Electrical Power-Line Installers and Repairers
49-9051.00Median wage $95,320/yr131,070 employed (US)Rank #918 of 923 scored · top 99% by substitution
Install or repair cables or wires used in electrical power or distribution systems. May erect poles and light or heavy duty transmission towers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution6
Exposure4
Augmentation29
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
23 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%3
panel mean rating 1.1/5 → substitution pressure 3/100
Technical feasibility todayw 20%5
panel mean rating 1.2/5 → substitution pressure 5/100
Cost vs. human wagew 15%3
panel mean rating 1.1/5 → substitution pressure 3/100
Adoption barriersw 20%inverted — strong barriers lower the score15
panel mean rating 4.4/5 (barrier strength) → substitution pressure 15/100
Sector adoption velocityw 10%5
panel mean rating 1.2/5 → substitution pressure 5/100
Task breakdown (23 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.
Coordinate work assignment preparation and completion with other workers.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail
Coordinate work assignment preparation and completion with other workers.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities and construction firms remain relatively conservative in automation of field coordination; most use incremental digital tools (work orders, GPS tracking) rather than autonomous crew management, and human foremen remain the norm. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utility/construction trades are slow AI adopters overall, with digitization concentrated in back-office scheduling tools rather than field coordination itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist coordinators by auto-generating shift schedules, flagging resource conflicts, and logging completion status, meaningfully reducing administrative burden while the human coordinator retains decision authority over crew safety and sequencing. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based scheduling, mapping, and communication tools can meaningfully assist supervisors in planning and tracking work assignments even though humans remain in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Coordination requires real-time communication, dynamic prioritization, and responsiveness to field conditions. While AI could support scheduling and documentation, the interpersonal negotiation and adaptive problem-solving central to construction coordination cannot be reliably automated end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | Coordinating work assignments involves scheduling, communication, and situational judgment tied to physical field conditions that current AI cannot fully manage end-to-end.rationale continues |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical infrastructure and union labor agreements typically mandate human supervisors who coordinate and sign off on work assignments. Liability for errors in crew deployment and sequencing on live lines creates legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety-critical utility work requires human oversight and accountability for crew coordination, creating moderate organizational and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI coordination assistants (scheduling, messaging summarization) would require significant oversight and fallback to human coordinators, raising total cost above the wage of a part-time crew coordinator. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human supervisors/dispatchers remain necessary for real-time field coordination, so AI tools reduce but don't eliminate labor cost, keeping cost ratio close to comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably orchestrates live crew coordination for power-line work; scheduling tools exist but do not autonomously manage the mutual adjustment and judgment calls required when crews encounter obstacles or resource conflicts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Scheduling and dispatch software assist with parts of coordination, but no deployed AI product autonomously manages line-crew work assignment coordination reliably. |
Drive vehicles equipped with tools and materials to job sites.
14CI 5–24 · exposure 13 · augmentation 25 · importance 4.8/5 · click for rater detail
Drive vehicles equipped with tools and materials to job sites.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power-line work is geographically dispersed, often in remote or rural areas with poor road infrastructure—the exact segments where autonomous vehicle adoption is slowest. Field service industries have shown minimal production adoption of autonomous delivery; adoption remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line work is a physical, low-digitization trade with minimal autonomous vehicle deployment in this sector; adoption of self-driving trucks for such tasks is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | GPS navigation and route-planning AI tools can assist human drivers in finding optimal paths to job sites, but the core task of safely operating the vehicle and managing materials remains human-dependent. Modest productivity gain from navigation assistance, but no transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route optimization, navigation, and scheduling to job sites, but it does not materially transform the core physical task of driving equipped vehicles to remote work locations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can handle navigation and some driving aspects, but the task includes delivery of tools and materials to job sites—requiring vehicle operation in varied field conditions, safe placement of equipment, and coordination with on-site work. Full autonomous delivery to construction/field sites remains unreliable; human oversight is nearly always necessary. |
| Task automatability | claude-sonnet-5 | 1/5 | Driving a specialized work vehicle to remote/varied job sites, often off-road or in adverse conditions, is not something current AI or autonomous driving systems can reliably perform end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory barriers are substantial: autonomous vehicles operate under restricted permits in limited geographies; liability for tool/materials delivery to job sites falls on the operator; and most field work sites require an authorized human to sign off on material delivery and site safety. Legal and insurance frameworks heavily protect the human-driver role. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing barrier prevents automation of driving itself, commercial driving requires certification, insurance, and liability considerations that create meaningful friction against replacing human drivers on job-site routes. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous vehicle deployment for each job site, including vehicle acquisition, insurance, maintenance, and remote oversight, remains more expensive than paying a worker to drive a standard equipped vehicle. The infrastructure and liability costs outweigh human wage savings at this task scope. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this driving task, so any AI-based attempt would require far more investment (specialized autonomous vehicle systems) than simply paying a human driver. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While autonomous vehicle research is mature in controlled environments, production systems for reliably delivering tools and materials to variable field job sites (unpaved, construction zones, tight access) remain limited. Waymo and similar offer rides in select cities but not general tool-delivery logistics to diverse industrial sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously drives utility trucks loaded with line equipment to job sites; autonomous vehicle tech remains limited to controlled routes and contexts, not utility fieldwork. |
Travel in trucks, helicopters, and airplanes to inspect lines for freedom from obstruction and adequacy of insulation.
14CI 7–21 · exposure 5 · augmentation 50 · importance 4.3/5 · click for rater detail
Travel in trucks, helicopters, and airplanes to inspect lines for freedom from obstruction and adequacy of insulation.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utility companies are adopting drone-based inspection as a tool to assist human technicians, but human travel and on-site judgment remain mandatory for most inspection and repair work in this regulated, safety-critical sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utility and infrastructure sectors are historically slow adopters of AI/automation due to safety-critical nature, regulatory oversight, and capital-intensive legacy systems, though drone inspection pilots are growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Drone inspection systems and thermal imaging tools can assist human technicians by identifying problem areas before travel and reducing unnecessary site visits, moderately improving productivity in the planning and initial assessment phase. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered image analysis from drone or aerial photography can help flag obstructions or insulation issues for human reviewers, meaningfully assisting inspection but not replacing the travel and final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical travel to remote locations and visual inspection of power lines in diverse environmental conditions. Current AI systems cannot operate vehicles independently or conduct on-site physical inspections without human operators. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical travel and inspection of physical infrastructure in the field; current AI cannot perform the physical travel or hands-on inspection tasks involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory requirements mandate licensed electricians for power-line work and safety inspections; liability and safety-critical nature of electrical infrastructure create hard barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Utility safety regulations, certification requirements for line workers, and liability around infrastructure failures create moderate barriers, though inspection specifically (versus repair) has fewer licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous travel and inspection systems remain expensive to deploy, maintain, and integrate into utility workflows. The loaded cost of current AI solutions (drones, pilot labor, integration) exceeds the cost of human field crews for this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While drone-based inspection can reduce costs versus helicopter patrols, the described task still requires vehicles, human oversight, and physical presence, keeping AI-alone costs from being dramatically cheaper than human labor for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously travel to inspect power lines and assess insulation adequacy. While drone inspection systems exist, they require human pilots and on-site technicians to validate findings and make safety judgments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Drone and satellite imagery inspection products exist and are used for some line inspection, but the task as described includes manned vehicle/helicopter travel and comprehensive human judgment not yet fully replaced by deployed AI systems. |
Adhere to safety practices and procedures, such as checking equipment regularly and erecting barriers around work areas.
13CI 0–25 · exposure 13 · augmentation 38 · importance 4.8/5 · click for rater detail
Adhere to safety practices and procedures, such as checking equipment regularly and erecting barriers around work areas.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities adopt specific tools (drones, sensors) for inspection but maintain human-centric safety practices due to regulatory requirements and risk aversion; actual displacement of safety compliance tasks remains minimal despite available technologies. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical line work is a physical, low-digitization trade with minimal AI/robotics adoption for on-site safety enforcement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered inspection tools (thermal imaging, computer vision for equipment assessment) assist field personnel in identifying hazards and maintenance needs more quickly, modestly improving their productivity while human judgment and physical execution remain essential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensors or checklists/apps could help track maintenance schedules or flag anomalies, but the core physical safety actions still require full human execution with limited AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While equipment checking and barrier placement have some automatable components (e.g., automated inspection tools, drone surveys), the task inherently requires physical presence, judgment about site-specific hazards, and compliance verification that current AI systems cannot perform end-to-end at the required safety standard. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, judgment-based safety task performed on-site (checking equipment, erecting barriers) that requires physical presence and manipulation; current AI cannot physically perform these actions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Electrical utility work is heavily regulated (OSHA, National Electrical Code, utility-specific protocols), and safety sign-off typically requires a licensed electrician or qualified supervisor, creating strong legal and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Safety procedures for high-voltage line work are governed by strict OSHA and utility regulations requiring qualified, trained personnel to physically inspect equipment and secure work zones, with high liability for failure. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Safety compliance automation (drones, sensors, monitoring systems) carries high integration and liability costs; when combined with mandatory human verification and oversight, total cost remains comparable to or exceeds the cost of human field workers performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for the physical labor and on-site judgment required, so no meaningful cost comparison favors AI. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products exist for some sub-tasks (thermal imaging drones, equipment diagnostics), but no current system reliably executes the full safety compliance workflow—checking diverse equipment types, assessing dynamic hazards, and erecting barriers—at production scale without significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical equipment checks or erects physical barriers around power-line work areas; this remains a manual field task. |
Inspect and test power lines and auxiliary equipment to locate and identify problems, using reading and testing instruments.
12CI 7–16 · exposure 9 · augmentation 50 · importance 4.5/5 · click for rater detail
Inspect and test power lines and auxiliary equipment to locate and identify problems, using reading and testing instruments.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is limited to early-stage drone and sensor pilots in some utilities; the vast majority of power-line inspection remains manual. Physical infrastructure work, especially under high safety and regulatory constraints, shows slow AI integration compared to information-sector tasks. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are a traditionally slow-adopting, highly regulated, physically-oriented sector; AI adoption is mostly limited to pilot drone/imagery programs rather than widespread deployment of diagnostic automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist through automated analysis of historical maintenance data, anomaly detection in sensor readings, or route optimization for inspections, helping technicians prioritize work. However, the core diagnostic and physical testing task still requires human expertise and decision-making. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered image analysis, predictive maintenance software, and sensor data analytics can help identify anomalies and prioritize inspections, meaningfully aiding but not replacing the technician's fieldwork and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence at height on power lines, hands-on instrument operation in variable field conditions, and expert judgment to diagnose complex electrical faults. Current AI cannot perform the core inspection activities end-to-end without human presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence at power lines, climbing structures, and hands-on use of testing instruments in outdoor field conditions—no current AI system can perform the physical inspection and testing itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Power-line work requires licensure and strict OSHA/electrical safety regulations mandate that qualified electricians perform or directly supervise inspections of live electrical systems. Liability for failures is severe, and safety requirements create hard legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility work is heavily regulated for safety, requires certified/licensed linemen, and involves high liability for misdiagnosis leading to outages or hazards, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and operational costs of deploying autonomous systems (drones, robotics) capable of safely operating on live power lines, plus required human oversight and validation, exceed the cost of trained electricians performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Drone/sensor inspection systems can reduce some inspection costs but still require skilled technicians for interpretation, physical testing, and repairs, so overall cost savings versus a lineworker are modest, not order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in analyzing readings from remote sensors or historical data, no deployed system performs the full task of on-site inspection and field testing of power lines autonomously. Drones with specialized sensors exist but require expert human interpretation and cannot fully replace manual testing. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some drone-based visual inspection and sensor analytics products exist for identifying line defects, but they supplement rather than replace the hands-on testing and diagnostic work described, and are narrow in scope. |
Attach cross-arms, insulators, and auxiliary equipment to poles prior to installing them.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Attach cross-arms, insulators, and auxiliary equipment to poles prior to installing them.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Electrical utility operations remain heavily physical, geographically distributed, and subject to safety-first culture and regulatory constraints; adoption of field automation remains minimal and confined to planning/design stages rather than active deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line construction and maintenance is a physically intensive, low-digitization trade with minimal AI/robotics adoption in the field to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers limited augmentation for the core physical task; design software and planning tools can assist with pole specifications and layout, but the hands-on attachment work itself receives minimal AI assistance today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, scheduling, or design specifications for pole equipment, but offers little direct assistance during the physical attachment work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical assembly and attachment of heavy equipment to poles in outdoor environments, requiring dexterous manipulation, spatial reasoning, and real-time adaptation to variable physical conditions. Current AI systems cannot perform such integrated physical work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical assembly task involving manual manipulation of heavy hardware onto poles, requiring dexterity and physical presence that current AI systems cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Installation of electrical utility infrastructure is subject to strict regulatory oversight, safety codes (OSHA, IEEE), and often requires licensed electricians or utility technicians to perform or inspect the work, creating strong legal and safety-based barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like an electrician's stamp, utility safety regulations, union labor practices, and physical liability create real organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized industrial robots capable of performing this task would require significant capital investment, custom integration, and infrastructure, far exceeding the loaded hourly wage of skilled trades workers in this domain. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a human lineworker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the physical assembly, alignment, and fastening of utility pole components in production environments. Robotic systems exist for narrow, controlled settings but not for the variable field conditions and spatial complexity here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs this pole-assembly work in production; it remains fully manual with hand tools and human labor. |
Test conductors, according to electrical diagrams and specifications, to identify corresponding conductors and to prevent incorrect connections.
7CI 0–14 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Test conductors, according to electrical diagrams and specifications, to identify corresponding conductors and to prevent incorrect connections.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The electrical utility and power-line installation sector remains heavily regulated and traditional, with slow adoption of field automation. Physical on-site tasks requiring human presence, safety sign-off, and licensing remain largely manual, with minimal displacement by AI even in forward-looking utilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical power-line work is a physical, on-site trade with very low AI/robotic adoption to date; this sector lags far behind digital/office sectors in automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by analyzing electrical diagrams, cross-referencing specifications, or organizing test checklists, but the physical testing itself and final conductor identification judgment remain firmly in the technician's domain. Such assistance is limited in scope and does not significantly amplify human productivity on the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help by digitizing diagrams, cross-referencing specifications, or flagging expected wiring configurations, but it offers limited direct assistance during the physical testing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI systems cannot perform end-to-end conductor testing in the field—this requires physical interaction with live or de-energized lines, specialized electrical test equipment operation, and real-time safety decision-making. While AI could assist in diagram interpretation or data analysis of test results, the core testing action and on-site conductor identification remains manual and physical. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically handling conductors with test equipment in the field, matching them to diagrams, and manipulating hardware—no current AI system can perform this physical testing task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task carries high regulatory and legal barriers: only licensed electricians are legally authorized to test live power lines; incorrect conductor identification poses catastrophic safety and liability risks; and OSHA regulations mandate human certification and responsibility for electrical safety work. Automation of this task is heavily restricted by law and industry standards. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Electrical work of this nature typically requires licensed/certified electricians or line workers due to safety and liability concerns, and errors in conductor identification can cause serious injury or equipment damage. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI solutions that could theoretically assist (robotic arms with sensors, computer vision for diagram matching) remain far more expensive than the relatively low cost of a trained technician performing the test manually, particularly given the narrow, specialized nature of this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical testing at all, so there is no viable cost comparison—a human lineworker with test equipment is the only current option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs live electrical conductor testing and identification in production environments today. This task demands specialized hardware (multimeters, continuity testers, high-voltage test equipment) and physical presence on power lines, which no general-purpose AI system handles autonomously. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical conductor testing and identification; this remains a manual electrical trade task requiring hands-on instrumentation. |
Identify defective sectionalizing devices, circuit breakers, fuses, voltage regulators, transformers, switches, relays, or wiring, using wiring diagrams and electrical-testing instruments.
5CI 0–10 · exposure 5 · augmentation 50 · importance 4.2/5 · click for rater detail
Identify defective sectionalizing devices, circuit breakers, fuses, voltage regulators, transformers, switches, relays, or wiring, using wiring diagrams and electrical-testing instruments.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities are capital-intensive, risk-averse, and highly regulated; adoption of autonomous electrical diagnostics remains negligible. Safety criticality and liability concerns create structural resistance to full automation in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical utility line work is a physical, low-digitization trade with minimal AI agent deployment in the field; adoption in this specific task is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by analyzing wiring diagrams, cross-referencing fault patterns against historical data, and interpreting instrument readings, thereby helping a technician diagnose faults more quickly. However, the human expert must remain in the loop for final judgment and field verification. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist via diagnostic software, predictive maintenance analytics, and digital wiring diagram lookup/interpretation support, helping technicians narrow down likely fault locations before physical testing. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical inspection and hands-on testing of electrical equipment in the field, along with safety-critical judgment about live power systems. Current AI cannot physically access, probe, or test electrical equipment, and the safety liability of misidentification is too severe for end-to-end automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection, hands-on use of electrical-testing instruments, and climbing/accessing equipment in the field, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical utilities operate under strict regulatory frameworks (OSHA, NFPA, state utility commissions) that require licensed electricians to perform high-voltage equipment diagnosis and repair. A human license-holder must legally be responsible for certifying the safety and correctness of electrical work on live systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility work involves safety-critical high-voltage systems typically requiring certified/licensed linemen, strict utility safety protocols, and liability concerns that keep humans directly responsible for diagnosis and sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and operational costs of deploying robotic systems or specialized sensors to perform electrical diagnostics would far exceed the labor cost of a trained technician, especially given the low-volume, site-specific nature of power-line work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical diagnostic task at all, so there is no viable cost comparison to a human lineworker performing this hands-on work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in interpreting wiring diagrams and analyzing test data from instruments, no deployed product reliably performs the full task of field diagnosis and defect identification without human intervention. The need for physical measurement and context-dependent safety assessment remains outside production AI systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously diagnoses defective line equipment in the field; diagnostic AI exists only in research or narrow sensor-based monitoring contexts, not as a replacement for the physical troubleshooting task. |
Pull up cable by hand from large reels mounted on trucks.
5CI 0–10 · exposure 0 · augmentation 13 · importance 3.8/5 · click for rater detail
Pull up cable by hand from large reels mounted on trucks.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities operate in low-digitization, physically constrained environments with heavy regulatory oversight; automation of linework is nascent and limited to research pilots. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical line work is a physical, low-digitization trade with minimal AI/robotics adoption for manual field tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or real-time hazard detection via computer vision on wearables, but the core cable-pulling task itself offers minimal augmentation potential today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of pulling cable by hand from a truck-mounted reel. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Pulling cable by hand from reels is fundamentally a physical task requiring strength, dexterity, and real-time environmental adaptation in outdoor conditions; current AI systems have no robotic embodiment to perform this work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual materials-handling task requiring strength and dexterity in outdoor field conditions; no AI system performs this physical action.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations, licensing requirements for electrical work, and OSHA oversight create hard barriers; utilities are legally liable for work quality, and the physical demands make human supervision/sign-off mandatory. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically restricts pulling cable, but it occurs within a regulated utility work context with safety protocols, though the barrier is more physical/practical than legal. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotics (hardware, maintenance, integration) vastly exceeds the loaded wage of a lineman performing manual cable work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical task, so AI cost is not comparable; the human remains the only viable performer at any cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system or autonomous robot reliably performs cable-pulling work in production utility environments today; this remains research-stage for robotics. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product pulls cable from reels; this is purely a mechanical/manual labor task with no software or robotic product addressing it in production. |
Cut trenches for laying underground cables, using trenchers and cable plows.
5CI 0–10 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail
Cut trenches for laying underground cables, using trenchers and cable plows.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Electrical utility companies are traditional, regulation-bound organizations with long infrastructure deployment cycles. Adoption of autonomous trenching and cable-laying robotics is negligible in production, with most work still performed by licensed crews using conventional equipment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and utility trenching is a low-digitization, physical-labor sector with minimal AI/robotic adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | GPS-guided equipment and soil analysis tools can assist operators, but current AI offers limited augmentation for the core task of trench cutting and cable placement. The human operator remains essential for safety, navigation around obstacles, and compliance with complex underground utility marking and depth regulations. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, utility mapping, and GPS-guided equipment positioning, but offers limited direct enhancement to the physical trenching operation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires operating heavy equipment in variable field conditions, navigating obstacles, and making real-time spatial judgments about cable placement depth and soil conditions. Current AI systems cannot operate physical machinery autonomously or reliably in unstructured outdoor environments at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical field task requiring heavy equipment operation on uneven terrain; no AI system today can perform the physical trenching work end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical power-line work is heavily regulated and licensed; workers must be certified and authorized. Liability for underground utility damage (hitting gas, water, or telecom lines) is high, and regulatory standards mandate human inspection and sign-off on cable installation for safety and code compliance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the same sense as electrical work, safety regulations, utility locating requirements, and liability for damaging buried infrastructure create real operational barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Equipment operation, trenching, and cable laying remain labor-intensive field tasks where human operators command full wages plus equipment fuel and maintenance. AI solutions capable of autonomous trenching would require prohibitively expensive robotics, making them more costly than deploying skilled workers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so AI cost is not comparable—human/mechanical operation remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product autonomously digs trenches and lays cables with the precision, safety, and contextual judgment required for electrical infrastructure. Autonomous trenching in unpredictable terrain remains a research and specialized equipment problem, not a solved production system. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously operate trenchers or cable plows in real-world utility work; this remains manual heavy-equipment operation. |
Cut and peel lead sheathing and insulation from defective or newly installed cables and conduits prior to splicing.
5CI 5–5 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Cut and peel lead sheathing and insulation from defective or newly installed cables and conduits prior to splicing.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Electrical utility and construction sectors show minimal automation of hands-on cable preparation; this task remains firmly in the domain of skilled trades with low digital transformation and strong reliance on field expertise and licensing. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line work is a physical, low-digitization trade with minimal AI/robotics adoption for hands-on tasks like this in current industry practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful productivity assistance for the core physical task of cutting and peeling lead sheathing; the work is inherently manual and tactile with no established AI-augmented workflows in the field. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of cutting and peeling cable sheathing; this is a manual dexterity task outside AI's current assistive capabilities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in variable real-world conditions (outdoor environments, different cable types, lead hazards) where current AI robotics cannot reliably perform end-to-end with equivalent quality and speed. The task involves tactile feedback, hazard handling, and adaptation to defects that exceed today's autonomous manipulation capabilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise physical manipulation task requiring hand tools on live or newly installed cables in variable field conditions; no current AI system can perform this physical cutting/stripping work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and safety barriers exist: lead handling is EPA-regulated, electrical work requires licensed electricians in most jurisdictions, and liability for improper cable preparation is high. Human certification and accountability are legally mandated for electrical infrastructure work. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Electrical line work is heavily regulated with safety certifications, and utilities require licensed/trained personnel for cable splicing tasks near high voltage, creating strong barriers to non-human performance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this task (if they existed) would require significant capital, maintenance, and setup costs far exceeding the loaded wage of a skilled electrical worker performing this intermittent field task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI/robotic substitute performing this task, so any hypothetical automation would require expensive custom robotics far exceeding a lineman's wage cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system or robotic product reliably performs lead sheathing removal and insulation peeling at production scale in field conditions. Specialized industrial robots exist for structured factory tasks but not for the variable, hazardous field conditions electrical work demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs cable sheathing/insulation removal in field electrical work; this remains a manual trade skill with no commercial automation. |
Trim trees that could be hazardous to the functioning of cables or wires.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Trim trees that could be hazardous to the functioning of cables or wires.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Utility vegetation management remains highly human-dependent and localized; adoption of autonomous or remote robotic trimming is minimal in the industry, with only early-stage pilots. Most jurisdictions require human certification and on-site judgment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utility/vegetation management sector is a physical, safety-critical field with slow AI adoption; some drone/satellite imagery analytics are being piloted for vegetation risk detection but the trimming itself remains manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Drones and remote sensing can assist in tree surveying and hazard identification, but the core physical task of safe trimming at height near live lines still requires a skilled human operator making critical safety calls in real time. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered imagery analysis (satellite, LiDAR, drone) can help identify hazardous tree growth and prioritize trimming schedules, improving planning even though execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Trimming trees near power lines requires navigating complex physical environments, assessing variable branch configurations, and making safety-critical decisions about what to remove. Current AI cannot reliably execute this in the real world with comparable safety and quality outcomes. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical outdoor task requiring climbing, chainsaw/pruning operation near live electrical infrastructure; no current AI system can perform the physical trimming work itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Tree trimming near electrical lines is strictly regulated; operators must be licensed, certified arborists or electricians, and work must comply with OSHA and utility safety standards. Liability and legal responsibility for outages or injuries create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, utility certification requirements, and liability around working near energized lines create strong barriers to any non-human or unsupervised automated approach to the physical trimming. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized tree trimming crews with arborists, safety certifications, and equipment for working near power lines cost tens of thousands per job; no AI system can perform this remotely or at lower cost with equivalent safety and compliance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for the physical labor, so AI cost is not comparable—human labor (often specialized crews) remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product autonomously performs tree trimming near live electrical infrastructure. This task requires physical manipulation, situational judgment about electrical hazard distances, and regulatory compliance that no commercial system reliably handles today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs vegetation trimming near power lines; this remains entirely a human physical labor task, sometimes aided by drones for detection only. |
Climb poles or use truck-mounted buckets to access equipment.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Climb poles or use truck-mounted buckets to access equipment.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The utility and construction sectors have historically lagged in automation of physical field work; pole climbing remains a fundamentally human task with minimal AI or robotics deployment to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility field work is a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on infrastructure access tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or equipment diagnostics before climbing, but the core physical act of ascending poles and accessing equipment offers limited scope for AI augmentation while humans remain in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, safety checklists, or drone-based pre-inspection, but offers little direct assistance to the physical act of climbing or positioning at the equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Climbing poles and accessing equipment at height requires physical manipulation in unstructured outdoor environments with safety-critical constraints. Current AI systems cannot perform embodied tasks of this complexity and danger independently. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical climbing/access task in outdoor, unstructured environments; no current AI or robotic system can perform this end-to-end at equal quality with time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations, worker licensing (journeyman certification in many jurisdictions), liability for work at height, and legal requirements for qualified personnel to perform electrical utility tasks create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Working on live electrical infrastructure at height requires licensed, trained linemen due to severe safety and liability risks, creating strong regulatory and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of safe pole climbing and equipment access would require specialized robotics costing far more than the hourly wage of a trained power-line worker, with significant ongoing maintenance and oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive specialized robotics far exceeding human labor costs for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs pole climbing or bucket-truck operation autonomously. Research robots exist but cannot handle the variability, weather, and safety demands of real electrical infrastructure work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that autonomously climb poles or operate bucket trucks to access electrical equipment; this remains far beyond current robotics capability. |
Replace or straighten damaged poles.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Replace or straighten damaged poles.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Utility companies are capital-intensive, slow-moving regulated monopolies with strong union presence and high switching costs; adoption of automation in physical field work like pole replacement remains minimal and concentrated in only the largest operators. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical utility field work is a physically-intensive, low-digitization sector with minimal AI/robotic adoption for manual infrastructure repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with damage assessment via drone imagery or predictive maintenance planning, the core task of physically replacing or straightening poles offers limited augmentation potential given that the skilled lineworker must remain in direct control of the heavy machinery and structural work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, scheduling, and diagnostics (e.g., identifying damaged poles via imagery or drones) but offers little direct assistance during the physical replacement or straightening process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Replacing or straightening damaged poles requires physical manipulation in outdoor environments with high safety risks, precise positioning of heavy equipment, and assessment of structural integrity—tasks that current AI and robotics cannot reliably perform end-to-end without human oversight and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical replacement and straightening of utility poles requires heavy equipment operation, climbing, rigging, and fine motor manipulation in outdoor environments that no current AI or robotic system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Utility pole work is heavily regulated by federal and state safety standards, requires licensed electricians/lineworkers, involves public safety and infrastructure critical to national systems, and demands human judgment in hazardous conditions—creating hard legal and safety barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility work involves high-voltage safety hazards, licensing/certification requirements, and liability concerns that mandate trained, authorized linemen to perform pole work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotics, equipment, safety systems, and human oversight required to automate pole replacement would far exceed the wages of skilled electrical power-line workers who perform this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical labor, so any AI-based approach would be more expensive or simply infeasible compared to a human crew. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products today can autonomously replace or straighten damaged utility poles; this remains a highly specialized, physically demanding field task requiring human expertise, heavy machinery operation, and real-time decision-making in variable field conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously replace or straighten utility poles; this remains a manual, crew-based physical task performed with bucket trucks, cranes, and hand tools. |
String wire conductors and cables between poles, towers, trenches, pylons, and buildings, setting lines in place and using winches to adjust tension.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
String wire conductors and cables between poles, towers, trenches, pylons, and buildings, setting lines in place and using winches to adjust tension.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The electrical power sector is capital-intensive and safety-critical with long asset lifecycles; adoption of automation for field installation tasks remains minimal and confined to remote monitoring rather than physical stringing operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility/construction trades are among the slowest sectors to adopt AI or robotics for physical field tasks, with minimal digitization of this specific work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or tension calculation, but the physical and safety-critical nature of the work limits meaningful real-time augmentation beyond planning phases. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning routes, load calculations, or drone-based inspection of lines, but offers little direct help during the physical stringing and tensioning process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in outdoor environments, precise spatial coordination, safety-critical decisions, and real-time judgment about tension and line placement—capabilities far beyond current AI systems today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, outdoor task involving heavy equipment, climbing, and precise manual tensioning in variable field conditions—no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical utility work is heavily regulated and licensed; jurisdictions require certified electricians to perform line installation, and liability for power grid failure creates hard legal and organizational barriers to autonomous automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility work requires certified linework training, safety certification, and often union/regulatory oversight due to high-voltage risk and fall hazards, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of outdoor electrical line work do not exist commercially; the human cost remains the baseline and any theoretical automation would require custom robotics prohibitively more expensive than manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the cost comparison favors the human worker entirely; any hypothetical robotic solution would be far more expensive than a lineworker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously string conductors and cables between structures while managing tension and safety constraints; the task remains exclusively human-performed in all production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product strings and tensions power lines in production; this remains firmly a human physical trade. |
Dig holes, using augers, and set poles, using cranes and power equipment.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Dig holes, using augers, and set poles, using cranes and power equipment.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The physical construction and utility sectors adopt automation very slowly. Power-line installation requires presence in variable outdoor conditions, precise coordination with existing infrastructure, and safety-critical decision-making—conditions that resist fast automation adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility/construction trades are among the slowest sectors to adopt AI-driven automation for physical fieldwork, with heavy equipment operation still fully human-performed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While equipment manufacturers have added some automation features (e.g., assisted controls on cranes), AI offers limited assistance to the core digging and pole-setting tasks, which remain largely manual and operator-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI may assist with planning routes, permitting, or scheduling, but offers minimal direct assistance to the physical act of digging and pole-setting itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment in variable outdoor terrain with precise positioning of poles. Current AI systems cannot operate excavation augers, crane controls, or power equipment in unstructured environments without human operators. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, outdoor task requiring operation of heavy machinery (augers, cranes) in variable terrain and weather; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations, OSHA requirements, and liability concerns create strong barriers to automating power-line work. Additionally, site conditions vary significantly, and human judgment about terrain, weather, and electrical hazards is legally and practically required. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility pole installation involves safety regulations, specialized equipment licensing/certification, and liability concerns around public infrastructure, creating strong barriers to any non-human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of this work would require specialized hardware (autonomous heavy equipment), integration, and site supervision that would exceed the cost of a trained operator performing the task manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical labor, so AI cost is effectively infinite relative to human labor for this specific task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products autonomously perform hole digging and pole setting at scale. While some heavy equipment has automation features, the task as stated—digging holes and setting poles using cranes and power equipment—requires human operators in production today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously digs holes and sets utility poles; this remains manual/heavy-equipment operator work with no robotic automation in production. |
Lay underground cable directly in trenches, or string it through conduit running through the trenches.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Lay underground cable directly in trenches, or string it through conduit running through the trenches.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a physical field-based task in traditional utility and infrastructure sectors that have lagging digital adoption. The specialized nature and regulatory constraints mean adoption of automation remains minimal and slow, with crews still performing this work manually. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electrical utility field construction is a low-digitization, physically intensive sector with minimal AI/robotic adoption for trenching and cable-laying tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist in planning trench routes, managing documentation, or monitoring cable placement via sensors, but these are ancillary to the core physical labor. Current systems offer minimal real-time assistance to workers actively laying cable and stringing conduit. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, GIS mapping, or scheduling logistics around cable-laying, but offers little direct help with the physical act of laying cable in trenches. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy cable in trenches, precise positioning, and handling of conduit systems—capabilities far beyond current AI/robotics deployed at scale. No end-to-end automation system exists that can reliably perform the full cable-laying and stringing operation in varied underground conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task involving heavy cable handling, trenching, and precise placement in outdoor field conditions, none of which current AI systems can perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Power-line work is highly regulated; only licensed electricians and certified line installers are legally permitted to install underground electrical cable and conduit systems. There is both a formal licensing barrier and significant safety/liability requirements that legally mandate human oversight and sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Utility work often requires licensed/certified linemen, safety regulations, and physical dexterity in hazardous trench environments, creating strong practical and regulatory barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems for underground cable work are extremely expensive, rare, and narrowly applicable. A human line installer's fully-loaded cost remains far lower than the capital and maintenance cost of equipment capable of doing this work reliably. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any AI-based approach would require expensive robotics far exceeding the cost of a human crew for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs underground cable installation and conduit stringing autonomously. This remains a specialized manual task performed by licensed field crews; no production systems exist that can replace this work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product lays underground cable in trenches or pulls it through conduit; this remains a manual/heavy-equipment-assisted human task. |
Open switches or attach grounding devices to remove electrical hazards from disturbed or fallen lines or to facilitate repairs.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Open switches or attach grounding devices to remove electrical hazards from disturbed or fallen lines or to facilitate repairs.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utility operations remain heavily human-dependent for field work; automation adoption is minimal and limited to narrow, controlled scenarios (substations). Fallen-line and outdoor emergency response are overwhelmingly manual. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line work is a physically demanding, low-digitization trade with minimal AI/robotic adoption for hands-on hazard mitigation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostics (identifying which switches to open via network analysis) or planning, but the actual physical intervention must remain human-performed, limiting augmentation impact on the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with hazard detection, outage mapping, or dispatch optimization, but offers little direct augmentation to the physical act of opening switches or attaching grounding devices. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of high-voltage equipment in hazardous, variable field conditions with critical safety implications. Current AI systems lack the embodied robotics, real-time environmental perception, and fail-safe mechanisms needed to safely handle live or potentially live electrical lines. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, safety-critical manual task requiring hands-on manipulation of switches and grounding equipment on live/de-energized lines; no AI system can physically perform it end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has strong legal and regulatory barriers: electrical codes require licensed electricians to perform high-voltage work, and liability for equipment damage or worker injury from automation failures creates significant legal exposure that deters substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Safety regulations (OSHA, utility lockout/tagout procedures) require qualified, trained, often certified lineworkers to perform grounding and switching to prevent electrocution, making human authorization legally mandatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware cost for robotics capable of this task (specialized arms, sensors, safety systems) far exceeds the wage of a trained electrician, and integration and maintenance overhead are substantial. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so the comparison defaults to AI being infeasible/more costly since a human with specialized equipment must be dispatched regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform the full end-to-end task of opening switches or attaching grounding devices on live power lines in production environments. While experimental robotics exist for narrow controlled scenarios, they are not in general use by utilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical switching or grounding operations on power lines; this remains entirely a human field task requiring robotic manipulation not available at scale. |
Install, maintain, and repair electrical distribution and transmission systems, including conduits, cables, wires, and related equipment, such as transformers, circuit breakers, and switches.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Install, maintain, and repair electrical distribution and transmission systems, including conduits, cables, wires, and related equipment, such as transformers, circuit breakers, and switches.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Electrical utilities and construction remain capital-driven, geographically dispersed, and safety-regulated sectors with slow digitization of field operations. Adoption of advanced automation is limited to narrow, high-volume tasks like underground cable laying, not the full spectrum of installation and repair work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility field work is a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on infrastructure repair, unlike office-based information work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostics (anomaly detection in thermal or electrical data from sensors) and work planning, but augmentation is limited; the bulk of the task—hands-on installation, splicing, climbing, and live-circuit repair—requires human judgment and skill that AI tools cannot materially enhance in situ. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, scheduling, predictive maintenance alerts, or documentation support, but offers little direct help with the hands-on physical installation and repair process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment, climbing poles, working at heights, and real-time diagnosis in live electrical environments—capabilities far beyond current AI or robotic systems in unstructured outdoor settings. No current AI system can autonomously install, maintain, or repair distribution systems end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical fieldwork involving climbing poles, handling high-voltage equipment, and manual installation/repair that current AI systems cannot perform, as they lack physical embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical power-line work is heavily regulated and licensed; only qualified, certified electricians are legally permitted to perform live-line work due to safety codes (OSHA, NEC) and liability. Customers and grid operators require human accountability and sign-off. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Electrical line work requires licensing, certification, strict safety protocols, and often union/regulatory oversight due to high-voltage danger, making unauthorized or automated substitution legally and physically prohibited. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of electrical work are capital-intensive and require extensive setup, maintenance, and human oversight, making them far more expensive per task instance than a trained electrician's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so any comparison favors the human worker who must physically execute the repair or installation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While robotic arms exist in controlled factory settings, no deployed product reliably performs live electrical installation and repair in field conditions. The task demands dexterous manipulation, electrical safety verification, and navigation of variable infrastructure that production systems do not handle. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product installs or repairs physical electrical distribution equipment; this remains entirely a human manual labor task requiring physical dexterity and on-site presence. |
Install watt-hour meters and connect service drops between power lines and consumers' facilities.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Install watt-hour meters and connect service drops between power lines and consumers' facilities.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Utility companies and power-line contractors have shown limited adoption of automation for this task; it remains highly labor-dependent and performed by licensed human workers in traditional ways. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line work is a physical, low-digitization trade with minimal AI/robotic adoption in the field; automation here lags far behind office-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with route planning, safety compliance checklists, or documentation after the fact, but the core physical task of installing meters and connecting service drops offers minimal scope for real-time AI assistance to a human worker. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with scheduling, diagnostics, or documentation related to this work, but offers little direct assistance during the physical installation and connection process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment, climbing poles, outdoor work in variable conditions, and precise connection of high-voltage systems—capabilities that current AI systems lack entirely. No meaningful part can be automated without deploying specialized robotics, which is not generally available today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical task requiring climbing, working with high-voltage lines, and precise manual installation; no current AI or robotic system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task involves working with high-voltage power systems and requires electricians to be licensed, bonded, and legally liable for safety compliance; regulatory and safety standards mandate qualified human oversight and sign-off. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Utility work involves licensing, safety certification, high-voltage liability, and often union/regulatory requirements mandating qualified electrical workers to perform installations and connections. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Skilled electrical line workers command high wages and the capital cost of developing and deploying specialized robotic systems to perform this task would far exceed the cost of human labor in most contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system performing this physical work, so AI cost is not applicable/comparable and the human remains the only viable option, making AI more expensive by default (infinite substitution cost). |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task in any form. The physical and environmental constraints require either human workers or purpose-built, industry-specific robotic systems that do not exist at scale in commercial use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs meters or connects service drops autonomously; this remains entirely a skilled human field task. |
Place insulating or fireproofing materials over conductors and joints.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Place insulating or fireproofing materials over conductors and joints.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Electrical utilities are geographically dispersed, safety-critical sectors with strong unionization and regulatory compliance requirements; automation adoption for physical field tasks remains minimal and pilots are rare. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility field trades are a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on line work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning, material pre-positioning, or hazard documentation, but the core manual placement task itself offers limited scope for meaningful AI assistance while the worker remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, documentation, or safety checklists, but offers little direct help with the physical act of applying insulating materials. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of materials in high-risk, variable field conditions with safety-critical consequences. Current AI systems cannot reliably handle the dexterity, environmental adaptation, and safety validation needed to place insulating materials on live or de-energized conductors. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical task requiring climbing, dexterity, and precise manual application of materials on live or de-energized lines; no current AI or robotic system can perform this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical utility work is heavily regulated and licensed; OSHA, NFPA, and utility-specific rules require qualified human workers to perform or directly supervise live-line insulation work, creating hard legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Working on electrical conductors requires licensed, trained linemen due to extreme safety/liability risk (electrocution, fire), creating hard regulatory and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics for high-altitude electrical work remain experimental and expensive; the integration, safety certification, and human oversight costs far exceed the loaded wage of trained line workers who perform this task efficiently. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so AI cost is effectively irrelevant/infinite compared to a human lineworker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI system reliably performs this task in production utility environments. The combination of electrical hazards, variable pole/conductor geometry, and regulatory safety requirements means no commercial product offers end-to-end capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical insulation/fireproofing placement on power lines; this remains squarely a human field task. |
Splice or solder cables together or to overhead transmission lines, customer service lines, or street light lines, using hand tools, epoxies, or specialized equipment.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Splice or solder cables together or to overhead transmission lines, customer service lines, or street light lines, using hand tools, epoxies, or specialized equipment.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities and electrical contractors operate in capital-intensive, risk-averse, regulated sectors with strong union presence and strict safety oversight. Adoption of autonomous automation for field electrical work remains minimal; pilots are rare and production deployment is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility field trades are a low-digitization, physically-demanding sector with minimal AI/robotics adoption for hands-on line work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with diagnostics (identifying fault locations) or planning splice locations via imagery, but the core physical task of splicing and soldering offers limited scope for assistive AI while a human remains in the loop. The bottleneck is physical execution, not decision-making. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, diagnostics, or documentation, but offers little direct help during the physical splicing/soldering act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in outdoor, variable environments (climbing poles, handling live or de-energized lines, precision hand soldering/splicing). Current AI systems cannot perform the end-to-end physical work; robotic alternatives exist only in narrow lab settings, not in general field deployment. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, dexterous manual task requiring climbing, handling live/de-energized high-voltage cables, and precise splicing in variable field conditions—no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical work on live or de-energized power lines is heavily regulated; a licensed electrician or power-line technician must legally perform or directly supervise splicing and soldering tasks. Liability, safety certification, and regulatory requirements create hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Utility work is heavily regulated, requires licensed/certified linemen, involves lethal electrical hazards, and strict safety and liability regimes mandate qualified human personnel. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment cost (robotic arms, computer vision, specialized tools), integration, safety systems, and ongoing maintenance far exceed the loaded wage of a skilled electrician for field work. Human labor remains vastly cheaper for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable—any hypothetical robotic solution would be far more costly than a trained lineworker today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs general cable splicing and soldering on transmission or service lines in production. Specialized robotic arms exist for controlled factory settings but not for field-deployed, variable outdoor electrical work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs cable splicing/soldering on transmission or service lines; this remains entirely a human field craft skill. |
Clean, tin, and splice corresponding conductors by twisting ends together or by joining ends with metal clamps and soldering connections.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Clean, tin, and splice corresponding conductors by twisting ends together or by joining ends with metal clamps and soldering connections.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities operate in heavily regulated environments with entrenched labor practices and safety protocols. Adoption of autonomous systems for field electrical work is minimal; the sector remains labor-dependent and risk-averse to automation of critical infrastructure tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Utility line work is a physical, low-digitization trade with minimal AI/robotic adoption for hands-on tasks like splicing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with documentation, diagnostics, or pre-planning of splicing procedures, but the hands-on soldering and conductor joining itself offers minimal opportunity for meaningful AI augmentation while a human holds tools in the field. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with training simulations, documentation, or diagnostic guidance, but offers little direct help during the physical splicing and soldering process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in confined spaces (often at height on poles or underground), fine motor control to twist conductors or position clamps, and judgment about solder quality and connection integrity. Current AI systems cannot perform end-to-end physical assembly of electrical connections reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical task requiring fine motor skill, dexterity, and judgment working with live/de-energized conductors at height or in trenches; no current AI system or robot can perform this manipulation.“}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical work is tightly regulated; most jurisdictions require a licensed electrician to perform or sign off on power-line splicing and soldering. Safety standards, liability for connection failure, and legal requirements create hard barriers to unsupervised automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Electrical utility work requires certified/licensed linemen, strict safety protocols, and regulatory oversight (OSHA, utility codes), making human physical performance and sign-off mandatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, safety compliance, precision requirements, and liability exposure make any hypothetical automation system far more expensive to deploy and maintain than paying a trained technician to perform the work on-site. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so AI cost is effectively infinite relative to a human lineworker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs field-level electrical conductor splicing and soldering autonomously in production. This remains a task requiring human technicians in the field; robotic prototypes exist but are not commercially deployed at scale in power-line work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs conductor splicing, cleaning, tinning, or soldering in field power-line contexts; this remains purely manual skilled labor. |
Related occupations — Installation, Maintenance & Repair
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.