Floor Layers, Except Carpet, Wood, and Hard Tiles
47-2042.00Median wage $56,460/yr23,640 employed (US)Rank #874 of 923 scored · top 95% by substitution
Apply blocks, strips, or sheets of shock-absorbing, sound-deadening, or decorative coverings to floors.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution11
Exposure1
Augmentation14
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
14 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%1
panel mean rating 1.0/5 → substitution pressure 1/100
Technical feasibility todayw 20%0
panel mean rating 1.0/5 → substitution pressure 0/100
Cost vs. human wagew 15%0
panel mean rating 1.0/5 → substitution pressure 0/100
Adoption barriersw 20%inverted — strong barriers lower the score51
panel mean rating 3.0/5 (barrier strength) → substitution pressure 51/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (14 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.
Sweep, scrape, sand, or chip dirt and irregularities to clean base surfaces, correcting imperfections that may show through the covering.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.2/5 · click for rater detail
Sweep, scrape, sand, or chip dirt and irregularities to clean base surfaces, correcting imperfections that may show through the covering.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor preparation remains a manual, on-site physical task dominated by small teams and craft workers; digital adoption in the sector is minimal and robotics adoption in this context is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, lowest-automation-adoption sectors, with essentially no AI/robotic penetration into manual surface prep. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful augmentation for surface preparation work; computer vision systems for defect detection exist but are not integrated into worker tools, and this task fundamentally requires physical human presence. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a worker physically sweeping, scraping, or sanding a floor surface. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in unstructured environments, tactile feedback to detect surface imperfections, and real-time adaptation to varying conditions. Current AI systems cannot reliably perform end-to-end autonomous floor preparation with the dexterity and spatial reasoning required. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring dexterous manipulation of tools on irregular surfaces; no AI system can perform the physical sweeping, scraping, or sanding itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no explicit licensing barriers, the requirement for human judgment about surface quality and the organizational preference for skilled labor inspecting their own work provide modest friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, but physical presence and manual skill are inherent requirements that prevent any software-based substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of this task remain prohibitively expensive (often hundreds of thousands of dollars), with high integration and maintenance costs far exceeding the loaded hourly wage of a floor layer. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical prep work, so human labor remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems currently perform autonomous floor preparation, surface inspection, or imperfection correction at scale. The task demands precise physical control and environmental assessment that only exists in research robotics prototypes, not production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic product performs autonomous floor surface preparation in real construction/renovation settings today; this remains far outside current robotics deployment. |
Cut covering and foundation materials, according to blueprints and sketches.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Cut covering and foundation materials, according to blueprints and sketches.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a traditional skilled trade with low digitization and slow adoption of advanced automation; most firms remain small and rely on experienced workers with adaptive, on-site judgment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are a low-digitization, physically-oriented sector with minimal AI/robotic adoption for material cutting tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with blueprint interpretation and material calculations, but the core physical cutting task itself offers limited augmentation opportunity; the worker's experience and spatial judgment remain central. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help interpret blueprints or generate cut lists and layout diagrams, offering modest planning assistance, but does not meaningfully augment the physical cutting process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cutting materials to specification requires physical manipulation, precise spatial reasoning tied to real-world blueprints, and adaptive handling of varied materials and conditions. Current AI systems have no embodied capability to perform this end-to-end in real construction environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Cutting physical flooring materials to fit a space requires physical dexterity, measurement, and manipulation of tools that no current AI system can perform end-to-end without robotic hardware far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no explicit licensing barriers for automating cutting itself, the task occurs in regulated construction environments with high liability for material waste and safety hazards, and human judgment about material properties and site-specific conditions is valued. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this cutting task to certified professionals, but physical workspace constraints and customer expectation of skilled manual work create some friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital, maintenance, and integration cost of robotic systems capable of material cutting far exceeds the loaded wage of a skilled floor layer who performs this task as part of their normal work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical automation (e.g., robotic cutting rigs) would be far more costly than a human tradesperson today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously cut physical materials according to blueprints in field conditions. This task requires robotics and real-world sensorimotor feedback that are not yet reliably available in production at scale for this domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical cutting of flooring materials from blueprints in production; this remains firmly in the domain of skilled manual labor. |
Form a smooth foundation by stapling plywood or Masonite over the floor or by brushing waterproof compound onto surface and filling cracks with plaster, putty, or grout to seal pores.
13CI 10–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Form a smooth foundation by stapling plywood or Masonite over the floor or by brushing waterproof compound onto surface and filling cracks with plaster, putty, or grout to seal pores.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a traditional construction trade with low digitization and a fragmented, small-firm base. Adoption of automation technologies is minimal, with work remaining highly manual and site-specific. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, lowest AI-adoption sectors, with physical on-site manual labor dominating. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers negligible assistance for floor surface preparation; there are no practical tools that augment a worker's ability to staple, brush sealants, or fill cracks more effectively. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical acts of stapling, brushing compound, or filling cracks, though it might help with material planning elsewhere in the job. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials (stapling plywood, brushing waterproof compounds, filling cracks) and real-time tactile feedback to assess surface smoothness and texture. Current AI systems cannot execute these fine motor and pressure-sensitive operations on variable floor conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual construction task requiring dexterity, tool use, and adaptation to irregular surfaces; no current AI system can perform stapling, brushing, or filling actions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers for this task, customer expectations for human workmanship, liability concerns for defective floor prep (which affects downstream flooring), and the highly variable nature of job sites create moderate friction to automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human floor layer, but physical dexterity, judgment on surface conditions, and liability for faulty installation create practical barriers to any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robotic system capable of floor preparation, integration, and site setup would far exceed the hourly wage of a skilled floor layer performing this work directly. |
| 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 human labor costs for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product exists that can autonomously staple plywood, apply waterproof coatings, or fill cracks to professional floor-laying standards. This remains in the domain of specialized robotics research, not production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs subfloor preparation in real construction settings; this remains purely a research-stage robotics challenge if attempted at all. |
Roll and press sheet wall and floor covering into cement base to smooth and finish surface, using hand roller.
13CI 10–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Roll and press sheet wall and floor covering into cement base to smooth and finish surface, using hand roller.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The construction and flooring sectors show low automation rates for manual, on-site finishing tasks; most work remains labor-intensive and site-specific, with limited capital deployment of robotics for this class of work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring installation trades are among the least digitized and slowest to adopt AI or robotic automation for physical manual tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for rolling and pressing operations, though computer vision might help in surface inspection or planning. The core task depends on real-time tactile feedback and adaptive hand pressure that AI tools cannot meaningfully augment today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI tools offer no meaningful assistance to the physical act of rolling and pressing flooring material; there is no digital or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in a variable, real-world environment—rolling and pressing sheet material onto uneven cement surfaces with tactile feedback. Current AI systems cannot perform physical manipulation at this level of dexterity and adaptability. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hand-eye coordination and tactile pressure control to smooth flooring material; no current AI or robotic system performs this end-to-end in real installations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for this task in most jurisdictions, the physical nature of the work and customer preference for human craftwork creates modest friction to automation. Safety and quality assurance on construction sites also favor human oversight. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the way medical or legal tasks are, physical dexterity, workplace safety standards, and the physical nature of the work create strong practical barriers to automation absent specialized robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of surface-following pressure control and material handling would require significant capital investment and integration costs, far exceeding the marginal labor cost of a skilled floor layer per task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven equivalent for this task, so a human floor layer remains the only viable and cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic systems reliably perform this specific task (rolling and pressing sheet coverings onto cement bases) in production settings. This remains a manual, skilled craft with few automation precedents in real-world construction workflows. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that install or press sheet flooring/wall covering into cement bases; this remains purely a manual trade skill. |
Apply adhesive cement to floor or wall material to join and adhere foundation material.
13CI 10–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Apply adhesive cement to floor or wall material to join and adhere foundation material.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor installation remains a low-digitization, site-based physical trade dominated by small firms and independent contractors with minimal production-scale automation adoption to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades are a low-digitization, physical-labor sector with minimal AI or robotics adoption for hands-on installation tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI/robotic tools offer minimal assistance to human floor layers; the task is inherently hands-on and environment-responsive, with few opportunities for meaningful augmentation from existing systems. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of applying adhesive and joining flooring materials; this is a purely manual craft task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Applying adhesive cement requires precise physical manipulation in unstructured environments (varying floor/wall conditions, moisture, temperature), real-time spatial judgment, and adaptive pressure control—capabilities current robots and AI systems lack reliably at scale outside controlled labs. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical task requiring hand-eye coordination and dexterity to spread adhesive and lay flooring materials precisely; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally restricted, adoption is hindered by safety/liability concerns (adhesive fumes, installation quality affecting building integrity), customer preference for human oversight, and site-specific conditions that demand experienced judgment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this sub-task, but the physical nature of construction work and liability for improper installation create practical barriers to any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of material handling and adhesive application are capital-intensive and require extensive on-site setup, making them far more expensive than skilled manual labor for typical projects. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this at any cost, so the human worker remains the only available and cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous adhesive application for floor/wall materials in production settings; the task demands dexterous manipulation and environmental adaptation beyond current robotic offerings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs adhesive application and material joining for flooring installation; this remains firmly in the domain of human manual labor with no robotic products in commercial deployment. |
Remove excess cement to clean finished surface.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Remove excess cement to clean finished surface.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and floor-laying remain sectors with low AI and robotics adoption; most firms are small, rely on manual labor, and show limited investment in automation for finishing tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, lowest AI-adoption sectors, with essentially no robotic deployment for this specific finishing task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision tools could potentially assist by detecting areas of excess cement or suggesting cleanup patterns, but such assistance has not matured to practical deployment and would add only marginal value to an experienced floor layer's judgment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for this physical, tactile cleanup task; there is no software or planning component AI could enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Removing excess cement from a finished surface requires tactile precision, dynamic adaptation to surface texture and material properties, and spatial judgment that current robotic systems cannot reliably perform. The task involves detecting varying cement buildup, adjusting pressure and angle in real-time, and avoiding damage to the finished surface—capabilities well beyond automated systems today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation, dexterity, and situational judgment about surface finishing that no current AI-driven robotic system can perform generally in unstructured job-site conditions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally restricted to licensed workers, the task occurs on-site in active construction environments with safety and quality standards, and substitution would require building contractor confidence in robotic systems—moderate organizational friction exists. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but physical, unstructured work environments and liability for damaged floors create practical friction against any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized hardware and vision systems required to approach this task would far exceed the hourly labor cost of a skilled floor layer, especially given the need for custom integration and extensive oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this manual finishing task, so the human laborer remains the only cost-effective option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform autonomous cement cleanup on finished surfaces at production quality. While some construction robotics exist, none have demonstrated reliable performance on this specific, delicate finishing task in real-world conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial robotic or AI product performs cement cleanup on finished floor surfaces in real installations; this remains outside current product offerings. |
Cut flooring material to fit around obstructions.
12CI 5–19 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Cut flooring material to fit around obstructions.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a hands-on trade with low digitization, performed by small and mid-size contractors. AI adoption in this sector remains minimal; most work is still done manually by skilled craftspeople with no significant production-level automation evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by analyzing photos to suggest cutting patterns or detect obstructions, but the core task—taking physical measurements and executing precise cuts—offers limited augmentation potential. Assistance would be peripheral to the main manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations or cut-layout planning via apps, but it offers little assistance for the physical act of cutting and fitting material around obstructions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cutting flooring material to fit around obstructions requires spatial reasoning, precise measurement of irregular shapes, and physical manipulation. While AI can compute cutting patterns from images or dimensions, the end-to-end task—including measuring on-site, accounting for material thickness and installation context, and executing the cut—remains heavily dependent on human judgment and manual labor that current systems cannot perform reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of materials with tools in a real-world space, which no current AI system can perform end-to-end; it is a manual dexterity task with spatial reasoning around physical obstructions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Floor installation is site-specific work requiring proper authorization and liability for fit and finish quality. Building codes, warranty obligations, and customer accountability create strong barriers to full automation; installers must sign off on work quality. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law strictly requires a human floor layer, but the task demands physical dexterity, on-site adaptability, and craftsmanship that create strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI vision and planning systems would require significant hardware (robotic arms, precision measurement tools) and setup costs that would exceed the labor cost of a skilled floor layer performing the task directly. Integration and oversight overhead further increases the cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical trade task, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this task independently. Vision systems can detect obstructions and AI can suggest cuts, but actual measurement, material handling, and cutting execution require skilled human workers. This remains research-stage for full automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical cutting and fitting of flooring materials; robotics for this exact task remain research-stage at best and are not in commercial deployment. |
Measure and mark guidelines on surfaces or foundations, using chalk lines and dividers.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Measure and mark guidelines on surfaces or foundations, using chalk lines and dividers.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a hands-on, physically embedded trade in the construction sector, which ranks among the lowest in AI adoption due to on-site variability, small firm dominance, and the capital-intensive, customized nature of automation. Current adoption of AI in this work remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, physically-dependent sectors with minimal AI or robotics adoption for hands-on layout tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with measurement and baseline calculations via computer vision or laser scanning before marking begins, but the physical act of marking itself remains entirely human-performed. The augmentation opportunity is narrow and at the planning stage rather than the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital measuring tools and laser layout devices offer some assistance, but general AI systems provide little direct augmentation to this specific manual marking task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of chalk lines and dividers on real-world surfaces, demanding real-time spatial judgment and fine motor control that current AI systems cannot perform. While vision systems could measure surfaces remotely, the actual marking operation—applying chalk lines and positioning dividers—requires embodied robotic capabilities that are not reliably deployed for construction work at scale today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, measurement of real-world irregular surfaces, and manual marking with tools like chalk lines and dividers; no current AI system can perform this physical layout task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical marking directly affects downstream work quality and safety, creating high error-cost asymmetry and implicit liability concerns. The task occurs on-site in construction environments with variable conditions, requiring human judgment about material properties and subsurface conditions that typically mandate direct worker involvement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically gates this sub-task, but it requires physical dexterity, on-site presence, and precise craftsmanship that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of surface measurement and marking would require substantial capital investment, site-specific setup, and integration costs, making them far more expensive than the labor cost of a skilled floor layer performing this task in situ. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical automation (e.g., robotics) would be far more costly than a human worker performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No current production AI system performs end-to-end physical marking tasks on construction surfaces. Autonomous construction robots exist in research but lack reliability and flexibility for the varied conditions and precision requirements of this task in real job sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product measures and marks physical guidelines on flooring surfaces; this remains firmly in the domain of skilled manual labor with no robotic or AI substitute in production. |
Inspect surface to be covered to ensure that it is firm and dry.
7CI 0–15 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Inspect surface to be covered to ensure that it is firm and dry.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and flooring installation remain largely non-digital, small-business-dominated sectors with low automation adoption overall. Surface inspection is a prerequisite step deeply embedded in manual workflows. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, physically-oriented sectors with minimal AI/robotic adoption for hands-on site inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by analyzing photographs of surfaces for visual defects or moisture patterns, but the tactile and judgment components of assessing firmness and dryness remain largely human-dependent, limiting meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Moisture meters and sensor-based tools (some app-connected) can assist workers in assessing dryness, but AI provides only marginal assistance beyond existing simple instrumentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Inspecting whether a surface is firm and dry requires tactile sensing, visual assessment of moisture, and judgment about structural integrity. Current AI systems lack the necessary hardware (touch sensors, moisture detection) and embodied presence to perform this inspection reliably on-site. |
| Task automatability | claude-sonnet-5 | 1/5 | Requires physical, on-site tactile and visual inspection of a subfloor surface using touch, moisture readings, and judgment; no current AI system can physically perform this inspection end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Building codes and construction standards typically require a licensed tradesperson to inspect and certify surface conditions before installation, and the liability for improper surface preparation falls on the installer, creating a strong legal and professional requirement for human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this inspection step, but it is embedded in a physical trade task requiring direct on-site presence and judgment, creating practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying a robotic or specialized sensing system to perform this inspection would far exceed the loaded wage of a worker performing a quick visual and tactile check on-site. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical inspection, so any AI-based approach (e.g., robotic sensors) would require costly hardware far exceeding the marginal cost of a human doing a quick manual check. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous surface firmness and moisture inspection for flooring work. This task fundamentally requires physical presence and multi-modal sensing that current deployed AI systems do not possess. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously inspects flooring substrates for firmness and dryness in the field; this remains a manual, hands-on task performed by the installer. |
Trim excess covering materials, tack edges, and join sections of covering material to form tight joint.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Trim excess covering materials, tack edges, and join sections of covering material to form tight joint.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a physical, on-site trade with high variability per job. Adoption of AI or robotics in this sector remains minimal; the industry remains labor-intensive with little evidence of significant automation deployment in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, lowest AI-adoption sectors, with minimal robotic deployment for finish trades work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with layout planning or material selection prior to the task, but once trimming and joining begins, the work is too hands-on and real-time for meaningful AI augmentation to support the worker without the AI itself performing the physical actions. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for the physical cutting, fitting, and seaming of floor covering materials. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation, spatial judgment, and tactile feedback to trim materials, secure edges, and align sections perfectly. Current AI systems lack the embodied dexterity, real-time environmental adaptation, and fine motor control needed to perform these operations reliably without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring precise hand-eye coordination, cutting, and fitting of flexible/rigid flooring materials in situ; no current AI system or robot can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Floor layers typically must be licensed or certified, and the quality of joints directly affects building integrity and liability. Customers expect and often contractually require a licensed professional to inspect and sign off on the work, creating a legal and contractual barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but physical dexterity requirements, variable job-site conditions, and customer expectations for quality craftsmanship create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware, integration, and safety overhead required for a robotic system capable of this work far exceed the cost of hiring a skilled floor layer, especially given the task's variability and the need for continuous human correction. |
| 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 floor layer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task autonomously today. Robotic systems exist for flooring but are narrowly scoped to specific materials and settings; they do not reliably handle the variable, non-standardized joining and trimming decisions required in general floor-laying work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform in-field floor covering trimming and seam joining; robotics research in flexible material manipulation for flooring is not commercially available. |
Determine traffic areas and decide location of seams.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Determine traffic areas and decide location of seams.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a physical, site-specific trade with low digitization. Adoption of AI is minimal; most firms use experienced workers' on-site assessment and decision-making without algorithmic support. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Flooring installation is a low-digitization, physical trade sector with minimal AI adoption in the field for spatial layout decisions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by providing heat maps of likely traffic zones or showing seam placement options, but the core task—deciding where seams belong—remains judgment-heavy and spatially context-dependent in ways current AI cannot reliably augment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with room measurement or seam-placement planning software, but this offers only marginal assistance to the core on-site judgment task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires on-site spatial reasoning, understanding of building layout patterns, and aesthetic judgment about seam placement. Current AI systems cannot reliably assess physical space geometry, traffic flow dynamics, or make contextual decisions about material appearance without extensive human supervision. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, spatial judgment of room usage patterns, and real-time assessment of physical space that current AI cannot perform end-to-end without a human on-site. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: flooring installation must meet building codes, seam placement affects safety and durability, and liability for poor decisions falls on the contractor. Professional judgment and site sign-off by skilled workers are typically contractually required. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically for this sub-task, but the inherently physical, on-site nature of assessing traffic and material layout creates strong practical barriers to remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The setup cost for custom computer vision, site analysis, and AI oversight would far exceed the loaded wage of a skilled floor layer performing this assessment, which typically takes minutes to hours per site. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical layout task, so any AI cost comparison is moot; the human tradesperson remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs traffic pattern analysis and seam placement decisions on actual job sites. This requires real-time 3D spatial understanding, walking-pattern prediction, and material-specific knowledge that no production system has demonstrated at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously determines traffic patterns and seam placement in physical flooring installation; this remains firmly a manual skilled-trade judgment task. |
Lay out, position, and apply shock-absorbing, sound-deadening, or decorative coverings to floors, walls, and cabinets, following guidelines to keep courses straight and create designs.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Lay out, position, and apply shock-absorbing, sound-deadening, or decorative coverings to floors, walls, and cabinets, following guidelines to keep courses straight and create designs.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a physical, site-dependent trade with low digitization and small firms dominant. Adoption of automation in flooring installation remains negligible; the sector has seen minimal AI/robotic deployment to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the least digitized, lowest AI-adoption sectors, with physical installation work seeing negligible automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance via design visualization or material-specification guidance, but offers limited augmentation for the core physical installation work; the task remains largely manual and craft-based. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with design layout planning, material estimation, or pattern visualization beforehand, but offers little help during the actual physical installation process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of materials (positioning, aligning, applying coverings) in real-world 3D space with visual-spatial judgment. Current AI lacks the embodied robotics capabilities to reliably handle the fine motor control, material properties understanding, and real-time surface adaptation needed for professional-quality installation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade requiring precise material handling, cutting, adhesive application, and fine motor skill in real-world variable spaces; no current AI/robotic system performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: building codes and warranties often require licensed or certified installers; liability for defects (water damage, buckling, accidents) falls heavily on the installer; and customer expectation for human craftsmanship and quality assurance is strong in this skilled trade. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing typically required, but physical dexterity, on-site variability, and customer expectations for quality craftsmanship create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of any floor-laying work remain prohibitively expensive in capital and integration costs compared to skilled human labor wages, with poor generalization across job sites and material variations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform full floor covering installation end-to-end. While robotic research exists, production systems capable of handling diverse materials, surfaces, and design specifications at construction-site quality standards do not exist in mainstream use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products install flooring or wall coverings autonomously; this remains purely a human manual trade with no commercial robotic solution. |
Heat and soften floor covering materials to patch cracks or fit floor coverings around irregular surfaces, using blowtorch.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Heat and soften floor covering materials to patch cracks or fit floor coverings around irregular surfaces, using blowtorch.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Flooring installation is a small-firm, physically on-site trade with limited digital infrastructure and slow technology adoption; no evidence of AI or robotic adoption in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are among the slowest sectors to adopt AI/robotics, with minimal digitization or automation investment for physical installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with temperature recommendations or material properties guidance via tablet/AR, but the core task—manipulating a blowtorch safely around irregular surfaces—offers minimal opportunity for meaningful human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of heating and shaping floor covering material with a blowtorch. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time sensory feedback (temperature, material consistency), precise manual control of a blowtorch, and adaptation to irregular physical surfaces. Current AI systems cannot execute the fine motor control and safety-critical judgment needed to handle open flame tools on building materials without human presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade task requiring precise heat application, dexterity, and real-time tactile judgment; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, liability concerns (open flame in occupied or sensitive spaces), OSHA standards, and worker compensation requirements create strong organizational and legal friction against autonomous automation of this task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but physical dexterity, safety handling of open flame, and irregular surface adaptation create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized equipment (blowtorch, safety gear), safety oversight, and the low-wage nature of this task mean automation would require significant upfront capital investment with questionable payoff against a skilled tradesperson's cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI or robotic substitute performing this task, so any AI cost comparison is moot—human labor is currently the only option and thus effectively cheaper than any nonexistent alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous heating and material manipulation with blowtorches in uncontrolled building environments. This remains beyond current robotic or AI capabilities in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs blowtorch-based floor covering fitting; this remains entirely human physical labor with no robotic or AI product in this niche. |
Disconnect and remove appliances, light fixtures, and worn floor and wall covering from floors, walls, and cabinets.
7CI 0–15 · exposure 0 · augmentation 0 · importance 3.3/5 · click for rater detail
Disconnect and remove appliances, light fixtures, and worn floor and wall covering from floors, walls, and cabinets.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floor laying is a low-digitization, physical sector dominated by small and mid-sized firms with limited automation adoption. No meaningful production deployment of AI agents for this type of task exists in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and flooring trades are a physically-demanding, low-digitization sector with minimal AI/robotic adoption for demolition-type tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to workers performing manual disconnection and removal of fixtures; the task is inherently physical and dependent on real-time environmental adaptation that augmentation tools cannot currently support. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical acts of disconnecting and removing appliances, fixtures, and worn coverings. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation, spatial reasoning, and safety judgments in varied real-world environments—disconnecting live appliances, removing fixtures safely, and handling worn materials. Current AI systems cannot perform these physical actions end-to-end or approach the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical demolition and disconnection task requiring manipulation of appliances, fixtures, and materials in varied real-world spaces; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task involves safety-critical work (electrical disconnection, hazardous material handling) that requires licensed professionals and on-site physical presence. Legal liability for improper appliance disconnection or injury creates strong barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically prevents automation of demolition tasks, though disconnecting appliances may involve electrical/plumbing considerations that could require certified trades in some jurisdictions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of this work, if it existed, would cost orders of magnitude more than hiring a floor layer, and integration costs would be prohibitive for the variable, site-specific nature of the work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic solution for this task at any comparable cost; a human worker remains the only practical and cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task. While robotics research exists, production-grade systems that safely disconnect appliances and remove fixtures from arbitrary locations do not exist in commercial deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical removal of flooring, wall coverings, or appliance disconnection; this remains firmly in the domain of human manual labor and robotics research at best. |
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.