaping and machine operation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy metal parts in 3D space using specialized equipment, precise spatial reasoning about custom fit-to-premise conditions, and real-time adaptation to material feedback—capabilities far beyond current AI systems' ability to execute end-to-end with quality parity. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical fabrication requiring manual manipulation of heavy metalworking machines and custom-fitting parts to specific site conditions; no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Boilermakers are typically licensed tradespeople, and pressure vessels, heat exchangers, and piping systems are heavily regulated for safety and code compliance; legal and liability barriers require a qualified human to perform and sign off on the work. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed per se in the same way as some trades, boilermaking involves safety-critical pressure vessel work often requiring certification, union labor, and on-site adaptability that create real organizational and safety-driven friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized metalworking equipment, integration complexity, and the need for human supervision and real-time problem-solving make end-to-end automation far more expensive than the loaded wage of a skilled boilermaker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical fabrication task, so the AI cost is effectively infinite relative to a human boilermaker's wage for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently operate heavy metalworking machines, perform custom fabrication with the tolerances and adaptations required, or handle the on-site environmental constraints and material variability this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates brakes, rolls, or drill presses to shape structural metal parts; this remains purely in the domain of skilled human tradespeople and robotics research at best. |
Position, align, and secure structural parts or related assemblies to boiler frames, tanks, or vats of pressure vessels, following blueprints.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Position, align, and secure structural parts or related assemblies to boiler frames, tanks, or vats of pressure vessels, following blueprints.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking remains a traditional, physically-located trade with low digital infrastructure penetration and strong craft expertise. Adoption of automation in this sector is minimal; the workforce is skilled, regulations are stringent, and batch-to-order production limits volume economies for automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Skilled trades construction and heavy manufacturing are among the slowest sectors to adopt AI/robotics for physical fabrication tasks, with adoption rates far behind office/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with blueprint interpretation or defect detection via computer vision, but provides minimal productivity gains for the core positioning and alignment tasks, which depend on human tactile feedback, spatial intuition, and real-time problem-solving on the shop floor. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with blueprint interpretation, planning layouts, or generating work instructions, but offers little direct assistance to the physical positioning and securing work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in 3D space, real-time spatial reasoning, and adaptation to site-specific constraints that current AI cannot perform end-to-end. While vision systems can inspect work, the actual positioning, aligning, and securing of heavy structural parts demands embodied robotic capability far beyond today's deployed systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fitting and alignment task requiring manual manipulation of heavy structural components in industrial settings; no current AI system can perform the physical positioning and securing work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | High barriers exist due to safety certification requirements for pressure vessels (ASME codes), the need for licensed, accountable human sign-off on structural integrity, and liability asymmetry where alignment errors in boilers create catastrophic risk. Regulatory frameworks require human inspection and approval. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel work is governed by codes (e.g., ASME) requiring certified welders/boilermakers and inspection sign-off, plus significant liability for structural failure, creating strong regulatory and safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of this work, combined with integration and safety infrastructure, vastly exceeds the cost of skilled boilermakers who can adapt to site variation and quality requirements. Amortization over typical task volumes makes automation economically unfeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so AI cost is effectively infinite relative to a human boilermaker's wage for this specific work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mature product reliably performs the full sequence of positioning, aligning, and securing structural boiler components in production environments. Research robots exist for narrow subtasks, but deployed solutions capable of this complex assembly work at scale do not exist in commercial boilermaking operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical boiler assembly and alignment; robotics in this domain remain research-stage or limited to highly structured factory welding, not complex pressure vessel assembly. |
Install manholes, handholes, taps, tubes, valves, gauges, or feedwater connections in drums of water tube boilers, using hand tools.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail
Install manholes, handholes, taps, tubes, valves, gauges, or feedwater connections in drums of water tube boilers, using hand tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional skilled trade in small shops and on-site construction; the sector has low digital maturity, dispersed operations, and strong union/craft traditions. Adoption of advanced automation is lagging compared to information and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking is a physical, hands-on trade in construction/industrial settings with minimal digitization or AI adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While digital tools (e.g., AR guides, thermal imaging for verification) could assist boilermakers in some planning and inspection tasks, the hands-on installation work itself offers limited opportunity for meaningful AI augmentation. The core task remains highly dependent on manual skill and tactile feedback. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with reading schematics, planning sequences, or documentation, but offers little direct help with the physical installation task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of small components in confined spaces within boiler drums, complex spatial reasoning about correct orientation and seating, and judgment about proper tightness—capabilities far beyond current robotic or AI automation. Current systems cannot reliably handle the variability of boiler geometries, component fit, or quality assurance without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is precise physical installation work requiring manual dexterity, fitting, and hand tool use inside boiler drums; no current AI system can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Boiler installation and maintenance is heavily regulated by ASME codes and state/federal safety standards; licensure and certification requirements apply to boilermakers, and liability for failure is severe. Regulatory frameworks require qualified human sign-off on critical safety components, creating legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Boiler work often involves certified welding/fitting standards, safety codes, and inspection sign-offs, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Boilermaker labor costs are moderate ($30–50/hour loaded), but implementing robotic systems capable of this task would require custom engineering, integration, and ongoing maintenance costing orders of magnitude more per installation, making human labor far cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding skilled boilermaker labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product exists that can autonomously install these mechanical connections in boiler drums. The task requires dexterous manipulation in confined spaces, real-time error correction, and quality verification that current robotics and computer vision systems cannot reliably perform in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product installs boiler components like manholes, valves, or gauges in industrial boiler drums today. |
Install refractory bricks or other heat-resistant materials in fireboxes of pressure vessels.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Install refractory bricks or other heat-resistant materials in fireboxes of pressure vessels.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a traditional trades sector with low digitization, small-batch or on-site work, and strong licensing/union requirements. Adoption of advanced automation in this domain has been minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial trades have very low AI/robotic adoption for hands-on fabrication tasks like this, with automation limited mostly to inspection or planning support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision tools might assist in inspecting refractory condition or suggesting repair patterns, but the core manual skill of brick placement offers limited productivity gain from current AI assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with specifications, material selection guidance, or thermal modeling, but offers minimal help with the actual hands-on installation process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise, custom placement of heat-resistant materials in confined, irregular spaces with real-time adjustment based on fit and thermal properties. Current AI cannot handle the dexterity, spatial reasoning, and physical adaptation needed for reliable end-to-end execution. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy heat-resistant materials in confined, hazardous industrial spaces—no current AI system can perform this manual construction task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Boilermakers' work is heavily regulated by building codes, pressure vessel standards (ASME), and often union contracts requiring licensed personnel to perform or certify this safety-critical work. Legal and safety liability create strong barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pressure vessel work is subject to safety codes (ASME) and typically requires certified boilermakers, plus the physical/manual nature and liability for structural integrity create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of handling refractory material installation would be extremely expensive to acquire, program, and maintain compared to a trained boilermaker's labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the all-in cost of any automated alternative vastly exceeds skilled human labor for this specialized physical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system can reliably install refractory bricks in fireboxes. This requires skilled robotic manipulation in constrained, high-temperature environments—a capability that exists only in narrow lab settings, not in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product installs refractory brick linings in pressure vessel fireboxes; this remains a skilled manual trade task. |
Conduct pressure tests on vessels, such as boilers.
3CI 0–5 · exposure 5 · augmentation 25 · importance 4.1/5 · click for rater detail
Conduct pressure tests on vessels, such as boilers.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The boilermaking trade is small, heavily unionized, and operates in physically distributed settings with strong regulatory gatekeeping. No measurable industry shift toward autonomous pressure testing exists; adoption velocity is near-zero due to legal and safety constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking and industrial vessel inspection is a low-digitization, physical trade sector with minimal AI agent adoption for hands-on testing procedures. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with pre-test checklists, post-test data analysis, and documentation generation, but these are small fractions of the task. The core work—executing the test under pressure, watching for failures, and certifying the result—remains firmly human-driven and offers minimal augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI/software can assist with logging results, predictive maintenance scheduling, or analyzing sensor data trends, but offers minimal assistance to the actual physical execution of the pressure test. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Pressure testing boilers requires physical inspection, hands-on valve operation, sensor placement, and real-time response to safety-critical outcomes. Current AI systems cannot autonomously perform the physical manipulation, real-time monitoring, or judgment calls needed when anomalies appear during high-pressure testing. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of equipment, gauges, and valves on-site to pressurize vessels and observe for leaks/failures—no AI system can perform this physical, hands-on inspection task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Pressure testing of boilers is heavily regulated under ASME, DOT, and state codes; certified boilermakers must personally conduct tests and certify results. Liability for catastrophic failure is severe, and regulations explicitly require licensed human sign-off, making this a hard barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Pressure vessel testing is heavily regulated (e.g., ASME codes, OSHA), typically requiring certified inspectors/boilermakers to physically conduct and sign off on tests, creating hard legal and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any hypothetical system would require significant custom hardware integration, real-time sensors, safety certification, and continuous human oversight. The loaded cost of a boilermaker performing this task is far lower than the capital and integration expense required for autonomous testing systems. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical test itself, so AI cost is not comparable—human labor with specialized equipment remains the only means to perform this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze pressure data logs after testing and assist with documentation, no deployed product performs autonomous pressure testing of physical vessels. Some computer vision systems exist for post-test inspection, but live testing control and decision-making remain human-dependent in all production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical pressure testing of boilers; this remains a manual, instrument-based procedure conducted by certified technicians. |
Attach rigging and signal crane or hoist operators to lift heavy frame and plate sections or other parts into place.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Attach rigging and signal crane or hoist operators to lift heavy frame and plate sections or other parts into place.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and heavy manufacturing sectors where boilermakers work remain low in overall AI/automation adoption due to on-site variability, safety requirements, and the need for skilled trade expertise. Automation of this specific task is not occurring in measurable production deployments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy industrial trades are among the slowest sectors to adopt AI/robotics for physical tasks, with rigging remaining manual due to safety and variability concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI-assisted visualization or pre-rigging planning tools could marginally support a boilermaker, the core task of physical attachment and real-time signaling offers minimal opportunity for AI to augment a human worker's capability in a meaningful way. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensors, load monitoring systems, or camera-assisted visibility tools could marginally assist crane operators, but the core rigging and signaling task itself sees little AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physically attaching rigging hardware to heavy components and coordinating real-time communication with equipment operators in a dynamic, safety-critical environment. Current AI systems cannot perform physical manipulation or reliably manage the spatial reasoning and dynamic hazard assessment needed on a construction site. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical rigging and hand-signaling task requiring on-site manipulation of heavy equipment and real-time physical coordination; no current AI system can perform the physical attachment or spatial hoisting judgment involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | OSHA regulations, liability concerns around heavy-load handling, safety certification requirements, and the legal responsibility for proper rigging placement create hard regulatory and liability barriers that require a licensed or certified human to perform or oversee this task. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations (OSHA), certification requirements for riggers and signal persons, and severe liability for crane accidents create strong barriers to automation of this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of performing rigging and signaling would be far more expensive to acquire, maintain, and deploy than the loaded wage of a skilled boilermaker, making human labor more cost-effective. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based alternative (e.g., robotic rigging) would be vastly more expensive than a human boilermaker, if even feasible. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously perform rigging attachment or coordinate crane operations in real industrial settings. This requires embodied robotics and real-world perception capabilities far beyond current production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical rigging attachment or crane signaling in production; this remains firmly in the domain of human physical labor and trained trades workers. |
Repair or replace defective pressure vessel parts, such as safety valves or regulators, using torches, jacks, caulking hammers, power saws, threading dies, welding equipment, or metalworking machinery.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Repair or replace defective pressure vessel parts, such as safety valves or regulators, using torches, jacks, caulking hammers, power saws, threading dies, welding equipment, or metalworking machinery.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boilermaking is a physical, low-volume trade with aging workforce; sectors are not actively adopting robotic or AI-driven automation in production, remaining largely manual. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Boilermaking is a physical, low-digitization trade with minimal AI adoption in the core repair work itself; sector shows little movement toward automation of physical tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with diagnostics, documentation, or CAD modeling of repairs, but current systems offer minimal real-time assistance during the hands-on repair work itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, documentation, or procedure lookup, but offers minimal direct assistance to the physical repair process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment, precise spatial judgment in confined spaces, and real-time decision-making about structural integrity. Current AI systems cannot operate torches, jacks, welding equipment, or other metalworking machinery in the real world. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical repair work requiring manual dexterity, welding skill, and precise use of heavy tools on pressure vessels; no current AI system can perform physical manipulation of metal parts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Pressure vessel work is regulated by ASME Boiler and Pressure Vessel Code and similar standards; licensed, certified boilermakers must legally certify repairs, creating hard regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Pressure vessel repair is heavily regulated (ASME codes, welding certifications, safety inspections) and typically requires certified/licensed welders and inspectors to sign off on repairs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of pressure vessel repair are prohibitively expensive to acquire, integrate, and maintain compared to the loaded cost of a skilled boilermaker, making AI deployment economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical repair, so any AI cost comparison is moot; human labor is the only option and remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or autonomous system reliably performs pressure vessel repair at scale; specialized human expertise and hands-on control remain necessary for safety-critical applications. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical welding, torch-cutting, or mechanical repair of pressure vessel components autonomously; this remains purely manual skilled trade work. |
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.