Terrazzo Workers and Finishers
47-2053.00Median wage $76,170/yr1,180 employed (US)Rank #873 of 923 scored · top 95% by substitution
Apply a mixture of cement, sand, pigment, or marble chips to floors, stairways, and cabinet fixtures to fashion durable and decorative surfaces.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution11
Exposure0
Augmentation11
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
26 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%0
panel mean rating 1.0/5 → substitution pressure 0/100
Technical feasibility todayw 20%0
panel mean rating 1.0/5 → substitution pressure 0/100
Cost vs. human wagew 15%1
panel mean rating 1.0/5 → substitution pressure 1/100
Adoption barriersw 20%inverted — strong barriers lower the score53
panel mean rating 2.9/5 (barrier strength) → substitution pressure 53/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (26 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Clean installation site, mixing and storage areas, tools, machines, and equipment, and store materials and equipment.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Clean installation site, mixing and storage areas, tools, machines, and equipment, and store materials and equipment.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo and construction trades remain physically intensive, site-specific, and operate with low capital investment in automation. Adoption of robotic or AI solutions for cleanup and storage tasks is negligible in these sectors, which typically rely on human crews. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction 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 | AI offers minimal assistance for physical site cleaning and material management. While digital asset tracking might help, the core task—manual cleaning and storage—gains little productivity benefit from current AI tools. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for physical cleaning, sorting, and storage of tools and materials on a job site.' |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical cleaning, moving, and storing of materials across a construction site—actions requiring mobile manipulation, navigation of unstructured environments, and judgment about proper storage conditions. Current AI systems lack the embodied dexterity and real-world reasoning needed to perform these activities autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning and material-handling task requiring mobile manipulation in unstructured construction environments, far beyond current AI/robotic capability.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for site cleaning and storage, the physical nature of the work and the need to adapt to varying worksite conditions create practical barriers to automation. Customer preference for human workers and organizational familiarity with labor-based solutions provide some protection. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically for cleanup, but physical unpredictability of job sites creates practical friction against automation.' |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated solutions for site cleaning and material handling remain expensive, fragile, and unreliable compared to the direct cost of unskilled or semi-skilled labor on construction sites. The all-in cost of robots and oversight exceeds simple human labor wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system to compare cost against; human labor remains the only practical option, making AI far more expensive or simply unavailable.' |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can reliably clean a terrazzo worksite, move tools and machines, or manage material storage without human intervention. This task falls outside the scope of current robotic and AI capabilities in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general construction-site cleanup, tool storage, and material organization autonomously; this remains research-stage robotics at best.' |
Mix cement, sand, and water to produce concrete, grout, or slurry, using hoe, trowel, tamper, scraper, or concrete-mixing machine.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Mix cement, sand, and water to produce concrete, grout, or slurry, using hoe, trowel, tamper, scraper, or concrete-mixing machine.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction, particularly terrazzo finishing, remains a low-digitization, physical-labor-heavy sector with minimal AI adoption. Automation of material mixing has historically relied on mechanical equipment rather than AI-driven systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and terrazzo finishing are low-digitization, physically intensive trades with minimal AI adoption for on-site material mixing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a worker mixing concrete; the task is fundamentally manual, requiring real-time sensory feedback and adjustment that AI cannot provide without robotic embodiment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with recipe calculations or mix-ratio guidance via an app, but offers little help with the physical mixing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials in a real construction environment—mixing cement, sand, and water with hand tools or machines, then assessing consistency by touch and sight. Current AI systems have no robotic embodiment to perform these physical actions reliably on job sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling and mixing task requiring manual dexterity and on-site judgment of material consistency; no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for mixing concrete, physical site constraints, variability in material quality, and the need for on-site judgment about consistency create practical adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically required for mixing, but the physical nature and on-site craft skill create practical barriers to any automation, AI or otherwise. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized concrete-mixing equipment and robotic systems that could replace this task are expensive to deploy and maintain, far exceeding the cost of a skilled laborer mixing materials by hand or machine. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so AI cost is not comparable; only traditional labor or mechanical mixers perform this work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously mix concrete or grout on-site with the sensorimotor precision required. This remains entirely dependent on human workers or specialized machinery not controlled by AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical mixing of construction materials; this remains purely a manual/robotic-mechanical task outside AI's domain. |
Precast terrazzo blocks in wooden forms.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.3/5 · click for rater detail
Precast terrazzo blocks in wooden forms.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The terrazzo industry is small, fragmented, and operates in traditional craft contexts with minimal digital infrastructure, limiting AI and automation adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI or robotic automation, with this specific craft task showing no meaningful digitization trend. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with design visualization or material calculation, but most of the task—form preparation, placement, finishing—remains manual and craft-dependent, offering limited augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI provides no meaningful real-time assistance for the physical process of mixing, pouring, and setting terrazzo in forms. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials, precise form setup, and real-time judgement in a physical environment. Current AI systems cannot autonomously handle material placement, compaction, and form management at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication task requiring manual mixing, pouring, and forming of terrazzo material, which current AI systems cannot perform since they lack physical embodiment for construction trades. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While no formal licensing bars automation, the work occurs on-site in varied conditions and requires apprenticeship-level skill, creating some organizational friction and worker preference for human craftspeople. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human for this task, but physical manipulation of materials, quality judgment, and on-site craftsmanship create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic solutions for precasting terrazzo blocks would require custom hardware, integration, and maintenance costs far exceeding the wages of skilled terrazzo workers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-based alternative to perform this physical task, so any AI cost comparison is moot; humans remain the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems perform end-to-end terrazzo block precasting today. The task demands physical dexterity, environmental adaptation, and quality control that remain in the research domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs precasting of terrazzo blocks in wooden forms; this remains a manual craft skill with no commercial automation offering. |
Produce rough concrete surface, using broom.
15CI 15–15 · exposure 0 · augmentation 0 · importance 2.9/5 · click for rater detail
Produce rough concrete surface, using broom.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo and concrete finishing occurs in small, physical job-site settings with low digitization. Adoption of automation in this sector is minimal and unlikely to accelerate in the near term given capital constraints and labor availability. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and finishing trades are among the slowest sectors to adopt AI/robotics due to physical variability, low digitization, and fragmented small-firm structure. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics offer no meaningful assistance to a human broom operator performing this surface-finishing task; the work is direct manual execution with no augmentation opportunity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for this manual, physical broom-texturing step; there is no digital or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of a broom on a concrete surface with tactile feedback and real-time adjustment—core physical automation challenges. Current robotics cannot reliably handle the dexterity, surface variability, and quality control needed for finishing work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring manipulation of a broom on wet concrete to create texture, which no current AI system can perform end-to-end; it requires embodied robotic action, not information processing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While no explicit licensing barriers exist for this task itself, the physical site constraints (active construction, varied surfaces, safety) and customer preference for proven human craftsmanship create modest friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier specifically protects this task, but the physical, unstructured job-site environment and lack of robotic solutions create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a mobile robot with manipulation, sensing, and environmental adaptation to perform broom finishing would cost orders of magnitude more than the hourly wage of a terrazzo worker, making it economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this task at any cost, so AI is effectively infinitely more expensive relative to a human worker who can do it directly with a simple tool. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system today can autonomously produce a rough concrete surface finish using a broom at acceptable quality. This remains a purely manual operation performed by human workers on job sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs concrete surface texturing with a broom; this remains outside the scope of commercial robotics or AI products in construction finishing. |
Remove frames when the foundation is dry.
15CI 15–15 · exposure 0 · augmentation 0 · importance 2.9/5 · click for rater detail
Remove frames when the foundation is dry.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a traditional craft in laggard sectors (small specialty shops, construction sites) with low digitization and minimal AI/robot adoption patterns to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades, especially specialized finishing work like terrazzo, show very low AI/robotics adoption due to low digitization and the bespoke, physical nature of the work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for a straightforward manual task that depends on physical presence, spatial judgment, and hands-on material handling. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of removing forms, though it might theoretically help schedule timing based on curing data, which is tangential to the task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Removing frames from dried terrazzo requires physical manipulation in variable spatial configurations, tactile feedback to avoid damage, and judgment about material condition—capabilities that current AI systems lack in uncontrolled job-site environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring dexterity to remove forms without damaging the drying terrazzo surface; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task occurs on active job sites with variable conditions and no legal licensing requirement, but physical accessibility and safety considerations create moderate friction for automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically required for frame removal itself, but it requires physical presence, judgment about drying/curing readiness, and care to avoid damaging finished work, creating moderate practical friction against remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems for frame removal would require expensive hardware, integration, and maintenance far exceeding the cost of a skilled worker performing this relatively quick task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so AI cost is not applicable/comparable and the human remains the only option, making AI effectively more expensive (infinite) than the human. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI system reliably performs frame removal from cured terrazzo in production settings; this remains a manual craft task without commercial automation solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical frame removal from construction surfaces; this remains purely a manual construction trade activity. |
Sprinkle colored marble or stone chips, powdered steel, or coloring powder over surface to produce prescribed finish.
14CI 10–19 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Sprinkle colored marble or stone chips, powdered steel, or coloring powder over surface to produce prescribed finish.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a small, traditionally-practiced craft sector with limited digitization, primarily composed of small trades firms with low capital availability and strong human-skill heritage, leading to slow AI/automation adoption even where technically feasible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the least digitized, lowest AI-adoption sectors, with virtually no robotic automation of decorative flooring application in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through vision-guided layout planning or material-mix recommendations before application, but the core manual sprinkle-and-finish work offers minimal opportunity for meaningful productivity augmentation while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of sprinkling and distributing chips or powders during terrazzo finishing. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise hand-application of decorative materials onto a wet surface with spatial awareness and manual dexterity that current AI systems cannot perform. The physical manipulation, depth perception, and real-time adjustment needed for aesthetic finish quality are beyond the scope of today's robotic or AI-driven automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, tactile craft task requiring hand-eye coordination to distribute materials evenly across a surface; no AI system can perform this physical manipulation.wireless robotics for this specific task do not exist commercially.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | The task sits in the skilled trades with some union representation and licensing in certain jurisdictions, plus customer preference for human craftsmanship and quality assurance; these create moderate friction but are not absolute legal barriers to automation attempts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for this specific step, but the physical, on-site nature of terrazzo finishing and lack of robotic manipulators create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of precision material application exist but are expensive, require extensive setup and calibration, and may not achieve the cost-per-unit economy needed to undercut skilled human terrazzo workers in most contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not applicable or is effectively infinite relative to a human finisher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this specialized decorative surfacing task in production settings. The task demands fine motor control, material texture assessment, and aesthetic judgment that current industrial robots and AI systems have not demonstrated at commercial scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical construction task; it remains entirely manual craftsmanship requiring skilled trowel and hand work on-site. |
Measure designated amounts of ingredients for terrazzo or grout, according to standard formulas and specifications, using graduated containers and scales, and load ingredients into portable mixer.
13CI 10–15 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Measure designated amounts of ingredients for terrazzo or grout, according to standard formulas and specifications, using graduated containers and scales, and load ingredients into portable mixer.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a small, fragmented, and labor-intensive trade operating in distributed physical job sites with low digitization; adoption of automation in this sector has been minimal and lags far behind professional services or information work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show very low AI/robotic adoption for physical material handling tasks, remaining a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital scales or automated recipe displays could marginally assist formula recall and reduce transcription errors, but the core task—physically measuring and loading—offers limited scope for AI-driven productivity gains while a human remains engaged. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital tools or apps could help calculate formula ratios or track inventory, offering minor assistance, but the core physical measuring and loading is unaided by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | While measuring and loading ingredients are individual mechanical steps, the task requires spatial judgment, environmental adaptation (humidity, temperature affecting formulas), and physical handling of bulk materials in varied work conditions—activities that current general-purpose AI systems cannot perform end-to-end in real construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring manual measuring and loading of heavy materials into equipment on a job site; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Construction sites operate under safety regulations and insurance requirements that favor qualified human workers; however, there is no strict legal prohibition on machine measurement and loading, creating moderate but not absolute barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically required for this measuring/mixing subtask, but it's embedded in a physical trade job requiring on-site human presence and coordination with other manual work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A terrazzo worker's loaded hourly cost is modest (~$20–30/hr all-in), and the manual task itself takes minutes; a custom robotic or vision-guided system for this narrow application would cost thousands to tens of thousands in capital and integration, making AI substantially more expensive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation solution (specialized robotics) would be far more costly than a human laborer today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs outdoor or construction-site ingredient measurement, formula verification, and loader operation as a complete system; mobile robotics for this niche application exist only in research or highly specialized industrial settings, not in production terrazzo work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical material measuring and mixer loading for terrazzo work; this remains outside the scope of current robotics or AI products in construction trades. |
Grind surfaces with a power grinder, or polish surfaces with polishing or surfacing machines.
13CI 10–15 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Grind surfaces with a power grinder, or polish surfaces with polishing or surfacing machines.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a traditional craft performed on-site with specialized equipment in fragmented, small-firm sectors. Digitization and AI adoption are minimal; the sector remains heavily reliant on skilled manual labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized sectors with minimal AI/robotics adoption in physical finishing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for the core grinding and polishing work itself. Computer vision for surface inspection or quality control could provide marginal support, but does not meaningfully augment the primary mechanical task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited assistance here beyond scheduling, quality inspection via imaging, or guidance systems; the core physical grinding/polishing work remains manual with little productivity transformation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Grinding and polishing terrazzo requires fine motor control, real-time tactile feedback, and adaptive response to surface irregularities. Current AI systems lack embodied manipulation capability and cannot reliably operate power tools to achieve consistent surface quality across varied terrazzo conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Grinding and polishing terrazzo requires physical manipulation of heavy power tools over irregular surfaces, sensing texture and pressure in real time; no current AI system can perform this physical task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no explicit legal licensing barriers specific to this task, the physical danger of power tools and the need for real-time human judgment create operational friction. Jobsite conditions and surface variation also require human adaptability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensure is typically required, but quality control, liability for surface damage, and customer expectations for craftsmanship create moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of equipping and maintaining robots capable of surface grinding and polishing, plus integration and safety oversight, substantially exceeds the loaded wage of skilled terrazzo workers performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic or AI-guided grinding system would require expensive specialized hardware, setup, and supervision, making it costlier than a skilled human finisher for typical jobs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs terrazzo grinding and polishing autonomously today. This task requires physical manipulation in unstructured environments—well beyond current robotic systems in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously grinds or polishes terrazzo floors; robotic floor finishing remains research/niche construction robotics, not standard practice. |
Blend marble chip mixtures, place into panels, and push a roller over the surface to embed the chips.
13CI 10–15 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail
Blend marble chip mixtures, place into panels, and push a roller over the surface to embed the chips.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a traditional craft concentrated in small, regional contracting firms with low technology penetration and limited capital for automation. Adoption of AI/robotics in this sector remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the slowest sectors to adopt AI/robotics, with manual craftsmanship still dominant and little digitization of this specific process. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI tools offer minimal assistance for the core task of physical chip embedding and surface finishing. Vision systems might help with design planning, but do not augment the worker during actual material placement and roller application. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical mixing, placing, and rolling actions involved in this hands-on task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy materials, precise spatial embedding of chips through roller pressure, and real-time tactile feedback to achieve even distribution. Current AI systems cannot perform end-to-end physical tasks of this complexity in unstructured jobsite environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual construction task requiring hands-on material handling, mixing, and manual roller pressure application, with no current AI or robotic system capable of performing it end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers, terrazzo work is performed by established skilled tradespeople with strong union presence in some regions, creating organizational friction. Customer expectations for human craftsmanship and on-site problem-solving provide moderate adoption resistance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but the physical dexterity, material judgment, and on-site variability create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robotic system capable of this task (custom manipulation, vision-guided placement, force control) would far exceed the loaded hourly wage of a skilled terrazzo worker, with additional integration and maintenance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No AI or robotic system exists for this task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human finisher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs manual blending, chip placement, and roller embedding for terrazzo finishing. The task demands dexterous manipulation in outdoor/indoor construction conditions with material variation that existing robotics cannot handle at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs terrazzo chip blending and rolling; this remains a skilled trade task done entirely by human craftspeople. |
Chip, scrape, or grind high spots, ridges, or rough projections to finish concrete, using pneumatic chisel, hand chisel, or other hand tools.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.9/5 · click for rater detail
Chip, scrape, or grind high spots, ridges, or rough projections to finish concrete, using pneumatic chisel, hand chisel, or other hand tools.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo and concrete finishing is a traditional, site-based, low-digitization trade sector with little demonstrated adoption of automation; firms remain small and rely on skilled craft labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show very low AI/robotics adoption for hands-on physical finishing work, with digitization and automation concentrated in design/scheduling rather than manual execution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for physically detecting and removing surface imperfections; the task is inherently manual and sensorimotor, with no meaningful role for machine intelligence to augment human performance on this specific work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools (vision, planning software) offer negligible direct assistance to the physical act of chipping and grinding concrete surfaces by hand. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise spatial awareness, fine motor control, and tactile feedback to detect and remove only high spots without damaging surrounding material. Current AI systems cannot physically manipulate tools or sense surface irregularities in the real world with the dexterity needed for consistent finishing work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, dexterous manual task requiring tactile feedback and tool manipulation on irregular concrete surfaces; no current AI system (software or robotic) can perform this end-to-end at equal quality with time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no hard legal licensing barriers specific to this finishing task, safety regulations, worksite-specific conditions, and the need for human judgment about finish quality create moderate friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically for this task, but physical workplace safety norms, tool handling liability, and lack of mature robotic alternatives create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this work (if they existed) would be extremely expensive to acquire, program, and maintain, far exceeding the hourly cost of a skilled terrazzo worker who can already do the job efficiently. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed at scale, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a skilled tradesperson for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system can autonomously perform finish grinding or chipping of concrete surfaces. This task demands physical embodiment, real-time environmental feedback, and adaptive tool control that existing automation does not reliably achieve in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs freeform chipping, scraping, or grinding of concrete finishes in production; this remains outside commercial robotics capability for variable, unstructured construction surfaces. |
Wet concrete surface and rub with stone to smooth surface and obtain specified finish.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Wet concrete surface and rub with stone to smooth surface and obtain specified finish.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing occurs primarily in small, specialized construction and restoration firms with low digitization, limited capital for automation investment, and strong reliance on human craft skill. Adoption of any automation in this sector has been minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics, especially for fine manual finishing work like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is minimal opportunity for AI to meaningfully assist a terrazzo worker in this primarily manual, sensorimotor task. The work is already highly skill-dependent and does not involve decision layers where AI tooling would enhance productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance to a worker physically rubbing and finishing a concrete surface. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Wetting a concrete surface and hand-rubbing it with stone to achieve a specified finish requires precise tactile feedback, real-time adjustment based on surface feel and appearance, and physical manipulation in an unstructured environment. Current AI systems cannot perform this sensorimotor task end-to-end with meaningful time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual finishing task requiring hand-eye coordination and tactile feedback that current AI systems cannot perform; no software-based automation applies to this physical labor.- |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no explicit licensing requirement for terrazzo finishing in most jurisdictions, the high skill requirement, customer preference for experienced human craftsmanship, and quality liability create moderate friction against automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandates a human specifically, but the physical dexterity, judgment on finish quality, and jobsite variability create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robots or AI-driven automation for terrazzo finishing would be prohibitively expensive to develop, maintain, and integrate compared to paying a skilled worker directly. Capital and integration costs far exceed the loaded wage of a terrazzo worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical robotic solution would be far more expensive than a human worker for this niche, low-volume task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform surface finishing of wet terrazzo through hand-rubbing techniques in production settings. The task requires continuous haptic feedback and adaptive pressure control that existing automation cannot dependably achieve at the quality standards required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic products perform terrazzo surface wetting and stone-rubbing finishing in production; this remains purely manual skilled labor. |
Wash polished terrazzo surface, using cleaner and water, and apply sealer and curing agent according to manufacturer's specifications, using brush or sprayer.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Wash polished terrazzo surface, using cleaner and water, and apply sealer and curing agent according to manufacturer's specifications, using brush or sprayer.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a traditional skilled trade in small, physical job-site environments with low digitization and capital constraints typical of laggard sectors. Current adoption of automation in this sector is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the slowest sectors to adopt AI/robotics, with this task requiring physical presence and manual skill not addressed by current automation deployment trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could potentially assist with mixing ratios or application guidelines, the core task—manual spraying and brushing with visual quality control—offers limited augmentation opportunity. AI assistance would be marginal compared to the human's embodied expertise. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical acts of washing, brushing, or spraying sealants onto terrazzo; this remains a hands-on craft process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of brushes/sprayers on vertical and horizontal surfaces, real-time quality assessment, and adaptation to varying surface conditions—capabilities far beyond current automation systems. End-to-end execution with 50% time savings is not feasible with existing robotics or AI. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring precise application of chemicals and washing on hardened, uneven surfaces; no current AI system (including robotics) can perform this end-to-end without human labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves direct manipulation of building surfaces and chemical application requiring skilled judgment about coverage, drying time, and surface quality assessment. Licensing and liability concerns around improper sealing (which affects durability) create moderate-to-strong adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandates a human specifically for this step, but physical dexterity, judgment on surface condition, and on-site variability create practical barriers to substitution beyond regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of this task would require significant capital investment, specialized fixtures, and integration costs far exceeding the loaded wage of a skilled terrazzo worker performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven robotic system priced competitively for this niche physical task; human labor with basic tools remains far cheaper than any hypothetical automation solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform autonomous cleaning, sealing, and curing agent application on terrazzo surfaces in production. This remains a manual trade task with no commercial automation solutions in real-world use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously washes, seals, and cures terrazzo surfaces in real-world job sites; this remains firmly manual craft work. |
Position and secure moisture membrane and wire mesh in preparation for pouring base materials for terrazzo installation.
10CI 5–15 · exposure 0 · augmentation 0 · importance 4.1/5 · click for rater detail
Position and secure moisture membrane and wire mesh in preparation for pouring base materials for terrazzo installation.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, especially specialized finishing work like terrazzo installation, are laggard sectors in automation adoption with mostly small firms, on-site variability, and low capital investment in robotics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades show very low AI/robotics adoption for physical installation tasks, remaining a laggard sector in automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers negligible assistance for physically positioning and securing materials; the task is dominated by manual, spatially-aware work where current AI tools (vision, planning) have no meaningful role in supporting human productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of positioning and securing membrane and mesh; this is a hands-on manual skill task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical positioning and securing of materials in three-dimensional space with site-specific variability, heavy manual handling, and precise spatial judgment that current AI systems cannot perform. Robots capable of this work exist only in narrow, controlled lab settings and are not integrated into construction workflows. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual construction task requiring precise placement and securing of materials on-site; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical presence and hands-on manipulation are inherently required; the task involves safety-critical foundation work where liability for errors rests heavily on the responsible party, creating strong organizational and legal friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this task to certain professionals beyond trade skill, but physical worksite conditions and safety/quality requirements create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this work are prohibitively expensive to acquire, integrate, and maintain compared to the relatively low hourly wage of terrazzo workers, making human labor far more cost-effective for this task today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive specialized robotics far exceeding the cost of skilled labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous positioning and securing of moisture membranes and wire mesh on construction sites. The task demands dexterous manipulation, environmental adaptation, and real-time problem-solving beyond current automation maturity. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product or robot performs this specific construction prep task; it remains fully manual and research on construction robotics has not reached this narrow application. |
Wet surface to prepare for bonding, fill holes and cracks with grout or slurry, and smooth with a trowel.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Wet surface to prepare for bonding, fill holes and cracks with grout or slurry, and smooth with a trowel.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a traditional craft-oriented trade with low digitization, predominantly small teams and regional operators; no meaningful AI or robotic displacement has emerged in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the slowest sectors to adopt AI/robotics, with this specific task showing no evidence of automation pilots or production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist in surface inspection or crack detection via computer vision, or provide material composition guidance, but the primary physical execution task offers limited augmentation opportunity since the worker's hands and judgment remain central. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for wetting surfaces, filling cracks, or trowel smoothing, as these are purely manual, tactile, judgment-based physical actions. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical dexterity, real-time sensory feedback (surface moisture detection, texture assessment), and adaptive hand movements with a trowel—capabilities that current robotics cannot reliably perform in unstructured construction environments with varied surfaces and material conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade task requiring surface wetting, precise grout/slurry application, and trowel finishing that current AI systems cannot perform end-to-end; robotics for this specific niche task are not deployed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes and finish specifications typically require certified human workers to sign off on terrazzo installation quality; customer expectations for human craftsmanship and liability concerns over automated finish work create strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate requires a human specifically for this action, but the tactile skill, material judgment, and physical dexterity needed create strong practical barriers to automation even without regulatory requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction robotics capable of this task would cost significantly more to acquire, program, and maintain per square foot of terrazzo finished than the loaded hourly rate of a skilled terrazzo worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic system exists for this task, so the human worker remains the only cost-effective option; any hypothetical robotic solution would be far more expensive than skilled labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products can autonomously wet surfaces, fill cracks with grout, and smooth finishes in terrazzo work at production quality today; the fine motor control and tactile feedback needed remain at research or prototype stage only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs terrazzo surface prep, grout filling, and trowel smoothing; this remains firmly in the domain of skilled manual labor with no robotic deployment in this trade. |
Move terrazzo installation materials, tools, machines, or work devices to work areas, manually or using wheelbarrow.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Move terrazzo installation materials, tools, machines, or work devices to work areas, manually or using wheelbarrow.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a physical, craft-intensive trade in small firms with low technology adoption rates. Automation adoption in construction trades remains slow and fragmented, with most firms retaining manual labor practices. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized sectors with minimal AI/robotics adoption for physical material transport tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for manual material movement in terrazzo installation; there is no significant productivity-enhancement opportunity through algorithmic or machine-learning support of human workers performing this task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for physically relocating heavy materials and equipment around a job site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Terrazzo installation materials, tools, and machines require navigation of uneven construction sites and careful placement in specific work areas. Current robotics and AI lack reliable spatial reasoning and physical dexterity for this task in complex, variable job-site environments without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical materials-handling task requiring mobility, strength, and navigation of job sites; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | On-site physical work in active construction zones carries liability and safety requirements; OSHA regulations and worksite supervision requirements create material friction. Worker familiarity with site layout and safety protocols is preferred and often mandated. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically for moving materials, but unstructured job sites, uneven terrain, and variable materials create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction-site robotics or autonomous systems would require significant capital investment and site integration far exceeding the cost of manual labor by construction workers, making automation economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this task, so any hypothetical automation (e.g., custom material-handling robots) would be far more expensive than a laborer using a wheelbarrow. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform this task autonomously on construction sites. While industrial robots exist, they are not widely deployed in terrazzo work and lack the adaptability needed for typical construction logistics. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product moves terrazzo materials/tools/machines to work areas; this remains a purely manual labor task with no robotic solution in production for this trade. |
Clean chipped area, using wire brush, and feel and observe surface to determine if it is rough or uneven.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Clean chipped area, using wire brush, and feel and observe surface to determine if it is rough or uneven.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a small, localized, physically-grounded trade with low digital infrastructure and slow adoption of industrial automation. Sector adoption of robotics for this specific task remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades show very low AI/robotics adoption for hands-on physical finishing tasks, remaining a laggard sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential; AI might flag images of severely chipped areas for review, but the core task of feeling surface texture and deciding finish quality depends on haptic feedback and expert judgment that AI cannot meaningfully assist with today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for a tactile, in-the-moment physical inspection and cleaning task like this. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation (wire brushing) and tactile feedback (feeling surface texture) in a variable, on-site environment. Current AI systems lack embodied robotics at construction-site reliability and cannot reliably assess surface quality through haptic feedback in real time. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of a wire brush on a physical surface plus tactile and visual inspection, which current AI systems cannot perform end-to-end without robotic embodiment far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Terrazzo finishing is a licensed skilled trade in many jurisdictions, and quality assurance requires in-person inspection and human judgment of surface finish. Customer expectations and quality liability also favor human craftsmanship oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical, tactile nature of the work and jobsite conditions create strong practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction robotics capable of this work remain expensive and require extensive setup and customization, making them costlier than deploying trained terrazzo workers for this manual, relatively quick task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at any cost point for this fine manual tactile task, so AI is effectively far more expensive or infeasible compared to a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs full-task automation of wire brushing and tactile surface assessment in terrazzo work. Robotics exist in controlled labs but not in production terrazzo finishing operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs tactile-feedback-based surface cleaning and roughness assessment in terrazzo finishing; this remains outside commercial robotics or AI product scope. |
Spread roofing paper on surface of foundation and spread concrete onto roofing paper with trowel to form terrazzo base.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.0/5 · click for rater detail
Spread roofing paper on surface of foundation and spread concrete onto roofing paper with trowel to form terrazzo base.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work occurs in small to mid-sized firms with low digital integration and heavy reliance on specialized craftsmanship. The construction sector overall lags in automation adoption, particularly for on-site finishing trades requiring dexterity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fieldwork, with adoption concentrated in design and scheduling rather than hands-on material placement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could theoretically assist with site planning or material estimation, but offers minimal productivity enhancement for the core task of spreading concrete with a trowel, which depends on human tactile feedback and skilled judgment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of spreading paper and troweling concrete, though it might help with planning quantities or scheduling elsewhere in the job. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in variable field conditions, including spreading materials evenly across uneven surfaces with hand tools. Current AI systems lack the embodied dexterity, real-time spatial reasoning, and adaptive force control needed to operate trowels reliably on-site. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical construction task requiring manual placement of roofing paper and troweling wet concrete over an uneven foundation surface, which no current AI or robotic system can perform outside narrow lab demos. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | The task requires on-site physical presence, real-time adaptation to surface conditions, and skilled judgment about material consistency and thickness. Building codes and quality standards typically require human oversight and certification, creating organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for this step, but practical barriers like handling wet materials on irregular job sites and lack of mobile manipulation robots make substitution impractical. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a robotic system capable of spreading concrete with a trowel would require significant hardware investment, integration, and maintenance, vastly exceeding the cost of a skilled terrazzo worker performing the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of skilled manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI robotic systems reliably perform trowel-based concrete spreading in production terrazzo work. While research exists in construction automation, commercial products for this specific task do not exist at scale in real jobsites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs this specific masonry/flooring preparation task; construction robotics remain research-stage for such varied, unstructured surface work. |
Cut metal division strips and press them into the terrazzo base for joints or changes of color to form designs or patterns or to help prevent cracks.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Cut metal division strips and press them into the terrazzo base for joints or changes of color to form designs or patterns or to help prevent cracks.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is a specialty trade in small crews and regional contractors with limited digitization. Adoption of automation in this sector is minimal; the workforce remains predominantly traditional and skill-based rather than technology-driven. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, slowest-adopting sectors for AI and robotics, with negligible deployment of automation for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While design software could help visualize patterns and generate cutting specifications, AI offers minimal real-time assistance during the hands-on pressing and fitting work itself. Limited scope for meaningful productivity augmentation in the core physical task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with design layout planning or pattern generation beforehand, but offers little direct assistance during the physical cutting and placement work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in a 3D space, cutting custom metal strips to fit irregular surfaces, and pressing them into wet/semi-cured terrazzo at exact depths and angles. Current AI systems lack the embodied dexterity, real-time sensorimotor feedback, and adaptive problem-solving needed for this hands-on construction work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise physical manual task involving cutting metal strips and manually pressing them into wet terrazzo mix, requiring dexterity and tactile feedback that no current AI or robotic system performs off-the-shelf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Terrazzo work traditionally requires apprenticeship, craft certification, and direct liability for finishing quality. The work is inherently site-specific with variable conditions, and aesthetic/structural judgment remains a human responsibility, creating meaningful adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically for this step, but the physical environment, custom worksite conditions, and craftsmanship standards create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Purchasing and maintaining a specialized robotic system capable of this task would far exceed the cost of hiring trained terrazzo workers. The equipment, integration, and oversight costs would make automation economically infeasible for typical job-site economics. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation solution (custom robotics) would be far more costly than paying a skilled terrazzo worker for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI system reliably performs the full cutting, fitting, and pressing of metal division strips in terrazzo applications at production scale. While industrial robots exist, they lack the adaptive sensing and decision-making required for the variability of on-site terrazzo installations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that cuts and places terrazzo divider strips; this remains a specialized trade skill performed entirely by hand tools and human craftsmanship. |
Modify mixing, grouting, grinding, or cleaning procedures, according to type of installation or material used.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Modify mixing, grouting, grinding, or cleaning procedures, according to type of installation or material used.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, especially high-touch finishing work, remain among the slowest sectors to digitize. On-site adaptation and human judgment dominate; AI adoption in terrazzo finishing is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the slowest sectors to adopt AI or robotics, with minimal digitization of on-site physical finishing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by recommending procedure modifications based on material specs or historical data, but the core task of hands-on sensing and physical adjustment remains human-led with minimal automation support available today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with reference information, mix ratios, or troubleshooting guides, but offers little direct assistance during the hands-on execution of mixing, grouting, or grinding. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time adaptation to physical materials, surface conditions, and equipment constraints that vary site-to-site. Current AI cannot perceive physical states, adjust machinery, or execute procedural modifications in the physical world end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of materials, tools, and on-site sensory judgment about surface conditions that current AI systems cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Terrazzo finishing is skilled trade work typically performed by union or licensed workers with apprenticeship-validated expertise. Liability, worker safety, and quality certification create strong organizational and regulatory friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law requires a human specifically, but physical dexterity, material handling, and craftsmanship create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Building a robotic system capable of autonomous procedure modification for terrazzo work would cost orders of magnitude more than hiring a skilled terrazzo worker. Equipment amortization and integration far exceed loaded labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so any AI-based approach (e.g., robotics) would be far more costly than employing a skilled human finisher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs autonomous adaptation of physical construction procedures. The task demands embodied sensing and fine-motor control that remain far beyond production-grade automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs terrazzo mixing, grouting, grinding, or cleaning adjustments in production; this remains firmly a manual skilled trade task. |
Spread, level, or smooth concrete or terrazzo mixtures to form bases or finished surfaces, using rakes, shovels, hand or power trowels, hand or power screeds, or floats.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Spread, level, or smooth concrete or terrazzo mixtures to form bases or finished surfaces, using rakes, shovels, hand or power trowels, hand or power screeds, or floats.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo and concrete finishing occurs in small crews, on varied jobsites with custom conditions, and in traditionally low-tech construction sectors. Adoption of automation in these trades remains minimal; most work is still done by hand by skilled workers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/automation for physical hands-on work, with minimal robotic deployment in terrazzo/concrete finishing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could theoretically assist with slope measurement or material consistency monitoring via sensors, but current assistive tools are not deployed in this domain. The task relies primarily on embodied skill and real-time sensorimotor feedback, where AI augmentation is minimal today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no meaningful real-time assistance to a worker physically spreading and troweling concrete or terrazzo mixtures. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Spreading, leveling, and smoothing concrete/terrazzo requires real-time physical manipulation in 3D space with high tactile feedback and judgment. Current AI systems cannot operate power trowels, floats, or screeds, nor adapt to variable terrain conditions and material consistency on-site. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, dexterity-intensive manual trade task requiring real-time tactile feedback and precise material handling that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | The task requires licensed tradecraft and on-site presence; building codes and union regulations often mandate human-supervised finishing of concrete/terrazzo surfaces. There are also high liability costs if automated systems produce defects in structural or aesthetic finishes. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate requires a human specifically, but the physical dexterity, judgment on material consistency, and jobsite variability create strong practical barriers to substitution, though not legal ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialised robotic systems for concrete finishing, where they exist, cost tens of thousands of dollars and require extensive setup, calibration, and site preparation—far exceeding the loaded wage of a terrazzo worker performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute for this task, so the effective cost of automation (were it possible) would far exceed the wage of a human finisher performing this skilled labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform autonomous concrete spreading and finishing at production scale. Robotic concrete finishing exists in narrow lab/pilot settings but does not reliably handle the full range of finish qualities, slopes, and material conditions this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs terrazzo/concrete spreading, leveling, and finishing at production scale; this remains firmly in the domain of skilled tradespeople with only experimental construction robotics research. |
Grind curved surfaces or areas inaccessible to surfacing machine, such as stairways or cabinet tops, with portable hand grinder.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Grind curved surfaces or areas inaccessible to surfacing machine, such as stairways or cabinet tops, with portable hand grinder.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a small, traditional trade with low digitization and physical on-site constraints. Adoption of manufacturing robots in the sector is minimal, and hand-finishing work remains labor-intensive by design. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and finishing trades are among the slowest sectors to adopt AI/robotics due to physical, unstructured environments and low digitization. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance here; power tool operation and curved surface grinding are fundamentally manual skills. Vision-guided marking or defect detection might slightly assist planning, but cannot substantially augment the core grinding task itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful real-time assistance for manual grinding of curved surfaces; there are no practical AI tools augmenting this specific physical craft task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of a handheld power tool in spatially complex, inaccessible areas (stairways, cabinet tops) where curved surfaces require tactile feedback and real-time adjustment. Current AI systems lack the embodied dexterity, spatial reasoning in confined spaces, and adaptive force control needed to operate a hand grinder safely and effectively. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity, precise hand-eye coordination, and manipulation of a hand grinder on irregular surfaces—no current AI system can perform this physical manual task at all. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves operation of potentially hazardous machinery (hand grinder) in tight spaces where safety and quality depend on human judgment and immediate corrective action. Liability for tool-related injuries and regulatory requirements around machinery operation create material barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but physical workspace constraints, safety requirements around power tools, and quality/liability concerns around damaging finished surfaces create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robot arm with gripper, positioning system, and safety infrastructure, combined with the need for task-specific programming and oversight per job, would far exceed the labor cost of a skilled terrazzo worker performing this grinding work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the human worker remains the only cost-effective option; any hypothetical robotic solution would require expensive custom engineering exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably perform handheld grinding of curved surfaces in confined spaces. This requires mobile manipulation, surface sensing, and safety awareness in unstructured environments—capabilities that exist only in research robotics, not in commercial systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic products perform fine-grained terrazzo grinding on curved or inaccessible surfaces like stairways; this remains far beyond current robotics manipulation capabilities in production. |
Fill slight grinding depressions with matching grout material and hand-trowel for a smooth, uniform surface.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Fill slight grinding depressions with matching grout material and hand-trowel for a smooth, uniform surface.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo finishing is performed by small, geographically dispersed specialty contractors with limited digitization and capital investment in automation. Adoption of robotics in this sector is negligible; the work remains largely manual craft-based. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, slowest-adopting sectors for AI or robotics in physical finishing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI vision could theoretically assist with defect detection or surface mapping, the core task—hand-troweling for surface uniformity—remains fundamentally manual. Current AI tools offer minimal productivity assistance to the human worker performing this skill. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this tactile, hands-on finishing task; there's no meaningful software or AI tool that aids the physical troweling process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three dimensions, real-time visual assessment of surface uniformity, and dexterous hand-troweling of grout on vertical or horizontal surfaces. Current AI lacks the embodied robotics and sensorimotor feedback to perform this end-to-end at the quality standards required. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of grout material with a hand trowel, precise tactile feedback, and fine motor control to match surface texture and color—no current AI system can perform this physical craft task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves safety-sensitive finishing work on surfaces where defects can affect building integrity and aesthetics. Industry standards, customer expectations for human craftsmanship, and quality liability create substantial adoption friction, though no strict licensing requirement exists for the task itself. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate specifically requires a human, but the physical dexterity, quality/aesthetic judgment, and on-site variability create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of this task, integration, training, and maintenance far exceeds the loaded wage of a skilled terrazzo worker, especially given the low volume and high customization of individual terrazzo projects. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a skilled tradesperson. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs grout filling and hand-troweling of terrazzo surfaces in production. While research robots exist for some construction tasks, none have demonstrated reliable, unsupervised performance of this specific craft skill at commercial scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs terrazzo grout-filling and hand-troweling finishing work; this remains a specialized manual trade skill. |
Mold expansion joints and edges, using edging tools, jointers, or straightedges.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Mold expansion joints and edges, using edging tools, jointers, or straightedges.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a traditional, low-digitization construction trade performed by small specialized firms. Adoption of automation in this sector remains minimal; the workforce is aging and skilled, and there is no evidence of AI or robotic displacement in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fabrication tasks, with minimal automation penetration to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a worker molding edges and joints in real time. The task is fundamentally about precise physical execution, not information processing, design iteration, or decision support that AI could augment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for the physical act of molding and finishing terrazzo edges and joints. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Molding expansion joints and edges requires precise physical manipulation of tools on a three-dimensional surface (terrazzo floor), real-time tactile feedback, and adaptive response to material properties. Current AI systems lack embodied robotic capabilities at the dexterity and reliability level needed for this construction task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise, tactile physical task involving hand-tool manipulation of wet terrazzo material, requiring dexterity and real-time feedback that current robotics/AI cannot replicate outside controlled labs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: terrazzo finishing is a licensed trade in many jurisdictions requiring apprenticeship and certification; quality and safety liability fall on the responsible tradesperson; and the task occurs on-site in variable conditions requiring human judgment and adaptation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate specifically requires a human for this narrow task, but construction site conditions, material variability, and quality/liability concerns create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this level of precision construction work (if they existed) would require custom engineering, installation, and maintenance costs far exceeding the loaded wage of a terrazzo worker per task unit. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute in production, so the human worker remains the only cost-effective option for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic system currently performs terrazzo edge molding reliably in production. This task requires specialized physical dexterity, on-site adaptation to variable surface conditions, and the ability to work within tight tolerances—capabilities not available in general-purpose commercial products. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs this specific masonry finishing task; it remains fully manual work done by skilled tradespeople. |
Repair concrete by cutting out damaged areas, drilling holes for reinforcing rods, and positioning reinforcing rods, using power saw and drill.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Repair concrete by cutting out damaged areas, drilling holes for reinforcing rods, and positioning reinforcing rods, using power saw and drill.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and skilled trades are laggard sectors for automation: highly fragmented, variable project conditions, small firms, and strong craft culture. Adoption of robotic concrete work remains negligible in production despite decades of R&D interest. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal production deployment of automation for concrete repair. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for this task. Some computer vision might flag concrete damage, but the actual cutting, drilling, and rod positioning still depend entirely on human skill and judgment in variable field conditions. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, measurement calculations, or diagnostic assessment of damage, but offers little direct assistance during the physical cutting, drilling, and rod placement process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in variable, on-site conditions—cutting and drilling concrete, then positioning reinforcing rods—which demands real-time sensory feedback, spatial reasoning, and adaptation. Current AI and robotics cannot reliably perform this coordinated physical work end-to-end in unstructured construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade task requiring skilled use of power saws and drills on concrete surfaces; no current AI system can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Construction and structural repair work have significant regulatory oversight, liability concerns (failed reinforcement affects building safety), and often explicit licensing/inspection requirements. Human accountability and sign-off are typically mandated by building code. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed like a professional trade in most jurisdictions, physical worksite safety requirements, structural integrity concerns, and quality/liability issues around reinforcement work create moderate barriers to non-human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment, integration, safety oversight, and maintenance required for reliable on-site concrete repair robotics far exceeds the cost of a skilled terrazzo worker performing the task manually at construction wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this task, so any hypothetical automation would require expensive specialized robotics far exceeding human labor costs for this niche task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform concrete repair with reinforcing rod positioning at scale. Robotic concrete cutting and drilling exist only in controlled lab settings or highly specialized, fixed applications, not in general field repair scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs concrete cutting, drilling, and rebar positioning autonomously in commercial terrazzo/concrete repair work today. |
Build wooden molds, clamping molds around areas to be repaired, or setting up frames to the proper depth and alignment.
7CI 0–15 · exposure 0 · augmentation 13 · importance 3.2/5 · click for rater detail
Build wooden molds, clamping molds around areas to be repaired, or setting up frames to the proper depth and alignment.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Terrazzo work is a traditional craft sector with low digitization, small firms, and physical on-site constraints; adoption of construction automation remains minimal in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fieldwork, with minimal production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance in physically building molds, clamping them, or setting frames to correct depth and alignment on construction sites. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, measurements, or generating cut lists, but offers little direct help with the physical building and fitting of molds. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of wooden molds, precise clamping in 3D space, and on-site spatial judgment. Current AI systems lack embodied capability to perform construction setup work end-to-end in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical construction task requiring hands-on carpentry, measurement, and fitting on-site that no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task inherently requires human physical presence on-site, hands-on judgment of alignment and depth, and real-time problem-solving in variable construction conditions—hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically bars automation, but the physical nature, need for on-site judgment, and variability of repair sites create practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves custom fabrication and spatial setup that would require specialized robotics far more expensive than the loaded wage of a skilled trades worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic solution for this task, so any hypothetical automation would be far more expensive than a skilled tradesperson. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical mold construction, clamping, and frame alignment in real terrazzo worksites. This remains entirely manual labor with no production automation systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product builds or fits wooden molds/frames for terrazzo repair; this requires physical dexterity and robotic manipulation far beyond current commercial capability. |
Signal truck driver to position truck to facilitate pouring concrete and move chute to direct concrete on forms.
5CI 0–10 · exposure 0 · augmentation 0 · importance 2.8/5 · click for rater detail
Signal truck driver to position truck to facilitate pouring concrete and move chute to direct concrete on forms.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction trades, particularly on-site skilled coordination work, remain in laggard sectors with minimal AI adoption; current industry practices rely heavily on experienced human workers for safety-critical equipment coordination. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and terrazzo finishing are low-digitization, physically-oriented trades with minimal AI/robotics adoption for real-time equipment coordination tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | This task is inherently dependent on real-time human presence and decision-making with heavy equipment; there is no meaningful way AI can augment a human already performing live truck and chute positioning on a construction site. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for hand-signaling truck positioning or chute direction during concrete pours. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time coordination with a truck driver on an active construction site, involving spatial judgment, hand signals, and dynamic response to changing conditions. Current AI systems cannot reliably perceive, communicate with, and coordinate physical equipment movement in outdoor construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, real-time coordination task on a job site requiring hand signals, situational awareness, and precise chute/truck positioning—no off-the-shelf AI system performs this physical action end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Construction sites have strict safety protocols and liability requirements; a human must be present and accountable for directing heavy equipment like concrete trucks. Regulatory and safety-critical nature creates hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this signaling task, but safety liability, coordination with heavy machinery, and physical presence requirements create real practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying a robotic system capable of safe, autonomous positioning and signaling would vastly exceed the wages of a terrazzo worker performing this task, making AI economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical directing/positioning function, so any hypothetical automation (robotics, sensors) would require costly specialized hardware exceeding simple human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product today can autonomously signal a truck driver and manage concrete chute positioning on an active construction site. This requires embodied presence, real-time perception, and safety-critical coordination that remains firmly in research territory. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product directs concrete trucks or manipulates chutes on active construction sites; this remains firmly a manual, in-person 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.