Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons
47-3014.00Median wage $40,470/yr7,490 employed (US)Rank #807 of 923 scored · top 87% by substitution
Help painters, paperhangers, plasterers, or stucco masons 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
Substitution14
Exposure1
Augmentation4
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
11 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%2
panel mean rating 1.1/5 → substitution pressure 2/100
Technical feasibility todayw 20%0
panel mean rating 1.0/5 → substitution pressure 0/100
Cost vs. human wagew 15%0
panel mean rating 1.0/5 → substitution pressure 0/100
Adoption barriersw 20%inverted — strong barriers lower the score68
panel mean rating 2.3/5 (barrier strength) → substitution pressure 68/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (11 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.
Clean work areas and equipment.
19CI 15–24 · exposure 8 · augmentation 0 · importance 4.1/5 · click for rater detail
Clean work areas and equipment.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-digitization, highly physical sector where small firms predominate; autonomous cleaning robots see minimal production deployment in painting/drywall trades, reflecting both technical immaturity and lack of economic pressure relative to low-wage manual labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and physical trades are among the slowest sectors to adopt AI/robotics for manual labor tasks like site cleanup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Cleaning is a largely manual, low-skill task with limited cognitive components; current AI tools offer no meaningful productivity enhancement for a worker physically cleaning equipment and sites. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for physical cleaning of tools and work areas, as this is a hands-on manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical cleaning of diverse work areas and equipment requires dexterous manipulation, obstacle navigation, and adaptability to variable conditions—capabilities that current robotics cannot reliably perform end-to-end. While partial automation of repetitive cleaning is possible in controlled environments, the mixed indoor/outdoor construction settings and varied equipment types make widespread 50%-time-saving automation infeasible with today's systems. |
| Task automatability | claude-sonnet-5 | 1/5 | Cleaning physical work areas, tools, and equipment involves manual dexterity, mobility, and physical manipulation of objects in unstructured environments that current AI systems cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No strict licensing requirement exists for basic cleaning tasks, but practical barriers include safety liability (robots in active construction zones), variable site conditions, and organizational reliance on workers already present to perform cleanup as part of job workflow. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform cleanup, but physical unpredictability of jobsites creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic cleaning systems that could handle outdoor construction sites are capital-intensive and require significant setup/oversight, making their per-task cost substantially higher than paying a laborer to clean during normal workday routines. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this physical task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of low-wage helper labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform general construction site cleaning autonomously in production environments. Specialized cleaning robots exist for narrow domains (e.g., floor buffing in controlled spaces), but none address the full scope of painter/plasterer worksite cleanup at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general jobsite cleanup of painting/plastering equipment and debris; robotic cleaning is limited to narrow, structured tasks like floor vacuuming, not construction-site cleanup. |
Remove articles such as cabinets, metal furniture, and paint containers from stripping tanks after prescribed periods of time.
19CI 15–24 · exposure 8 · augmentation 0 · importance 2.9/5 · click for rater detail
Remove articles such as cabinets, metal furniture, and paint containers from stripping tanks after prescribed periods of time.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in small, local paint and finishing shops with low digitization, limited capital budgets, and minimal technology adoption patterns. Sector-wide automation velocity is very slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades helper occupations show minimal AI/robotics adoption for physical manual tasks, remaining a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful AI assistance applicable to physically removing items from tanks; monitoring timers could be digitized but offers negligible productivity gain for a simple manual operation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of removing objects from stripping tanks. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | The task involves physical manipulation of items from tanks after timing, which requires robotic arms or mobile systems not yet economically deployed in this setting. While timing can be automated, the unstructured removal of varied objects from liquid tanks with precise handling remains difficult for current general-purpose systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring lifting and handling items from chemical stripping tanks; no current AI system can perform this physical manipulation.6 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Safety requirements around chemical exposure and proper handling of potentially hazardous tanks introduce some friction, but no hard legal licensing requirement exists for this helper-level task, and organizational adoption barriers are modest. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, but handling chemical stripping tanks may involve safety/hazmat protocols that add some procedural friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics systems capable of safely removing items from corrosive stripping tanks would cost tens of thousands of dollars in capital and integration, far exceeding the annual wage of a helper performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed for this task, so AI cost is effectively infinite relative to a low-wage helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous removal of mixed items from chemical stripping tanks in production paint shops. Specialized tank-clearing robots exist only in research or niche industrial contexts, not at the scale of helper operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs tank-stripping removal in painting/plastering trades; this remains a manual labor task. |
Perform support duties to assist painters, paperhangers, plasterers, or masons.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Perform support duties to assist painters, paperhangers, plasterers, or masons.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt automation; they remain labor-intensive, geographically dispersed, and highly variable. Current AI/robotics adoption in this space is minimal and experimental. |
| 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 AI integration in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a helper performing physical support duties like material preparation, tool positioning, or site organization. The task is inherently manual and coordination-based rather than information-intensive. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a helper performing physical support tasks like carrying materials, cleaning, or basic setup work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Support duties for skilled trades (mixing materials, holding tools, moving equipment, site prep) require physical manipulation in unstructured environments—tasks current AI cannot perform reliably. The role is fundamentally assistive to human craftspeople and involves real-time coordination on job sites where automation would need full robotic capability. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, general-labor support task (moving materials, setting up scaffolding, cleaning surfaces, mixing compounds) that requires manipulation of physical objects in unstructured environments, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no hard legal or licensing barriers to automating helper roles, the physical nature of the work, coordination requirements with licensed tradespeople, and job-site variability create moderate friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing is typically required for helper roles, but the physical nature of the work and need for on-site coordination with skilled tradespeople create practical barriers to any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Helper labor is low-wage ($15–18/hr in many markets), while deploying robotics or autonomous systems to replace physical job-site support would require significant capital investment far exceeding the hourly wage, making AI far more expensive all-in. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for a physical helper, so any comparison would require expensive robotics hardware plus supervision, making AI far more costly than a low-wage human helper today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the physical tasks inherent in on-site support work for painters, plasterers, or masons. This is not yet a solved problem in robotics or AI systems in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical helper duties on construction/renovation sites; this remains firmly in the domain of human physical labor and robotics research at best. |
Apply protective coverings, such as masking tape, to articles or areas that could be damaged or stained by work processes.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.9/5 · click for rater detail
Apply protective coverings, such as masking tape, to articles or areas that could be damaged or stained by work processes.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades remain among the slowest sectors for AI/robotic adoption; no meaningful automation is occurring for helper-level taping tasks in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and physical trades are among the slowest sectors to adopt AI, especially for manual prep work, with virtually no automation deployment for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for a purely physical manual task; there is no information-processing, decision, or drafting component that an AI system could augment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful assistance for the physical act of applying masking tape or protective coverings, as this is a purely manual task with no digital/cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Applying masking tape and protective coverings requires fine motor coordination, spatial reasoning about edges and contours, and judgment about which areas need protection—capabilities that current robotic systems lack in unstructured physical environments like job sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on task requiring manipulation of tape and coverings across irregular surfaces in variable job-site conditions; no off-the-shelf AI system performs this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This is an unskilled task with no licensure or regulatory requirement, and it involves no sensitive decisions, but the physical complexity and site variability create practical deployment barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but practical barriers are high due to the need for physical dexterity and adaptability to varied job sites, though this reflects capability gaps rather than regulatory protection. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A robot or robotic system capable of adaptive taping would cost tens of thousands of dollars and require extensive integration, far exceeding the hourly wage of a helper performing this straightforward manual task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic solution for this task at any cost comparable to a low-wage helper, so AI is effectively far more expensive or infeasible. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task autonomously on real job sites with variable geometries, materials, and surface conditions. Robotic systems for taping exist only in narrow lab or manufacturing contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical masking/covering tasks in construction/painting settings; this remains firmly in the domain of human manual labor and robotics research at best. |
Smooth surfaces of articles to be painted, using sanding and buffing tools and equipment.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.8/5 · click for rater detail
Smooth surfaces of articles to be painted, using sanding and buffing tools and equipment.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Painting and surface preparation remain dominated by small, geographically distributed trades with low capital budgets and high variability. Digitization and automation adoption in this sector lag information and finance sectors significantly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and painting trades are low-digitization, physical-labor sectors with minimal AI/robotics adoption for manual surface prep tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-guided power tools or computer vision feedback systems could assist in detecting missed spots or optimizing sanding patterns, but the core task remains inherently tactile and manually executed. Practical augmentation tools remain limited today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a human physically sanding and buffing surfaces; this is a manual, tactile task outside current AI's assistive scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual dexterity, real-time tactile feedback to detect surface imperfections, and adaptation to variable surface geometries and material properties. Current AI robots lack the manipulation finesse and sensory integration to reliably smooth surfaces to painting standards without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical task requiring hand-held sanding and buffing tools on varied surfaces; no current AI system can perform this end-to-end without robotic embodiment, which is not off-the-shelf available. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This is manual, on-site work with minimal licensing barriers, but high organizational friction exists around equipment transport, jobsite variability, and customer expectations for human-performed finishing work. No hard legal requirement for human labor, but adoption faces practical constraints. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical dexterity, variable surfaces, and workspace conditions create practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic sanding equipment, integration, and safety systems are capital-intensive and require significant setup per site. The loaded cost per task remains well above unskilled manual labor wages, especially accounting for customization and downtime. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation solution for this physical task, so any hypothetical robotic system would be far more expensive than a low-wage helper performing manual sanding. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic systems currently perform autonomous surface smoothing for painting at production scale. While research robots demonstrate isolated sanding, they lack the reliability, speed, and generalization needed for real jobsites with varied substrates and geometries. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or industrial product performs general-purpose surface sanding/buffing prep for painting; robotic sanding exists only in narrow, fixed industrial contexts, not for varied helper tasks. |
Mix plaster, and carry plaster to plasterers.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Mix plaster, and carry plaster to plasterers.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction helpers remain in laggard sectors with low automation; most plastering work is performed by small trades firms with limited digitization and capital for robotics. |
| 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 virtually no production deployment for tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Mixing and carrying plaster offers minimal opportunity for AI assistance; the task is mechanical and does not benefit meaningfully from AI-generated insights or recommendations. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical acts of mixing and carrying plaster; there is no software layer that meaningfully augments this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Mixing plaster and carrying it requires physical manipulation in real-world conditions, precise material preparation, and adaptive handling across unstructured job sites—capabilities far beyond current AI systems. No end-to-end automation or 50% time saving is achievable with off-the-shelf technology today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling and mixing task requiring mobility, strength, and coordination that no current AI system (software or generally available robotics) can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical construction work has low regulatory barriers to automation itself, but on-site coordination, safety requirements, and the practical need for adaptive physical labor create modest friction to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical site variability, uneven surfaces, and lack of mature mobile manipulation robotics create strong practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated plaster mixing and transport would require specialized robotics with end-to-end integration, which is far more expensive than a helper's loaded wage; current systems cannot cost-effectively replace this labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute being deployed, so the human laborer remains the only cost-effective option; any robotic equivalent would be far more expensive than a helper's wage today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI systems reliably perform plaster mixing and transport in construction environments at production scale. This remains a manual labor task with no commercial automation products in real use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs plaster mixing and carrying on construction sites; this remains entirely a manual labor task in practice. |
Supply or hold tools and materials.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.5/5 · click for rater detail
Supply or hold tools and materials.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and trades sectors show low AI adoption overall, and this specific task—requiring embodied robotics on job sites—has virtually no production deployment in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades are among the least digitized, lowest AI-adoption sectors, with physical helper tasks seeing essentially no AI/robotic deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully augment a helper's ability to supply or hold tools and materials, as the task is primarily physical manipulation without analytical or information-processing components. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a human performing this physical handing/holding task on a job site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence on a job site to hand tools and materials to workers in real time, which current AI systems cannot perform. No meaningful automation of the core physical supply and holding function exists today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically handing tools and materials to a tradesperson requires embodied manipulation and mobility in unstructured environments, which current AI systems cannot perform.rating reflects no software-only automation potential. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers, the physical and spatial constraints of construction sites, unpredictable tool layouts, and the need for real-time responsiveness create moderate practical friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but physical dexterity, mobility, and situational awareness needs create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a mobile robotic system capable of this task would far exceed the loaded wage of a helper, making AI substantially more expensive than human labor for this application. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute deployable at any reasonable cost for this physical task, making a human helper the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably supply or hold tools and materials on construction sites. This is fundamentally a physical task requiring embodied robotics at scale, which does not exist in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotics product reliably performs general tool-supplying and holding for construction trades in real job sites today; this remains beyond current robotics capability outside narrow research demos. |
Place articles to be stripped into stripping tanks.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.0/5 · click for rater detail
Place articles to be stripped into stripping tanks.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The painting and plastering trades are low-digitization, physically distributed sectors with limited automation adoption; task-specific robotics for helper-level work sees negligible deployment in these laggard industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades support occupations are among the least digitized and slowest to adopt AI/robotics for physical manual tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful AI system that assists a human in placing articles into stripping tanks; the task is purely manual and offers no surface for computational augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this simple physical placement task; there is no cognitive or drafting component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of objects in 3D space and placement into tanks, which demands dexterous robotic systems and spatial reasoning that current AI/robotics cannot reliably accomplish at cost-competitive levels for this low-skill task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring picking up and placing objects into chemical tanks; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This task has minimal legal or regulatory barriers to automation, but it remains labor-intensive and embedded in small-to-medium-scale operations with limited capital investment in automation infrastructure. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but handling stripping chemicals involves safety/liability considerations and workplace physical constraints that create some friction for automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of handling physical placement tasks remain significantly more expensive than the loaded wage of a helper in this domain, with integration and maintenance costs making them uneconomical. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic automation for this task would require expensive custom engineering, far exceeding the cost of a low-wage helper performing the manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system performs this specific task reliably in production; general-purpose robotic systems capable of object handling exist but are not economically deployed for this application in commercial settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists for this narrow physical task; it would require custom robotics, which is not commercially available for this use case. |
Fill cracks or breaks in surfaces of plaster articles or areas with putty or epoxy compounds.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Fill cracks or breaks in surfaces of plaster articles or areas with putty or epoxy compounds.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and skilled trades remain among the slowest sectors to adopt automation; small painting crews dominate this work and show minimal robotic adoption. The fragmented, project-based nature of the industry and low margins on helper tasks limit investment in automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics due to physical variability, low digitization, and fragmented small-business structure. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI/robotic systems offer minimal assistance to a human performing this task; computer vision could theoretically guide crack detection, but no mainstream tool meaningfully augments a helper's productivity in putty application itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with estimating materials needed or diagnosing crack causes via image analysis, but offers minimal help with the actual hands-on filling and smoothing work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Filling cracks with putty or epoxy requires precise manual dexterity, three-dimensional spatial judgment, and tactile feedback that current AI systems cannot perform end-to-end. Robots exist for some construction tasks but are not deployed at scale for fine crack-filling work, which demands real-time adaptation to surface irregularities. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of tools and materials to detect, fill, and smooth cracks—no off-the-shelf AI system can perform this physical manual labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally licensed, the task is embedded in manual trades with modest organizational friction; many small painting contractors lack infrastructure for robotic integration. Customer expectations for hand-finished work and the need for on-site adaptation provide moderate adoption friction, though no hard legal barriers exist. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, but physical dexterity, judgment on material consistency, and jobsite variability create practical barriers to automation with current robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics systems capable of fine putty or epoxy application remain prohibitively expensive compared to the hourly wage of a helper ($15–25/hr), especially when accounting for setup, training, and oversight costs. Capital investment far exceeds labor savings for this low-cost task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic solution for this task at any comparable cost; human labor remains the only practical option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task autonomously in production settings. While research robots can manipulate materials, they lack the sensorimotor precision and environmental adaptability required for consistent quality crack-filling across varied plaster surfaces. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical patching/filling task in real work settings; it remains a purely manual construction/trade activity. |
Erect scaffolding.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Erect scaffolding.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-automation sector with limited digital infrastructure, and physical on-site tasks like scaffolding have seen minimal AI adoption compared to information-work sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and manual trades are among the slowest sectors to adopt AI/robotics for physical tasks, with minimal automation deployed for scaffolding work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance to workers performing physical scaffolding erection; the task is primarily manual and spatial reasoning that remains outside AI's productive scope. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer negligible assistance for the physical act of erecting scaffolding, though planning software may help elsewhere in the job. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Erecting scaffolding requires physical manipulation in three-dimensional space with significant safety implications and site-specific adaptation. Current AI systems cannot operate robotic arms or coordinate multi-step physical assembly tasks reliably in real construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Erecting scaffolding is a physical, manual construction task requiring strength, spatial judgment, and manipulation of heavy metal components; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Scaffolding erection is subject to OSHA and other regulatory safety requirements where a qualified, licensed human must perform and sign off on the installation, creating hard legal and liability barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no licensing law mandates a human specifically for scaffold erection, OSHA safety regulations, competent-person inspection requirements, and liability for structural failure impose meaningful oversight and safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of scaffolding assembly are prohibitively expensive per-unit and require extensive site setup and integration costs that far exceed the loaded wage of skilled scaffold workers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so the effective AI cost is undefined/infinite relative to a human laborer's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs on-site scaffolding erection autonomously. This remains firmly in the domain of human skilled labor with no production-scale AI systems performing this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously erects scaffolding; this remains squarely a human physical labor task with no robotic automation in commercial use. |
Pour specified amounts of chemical solutions into stripping tanks.
7CI 5–10 · exposure 0 · augmentation 13 · importance 2.6/5 · click for rater detail
Pour specified amounts of chemical solutions into stripping tanks.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in construction and trades sectors with low automation adoption rates, fragmented small-firm structures, and physical-site constraints that inhibit capital investment in specialized robotics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades and manual helper roles are among the least digitized, slowest-adopting sectors for AI or robotic automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist via tank inventory tracking, chemical-mixing calculations, or safety checklist reminders, but the core physical pouring task offers limited room for human-AI co-productivity enhancement. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of measuring and pouring chemicals into tanks, though it could conceivably provide instructions or safety data, which is marginal to this specific physical task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Pouring chemicals into tanks requires precise physical manipulation, environmental sensing, and real-time safety judgment in a physical workspace. Current AI systems lack the embodied capabilities, dexterous manipulation, and hazard-response autonomy needed to reliably perform this chemical handling task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring hands to measure and pour chemicals into tanks; current AI systems (software/LLMs) cannot physically perform this, and robotic solutions for this narrow task are not off-the-shelf deployed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chemical handling is subject to OSHA and EPA regulations requiring proper training, certification, and documented compliance; liability for spills or exposure is severe; and the human worker's sign-off on quantities and safety is legally expected. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Handling chemical solutions often involves safety protocols and sometimes hazmat handling guidelines, creating moderate liability and safety-compliance friction, though not a licensure requirement specifically. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of autonomous chemical handling, safety compliance, and tank operation would cost orders of magnitude more than the loaded wage of a helper, with significant integration and maintenance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute readily available for this task, so any hypothetical automation would require custom hardware integration exceeding the cost of a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform autonomous chemical pouring and tank management in unstructured job-site environments. Robotic systems capable of this exist only in controlled lab or industrial settings, not in the variable field conditions where painters and helpers work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specific physical chemical-pouring task in construction/painting settings; any robotic dosing systems are industrial/lab-specific, not adapted to this trade context. |
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.