Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters
47-3011.00Median wage $47,550/yr14,170 employed (US)Rank #833 of 923 scored · top 90% by substitution
Help brickmasons, blockmasons, stonemasons, or tile and marble setters 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
Substitution13
Exposure2
Augmentation16
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
15 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%3
panel mean rating 1.1/5 → substitution pressure 3/100
Technical feasibility todayw 20%1
panel mean rating 1.0/5 → substitution pressure 1/100
Cost vs. human wagew 15%1
panel mean rating 1.0/5 → substitution pressure 1/100
Adoption barriersw 20%inverted — strong barriers lower the score60
panel mean rating 2.6/5 (barrier strength) → substitution pressure 60/100
Sector adoption velocityw 10%1
panel mean rating 1.0/5 → substitution pressure 1/100
Task breakdown (15 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Mix mortar, plaster, and grout, manually or using machines, according to standard formulas.
27CI 19–35 · exposure 20 · augmentation 25 · importance 4.5/5 · click for rater detail
Mix mortar, plaster, and grout, manually or using machines, according to standard formulas.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Construction remains low-digitization with heavy reliance on manual labor and small-firm operators; while large contractors use some automated batching, penetration among the broader helper workforce is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and masonry trades show very low AI/robotics adoption, being physical, low-digitization, small-firm dominated work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with recipe recall, timing reminders, or basic consistency checks via mobile imaging, but the core sensorimotor task of adjusting mix texture and slump by hand remains human-led with limited AI value-add. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help calculate mix ratios or troubleshoot formulas via a mobile app, but offers little assistance for the core physical mixing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While mixing itself is mechanically straightforward and some automated batching systems exist, the task requires judgment about consistency, adjustment for ambient conditions, and real-time quality assessment—outcomes current AI cannot reliably achieve end-to-end in uncontrolled jobsite environments. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical mixing of mortar/plaster/grout requires manipulating materials, equipment, and judging consistency on-site, which current AI systems cannot perform end-to-end; only measurement/formula guidance could be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some unionized trades require humans to supervise or perform mixing, and quality verification is customarily done by experienced workers; however, mechanical automation is already deployed in industrial settings with fewer restrictions. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for mixing mortar, but physical site conditions, safety practices, and quality control create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated mixing equipment exists but requires significant capital investment, operator oversight, and maintenance; the total cost per batch is often comparable to or exceeds hiring a skilled helper, especially for small jobs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical mixing, so any AI cost comparison is moot—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial concrete/mortar batching plants use automated systems, but these are fixed installations with controlled inputs; no deployed mobile AI system reliably mixes to craft specifications on-site across variable conditions and material batches. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product mixes construction materials; this remains a physical labor task requiring robotics not yet in production for this trade. |
Arrange or store materials, machines, tools, or equipment.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail
Arrange or store materials, machines, tools, or equipment.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-automation, physical-task-heavy sector with fragmented, small-team work patterns. Adoption of AI or robotics for material handling is minimal and remains largely experimental. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the slowest sectors to adopt AI/robotics for physical labor tasks, with minimal production deployment of automated material handling on job sites. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist through inventory tracking and workflow optimization apps, but the core physical task of arranging and storing materials offers limited augmentation value; most benefit would be logistical planning rather than real-time task execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with inventory tracking or logistics planning software, but offers little direct augmentation for the physical act of arranging and storing materials on-site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Physical arrangement and storage of materials, machines, and tools in construction sites requires dexterous manipulation, spatial reasoning in unstructured environments, and real-time decision-making that current AI systems cannot perform end-to-end. Current robotics cannot reliably handle the variety, weight, fragility, and site-specific logistics involved. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical materials-handling and organizing task on a job site; current AI systems cannot physically arrange bricks, tools, or equipment without robotic embodiment, which is not deployed in this trade. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal licensing barriers, job-site safety standards, liability for damage to materials or equipment, and the need for human judgment about efficient on-site logistics create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier prevents automation, but organizational and physical-environment friction (uneven terrain, varied materials, coordination with skilled tradespeople) create practical adoption barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of manipulating and organizing construction materials would require expensive robotic hardware, infrastructure modification, and ongoing maintenance—far exceeding the wage cost of a helper performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robotic material handling in unstructured construction settings would require expensive custom robotics far exceeding the low wage cost of a helper performing this task manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems reliably perform general material arrangement and storage on construction sites autonomously. While some warehouse automation exists, it operates in controlled settings and cannot generalize to the variable, outdoor conditions of bricklaying work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product exists that autonomously arranges or stores masonry materials/tools on active construction sites; this remains far from deployable robotics territory for unstructured environments. |
Transport materials, tools, or machines to installation sites, manually or using conveyance equipment.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Transport materials, tools, or machines to installation sites, manually or using conveyance equipment.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-digitization, physical labor sector with slow AI adoption. Material transport via human helpers is deeply embedded in site practices, and autonomous alternatives see negligible real-world deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction is a notoriously low-digitization, physically-oriented sector with minimal AI/robotics adoption for on-site material transport. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Powered conveyance equipment (forklifts, wheelbarrows with assist) offers modest productivity gains, but AI-driven augmentation of human transport tasks is minimal given the mechanical and navigational nature of the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some assistance exists via powered conveyance equipment (already in use, not AI-driven) and route/logistics planning apps, but AI provides minimal direct productivity enhancement for the physical act of transporting materials. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical material handling and on-site navigation in unstructured environments. Current AI systems cannot operate mobile robots reliably at construction sites to independently transport materials, tools, or heavy equipment to specific installation locations. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring mobility, manipulation of heavy/awkward objects, and navigation of job sites, which current AI systems (software-based) cannot perform; robotics for this remains research-stage. No off-the-shelf AI system can substitute for the physical labor involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory barriers exist, but practical barriers include unstructured job-site environments, safety concerns around autonomous equipment near workers, and the need for real-time coordination with human crews. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this specific task, but practical barriers like uneven terrain, safety concerns, and lack of mature robotic solutions create real friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated solutions (mobile robots, conveyance systems) remain expensive to acquire, maintain, and deploy, with high per-task costs far exceeding the wage of a helper performing manual transport. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic solution capable of this would require expensive specialized hardware, site engineering, and supervision, far exceeding the cost of a human helper's wage for the same output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform autonomous material transport to construction sites at scale. While research robots exist, they lack the dexterity, environmental perception, and cost-effectiveness needed for practical construction job-site deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously transports construction materials and tools around active job sites at scale; existing warehouse/logistics robots operate in controlled, flat, structured environments unlike variable construction sites. |
Clean installation surfaces, equipment, tools, work sites, or storage areas, using water, chemical solutions, oxygen lances, or polishing machines.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Clean installation surfaces, equipment, tools, work sites, or storage areas, using water, chemical solutions, oxygen lances, or polishing machines.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and masonry are low-digitization, site-based sectors with limited robotics deployment. Adoption of autonomous cleaning at job sites remains minimal and confined to large commercial projects. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized sectors with minimal AI/robotics adoption for physical site tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a helper performing manual cleaning tasks. This task does not benefit from language models, vision systems, or agents in a way that would augment human productivity on-site. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for physically cleaning tools, surfaces, and work sites using chemicals or machinery. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of tools and equipment (water sprayers, chemical applicators, polishing machines) in variable, unstructured outdoor/site conditions with real-time sensory feedback. Current AI systems cannot perform physical cleaning autonomously at scale or with the dexterity and adaptability needed for construction sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning task requiring manual dexterity, mobile manipulation, and judgment across varied surfaces and tools; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are modest barriers: OSHA regulations govern chemical handling and tool safety, and site supervisors typically prefer human presence for safety compliance. However, there is no licensing requirement and no legal bar to automation if a robot were capable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but physical site access, safety protocols, and unstructured environments create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous cleaning robots capable of handling construction sites remain in research/expensive prototype phases. The cost per task would far exceed hiring a helper at minimum wage for the foreseeable future. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical labor task, so AI cost per task-equivalent is effectively infinite relative to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs construction site cleaning autonomously today. Robotics for cleaning exist in narrow, controlled domains (floors in warehouses), but not for the diverse surfaces, tools, and chemical handling required in masonry work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general construction-site cleaning with chemical solutions, oxygen lances, or polishing machines; robotics for this remains research-stage at best. |
Remove excess grout or residue from tile or brick joints, using sponges or trowels.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Remove excess grout or residue from tile or brick joints, using sponges or trowels.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-digitization, physically-intensive sector with fragmented firms and slow adoption of automation. AI-driven robotic task adoption in masonry helper roles is negligible in the industry today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the least digitized and slowest-adopting sectors for AI/robotic automation of manual physical tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a human performing this largely manual, hands-on task; augmentation would require autonomous or collaborative robots, which are not yet practical for this use case. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for this tactile, physical finishing task performed by hand with sponges or trowels. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials in outdoor/indoor construction environments with fine motor control and visual inspection. Current AI systems lack the embodied dexterity, real-time environmental adaptation, and spatial reasoning needed to autonomously use sponges or trowels on vertical or complex surfaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires fine physical manipulation, dexterity, and visual judgment of wet materials in unstructured environments, which is far beyond current AI/robotics capabilities deployed today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no hard licensing requirements for this helper-level task, physical presence on job sites, safety regulations, and integration into construction workflows create moderate organizational friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific task, but physical site conditions, variability of materials, and lack of automation infrastructure create practical friction rather than legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robotic system capable of this task would require expensive hardware (articulated arms, vision systems, environmental sensors) with high integration costs, far exceeding the loaded wage of a helper laborer on a construction site. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable automated substitute, so any AI/robotic solution would cost far more than a low-wage helper performing this task manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products can reliably perform this task end-to-end in production settings. This requires mobile manipulation and tactile feedback in unstructured environments, which remains at research stage for current robotics and AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs autonomous grout/residue removal from tile or brick joints; this remains a manual construction task with no robotic deployment. |
Apply grout between joints of bricks or tiles, using grouting trowels.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Apply grout between joints of bricks or tiles, using grouting trowels.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-digitization sector with limited automation adoption. Small firms and trade work lag in robotics investment; no measurable displacement of grout-application labor is evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical manual tasks, with minimal production deployment of automation for grouting or masonry work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered vision systems could potentially guide workers or detect joint defects, but current tools offer minimal assistance. The task is fundamentally manual and embodied, limiting augmentation upside. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no direct assistance to a worker physically applying grout with a trowel, as this is a manual, tactile task outside AI's current scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual dexterity, real-time tactile feedback, and adaptation to variable joint widths and material surfaces. Current robotic systems cannot reliably perform grouting with the quality and speed of trained workers in unstructured construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Grout application requires fine physical dexterity, force modulation, and real-world manipulation in unstructured environments, which current AI systems (including robotics) cannot perform end-to-end reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task occurs on construction sites with variable conditions, safety requirements, and inspection standards, but no strict licensing requirement mandates human performance. Organizational friction and quality liability are the main barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical, tactile nature of the work and variable job-site conditions create substantial practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems for grouting, if available, would cost far more than the loaded wage of a helper performing this task, given the relatively low skill premium and straightforward labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system for this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs grout application at the precision and consistency required for finished masonry or tile work. Experimental robotics exist but are not production-ready for typical job sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs grouting of bricks or tiles autonomously; this remains far outside current robotic manipulation capabilities in construction settings. |
Correct surface imperfections or fill chipped, cracked, or broken bricks or tiles, using fillers, adhesives, or grouting materials.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Correct surface imperfections or fill chipped, cracked, or broken bricks or tiles, using fillers, adhesives, or grouting materials.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction, especially small-scale surface repair work, remains among the slowest sectors to adopt automation. Adoption is confined to large commercial projects and is negligible for routine helper tasks like surface correction. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, slowest-adopting sectors for AI and robotics, with physical repair tasks seeing negligible automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with inspection—identifying defects via computer vision—but human judgment and dexterity remain essential for applying materials correctly. Augmentation is limited to defect detection rather than material application. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with identifying color-matched grout mixes or providing repair instructions via visual recognition apps, but this offers only marginal assistance to the hands-on repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three dimensions—applying fillers, adhesives, or grout to specific surface defects on bricks or tiles. Current AI and robotics cannot reliably perform fine hand-level construction work on varied, irregular surfaces in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual dexterity task requiring hands-on manipulation of fillers, adhesives, and grouting materials on irregular surfaces; no AI system can perform the physical work itself.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task is site-based, outdoor-prone, and involves physical presence on active construction sites. While there are no strict licensing requirements for helpers, safety regulations and site coordination requirements create modest friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for minor surface repair, but the physical, tactile, and judgment-based nature of matching materials and finishes creates practical barriers to remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of any precision material handling cost tens of thousands of dollars, far exceeding the loaded wage of a helper performing this task. The integration and setup costs are prohibitive relative to low-skill manual labor wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical repair, so the human remains the only cost-effective option; any robotic attempt would be far more expensive than a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs surface repair on bricks or tiles at construction scale. While robotic arms exist, none are in production use for this specific task of correcting surface imperfections with variable materials and geometries. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical repair of masonry or tile surfaces; this remains firmly in the domain of robotics research at best, not production systems. |
Modify material moving, mixing, grouting, grinding, polishing, or cleaning procedures, according to installation or material requirements.
13CI 10–15 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Modify material moving, mixing, grouting, grinding, polishing, or cleaning procedures, according to installation or material requirements.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction, especially skilled trades work, remains a low-adoption sector for AI and robotics. Physical, unstructured environments, low digitization, and craft-based decision-making mean adoption velocity is slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the least digitized sectors with minimal AI/robotics adoption for hands-on physical tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance in modifying grouting, grinding, polishing, or cleaning procedures on-site. Procedural guidance documents exist but do not augment the actual task execution meaningfully. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer minor assistance via reference guides or mix-ratio calculators, but it doesn't meaningfully enhance the hands-on physical adjustment process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires on-site physical manipulation, real-time adaptation to material properties and environmental conditions, and judgment calls about installation requirements that current AI cannot perform. No end-to-end automation is feasible with today's robotics or AI systems for the full range of masonry and tile work modifications. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity, on-site judgment about materials and conditions, and manual adaptation of procedures—far beyond current AI's physical and perceptual capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers for helpers themselves, the task occurs in regulated construction environments with safety and quality standards, and requires a human on-site to assess and respond to material and installation requirements in real time. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but physical presence, tactile judgment, and jobsite variability create practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Given the specialized physical skills, equipment, and on-site presence required, any hypothetical automation system would be far more expensive than paying a skilled helper, making the cost ratio unfavorable for AI deployment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical automation (e.g., robotics) would be far more costly than a human helper today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform adaptive modification of masonry/tile procedures in real construction environments. This task sits at the intersection of physical robotics, material science judgment, and dynamic environmental adaptation—none of which have production-grade AI solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical material handling, mixing, grinding, or polishing adaptation in construction settings; this remains firmly in the domain of human labor and robotics research. |
Apply caulk, sealants, or other agents to installed surfaces.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.6/5 · click for rater detail
Apply caulk, sealants, or other agents to installed surfaces.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a low-digitization, physical-site-dependent sector where on-site caulking and sealing are performed by human workers; no evidence of meaningful AI or robotic adoption for this specific task in mainstream practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics, with physical, unstructured environments and low digitization limiting any meaningful automation uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a worker applying caulk or sealants; the task is fundamentally hands-on and does not benefit from typical AI productivity tools like document analysis, pattern matching, or information retrieval. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for a manual, tactile task like applying caulk or sealant; there's no software layer that meaningfully improves this hands-on process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Caulking and sealing installed surfaces requires precise spatial navigation, fine motor control, and real-time adaptation to surface irregularities. Current AI systems cannot reliably operate robotic arms on construction sites to apply sealants with the quality and speed comparable to human workers, and end-to-end automation is not deployable today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation, dexterity, and situational judgment on-site (uneven surfaces, material properties) that no general AI system can perform end-to-end today; it needs robotic embodiment, not just software. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no strict licensing requirement for applying caulk as a helper-level task, general contractor oversight, quality standards, and the physical site context create moderate friction to full automation; customer expectations and liability for poor sealing also provide some protection. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for caulking, but physical site conditions, safety practices, and quality standards on finished surfaces create practical friction against any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of caulking would require significant hardware investment and integration costs far exceeding the wage of a helper working on this single task, making the all-in cost multiple times that of human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic system exists to compare cost against; a human helper's wage is far cheaper than any hypothetical bespoke robotic solution for this variable, low-volume task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform caulking and sealing on active job sites. While research exists into robotic sealing, no commercial systems demonstrate production-ready performance at construction scale or cost-effectiveness. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products performing caulking or sealant application autonomously in construction settings; this remains a manual trade task with no robotics-as-a-service equivalent in general use. |
Remove damaged tile, brick, or mortar, and clean or prepare surfaces, using pliers, hammers, chisels, drills, wire brushes, or metal wire anchors.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.5/5 · click for rater detail
Remove damaged tile, brick, or mortar, and clean or prepare surfaces, using pliers, hammers, chisels, drills, wire brushes, or metal wire anchors.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction helpers work in highly physical, on-site, low-digitization environments where equipment is specialized and labor remains cheaper than automation; AI and robotic adoption in this sector remains nascent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, slowest-adopting sectors for AI/robotics, especially for demolition and manual site prep work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance for identifying damaged materials or guiding tool use in real-time on physical tasks; computer vision or planning tools remain too immature to augment this workflow. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful in-task assistance for physically removing damaged materials or preparing surfaces with hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires fine motor control, tactile feedback, and real-time decision-making in physical space to identify and remove damaged materials without harming surrounding structures—capabilities that current AI systems cannot perform end-to-end in unstructured construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical demolition and surface-preparation task requiring manual dexterity, force application, and situational judgment about material condition; no AI system can perform this end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety requirements, job-site variability, and lack of regulatory mandate for automation create some friction, though no formal licensing or legal requirement for a human to perform this specific task exists. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this subtask, but physical robotics for demolition/surface prep face high organizational, safety, and capital barriers to deployment in construction settings. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of any part of this task (if they exist in production) are orders of magnitude more expensive than the loaded hourly wage of a construction helper, and still require significant human supervision and rework. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation for this physical task, so AI cost is not comparable — human labor with hand tools remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI systems reliably perform selective demolition, damage assessment, and surface preparation on job sites at production scale; this remains research-stage or limited to highly structured, controlled settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical tile/brick removal or surface prep; this remains firmly in the domain of human manual labor with hand tools. |
Cut materials to specified sizes for installation, using power saws or tile cutters.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Cut materials to specified sizes for installation, using power saws or tile cutters.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction, particularly masonry helpers, remains a low-automation, physical-labor-intensive sector with minimal AI/robotic adoption. The distributed, small-team nature of job sites and the capital requirements for robotic solutions limit adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, slowest-adopting sectors for AI and robotics, with physical on-site tasks like material cutting seeing minimal automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically assist with layout planning or cutting specifications, the core task of physically operating power tools offers minimal augmentation opportunity; human workers continue to perform the task with traditional power tools largely unchanged. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with measurement calculations, cut-list optimization, or layout planning software, but it offers little direct assistance to the physical act of operating a saw or tile cutter. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cutting materials to specified sizes requires physical manipulation in real-world environments with variable material properties, precise positioning, and safety-critical operation of power tools. Current AI cannot reliably perform the end-to-end physical task of measuring, positioning, and operating equipment on a job site. |
| Task automatability | claude-sonnet-5 | 1/5 | Cutting tile, brick, or stone to precise dimensions requires physical manipulation, measurement, and dexterous handling of materials and power tools on-site, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Operation of power saws and tile cutters involves inherent safety hazards and liability exposure; regulatory safety standards and worker-protection requirements create friction against full automation. Additionally, physical job-site conditions and material variability require human oversight and judgment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this cutting task to certified humans, but physical site conditions, safety requirements around power tools, and variable material specs create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of material cutting would require significant capital investment, installation, and maintenance costs that far exceed the loaded wage of helper-level workers, making the cost ratio unfavorable for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic system for this task in typical job-site conditions, so any hypothetical automation would require expensive custom robotics far costlier than a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task autonomously and reliably in construction settings. While robotic systems exist in some industrial contexts, they are not general-purpose solutions adapted to the varied and dynamic conditions of bricklaying and stonework job sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously operates power saws or tile cutters to cut construction materials; this remains a manual physical trade skill with only nascent robotic cutting research in controlled factory settings. |
Provide assistance in the preparation, installation, repair, or rebuilding of tile, brick, or stone surfaces.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Provide assistance in the preparation, installation, repair, or rebuilding of tile, brick, or stone surfaces.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and masonry trades remain low-digitization sectors with strong preference for skilled human labor, site-specific judgment, and proven craftsmanship. Automation adoption in helper roles is negligible despite labor shortages. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the least digitized and slowest to adopt AI/robotics due to physical, unstructured work environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI tools provide minimal assistance to helpers preparing or installing tile and stone—there are no mainstream AI applications that measurably increase helper productivity on these physical, spatially embedded tasks. Limited potential for augmentation beyond basic project planning. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, material estimation, or instructional guidance via apps, but offers minimal help with the hands-on physical execution of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials in 3D space, precise spatial positioning, and real-time adaptation to site conditions—capabilities that current AI systems fundamentally lack. No off-the-shelf system can autonomously prepare, install, or repair tile, brick, or stone surfaces at equal quality with 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task requiring dexterity, material handling, and spatial judgment on-site; no current AI system or robot can autonomously prepare, install, or repair tile/brick/stone surfaces. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist due to the requirement for licensed masons to oversee and ultimately certify work quality; safety liability for defective installations; building codes and inspections; and the need for on-site human presence and adaptation. Substitution is legally and practically constrained. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically blocks automation, but physical environment variability, safety concerns, and lack of mature robotic hardware create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current specialized robotic systems for masonry or tiling are extremely expensive to acquire, program, and maintain compared to hiring skilled or semi-skilled helpers. The all-in cost per task substantially exceeds loaded human wages for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so any hypothetical robotic system would be far more costly than a human helper's wage today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs physical masonry, tiling, or stonework installation at production scale. While robotics research exists in this space, no mature commercial system reliably executes the full range of preparatory and installation tasks in real construction environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical masonry helper work; robotics in construction remain research/pilot stage for narrow bricklaying tasks, not general helper duties. |
Locate and supply materials to masons for installation, following drawings or numbered sequences.
7CI 0–15 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Locate and supply materials to masons for installation, following drawings or numbered sequences.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains one of the least digitized and most physically-grounded sectors. Material supply on active job sites shows negligible AI adoption and remains heavily manual and on-site. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical tasks, with minimal automation of on-site material handling in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Basic AI could assist marginally via digital inventory systems or workflow optimization software to plan supply sequences, but this addresses only planning, not the core physical task of locating and delivering materials on-site. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with logistics planning or reading drawings/numbered sequences to guide material staging, but it offers little direct assistance to the physical fetch-and-carry component of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence on a construction site, spatial reasoning about material placement, and dynamic responsiveness to mason requests. Current AI systems cannot physically locate, handle, or transport materials to workers in real-world building environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical navigation of a job site, locating and moving heavy materials, and coordinating with a mason in real time—no off-the-shelf AI system can perform this physical logistics task end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | The task is intrinsically human-contact dependent—a mason cannot work effectively without someone physically present to supply materials. Liability and safety regulations on construction sites also require direct human accountability for material handling and site coordination. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but practical barriers are high due to physical site variability and safety considerations around handling materials near workers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no capability to perform this task, making cost comparison moot; human labor remains the only viable option and thus is cheaper than any attempted AI solution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this task at any cost point comparable to a low-wage helper; equipment for such physical material handling would be far more expensive than the human wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs end-to-end material location and supply on active construction sites. This is fundamentally a physical coordination task that requires embodied presence and real-time adaptation to site conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product reliably locates and delivers masonry materials on construction sites; this remains far from production reality, requiring dexterous manipulation and unstructured environment navigation. |
Move or position materials such as marble slabs, using cranes, hoists, or dollies.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Move or position materials such as marble slabs, using cranes, hoists, or dollies.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and masonry remain low-digitization, on-site sectors with limited automation adoption; material-handling automation is not yet demonstrably deployed in this occupational context at scale. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical labor tasks, with minimal production deployment of automated material handling on job sites. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted positioning via computer vision or path optimization for crane operators could offer modest assistance, but the task is already equipment-mediated and does not lend itself to significant productivity augmentation through AI. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled sensors or crane-assist systems can provide minor guidance (e.g., load balancing alerts), but they offer limited productivity transformation for this specific manual positioning task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy materials in unstructured outdoor/construction environments with spatial reasoning, safety awareness, and real-time hazard detection. Current AI lacks embodied robotics at the scale and dexterity needed for reliable end-to-end automation of marble handling. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring manipulation of heavy, fragile slabs in variable job-site conditions; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, licensing requirements for crane operation, liability for equipment damage and worker injury, and OSHA oversight create substantial legal and organizational barriers to unsupervised automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety regulations (OSHA equipment operation rules) and site-specific physical constraints create moderate friction, though no formal licensing is strictly required for a helper role. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized heavy-duty construction robots capable of autonomous marble handling remain far more expensive than paying a helper, especially when factoring in integration, site setup, and liability costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic or automated crane/hoist solution capable of this task would require far higher capital and integration costs than simply paying a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While material-handling robots exist in controlled warehouse settings, no deployed products reliably perform crane/hoist operation and marble positioning in typical construction job sites with the variability and safety requirements this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product moves or positions marble slabs on construction sites; robotic material handling for such irregular, heavy, fragile items remains research-stage or limited to controlled factory settings. |
Erect scaffolding or other installation structures.
5CI 0–10 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Erect scaffolding or other installation structures.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The construction sector remains low in digital adoption for physical labor tasks, and no meaningful production deployment of scaffolding automation exists in the industry. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction is a low-digitization, physically intensive sector with minimal AI/robotics adoption for manual erection tasks, lagging far behind information-sector automation trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI/AR tools could assist with planning and visualization of scaffolding layouts or safety compliance checking, but the core physical erection task offers limited augmentation opportunities because human judgment and hands-on control remain essential. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for the physical act of erecting scaffolding; any planning software support is tangential to the task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Erecting scaffolding requires physical manipulation of heavy materials in unpredictable outdoor environments, spatial reasoning about site-specific constraints, and real-time safety assessment. Current AI and robotics cannot reliably perform this complex physical task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Erecting scaffolding requires physical manipulation of heavy materials in variable environments, which is beyond current AI capabilities without embodied robotics that don't exist at commercial scale for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Scaffolding erection is heavily regulated by OSHA and similar bodies; a licensed, trained human must design, supervise, and certify structural safety. Legal liability for failure creates hard barriers to autonomous automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing is required for this specific task, safety regulations (OSHA scaffolding standards) and physical risk create real barriers to any automated substitution, though not a hard legal requirement for human performance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics systems capable of this task (if they existed) would require significant capital investment, site-specific setup, and specialized hardware far exceeding the loaded wage of a skilled laborer performing the work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven substitute for this physical labor, so the human remains the only cost-effective option; robotic alternatives would be far more expensive than a helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably erects scaffolding autonomously. While some robotic arms exist in controlled lab settings, production systems for general-purpose scaffolding erection at job sites do not exist at any meaningful scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously erects scaffolding on construction sites; this remains a purely physical, manual task performed by human workers. |
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.