Plasterers and Stucco Masons
47-2161.00Median wage $57,660/yr19,310 employed (US)Rank #875 of 923 scored · top 95% by substitution
Apply interior or exterior plaster, cement, stucco, or similar materials. May also set ornamental plaster.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution11
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%2
panel mean rating 1.1/5 → substitution pressure 2/100
Technical feasibility todayw 20%2
panel mean rating 1.1/5 → substitution pressure 2/100
Cost vs. human wagew 15%2
panel mean rating 1.1/5 → substitution pressure 2/100
Adoption barriersw 20%inverted — strong barriers lower the score46
panel mean rating 3.2/5 (barrier strength) → substitution pressure 46/100
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.
Determine materials needed to complete the job and place orders accordingly.
34CI 28–40 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail
Determine materials needed to complete the job and place orders accordingly.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plastering and stucco masonry remains a fragmented, small-firm dominated sector with low digitization. Adoption of automated procurement or AI-assisted material planning is minimal; most firms use simple manual methods or basic spreadsheets for this task. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Construction trades are historically slow AI adopters; small plastering/stucco businesses rarely use advanced estimating AI, relying on experience-based rules of thumb. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could meaningfully assist by auto-calculating material quantities from uploaded job plans or prior projects, generating preliminary estimates, or suggesting material options based on specifications. This would reduce manual calculation work while the plasterer retains final judgment on materials and ordering decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Estimating apps and spreadsheets/AI calculators can help masons quickly compute material quantities and generate purchase orders, improving speed and accuracy over manual calculation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Determining materials requires understanding job scope, specifications, and local material availability—tasks with significant human judgment and site-specific variables. While AI could assist in generating material lists from plans, the need to verify quantities against actual site conditions, account for waste, and handle supplier relationships means current systems cannot reliably automate this end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Estimating material quantities requires site-specific measurements and judgment about waste factors, surface conditions, and job specifics that current AI cannot independently assess without significant human input.itude |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers, liability concerns for incorrect orders, budget overruns, and material waste create organizational friction. The plasterer's professional judgment about material quality and supplier relationships also creates preference for human decision-making on ordering. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human specifically calculate material quantities; it's a business/logistics task with low formal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions for material estimation require significant integration, manual verification, and human oversight to prevent costly mistakes. The all-in cost of AI infrastructure plus required human review is comparable to or exceeds the time a skilled tradesperson would spend manually assessing materials and placing orders. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While software licenses are cheap, the human still must physically measure the job site and validate outputs, so overall cost savings versus a mason doing quick mental/manual takeoffs are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI products reliably perform complete material estimation and ordering for construction trades in production. While general inventory and procurement systems exist, none specifically handle the plastering trade's material requirements and supplier coordination without substantial human oversight and error correction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some construction estimating software includes material calculators, but these are decision-support tools requiring human-entered measurements and judgment, not autonomous ordering systems in production use by tradespeople. |
Apply coats of plaster or stucco to walls, ceilings, or partitions of buildings, using trowels, brushes, or spray guns.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Apply coats of plaster or stucco to walls, ceilings, or partitions of buildings, using trowels, brushes, or spray guns.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction, especially small-to-medium firms doing plaster and stucco work, remains largely low-digitization, physical labor-dependent, and slow to adopt automation. Sector adoption of robotics for finishing trades is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors for AI/robotic adoption due to low digitization, variable site conditions, and physical labor demands. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a plasterer actively applying coats; the task is inherently manual and requires continuous human judgment and physical execution that AI tools do not augment in practice. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited direct assistance to the physical application process itself, though it may help with mix calculations, scheduling, or design specifications adjacent to the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Applying plaster or stucco requires precise manual dexterity, real-time tactile feedback, and adaptation to surface irregularities that current AI systems cannot perform end-to-end. While spraying could be partially automated, the finishing work demands human skill that no deployed system can match at quality parity. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a highly physical, dexterous manual trade task requiring on-site material handling, surface adaptation, and tool manipulation that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are modest organizational barriers: construction sites are dynamic, subcontractor relationships are entrenched, and safety regulations apply. However, no hard licensing or legal requirement mandates human labor for this specific task, so substitution faces mainly practical and economic friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate requires a human specifically for the application step, but physical site conditions, safety requirements, and quality control create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized plasterer/stucco mason earns moderate wages (~$30–50/hour loaded), while robotic systems capable of this work would cost hundreds of thousands to millions upfront, with integration and maintenance overhead far exceeding human labor cost for this task today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation solution for this task, so any hypothetical robotic system would be far more costly than a human plasterer given current technology costs and lack of maturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform full plaster/stucco application in real construction environments. Prototype robotic arms exist in labs but struggle with surface variability, material consistency, and the manual trowel work that defines the task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product reliably applies plaster or stucco finishes in production construction settings today; this remains research/prototype territory at best for construction robotics. |
Cover surfaces such as windows, doors, or sidewalks to protect from splashing.
13CI 10–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Cover surfaces such as windows, doors, or sidewalks to protect from splashing.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a laggard sector for AI/automation due to site variability, fragmented supply chains, and physical work requirements. Adoption of automation in this task is minimal and slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are a low-digitization, physical-labor sector with minimal AI/robotic adoption for site prep tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for this task; basic task-planning software or site documentation might help with workflow scheduling, but does not substantively augment the core physical work of protecting surfaces during plastering. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of covering and protecting surfaces during plastering work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of protective coverings in real-world environments with spatial reasoning and manual dexterity. Current AI systems cannot autonomously perform physical actions like draping, taping, or securing protective materials on varied architectural surfaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual task requiring placing tarps, tape, and coverings on-site around irregular surfaces; no off-the-shelf AI system can perform this physical action.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not formally licensed, this task occurs in unionized construction sectors with established practices and customer expectations for skilled human labor, creating organizational and contractual friction to automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but the physical, situational nature of covering irregular surfaces on a job site creates practical friction against non-human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even accounting for emerging robotics, the cost of hardware, deployment, and site-specific configuration far exceeds the wage cost of a plasterer performing this straightforward manual task, making economic substitution infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any AI-based approach would require expensive robotics not currently deployed, making it more costly than a human worker doing simple manual prep. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously execute the physical coverage of surfaces to prevent splashing. This is a fundamentally embodied task requiring robotic manipulation in unstructured job sites, which remains beyond production-ready systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs on-site masking/covering for plastering work; this remains entirely manual labor. |
Clean job sites.
13CI 10–15 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Clean job sites.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a laggard sector for automation. Job-site cleaning is typically performed by entry-level workers or subcontractors; digitization and automation adoption in this domain remain low compared to information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the least digitized sectors and show minimal AI/robotics adoption for physical labor tasks like site cleanup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for manual cleaning tasks. Simple tools like autonomous vacuums or sweepers exist in controlled settings, but they provide limited augmentation to a worker actively cleaning diverse construction debris and hazards on an active site. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI tools offer essentially no assistance to a human physically clearing debris and materials from a job site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning job sites involves navigating unstructured physical environments with varied debris, obstacles, and hazards. Current AI systems cannot reliably control robots to perform this end-to-end with 50% time savings compared to human workers on typical construction sites. |
| Task automatability | claude-sonnet-5 | 1/5 | Cleaning a physical job site involves navigating debris, tools, and materials in an unstructured environment, which is beyond current off-the-shelf AI or robotic capability at equal quality and speed.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While cleaning is not a licensed task, adoption faces practical barriers: job-site safety standards, insurance requirements for automated equipment, need for human supervision, and customer preference for predictable human crews on active construction projects. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical site variability, safety concerns around debris and hazards, and lack of mature robotics create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robots capable of site cleaning are capital-intensive and require ongoing maintenance, oversight, and integration. Labor costs for manual cleaning remain substantially lower than the all-in cost of robotic systems today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Human laborers doing site cleanup are cheap and flexible, while robotic solutions capable of navigating construction debris would require expensive, unproven hardware exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous job-site cleaning at scale. Robotics in this domain remain largely research-stage or require highly controlled environments, far from the heterogeneous conditions of active construction sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general construction job-site cleanup; robotic cleaning solutions remain research-stage or limited to flat, structured indoor environments like warehouses. |
Clean and prepare surfaces for applications of plaster, cement, stucco, or similar materials, such as by drywall taping.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.9/5 · click for rater detail
Clean and prepare surfaces for applications of plaster, cement, stucco, or similar materials, such as by drywall taping.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, especially small and mid-sized shops doing surface preparation, operate in laggard sectors with low digitization and minimal AI agent adoption. Physical trades lack the information-intensity that drives rapid AI uptake. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are a low-digitization, physically-oriented sector with minimal AI/robotic adoption for hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a human plasterer actively cleaning and taping surfaces; the task is inherently manual and on-site, with no decision-support or analytical layer where AI could augment productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for the physical act of cleaning and preparing surfaces or taping drywall. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of surfaces, drywall taping, and preparation work that requires on-site presence, spatial reasoning, and manual dexterity. Current AI systems cannot perform mechanical surface preparation end-to-end with quality parity. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring on-site surface cleaning, scraping, and taping that no current AI system can perform end-to-end; it requires robotic manipulation far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task has substantial barriers: it requires physical on-site presence, skilled manual work, and is typically performed as part of licensed or union construction work with established labor practices and customer expectations for human craftsmanship. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically bars automation of surface prep, but physical site access, safety requirements, and craft-specific dexterity create practical organizational friction against remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction robots capable of surface preparation and taping remain extremely expensive to purchase, maintain, and deploy compared to the loaded wages of skilled plasterers and stucco masons. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so the effective cost of AI 'doing' the task is infinite relative to a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs physical surface cleaning and preparation, including drywall taping, in real construction environments. This task lies firmly in the physical domain where AI robotics remain at research or early-pilot stages. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs surface preparation or drywall taping autonomously today; this remains outside robotics deployment in construction trades. |
Rough the undercoat surface with a scratcher so the finish coat will adhere.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Rough the undercoat surface with a scratcher so the finish coat will adhere.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plastering and stucco work remains a low-digitization, small-firm-dominated sector with minimal AI or robotic adoption even for simpler tasks. The physical, craft-based nature of the work and fragmented industry structure create laggard adoption conditions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the slowest sectors to adopt AI/robotics, with minimal automation penetration into physical finishing tasks like plastering. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for the hands-on execution of surface roughing, which depends entirely on physical skill, material feedback, and real-time adaptation by the worker. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance to a plasterer physically scratching an undercoat; this is a tactile hand-tool activity with no digital or planning component AI can enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of a scratcher tool on a freshly applied surface to create a specific texture pattern for adhesion. Current AI systems lack the embodied dexterity, real-time sensory feedback, and adaptive force control needed to perform this construction task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise physical manipulation task requiring a tool (scarifier/scratcher) applied to wet plaster with tactile feedback; no current AI system or robotic platform performs this general construction task autonomously. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Construction work often involves site-specific licensing, building codes, and liability requirements for workmanship quality. Additionally, the output is a tactile, time-sensitive material preparation step that is tightly integrated into the workflow and may require human judgment about surface readiness. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically required for this micro-task, but it demands physical dexterity, judgment of surface texture and timing within wet material work that resists remote or software-based substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even experimental robotic systems for this task would require substantial equipment investment, integration, and site-specific calibration—far exceeding the cost of a skilled plasterer performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI/robotic alternative to compare cost against; a human plasterer with basic hand tools remains the only viable and far cheaper option than any hypothetical automated rig. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can reliably perform the physical act of roughing undercoat surfaces on construction sites. This is a hands-on, spatially-variable task requiring equipment operation and material-specific judgment that remains beyond current robotic capabilities in real-world deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform plaster scratch-coat texturing; this remains outside any commercial robotics or AI offering and is purely manual craft work. |
Mix mortar and plaster to desired consistency or direct workers who perform mixing.
7CI 0–15 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Mix mortar and plaster to desired consistency or direct workers who perform mixing.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains one of the least digitized and most labor-intensive sectors; autonomous on-site material preparation has seen minimal real-world adoption due to site variability and cost barriers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal production deployment of any automation for mixing or crew direction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential: AI monitoring systems could flag consistency deviations via sensors, but the core task of mixing and directing workers depends on tacit skill and on-site judgment that AI cannot substantially enhance in production. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist indirectly (e.g., mix ratio calculators, scheduling or training guidance for directing workers) but offers little direct assistance to the hands-on mixing and physical judgment involved. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Mixing mortar and plaster to precise consistency requires real-time sensory feedback (touch, visual assessment of texture) and physical manipulation that current AI systems cannot perform autonomously. Directing workers is also context-dependent and requires on-site presence and judgment. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task involving handling materials, judging consistency by feel/appearance, and operating mixing equipment on a job site; no off-the-shelf AI system can perform this physically end-to-end today.9 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong regulatory and safety barriers exist: construction work requires licensed tradespeople, liability for material defects rests on qualified workers, and worker direction authority is legally vested in certified foremen/supervisors. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly required for mixing itself, but the physical, on-site, tactile nature of judging material consistency and directing a crew creates strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of autonomous mixing would require expensive robotic hardware, sensors, and integration; human workers performing this task cost far less and are already skilled at it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical mixing task, so the comparison defaults to AI being effectively infinitely costlier than a human worker for this specific action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous mortar/plaster mixing or on-site worker direction at construction quality standards. This remains a manual, skilled task with no production-ready AI substitute. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product mixes mortar/plaster or directs on-site workers; this remains firmly in the domain of human physical labor and supervision. |
Cure freshly plastered surfaces.
7CI 0–15 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Cure freshly plastered surfaces.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades including plastering remain low-adoption sectors for AI; the physical nature of the work and site-specific conditions create structural resistance to automation beyond basic monitoring. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the slowest sectors to adopt AI, especially for hands-on physical finishing tasks like curing plaster. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Simple sensors or alerts about temperature and humidity could assist a plasterer in managing conditions, but the core curing process is environmental control, not a knowledge task where AI augmentation meaningfully improves human productivity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide minor assistance such as weather/humidity monitoring alerts or optimal curing time recommendations, but does not materially transform the physical execution of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Curing freshly plastered surfaces requires active monitoring and environmental control (humidity, temperature, airflow) in a physical space over extended periods. Current AI systems cannot perceive, adjust, or maintain the real-world conditions necessary for proper cure without human oversight and physical intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | Curing plaster requires physical monitoring of environmental conditions and manual application of water/curing compounds over time; no AI system can perform this physical process end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Curing is not a task that can be legally delegated; a licensed plasterer or mason must be responsible for ensuring proper cure conditions and inspecting the final surface quality, creating a hard regulatory barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically for curing, but it requires physical presence, judgment on timing/weather conditions, and hands-on materials work that resist remote or software-based substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Curing requires physical presence and environmental control equipment (humidifiers, dehumidifiers, ventilation, heaters) that are already in place; adding AI monitoring would increase costs without meaningful labor displacement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable—human labor or simple mechanical/timed sprinkler systems (non-AI) remain the only options. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product autonomously manages the curing process for plaster. Curing is a passive chemical/physical process that requires environmental sensors and climate control infrastructure, not AI decision-making—this falls outside what AI systems are designed to do. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical curing of plaster surfaces; this remains a purely manual, physical-world task with no automation products in production. |
Install guide wires on exterior surfaces of buildings to indicate thickness of plaster or stucco and nail wire mesh, lath, or similar materials to the outside surface to hold stucco in place.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Install guide wires on exterior surfaces of buildings to indicate thickness of plaster or stucco and nail wire mesh, lath, or similar materials to the outside surface to hold stucco in place.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Plastering and stucco work remains a highly fragmented, craft-based sector with limited digitization, small job sites, and strong reliance on skilled manual labor; automation adoption is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the least digitized and slowest-adopting sectors for AI/robotic automation of hands-on physical installation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance in performing or planning wire installation and mesh attachment on exterior surfaces; the task is fundamentally manual and site-specific, with little room for algorithmic support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning material quantities, guide-wire placement calculations, or generating specifications, but offers minimal direct assistance to the physical act of installing wires and lath. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in outdoor, variable conditions—climbing, fastening materials, and precise spatial positioning. Current AI/robotics cannot reliably perform end-to-end exterior installation with equal quality and cost savings in the field today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical construction task requiring precise manual manipulation of wire, lath, and fasteners on exterior surfaces; no current AI system can perform this physical work end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes typically require licensed plasterers or masons to perform or supervise exterior stucco/lath installation; liability for structural failure (water infiltration, adhesion) is high; and safety regulations mandate human presence and oversight on exterior work. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically for this subtask, but physical site conditions, safety requirements, and the need for hands-on precision create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment, robotics, and oversight required for autonomous exterior wire and mesh installation would far exceed the loaded wage of a skilled plasterer or stucco mason on a per-task basis. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute performing this task, so the comparison defaults to AI being effectively unusable and thus not cost-competitive with human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products in production perform this task reliably. Specialized masonry and stucco work requires dexterous manipulation, real-time environmental assessment, and adaptation to building irregularities—beyond current commercial offerings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products install guide wires or attach lath/mesh to building exteriors; this remains firmly in the domain of skilled manual trades, with robotics for such irregular exterior work still research-stage at best. |
Apply weatherproof, decorative coverings to exterior surfaces of buildings, such as by troweling or spraying on coats of stucco.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Apply weatherproof, decorative coverings to exterior surfaces of buildings, such as by troweling or spraying on coats of stucco.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, especially small-firm masonry and plastering, show low automation adoption. The sector remains labor-intensive and physically site-specific, with limited digitization and high resistance to capital-intensive automation in fragmented markets. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are a laggard sector for AI/robotics adoption due to physical variability, outdoor conditions, and low digitization of on-site manual work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with design visualization or surface preparation planning, but the core manual dexterity and real-time environmental adaptation required for troweling and spraying offer minimal scope for AI augmentation while a human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, material estimation, mixing ratios, or scheduling, but offers minimal direct assistance to the physical troweling/spraying application itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of materials (troweling or spraying stucco) on vertical and angled surfaces with aesthetic judgment and environmental responsiveness. Current AI systems lack embodied robotics capability to perform this end-to-end with quality parity in uncontrolled outdoor settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual construction task requiring skilled hand-eye coordination, material handling, and site-specific judgment that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This work requires skilled tradesperson licensing and inspection sign-off in most jurisdictions, and building code compliance for weatherproofing creates legal and liability requirements that mandate human expertise and accountability for outcomes. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed everywhere, exterior work involves building codes, weatherproofing liability, and quality standards that create moderate friction against unproven automation, though not a strict legal requirement for a human. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Roboticized stucco application systems, where they exist in research or limited deployment, remain capital-intensive with high setup and maintenance costs compared to the loaded wage of skilled plasterers, making AI significantly more expensive per unit output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation would be far more costly than skilled labor, if it existed at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform full stucco application autonomously. While some construction robotics research exists, production systems for weatherproof decorative exterior coating application at scale do not exist in real organizations today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product or robot reliably applies exterior stucco coatings in production; construction robotics for this specific trade remain research/prototype stage at best. |
Create decorative textures in finish coat, using brushes or trowels, sand, pebbles, or stones.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.9/5 · click for rater detail
Create decorative textures in finish coat, using brushes or trowels, sand, pebbles, or stones.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The plastering and masonry trades remain low-digitization sectors with high geographic fragmentation, small firms, and physical on-site constraints. Adoption of automation in this task is minimal and progressing very slowly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are among the slowest sectors to adopt AI/robotics due to physical, unstructured work environments and low digitization. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a plasterer creating textures; the task depends entirely on human sensorimotor skill, material feel, and aesthetic judgment applied in real time during application. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of texturing plaster with hand tools; software tools may help with design visualization but not the hands-on task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Creating decorative textures with hand tools on vertical/overhead surfaces requires real-time spatial judgment, haptic feedback, and fine motor control that current AI systems cannot perform. No deployed autonomous system can reliably manipulate brushes or trowels to produce consistent aesthetic finishes on-site. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, tactile craft skill requiring hand-eye coordination and material manipulation that no current AI/robotics system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Decorative finish work requires local building codes compliance, customer aesthetic approval, and typically direct on-site execution by licensed tradespeople. Union rules and professional standards create friction against substitution with automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically bars automation, but the physical dexterity, on-site variability, and craftsmanship expectations create strong practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any hypothetical robotic system capable of this task would cost orders of magnitude more than hiring a skilled plasterer, both in capital equipment and integration, making it economically infeasible at current technology maturity. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any hypothetical robotic system would require far more capital and setup cost than a skilled tradesperson. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production system exists that autonomously creates decorative textures in finish coats. This is a skilled manual craft requiring embodied dexterity, material handling, and live adjustment to substrate conditions that current robotics and AI cannot achieve reliably. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product or robot performs decorative plaster texturing in production; this remains firmly a human manual craft. |
Apply insulation to building exteriors by installing prefabricated insulation systems over existing walls or by covering the outer wall with insulation board, reinforcing mesh, and a base coat.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Apply insulation to building exteriors by installing prefabricated insulation systems over existing walls or by covering the outer wall with insulation board, reinforcing mesh, and a base coat.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, particularly specialized exterior work like stucco and insulation, operate in small firms and physical job sites with low digitization. Adoption of automation in this sector remains negligible, with little evidence of pilots or production deployments. |
| 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 exterior finishing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal productivity boost for the core task; thermal imaging or substrate analysis tools could provide minor diagnostic support, but the installation work itself—positioning, securing, finishing—remains dependent on skilled human execution and judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, material estimation, or scheduling around this task, but offers little direct assistance to the hands-on application of insulation boards and coatings. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three dimensions—positioning prefabricated systems, securing mesh, and applying base coat—in outdoor conditions with variable substrates. Current AI systems cannot operate construction equipment, assess wall conditions, or perform the dexterity-intensive installation work autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical construction task requiring manipulation of materials, tools, and precise application on exterior walls at various heights; no current AI system can perform this manual work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes, safety regulations, and liability requirements typically mandate that insulation installation be performed by licensed tradespeople who sign off on quality and compliance. Customer preference for skilled human oversight and on-site problem-solving creates strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate strictly requires a human for this specific task, but physical dexterity, judgment on substrate conditions, and quality/weatherproofing liability create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying autonomous systems for exterior insulation work would require specialized hardware, site-specific setup, and integration costs far exceeding the loaded wage of a skilled tradesperson per installation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would be far more costly than a human mason performing this task with existing tools. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs exterior insulation installation. Robotic systems for construction remain research-stage or prototype, with no evidence of reliable production use for this specific multi-step, site-adaptive task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product installs exterior insulation systems, boards, mesh, or base coats in production; this remains purely manual skilled trade work. |
Spray acoustic materials or texture finish over walls or ceilings.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Spray acoustic materials or texture finish over walls or ceilings.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The plastering and stucco sector is characterized by small firms, on-site manual work, and low digitization. Adoption of advanced automation lags far behind information and professional services; most work remains artisanal and distributed across fragmented job sites. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical execution tasks, with minimal production deployment of spraying automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer limited assistance via computer vision for surface mapping or spray parameter suggestions, but current systems provide minimal productivity boost to the core task of material application. The task remains fundamentally dependent on skilled human control. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited assistance here, perhaps in estimating material quantities or planning coverage, but does not meaningfully enhance the physical spraying process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Spraying acoustic materials or texture finish requires precise control of equipment in three-dimensional space, adaptation to surface irregularities, and real-time adjustments for material consistency and coverage. Current AI systems cannot autonomously operate spray equipment with the dexterity and environmental awareness needed to match human craftsmanship at acceptable quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical spraying task requiring manual dexterity, ladder/scaffold work, and on-site material handling that current AI systems cannot perform end-to-end.It requires embodied robotic execution, not just cognitive processing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: skilled trades licensing requirements in many jurisdictions, liability concerns for material application quality and structural integrity, building code compliance oversight, and the human-contact/site-specific nature of construction work all limit substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate requires a human specifically, but physical site variability, safety considerations, and lack of mature robotic tooling create substantial practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment cost, integration complexity, specialized setup per job site, and safety systems required for autonomous spraying would far exceed the loaded wage of skilled plasterers, especially for the small-to-medium projects that dominate the sector. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed at scale, so the all-in cost of automation (equipment, setup, calibration for varied surfaces) far exceeds a human tradesperson's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task end-to-end. While robotic spraying exists in controlled factory settings (e.g., automotive), the variability of residential/commercial construction surfaces, access constraints, and safety requirements place this firmly outside current production-ready automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs spray application of acoustic or texture finishes in real construction settings; robotic spraying exists only in narrow, research or highly controlled industrial contexts. |
Mold or install ornamental plaster pieces, panels, or trim.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Mold or install ornamental plaster pieces, panels, or trim.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The construction trades, particularly specialty plaster work, remain low-digitization sectors with slow technology adoption; most firms are small, work is site-specific and variable, and labor is embedded in physical processes resistant to automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics, with this artisanal task showing essentially no measurable automation or AI deployment in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI tools offer minimal assistance for the core task of molding and installing ornamental plaster; design visualization aids exist but do not meaningfully augment the craftwork itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with design visualization, generating ornamental patterns, or CAD-based mold design, but offers minimal help with the physical molding and installation process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Molding or installing ornamental plaster requires precise physical manipulation, spatial judgment, and adaptability to on-site conditions that current AI cannot perform autonomously. No end-to-end automation solution exists that meets the 50% time-saving threshold for this hands-on fabrication and installation task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on craft task requiring manual dexterity, material handling, and precise on-site fitting that current AI systems cannot perform end-to-end; no software-only or robotic system generally available today does this reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task has meaningful barriers: ornamental plaster work often requires licensed tradespersons in many jurisdictions, involves custom on-site judgment and finishing, and carries liability for structural or aesthetic failure that favors human accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed like medicine or law, ornamental plasterwork requires skilled craftsmanship, on-site judgment, and physical presence, creating substantial practical barriers to automation even though no formal licensing mandate exists. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of plaster molding and installation would be prohibitively expensive to develop, deploy, and maintain compared to the relatively low loaded cost of skilled tradespeople performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative to human labor for this physical fabrication and installation task, so AI is not a cost-competitive substitute at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can reliably mold, shape, or physically install ornamental plaster in real-world construction settings. This task fundamentally requires embodied robotics and dexterous manipulation at a level far beyond current production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products install or mold ornamental plaster; this remains a specialized manual trade with no robotic or AI production systems in use for this specific craft. |
Set up scaffolds.
3CI 0–5 · exposure 0 · augmentation 0 · importance 4.1/5 · click for rater detail
Set up scaffolds.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction remains a physically laggard sector with limited AI adoption; scaffold setup is inherently on-site and requires embodied work unsuitable for current digitization patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are a low-digitization, physically-dominated sector with minimal AI/robotics adoption for manual site tasks like scaffolding. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for the physical task of erecting scaffolds; there are no deployed tools that augment a worker's ability to assemble structures. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers negligible assistance for the physical act of assembling scaffolding, though it might help with planning or safety checklists tangentially. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Setting up scaffolds requires physical manipulation, spatial reasoning in real environments, and safety-critical assembly that current AI systems cannot perform end-to-end. No autonomous system today can independently construct scaffolding structures. |
| Task automatability | claude-sonnet-5 | 1/5 | Setting up scaffolds is a physical, spatial task requiring manual assembly, load assessment, and site-specific adaptation that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Scaffold setup is subject to strict OSHA and building codes requiring certified personnel; liability and safety regulations legally mandate human oversight and sign-off on load-bearing structures. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Scaffold erection is governed by strict safety regulations (e.g., OSHA) often requiring trained/certified personnel and physical human presence for setup and inspection. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous scaffolding systems would require specialized robotics hardware and integration costs vastly exceeding the labor cost of skilled workers, making AI substantially more expensive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical scaffold assembly, so any AI-based approach would be far more costly or simply infeasible compared to a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial AI system can reliably perform physical scaffold assembly. This task demands embodied robotics and real-world spatial problem-solving far beyond current production capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product or robotic system autonomously erects scaffolding on construction sites; this remains a manual trade task. |
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.