Helpers--Carpenters

47-3012.00

Median wage $43,780/yr21,680 employed (US)Rank #806 of 923 scored · top 87% by substitution

Help carpenters by performing duties requiring less skill. Duties include using, supplying, or holding materials or tools, and cleaning work area and equipment.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution14

Exposure4

Augmentation16

Substitution — the headline: capability discounted by cost, barriers and adoption.

Exposure — technical capability alone, regardless of whether anyone deploys it.

Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.

Tasks on the substitution scale

18 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

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%4

panel mean rating 1.2/5 → substitution pressure 4/100

Technical feasibility todayw 20%2

panel mean rating 1.1/5 → substitution pressure 2/100

Cost vs. human wagew 15%2

panel mean rating 1.1/5 → substitution pressure 2/100

Adoption barriersw 20%inverted — strong barriers lower the score60

panel mean rating 2.6/5 (barrier strength) → substitution pressure 60/100

Sector adoption velocityw 10%1

panel mean rating 1.1/5 → substitution pressure 1/100

Task breakdown (18 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.

Cut timbers, lumber, or paneling to specified dimensions.

CI 10–35 · exposure 13 · augmentation 38 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption is concentrated in large-scale fabrication shops and mills rather than general carpentry. Most construction sites and smaller shops continue manual or semi-manual cutting, reflecting slow real-world deployment in the broader carpentry sector.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and carpentry trades have very low AI adoption for physical tasks; this sector remains one of the least digitized in terms of hands-on labor automation.
Augmentation potential	claude-haiku-4-5-20251001	3/5	Power saws and CNC systems can assist carpenters in achieving precision cuts faster, reducing physical strain and rework. However, the assistance is primarily from the tool itself rather than AI-driven augmentation, limiting the rating to moderate improvement in productivity.
Augmentation potential	claude-sonnet-5	2/5	AI can help with measurement calculations, cut lists, or optimizing material usage via software, but it does not meaningfully assist the physical act of cutting itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While CNC machines and automated saws can cut lumber to precise dimensions, this task requires physical material handling, setup, and quality verification that current AI systems cannot reliably perform end-to-end. The task remains largely dependent on human operation of powered equipment.
Task automatability	claude-sonnet-5	1/5	This is a physical cutting task requiring manual dexterity, tool handling, and on-site judgment; current AI systems cannot perform physical labor and no software substitute exists for the act of cutting materials.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Physical safety regulations and workplace requirements around powered equipment create moderate barriers; machine operation often requires human certification or oversight. No strict licensing prevents automation, but liability and ergonomic constraints slow substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing typically required for cutting lumber, but physical presence, tool operation, and precision needs create practical barriers to any non-human execution; not a regulatory barrier so much as a physical one.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	CNC machinery and integration costs are substantial, while carpenter helper wages are modest. The amortization of equipment, setup time, and supervision required make this less cost-effective than human labor for variable, small-batch cutting tasks typical in carpentry.
Cost vs. human wage	claude-sonnet-5	1/5	AI has no mechanism to perform physical cutting, so there is no viable cost comparison—human labor with tools remains the only option and is far cheaper than any hypothetical robotic solution today.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	CNC cutting systems exist and perform reliably in controlled factory settings, but deployment in carpentry helper roles is limited; most on-site carpentry still relies on manual and semi-automated saws. Fully autonomous cutting without human oversight is not standard practice.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product cuts timber or lumber; this remains purely a physical trade task performed by humans with tools like saws, requiring no AI-based production system.

Smooth or sand surfaces to remove ridges, tool marks, glue, or caulking.

CI 15–30 · exposure 8 · augmentation 13 · importance 3.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Construction and carpentry remain low-digitization sectors with slow automation adoption. While some shops use CNC or industrial sanders for specific products, the broader helper-carpenter sanding workflow is largely manual and shows limited production automation.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical manual tasks, with minimal production deployment.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Power sanders and dust collection systems assist human workers, but AI-driven augmentation is minimal; sanding is already semi-mechanized. Vision-based defect detection or machine-learning surface-quality feedback could theoretically help, but such systems are not yet standard in carpentry workflows.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools (vision, planning software) offer negligible direct assistance to the physical act of sanding or smoothing surfaces.
Task automatability	claude-haiku-4-5-20251001	2/5	While sanding machines exist and can handle flat surfaces, this task requires significant human judgment to detect ridges, assess surface quality, and avoid over-sanding. Current AI lacks the tactile feedback and adaptive decision-making needed for consistent results across varied materials and surface conditions without substantial human oversight.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of tools against irregular surfaces with tactile feedback, which no current off-the-shelf AI system can perform end-to-end without robotic hardware far beyond typical deployment.
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are minimal legal or licensing barriers to automating this helper-level task. The main friction is organizational (existing workflows, equipment investment) and practical (task variability), not regulatory or liability-driven.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this task, but physical environment variability, safety concerns, and lack of mature manipulation robots create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Industrial sanding equipment requires significant capital investment, integration, and maintenance. For a helper-level task involving variable surfaces and inspection, the all-in cost per task unit remains higher than paying a low-wage worker, especially when human flexibility is factored in.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution priced or deployed for this task, so cost comparison favors the human helper by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercially deployed autonomous systems reliably perform multi-surface sanding and smoothing at the quality standards required in carpentry. Existing automation is rigid (CNC sanders for flat stock) and cannot adaptively handle the irregular, variable surfaces this task describes.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product performs freeform sanding/smoothing of carpentry surfaces in real jobsites; this remains at best a robotics research problem, not a shipped product.

Drill holes in timbers or lumber.

CI 10–33 · exposure 13 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Carpentry remains a labor-intensive, on-site physical trade with low digital maturity and minimal AI adoption; most firms are small, manage variable project conditions, and lack the infrastructure or ROI case for automated drilling systems.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and manual trades remain among the least digitized/automated sectors, with minimal AI or robotics adoption for hands-on tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI offers minimal assistance for this task; power drills are already mature, low-cost, and ergonomic, and there is little room for software to enhance drilling performance without introducing complexity that would impede a helper's workflow.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for the physical act of drilling holes in lumber; this is a manual dexterity task outside AI's scope.
Task automatability	claude-haiku-4-5-20251001	2/5	While drilling straight holes in lumber is technically straightforward for a robotic system, the task requires positioning and securing variable wood pieces, identifying grain direction, and handling exceptions—all of which lack the flexibility of current off-the-shelf AI systems without extensive custom setup and tooling.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring positioning a drill, applying force, and controlling depth/angle in real-world material; no off-the-shelf AI system can perform this end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Carpentry work sites have moderate barriers: OSHA regulations govern powered tool use and machine guarding, insurance and liability concerns arise around tool-caused injuries, and customer expectations on site safety and human oversight create organizational friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically drill holes, but physical presence, tool handling, and jobsite variability create practical barriers to any automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Deploying a robotic drilling system (hardware, integration, maintenance, safety compliance) typically costs far more than paying a helper to drill holes manually, especially for small-to-medium job sites where task volumes don't justify the capital investment.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute deployable at comparable cost for this manual task; equipment and setup costs would far exceed a helper's wage for this simple task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed AI product reliably performs full hole-drilling tasks end-to-end (positioning lumber, selecting drill parameters, adapting to wood variability, quality control) in production carpentry environments today; specialized industrial robots exist but are niche, custom, and not 'generally available' AI solutions.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs freeform drilling into timber/lumber on job sites; robotic drilling exists only in narrow, fixed factory contexts, not general carpentry helper work.

Clean work areas, machines, or equipment, to maintain a clean and safe job site.

CI 15–24 · exposure 8 · augmentation 0 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a laggard sector in automation and digitization, with high fragmentation, small firms, and significant physical and environmental variability. Adoption of cleaning robots on construction sites is virtually nonexistent in production today.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a physically-oriented, low-digitization sector with minimal AI/robotics adoption for manual labor tasks like site cleanup.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance to a carpenter's helper performing manual cleaning—this task does not benefit from AI-driven augmentation in its core execution (physical debris removal, surface preparation).
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer negligible assistance for physical cleaning of tools, machines, and job sites.
Task automatability	claude-haiku-4-5-20251001	2/5	Cleaning work areas requires navigation of variable physical spaces, handling of diverse debris types, and judgment about what constitutes 'clean and safe'—capabilities current AI robots lack reliably. While some narrow cleaning tasks (e.g., floor sweeping in defined areas) show proof-of-concept, end-to-end site cleaning with 50% time savings at equal quality remains out of reach for deployed systems.
Task automatability	claude-sonnet-5	1/5	Physical cleaning of a job site involves manipulating diverse debris, tools, and surfaces in unstructured environments, which current AI (software or robotic) cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are modest barriers: jobsites prioritize human safety and site familiarity, and human workers are expected to maintain their own work areas as part of the role. However, no legal licensing prevents automation, and the task is not inherently client-facing, creating relatively low adoption friction compared to regulated work.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human do this specific task, but job-site safety and liability concerns create some friction against unproven automated equipment.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized cleaning robots capable of jobsite work are prohibitively expensive ($100k+) compared to the loaded wage of a carpenter's helper ($30–50k/year), and require significant infrastructure investment, making AI far costlier than human labor for this task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this task, so any hypothetical system would cost far more than a low-wage helper performing manual cleanup.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercially deployed AI robots reliably perform comprehensive jobsite cleaning in construction environments. Existing cleaning robots operate in highly controlled settings (warehouses, offices) and cannot handle the unstructured, hazard-filled conditions of active construction sites.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously cleans construction job sites; robotic cleaning is largely confined to flat, predictable indoor floors, not construction debris and equipment.

Cover surfaces with laminated plastic covering material.

CI 15–24 · exposure 8 · augmentation 13 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Carpenter helpers work primarily in small-to-medium construction firms with low automation rates. These sectors show minimal adoption of advanced robotics or AI-driven automation systems.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and carpentry trades are among the least digitized sectors with minimal AI/robotics adoption for physical installation tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with design planning or cut-list optimization, but the core physical task of covering surfaces offers limited augmentation since the helper's presence and manual skill are inherent to safe, quality execution.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for the physical act of cutting, adhering, and fitting laminate covering material.
Task automatability	claude-haiku-4-5-20251001	2/5	Covering surfaces with laminated plastic requires precise measurement, cutting, alignment, and adhesive application in variable physical environments. While parts of the cutting process could theoretically be automated, the manual handling, surface preparation, and quality control remain dependent on human dexterity and judgment, preventing the ≥50% time-saving threshold.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring precise cutting, adhesive application, and fitting of laminate to surfaces; no current AI system can perform this physical labor.
Adoption barriers	claude-haiku-4-5-20251001	2/5	The task is not legally restricted to licensed professionals, but it requires physical presence on-site and real-time problem-solving, creating moderate friction to full automation without human oversight.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically bars automation, but physical dexterity, material handling, and quality-of-finish requirements create strong practical barriers to any current automation attempt.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI systems capable of mobile manipulation and precision surface work do not exist at scale; robotics solutions that could perform this task are significantly more expensive than the loaded wage of a carpenter helper.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI or robotic alternative for this task, so any hypothetical automation would require expensive specialized robotics far costlier than a helper's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system reliably performs end-to-end lamination of surfaces today. This task requires mobile manipulation, real-time visual feedback, and adaptation to diverse surface conditions—capabilities not yet demonstrated in production systems.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product or robotic system performs laminate installation in real construction/carpentry settings today; this remains purely manual skilled work.

Select tools, equipment, or materials from storage and transport items to work site.

CI 15–20 · exposure 5 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a low-digitization, physical-intensive sector with fragmented, small-scale operations. Adoption of robotics for material logistics in this domain is minimal and remains experimental rather than production-standard.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a notoriously low-digitization, physical-labor sector with minimal deployed robotic automation for material handling in variable field environments.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist via inventory management software or predictive logistics planning to optimize what materials to pre-stage, but the core task of physical selection and transport offers limited scope for real-time human-AI collaboration.
Augmentation potential	claude-sonnet-5	2/5	Basic AI/software tools like inventory apps or GPS/logistics tracking can help organize what to select and where to bring it, but they don't meaningfully change the physical labor or execution speed of retrieval and transport.
Task automatability	claude-haiku-4-5-20251001	1/5	The task requires physical navigation through storage, selection based on context-dependent project needs, and transport to a dynamic work site. Current AI systems cannot autonomously perform these embodied, real-world logistics operations without specialized robotics integration.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring locomotion, object recognition, grasping, and transport at a job site; no off-the-shelf AI system performs this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Physical job sites often have safety regulations and insurance requirements around material handling and transport. Although not legally mandatory to use human labor, liability and safety compliance create moderate friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically for this task, but physical site variability and safety/liability concerns around robots on active job sites create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of autonomous robotic systems, localization infrastructure, and integration needed to handle material selection and transport in unstructured construction environments far exceeds the loaded wage of a helper performing this task.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic systems capable of this unstructured physical task would require expensive hardware, mobility, and manipulation far exceeding a helper's wage, with no mature low-cost solution available.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While robotic inventory systems and autonomous delivery vehicles exist in narrow domains, no deployed general-purpose solution reliably selects and transports construction materials to job sites at production scale. Most systems are still in pilot phases or highly constrained environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products autonomously select construction tools/materials from storage and physically transport them to a work site; robotics for this remains research-stage or narrow warehouse-only pilots.

Glue and clamp edges or joints of assembled parts.

CI 15–15 · exposure 0 · augmentation 0 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction and carpentry remain low-digitization sectors with small, dispersed work sites where automation adoption has been minimal; this particular task sees negligible AI/robotic adoption in production.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and carpentry trades are among the least digitized, slowest-adopting sectors for AI/robotics, with physical assembly automation remaining rare and experimental.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance for gluing and clamping; the task requires continuous haptic feedback and real-time motor control that AI systems cannot augment from a helper-assistant perspective.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful real-time assistance for the physical act of gluing and clamping parts; it is not a task involving information processing where AI tools help.
Task automatability	claude-haiku-4-5-20251001	1/5	Gluing and clamping requires precise spatial coordination, force calibration, and real-time sensorimotor adaptation that current robotics and AI systems cannot reliably execute in unstructured carpentry environments with varied materials and joint geometries.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring hand-eye coordination, force control, and adaptive positioning that current AI systems cannot perform end-to-end; no off-the-shelf AI can physically apply glue and align/clamp parts.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Physical and sensorimotor requirements create some natural protection, though no licensing or liability barrier specifically guards the task; organizational friction around capital investment is the main barrier.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, but physical workspace variability, tool handling, and lack of mature robotic solutions create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of gluing and clamping are orders of magnitude more expensive to purchase, integrate, and maintain than hiring a helper carpenter for this task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution deployed for this task, so any hypothetical automation (custom robotics) would be far more expensive than a low-wage helper performing manual gluing and clamping.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI or robotic system reliably performs this task end-to-end in production carpentry settings; specialized industrial robots exist only for highly controlled, repetitive scenarios with standardized parts.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs this specific gluing-and-clamping manipulation task in production carpentry settings; robotic assembly exists only in narrow, highly controlled industrial contexts, not general carpentry helper work.

Cut and install insulating or sound-absorbing material.

CI 15–15 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction and carpentry remain low-digitization, physically-grounded sectors with slow capital investment in automation. Adoption of robotic systems for insulation work is minimal and confined to large industrial projects.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and physical trades are among the slowest sectors to adopt AI/automation, with minimal deployment of robotics for material installation tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with material estimation, cut layout optimization, or documentation, but these are peripheral to the core physical task. The primary work—cutting and installing—remains outside current AI augmentation capability.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with measurement calculations, material estimation, or cut-list generation, but offers little direct help with the physical cutting and installation itself.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of materials in varied spatial configurations, precise measurement relative to irregular building surfaces, and adaptive problem-solving for fit and placement. Current AI systems cannot perform end-to-end physical construction work.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring measuring, cutting, and fitting insulation or acoustic material in real-world spaces; no current AI system can perform this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing requirements for this helper-level task, significant barriers exist: physical environment unpredictability, need for real-time spatial reasoning, and practical limitations of current robotics in construction sites reduce near-term substitution risk.
Adoption barriers	claude-sonnet-5	2/5	No licensing typically required for this specific helper task, though jobsite safety standards and contractor supervision create some procedural friction, but not hard legal barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotics capable of cutting and installing insulation would require substantial capital investment, programming, and site integration—far exceeding the labor cost of a helper carpenter for this work.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute performing this task, so any hypothetical automation would cost far more than a human helper given current robotics costs and reliability.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products reliably perform material cutting and installation in real carpentry work. While vision systems and robotic platforms exist in research, none operate autonomously on-site with the flexibility required for this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product cuts and installs physical insulation materials; this remains research-stage robotics at best, not a production capability.

Perform tie spacing layout and measure, mark, drill or cut.

CI 5–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a laggard sector for automation with low digitization and fragmented job sites. Real-world adoption of autonomous systems for carpenter helper tasks is virtually non-existent, remaining limited to large commercial projects.
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 automation penetration in framing and layout work.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with measurement calculations or layout verification via computer vision, but the core physical task of marking, drilling, and cutting requires human motor control and judgment. Augmentation potential is limited to computational aids rather than transformative productivity gains.
Augmentation potential	claude-sonnet-5	2/5	Digital layout tools, laser measuring devices, and CAD-based markup apps can assist with planning and precision, but the core physical execution remains manual with limited AI-specific augmentation.
Task automatability	claude-haiku-4-5-20251001	2/5	Tie spacing layout requires interpreting blueprints and spatial reasoning in a physical environment. While AI could assist with measurement calculations, the full task of measuring, marking, drilling, or cutting requires robotic manipulation of construction materials and tools that current AI systems cannot reliably execute end-to-end on varied job sites.
Task automatability	claude-sonnet-5	1/5	This is a physical layout, measuring, drilling, and cutting task on-site that requires manipulation of materials and tools; no current AI system can perform this end-to-end without robotic hardware that doesn't exist in general deployment.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Safety regulations, liability for structural integrity errors, and OSHA requirements around tool operation create strong adoption barriers. The tie spacing directly affects load-bearing capacity, making human oversight and sign-off functionally mandatory rather than optional.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically for this task, but the physical nature of the work and need for on-site adaptability create practical barriers to automation via robotics or software.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current AI and robotics solutions capable of any portion of this task (measurement, layout) remain significantly more expensive than paying a carpenter's helper, especially when factoring in setup, calibration, and maintenance costs on diverse job sites.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven robotic system for this task at any deployable cost, making the human laborer the only economically available option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs this integrated task reliably in production. While computer vision can identify positions and robotics research exists, there is no commercial system that can autonomously perform tie spacing layout, marking, drilling, and cutting on active construction projects.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform physical tie spacing layout, measuring, marking, drilling, or cutting in carpentry contexts; this remains firmly in the domain of human manual labor with hand tools.

Fasten timbers or lumber with glue, screws, pegs, or nails and install hardware.

CI 10–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Carpentry and construction remain low-digitization sectors with minimal automation adoption. On-site helpers performing fastening work operate in dispersed, variable physical environments where AI/robotics adoption remains nascent and pilots are extremely rare.
Sector adoption velocity	claude-sonnet-5	1/5	Residential and commercial construction trades are among the least digitized sectors, with minimal AI/robotic adoption for hands-on fastening work.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers minimal assistance for fastening and hardware installation; no AI tool meaningfully augments a carpenter helper's productivity at this task, as it requires continuous sensorimotor feedback and real-time physical problem-solving.
Augmentation potential	claude-sonnet-5	2/5	AI offers limited direct assistance to this hands-on task itself, though it may help with related planning, measurements, or instructions rather than the physical fastening work.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation of materials, positioning timbers, and applying fasteners in specific orientations—capabilities that current AI systems cannot perform. Robotic systems exist for some fastening operations in controlled manufacturing, but end-to-end automation of ad hoc carpentry fastening with mixed materials, hardware installation, and quality assessment remains beyond deployed autonomous capabilities.
Task automatability	claude-sonnet-5	1/5	This is a physical manual construction task requiring dexterity, spatial judgment, and handling of tools and materials in varied real-world conditions; no off-the-shelf AI system can perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Structural barriers are moderate: carpenters retain high autonomy and no legal licensing requirement specifically governs fastening work, but practical friction includes job-site variability, safety liability for misinstalled hardware, and deep organizational reliance on on-site human judgment and adaptability.
Adoption barriers	claude-sonnet-5	2/5	No licensing specifically bars automation of this task, but physical environment variability, safety concerns, and lack of mature robotic hardware create substantial practical friction against substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of deploying robotic systems capable of timber fastening and hardware installation, including integration, safety compliance, and oversight, far exceeds the wage cost of a carpenter helper performing this task on a job site.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic system to compare cost against for this task, so the human remains far cheaper and more practical than any AI-driven alternative.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No general-purpose deployed product reliably performs this task autonomously in real-world carpentry contexts. While industrial robotic arms can do fastening in highly controlled settings, they cannot adapt to variable lumber dimensions, grain, hardware specifications, and on-site construction variability that carpenters encounter.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs freeform carpentry fastening and hardware installation in production; robotics in this space remains research-stage or limited to highly controlled factory settings, not job-site helper work.

Position and hold timbers, lumber, or paneling in place for fastening or cutting.

CI 10–15 · exposure 0 · augmentation 13 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Carpentry and construction remain highly manual, low-automation sectors with limited digital infrastructure. Current adoption of robotics in this domain is minimal and concentrated in factory prefabrication, not site work.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a low-digitization, physically-oriented sector with minimal AI/robotics adoption for hands-on material handling tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could theoretically provide visual guidance or measurement assistance to improve positioning accuracy, but such tools are not yet standard practice and the core manual holding task remains inherently human-dependent.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no assistance for the physical act of positioning and holding materials during cutting or fastening.
Task automatability	claude-haiku-4-5-20251001	1/5	This task fundamentally requires physical manipulation and real-time spatial positioning of materials in three-dimensional space. Current AI systems have no robotics deployment in carpentry settings that can reliably position and hold diverse timber sizes and shapes for fastening, and the task offers no meaningful automation boundary without full robotic manipulation.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterous handling of variable, heavy materials in unstructured job-site environments, which current AI/robotics cannot perform end-to-end reliably.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	While not legally restricted, the task has moderate adoption friction due to the need for flexible, adaptable physical systems that work in varied construction environments and the preference for human judgment on material placement quality.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for this task itself, but physical safety, liability for on-site injury, and variable job conditions create meaningful practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of holding and positioning timbers would be orders of magnitude more expensive than the loaded wage of a carpenter's helper, with high integration and maintenance costs.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable off-the-shelf robotic system for this task, so any hypothetical automation would require far more expensive custom hardware than paying a helper's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products exist that perform this physical task in production carpentry environments. While industrial robotics exists elsewhere, carpentry-specific positioning-and-holding systems are not in commercial use at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product positions and holds lumber or timbers for cutting/fastening in construction settings; this remains robotics research territory, not production use.

Construct forms and assist in raising them to the required elevation.

CI 10–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a low-digitization, highly physical sector with slow adoption of automation. Small to mid-sized firms dominate carpentry work, and production robotic deployment in this space is negligible.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a low-digitization, physically demanding sector with minimal AI/robotic adoption for manual formwork tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI provides no meaningful assistance to a carpenter's helper constructing and positioning forms; the task is fundamentally manual and spatial, unsuited to current augmentation tools.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with planning, measurements, or generating cut lists via digital tools, but offers little direct help with the physical act of building and raising forms.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of materials in unstructured, variable construction environments—setting up temporary wooden forms at precise elevations. Current AI systems cannot perform end-to-end physical construction work, and no robotic systems reliably do this at scale in field conditions.
Task automatability	claude-sonnet-5	1/5	This is a physical construction task requiring on-site manual labor, lifting, and precise placement of forms; no current AI system can perform this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are few hard regulatory bars to automation, construction sites involve significant safety hazards and coordination with skilled tradespeople, creating organizational friction and liability concerns that slow substitution.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement specifically bars automation, but safety regulations, physical site variability, and liability for structural work create real friction against unproven automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of robots capable of form construction, plus integration and maintenance, far exceeds the loaded wage of a carpenter's helper, making AI economically unfeasible today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI/robotic substitute performing this physical task, so any hypothetical automation would be far more expensive than a helper's wage today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI or robotic product reliably constructs and raises forms on construction sites at scale. While construction robotics exist in labs and narrow pilots, they lack the flexibility and real-world reliability needed for routine carpentry helper tasks.
Technical feasibility today	claude-sonnet-5	1/5	No deployed robotic or AI product constructs and raises concrete forms in real job-site conditions; this remains far outside current robotics deployment.

Align, straighten, plumb, or square forms for installation.

CI 5–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a laggard sector for automation due to site variability, unionization, skilled-trades culture, and the capital intensity of deploying suitable robotics. Meaningful adoption of autonomous form alignment has not occurred in production.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a notoriously low-digitization, physically-oriented sector with minimal AI/robotic adoption for on-site manual tasks like form alignment.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Limited augmentation is available; laser-guided leveling and digital level tools assist human workers in measurement and layout, but core alignment and physical straightening remain heavily dependent on human skill and judgment.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with planning, measurements, or laser-guided leveling tools support the task indirectly, but the core physical alignment work itself isn't meaningfully augmented by current AI systems.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation of heavy materials in unstructured job-site environments—aligning, straightening, plumbing, and squaring forms. Current AI systems lack the embodied manipulation capabilities, real-time spatial reasoning under site variability, and robust gripper control needed to perform this end-to-end.
Task automatability	claude-sonnet-5	1/5	This is a physical, hands-on manipulation task requiring precise positioning of construction forms in real-world space, which current AI systems (software-based) cannot perform without embodiment in capable robotics that don't exist for this application today.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Significant barriers exist: skilled human judgment is required to assess proper alignment across variable site conditions, liability and safety concerns around structural integrity favor human accountability, and OSHA regulations emphasize human oversight of form installation in safety-critical contexts.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically for this task, but physical site conditions, variability of forms, and coordination with other trades create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Deployed robotic systems capable of construction-site manipulation are extremely expensive (hundreds of thousands to millions), while carpenter helpers earn modest hourly wages, making automation cost-prohibitive for this task today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a helper's wages.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform autonomous form alignment and plumbing on construction sites. This task demands physical dexterity and environmental adaptation well beyond current robotic deployment in construction.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform physical alignment, plumbing, or squaring of construction forms; this remains entirely manual work done by human laborers on job sites.

Hold plumb bobs, sighting rods, or other equipment to aid in establishing reference points and lines.

CI 5–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction trades remain heavily laggard in AI/automation adoption, with very limited digitization. Physical assist tasks like holding equipment see negligible AI deployment velocity in real job sites.
Sector adoption velocity	claude-sonnet-5	1/5	Construction trades are a low-digitization, physical-labor sector with minimal AI/robotic adoption for on-site manual assistance tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI systems offer no meaningful assistance in holding or positioning physical equipment on a construction site; the task is purely manual and offers little leverage for AI augmentation.
Augmentation potential	claude-sonnet-5	2/5	AI could help plan reference points or interpret survey data beforehand, but it offers no direct assistance during the physical act of holding equipment.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical presence on-site to hold equipment in precise positions, which demands embodied manipulation and real-time spatial coordination that current AI systems cannot perform. No meaningful automation is feasible today without full robotic deployment, which is not standard in carpentry.
Task automatability	claude-sonnet-5	1/5	This requires physical presence on a job site holding equipment steady in coordination with a carpenter; current AI systems have no embodied capability to perform this physical assistance task.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Physical presence and hands-on equipment handling requirements create substantial barriers; no automation exists, and most construction workflows treat this as an integral human task with organizational and safety friction against substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but the task requires physical co-presence and manual dexterity making substitution by non-embodied AI impossible regardless of regulation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of robotics systems capable of holding and positioning equipment on construction sites vastly exceeds the loaded wage of a carpenter's helper, making automation economically infeasible today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for this physical labor task, so any hypothetical robotic solution would be far more expensive than a helper's wage today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs the on-site physical holding and positioning of construction equipment as a substitute for human labor in production carpentry work. This remains entirely a human task in current practice.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs this physical holding/assisting task; it requires a human body and hands on-site with a physical tool.

Install handrails under the direction of a carpenter.

CI 5–15 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a low-digitization, physical-task-dominant sector with slow adoption of autonomous systems. Most carpenter helper tasks continue to rely on human labor with traditional oversight.
Sector adoption velocity	claude-sonnet-5	1/5	Construction trades remain a laggard sector for AI/robotics adoption due to low digitization and physical variability of job sites.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could provide marginal assistance through measurement tools, code lookup, or installation sequence suggestions, but the core physical task of installation offers limited opportunity for meaningful human-AI collaboration or productivity enhancement.
Augmentation potential	claude-sonnet-5	2/5	AI could help with measurements, code lookups, or instructions via a mobile device, but offers minimal assistance to the actual physical installation work.
Task automatability	claude-haiku-4-5-20251001	1/5	Installing handrails requires precise physical manipulation in varied architectural contexts, drilling, anchoring, and alignment decisions that demand real-time spatial reasoning and adaptation. Current AI systems cannot perform the end-to-end physical installation task.
Task automatability	claude-sonnet-5	1/5	This is a physical installation task requiring manipulation of materials, precise fitting, and use of hand/power tools in variable job-site conditions; no current AI system can perform this end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Building codes, occupational safety regulations, and liability requirements create substantial barriers. Handrail installation affects public safety and structural integrity, often requiring sign-off by licensed professionals and inspectors.
Adoption barriers	claude-sonnet-5	2/5	No licensing generally required for a helper role, though safety codes and building inspections create some oversight friction, and physical dexterity/human presence is currently required regardless of formal barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic systems capable of construction tasks cost substantially more than the loaded wage of a carpenter's helper, with significant integration and safety oversight costs that are not yet offset by operational efficiency.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute performing this task, so any hypothetical automation would be far more costly than a helper's wage today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product today can autonomously install handrails in real buildings. Robotic systems for construction installation remain research-stage and cannot reliably handle the variability and precision required.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product installs handrails; robotics for this kind of unstructured physical assembly work remains research-stage at best.

Cut tile or linoleum to fit and spread adhesives on flooring for installation.

CI 5–15 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Carpenters and flooring helpers work in small-scale, distributed job-site environments with low digitization. Adoption of AI or robotics in this sector remains minimal, with most work still performed by hand in small crews and contractors.
Sector adoption velocity	claude-sonnet-5	1/5	Construction and physical trades are among the slowest sectors to adopt AI/robotics due to low digitization and high variability of job sites.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially assist by analyzing floor layouts and recommending cutting patterns or optimal adhesive application strategies, but such tools are not widely in use for this task. The manual, hands-on nature of the work limits meaningful augmentation opportunities compared to information-based tasks.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with measurement calculations or material estimation planning, but offers minimal direct assistance to the physical act of cutting and adhesive application.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise spatial judgment, physical dexterity, and real-time adaptation to irregular surfaces and measurements—capabilities that current AI systems lack. The cutting and spreading of adhesives involves direct physical manipulation that only robotic systems could attempt, and those are not widely deployed for this work.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring cutting materials to precise fit and manually spreading adhesive—no AI system today can perform these physical actions end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	This task has substantial adoption barriers due to its requirement for physical presence on-site, the need for real-time adaptation to uncontrolled environments, and the safety and quality liability risks if automation fails during installation. Flooring work also typically occurs in customer spaces where human oversight and accountability are expected.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier prevents automation of this task, but physical environment variability (uneven surfaces, custom cuts) creates practical friction against non-human execution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic or AI-assisted systems for tile cutting and adhesive spreading would require significant capital investment and specialized integration, making them far more expensive than the direct labor cost of a helper-carpenter in most markets.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute performing this task, so the cost comparison favors the human worker entirely; robotic systems capable of this would be far more costly than a helper's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercially deployed AI or robotic systems reliably perform tiling or linoleum installation at production quality. While some research robots exist, they lack the flexibility to handle variable job site conditions, material variations, and the judgment required to ensure proper fit and adhesive application.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs tile/linoleum cutting and adhesive spreading; this remains firmly in the domain of human manual labor and any robotics solution is research-stage at best.

Secure stakes to grids for constructions of footings, nail scabs to footing forms, and vibrate and float concrete.

CI 5–15 · exposure 0 · augmentation 0 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a low-digitization, fragmented sector with high physical and site-specific variability; automation adoption for manual helper tasks lags far behind information-intensive sectors.
Sector adoption velocity	claude-sonnet-5	1/5	Construction trades remain among the least digitized and slowest to adopt AI/robotics for hands-on physical tasks like concrete finishing.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI provides no meaningful assistance for physically securing stakes, nailing forms, or floating concrete; the task is fundamentally manual and does not benefit from generative or analytical AI augmentation.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no current assistance for the physical acts of staking, nailing, vibrating, or floating concrete on a job site.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of materials in unstructured construction environments (securing stakes, nailing, vibrating, floating concrete), which remains beyond the capabilities of current AI systems and available robotics at scale. No general-purpose end-to-end automation exists for these coordinated, position-dependent manual operations.
Task automatability	claude-sonnet-5	1/5	This is a physical construction task requiring manual dexterity, strength, and on-site manipulation of stakes, forms, and wet concrete that no current AI or robotic system can perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	This task requires licensed or trained personnel supervision on active job sites, and safety regulations mandate human presence and oversight for footing and concrete work. Physical site conditions and liability for structural integrity create significant adoption barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically bars automation, but physical site conditions, safety protocols, and lack of mature robotic tooling create strong practical barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current AI-enabled or robotic systems capable of performing any part of this work cost substantially more per task-unit than a helper-carpenter's loaded wage, and no integrated system exists to perform the full task cost-effectively.
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 custom robotics far exceeding the cost of a helper's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products reliably perform the full sequence of stake securing, nailing, and concrete finishing in real construction settings. Specialized construction robots exist for narrow subtasks but not for this integrated manual workflow at production scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product performs staking, nailing form scabs, and vibrating/floating concrete; robotics for this remains research-stage at best.

Erect scaffolding, shoring, or braces.

CI 0–5 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Construction remains a low-automation, labor-intensive sector with strong resistance to robotics in field work. Physical scaffolding erection is performed by small crews on variable sites, showing minimal AI/automation adoption to date.
Sector adoption velocity	claude-sonnet-5	1/5	Construction is a low-digitization, physically intensive sector with minimal AI/robotic adoption for on-site structural tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Limited augmentation potential exists; AI could assist with pre-planning or safety compliance checking, but the physical execution itself—balancing, securing, and adjusting structures in real-time—relies on human workers' judgment and dexterity with minimal AI support available today.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no direct assistance to a worker physically erecting scaffolding or shoring in the moment.
Task automatability	claude-haiku-4-5-20251001	1/5	Erecting scaffolding, shoring, or braces is a physical construction task requiring spatial reasoning, on-site assessment, and precise manual assembly in variable environments. Current AI systems cannot perform end-to-end physical construction work without human involvement.
Task automatability	claude-sonnet-5	1/5	This is a physical, hands-on manual task requiring dexterity, spatial judgment, and safe handling of heavy materials on-site, none of which current AI systems can perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	5/5	This task has strong legal and safety barriers: building codes mandate human inspection and certification; liability for structural failures falls on licensed professionals; OSHA regulations require trained workers to erect safety equipment, creating hard requirements for human involvement.
Adoption barriers	claude-sonnet-5	4/5	Scaffolding and shoring erection is subject to strict OSHA safety regulations, competent-person sign-off requirements, and liability concerns that heavily constrain automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized construction robots and AI-guided systems remain significantly more expensive than paying a carpenter's helper, including equipment, maintenance, and integration costs, making economic substitution infeasible today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute for this labor, so any hypothetical automation would be far more expensive than a helper's wage today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs physical scaffolding erection autonomously. While robotic systems and computer vision research exist, they are not production-ready for the complexity and safety-critical nature of this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously erects scaffolding or shoring; this remains firmly in the physical/robotics research stage, not production.

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.