Lighting Technicians

27-4015.00
Median wage $68,060/yr8,900 employed (US)Rank #732 of 923 scored · top 79% by substitution

Set up, maintain, and dismantle light fixtures, lighting control devices, and the associated lighting electrical and rigging equipment used for photography, television, film, video, and live productions. May focus or operate light fixtures, or attach color filters or other lighting accessories.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution18
Exposure12
Augmentation34

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

16 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%13

panel mean rating 1.5/5 → substitution pressure 13/100

Technical feasibility todayw 20%9

panel mean rating 1.4/5 → substitution pressure 9/100

Cost vs. human wagew 15%7

panel mean rating 1.3/5 → substitution pressure 7/100

Adoption barriersw 20%inverted — strong barriers lower the score47

panel mean rating 3.1/5 (barrier strength) → substitution pressure 47/100

Sector adoption velocityw 10%7

panel mean rating 1.3/5 → substitution pressure 7/100

Task breakdown (16 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.

Operate manual or automated systems to control lighting throughout productions.

57

CI 30–84 · exposure 58 · augmentation 88 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Theater, broadcast, concert, and corporate event sectors have already adopted automated lighting control extensively; large productions routinely program complex cue sequences pre-show. Adoption is mature in high-digitization entertainment venues and professional productions.
Sector adoption velocityclaude-sonnet-52/5Entertainment and live production industries have adopted automated lighting control systems for cueing, but adoption of AI-driven autonomous lighting decision-making is still nascent and slow-moving.
Augmentation potentialclaude-haiku-4-5-202510015/5AI-assisted lighting dramatically augments human operators by predicting cue timing, suggesting intensity changes based on visual analysis, and automating routine fades—allowing the operator to focus on artistic nuance and live adaptation rather than mechanical execution.
Augmentation potentialclaude-sonnet-54/5AI and automated lighting boards significantly assist technicians by pre-programming cues, syncing with sound/video, and enabling rapid adjustments, greatly boosting productivity while a human remains in control.
Task automatabilityclaude-haiku-4-5-202510015/5Lighting control systems are already heavily automated; modern theatrical and broadcast lighting can be programmed and executed by automated systems with cue sequences, intensity dimming, and color changes. AI-driven scheduling and adaptive lighting adjustment can achieve ≥50% time savings while maintaining or exceeding quality of lighting design execution.
Task automatabilityclaude-sonnet-52/5Live, real-time lighting operation during productions requires split-second adjustments to unpredictable performer movement, timing cues, and artistic judgment that current AI cannot reliably replicate end-to-end.'
Adoption barriersclaude-haiku-4-5-202510013/5Live entertainment still highly values human judgment for real-time responsiveness to performer movement, audience reaction, and artistic improvisation. Union agreements and venue policies may restrict full automation, and clients often prefer a human operator for creative control and live troubleshooting.
Adoption barriersclaude-sonnet-52/5No licensing requirement, but live event reliability, union labor practices in film/theater, and the need for real-time artistic judgment create moderate organizational friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510014/5Automated lighting control systems represent a significant upfront investment but dramatically reduce per-production operational costs; amortized over multiple productions, the cost per output is substantially lower than continuous human operator wages.
Cost vs. human wageclaude-sonnet-52/5Automated lighting rigs and software reduce some labor but still require skilled technicians for programming and live operation, so total cost savings versus a human operator are modest.
Technical feasibility todayclaude-haiku-4-5-202510014/5Mature lighting control software (ETC Eos, Hog, grandMA) exists in production at scale across theaters, concerts, and broadcasts. These systems reliably execute programmed cues and automate repetitive lighting transitions, though full autonomous adaptation to live performance variables remains partially constrained.
Technical feasibility todayclaude-sonnet-52/5Automated lighting consoles and programmed cue systems exist and are widely used, but they still require a human operator to program, trigger, and adjust in real time; fully autonomous show operation is not standard practice.

Match light fixture settings, such as brightness and color, to lighting design plans.

30

CI 25–35 · exposure 25 · augmentation 50 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Lighting technician work spans live events, theater, film, and installations—sectors with moderate-to-low AI adoption rates; while some venues use programmable lighting control, AI-driven autonomous matching to designs is not yet mainstream in production.
Sector adoption velocityclaude-sonnet-52/5Entertainment and live-event industries adopt digital lighting control tools steadily but remain a physically-oriented, lower-digitization sector with limited AI-driven automation in production.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist by analyzing design plans, suggesting fixture settings, and previewing color/brightness combinations, raising technician productivity in planning and iteration phases without replacing the need for on-site calibration and judgment.
Augmentation potentialclaude-sonnet-53/5Software-assisted lighting design tools and presets help technicians quickly recall and adjust settings to match design plans, improving efficiency while the technician still executes and verifies the physical setup.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can interpret lighting design plans and theoretically suggest settings, the task requires precise physical calibration and real-time environmental assessment that current systems cannot reliably execute end-to-end without human intervention and verification.
Task automatabilityclaude-sonnet-52/5This requires physical manipulation of fixtures and on-site sensory judgment of how light appears in a real space, which current AI systems cannot perform end-to-end without robotic embodiment.','the task is largely physical/perceptual on-site work
Adoption barriersclaude-haiku-4-5-202510014/5Lighting design matching often involves aesthetic judgment, venue-specific factors, and live event coordination where human presence and decision-making are preferred or contractually required; liability for poor lighting outcomes also creates organizational friction against full automation.
Adoption barriersclaude-sonnet-52/5No formal licensing is typically required, but venues and productions rely on skilled technicians for safety, on-site judgment, and live troubleshooting, creating moderate organizational friction against pure automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI systems capable of any part of this task (design interpretation, fixture control) still require significant overhead, setup, and human oversight that approaches or exceeds the cost of a technician performing it directly.
Cost vs. human wageclaude-sonnet-52/5Hardware, DMX control systems, and technician oversight still dominate costs; there is no cheap AI substitute for the physical setup and fine-tuning work involved.
Technical feasibility todayclaude-haiku-4-5-202510012/5No mature production systems currently automate this task; existing lighting control systems require manual input or pre-programmed scenes, and AI-driven automated calibration to match design specs remains research-stage with material error margins.
Technical feasibility todayclaude-sonnet-52/5Some lighting console software offers programmable presets and color-matching tools, but these are operator-assisted digital controls, not autonomous AI systems reliably executing the full matching task.

Notify supervisors when major lighting equipment repairs are needed.

26

CI 19–33 · exposure 20 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting technician roles are concentrated in small-to-medium entertainment and venue operations with low overall digitization and slow technology adoption; few venues have adopted predictive maintenance systems, and most rely on technician rounds and ad-hoc reporting.
Sector adoption velocityclaude-sonnet-51/5Live event, theatrical, and film production lighting work is a physical, low-digitization sector with minimal AI agent deployment for hands-on equipment assessment tasks.
Augmentation potentialclaude-haiku-4-5-202510013/5Sensor dashboards or AI-flagged anomalies could assist technicians by highlighting equipment that warrants inspection, reducing blind spots during routine rounds, though the final judgment and notification remain human-driven.
Augmentation potentialclaude-sonnet-52/5AI could help draft or format repair notification messages and track equipment maintenance logs, but offers little assistance for the core physical inspection and assessment work.
Task automatabilityclaude-haiku-4-5-202510012/5Detecting equipment malfunction requires visual inspection and experiential judgment about equipment degradation; while AI could flag simple sensor-based failures, most decisions about 'major' repairs require human assessment of performance nuance, cost-benefit, and operational context that AI systems struggle with at production scale.
Task automatabilityclaude-sonnet-52/5This requires physical inspection and hands-on assessment of lighting equipment condition before communicating findings, which current AI cannot perform independently, though the notification/reporting portion could be templated or drafted by AI.
Adoption barriersclaude-haiku-4-5-202510013/5The task requires accountability for equipment safety and operational readiness in live-event contexts; while no single licensed role legally mandates this decision, organizational liability, union agreements in some venues, and industry norms create moderate friction against automation without human oversight.
Adoption barriersclaude-sonnet-52/5No formal licensing barrier exists for this specific notification task, but practical barriers include physical presence requirements and safety-critical judgment about equipment condition that organizations rely on trained technicians for.
Cost vs. human wageclaude-haiku-4-5-202510012/5A lighting technician's loaded wage (~$30/hr) is very low for this routine judgment task; even cheap sensor-based monitoring plus integration overhead remains comparable to or exceeds the cost of occasional human checks, especially given low false-positive tolerance.
Cost vs. human wageclaude-sonnet-51/5AI cannot perform the physical inspection component at all, so there is no viable cost comparison for the core task; a human must still be present and paid to do this.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed AI products reliably diagnose lighting equipment need for major repair in real theaters, studios, or venues. Condition monitoring exists but is narrow and vendor-specific, with high false-positive rates and limited integration into technician workflows.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously inspects physical lighting rigs and determines repair needs; this remains a human judgment task requiring physical presence on set or venue.

Program lighting consoles or load automated lighting control systems onto consoles.

24

CI 14–35 · exposure 20 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI in live lighting control remains minimal; the sector is dominated by skilled manual technicians and incremental tool improvements rather than AI-driven automation. Pilots and experimental systems are rare, and production deployment is nearly absent.
Sector adoption velocityclaude-sonnet-51/5Live event and theatrical production sectors have low digitization of this specific task and show minimal evidence of AI adoption for console programming.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist by suggesting cue sequences, previewing effects, or auto-generating template configurations from scene descriptions, which would raise a technician's productivity. However, the human must remain central to validation, artistic refinement, and real-time execution, making this a moderate augmentation scenario.
Augmentation potentialclaude-sonnet-52/5AI can offer some assistance such as suggesting cue sequences or automating repetitive programming steps, but it plays a limited role in the hands-on console work itself.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could assist in generating basic lighting sequences or templates, the task requires creative decision-making, real-time adaptation to specific venue/event needs, and nuanced understanding of artistic intent that current AI systems cannot fully replicate end-to-end. The programming often involves bespoke configurations tied to unique production contexts, making 50% time savings at equal quality unlikely with current systems.
Task automatabilityclaude-sonnet-52/5Programming lighting consoles requires physical setup, venue-specific fixture patching, and iterative creative adjustment during rehearsals that current AI cannot handle end-to-end.imestamp No off-the-shelf system reliably automates this workflow today.
Adoption barriersclaude-haiku-4-5-202510014/5Licensing and regulatory requirements, union rules (particularly in theater and live events), liability for failed lighting cues affecting safety and performance, and the high cost of errors in live productions create substantial legal and organizational barriers to full automation. Human expertise and sign-off are typically required.
Adoption barriersclaude-sonnet-52/5No formal licensing is required, but venue-specific hardware, safety considerations for live events, and the need for real-time creative/technical judgment create moderate practical friction.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI tooling for lighting programming would require significant setup, integration, and human oversight costs that likely approach or exceed the cost of a skilled technician performing the task directly. The specialized nature of lighting consoles and venues limits economy of scale.
Cost vs. human wageclaude-sonnet-51/5No viable AI substitute exists for this physical, hands-on console programming task, so the human remains the only cost-effective option.
Technical feasibility todayclaude-haiku-4-5-202510012/5No mature, production-deployed AI systems reliably program lighting consoles or load control systems independently. Some research-stage tools exist for generating lighting cues, but deployed products demonstrating reliable, real-world automation of this task at scale do not exist today.
Technical feasibility todayclaude-sonnet-51/5There are no deployed AI products that program lighting consoles or load control systems autonomously in production venues; this remains a manual technical task performed by trained operators.

Test lighting equipment function and desired lighting effects.

24

CI 19–29 · exposure 20 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting technicians work in film, theater, events, and construction—sectors with lower digital automation adoption and strong preference for skilled tradework. Physical presence requirements and specialized venue conditions limit remote or automated substitution.
Sector adoption velocityclaude-sonnet-51/5Lighting technician work in film/theater/event production is a low-digitization, physically embedded trade with minimal AI agent adoption in production environments today.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-powered visualization tools, augmented reality previews of lighting effects, and automated diagnostic systems for equipment status could meaningfully assist technicians in planning and problem-solving, though the core task remains human-driven.
Augmentation potentialclaude-sonnet-52/5AI-based lighting control software and simulation tools can help preview or program effects, offering some assistance, but hands-on testing and effect verification still rely primarily on human judgment on-site.
Task automatabilityclaude-haiku-4-5-202510012/5Testing lighting equipment function requires physical interaction with fixtures, dimming systems, and spatial assessment of light distribution—tasks that demand hands-on manipulation and real-time visual evaluation in three-dimensional space. While AI could potentially assess images of lighting effects, the full end-to-end task of testing equipment across multiple fixtures and adjusting for desired effects remains heavily dependent on manual operation and spatial reasoning beyond current AI capabilities.
Task automatabilityclaude-sonnet-52/5Testing lighting equipment involves physical setup, hands-on adjustment, and visual/aesthetic judgment on-site that current AI cannot perform end-to-end; only diagnostic subcomponents (e.g., sensor-based fault detection) could be automated.
Adoption barriersclaude-haiku-4-5-202510013/5Physical presence on set or in venue is typically required to assess lighting effects in context and ensure equipment safety and proper function. Professional standards and client satisfaction preferences create some friction toward human involvement, though no strict legal licensing barrier exists for testing itself.
Adoption barriersclaude-sonnet-52/5No licensing requirement, but the task requires physical presence, manual dexterity, and subjective aesthetic judgment tied to a live set, creating practical (not regulatory) barriers to remote automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Current AI systems cannot perform this task end-to-end, making direct cost comparison unfavorable. Integration of vision systems, robotics for physical testing, and specialized hardware oversight would exceed the cost of a trained lighting technician for meaningful automation.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this physical task, so AI cost comparison is moot—human labor with basic tools remains the only functional option.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed products reliably perform the full spectrum of lighting equipment testing and effect evaluation. While computer vision can analyze images of lighting results, no production systems autonomously test physical equipment function, diagnose failures, and validate effects to professional standards without human intervention.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously tests physical lighting rigs and evaluates desired artistic lighting effects in production settings; this remains a manual, on-site task.

Load, unload, or position lighting equipment.

19

CI 10–29 · exposure 13 · augmentation 13 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Adoption is minimal in entertainment and event production sectors, which remain labor-intensive and resistant to full automation of creative/technical setup roles. Pilots are rare, and production-scale deployment of robotic lighting positioning is not evident in the industry.
Sector adoption velocityclaude-sonnet-51/5Film, TV, and live-event production is a physically intensive, low-digitization sector with minimal AI/robotics adoption for equipment handling tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI-assisted tools could help with lighting layout planning and equipment recommendations via software, but on-site physical loading and positioning offers limited augmentation because the task is primarily manual labor; current computer vision or autonomous guidance systems do not yet materially boost human productivity during active setup.
Augmentation potentialclaude-sonnet-51/5Current AI tools offer essentially no assistance with the physical act of loading, unloading, or positioning lighting gear.
Task automatabilityclaude-haiku-4-5-202510012/5Loading and positioning lighting equipment requires physical manipulation in variable, unstructured environments (stage setups, outdoor locations, confined spaces). Current robotic systems lack the dexterity, real-time spatial reasoning, and adaptability to handle diverse equipment types and fragile optical components reliably, and no integrated end-to-end automation achieves 50% time savings at equal quality.
Task automatabilityclaude-sonnet-51/5This is a physical materials-handling task requiring manual dexterity, spatial judgment, and mobility on set; no off-the-shelf AI system performs this end-to-end today.
Adoption barriersclaude-haiku-4-5-202510013/5Safety and liability concerns exist when automating equipment handling on active production sets with crew present, and unions in entertainment typically require humans to perform or directly supervise rigging and positioning. However, there are no hard legal prohibitions on automation of the physical task itself.
Adoption barriersclaude-sonnet-52/5No licensing requirement exists, but physical safety, equipment handling protocols, and on-set coordination create practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Current robotic and autonomous systems capable of manipulation remain capital-intensive; deployment, integration, and maintenance costs substantially exceed the wage of a skilled lighting technician for this task, especially considering the infrequent or batch nature of setup work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-driven substitute; any robotic solution would require expensive custom hardware far exceeding the cost of a human technician for this task.
Technical feasibility todayclaude-haiku-4-5-202510012/5While some warehouse automation and robotic arms exist, they operate in highly controlled settings with standardized objects. Lighting equipment positioning in production environments—requiring precise placement, angle adjustment, cable routing, and collision avoidance around people and sets—lacks mature deployed products performing this task reliably in production.
Technical feasibility todayclaude-sonnet-51/5No deployed product loads, unloads, or positions lighting equipment; this remains squarely a human/robotics research problem not addressed by current commercial offerings.

Install color effects or image patterns, such as color filters, onto lighting fixtures.

17

CI 10–24 · exposure 8 · augmentation 25 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting installation remains a largely manual, on-site craft performed in venues and events with low digitization. Adoption of AI or robotics for this task is minimal and primarily confined to research or conceptual stages.
Sector adoption velocityclaude-sonnet-51/5Entertainment and live event lighting is a physically-oriented, low-digitization sector where robotic or AI automation of fixture installation has seen negligible real-world adoption.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist by recommending color palettes, suggesting filter types for specific effects, or providing digital previews, but the core physical installation task offers limited opportunity for AI to augment human productivity directly.
Augmentation potentialclaude-sonnet-52/5AI could assist with planning color schemes, generating lighting design suggestions, or programming effects, but offers little direct help with the physical act of installing filters onto fixtures.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could potentially guide tool selection and provide installation instructions, the physical manipulation of color filters and precise mounting onto lighting fixtures requires dexterous robotics and spatial reasoning. Current AI systems lack reliable embodied capability for this hands-on assembly work at scale.
Task automatabilityclaude-sonnet-51/5This is a physical hands-on task requiring installing gel filters or gobos onto lighting fixtures, which requires manipulation of physical equipment that current AI systems cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5While there is no legal licensing requirement for installing color filters, organizational friction and the need for spatial precision in live event or venue settings create moderate adoption barriers to full automation.
Adoption barriersclaude-sonnet-52/5No licensing requirement exists specifically for this task, but physical access to equipment, on-site presence, and coordination with production needs create moderate practical friction against remote automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of robotic systems capable of precise filter installation, integration, and oversight would far exceed the loaded wage of a skilled lighting technician performing this work manually.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for this physical installation task, so AI cost comparison is essentially moot; a human technician remains necessary and cheaper than any hypothetical robotic solution.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product today reliably performs physical installation of color filters onto lighting fixtures without human intervention. This task fundamentally requires robotic arms or human technicians in production environments.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product or robotic system performs physical installation of color filters or gobos onto lighting fixtures in production settings; this remains a manual technician task.

Perform minor repairs or routine maintenance on lighting equipment, such as replacing lamps or damaged color filters.

17

CI 10–24 · exposure 8 · augmentation 25 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting maintenance remains a traditional, hands-on craft in most venues (theaters, studios, venues); adoption of robotics or autonomous systems is minimal and confined to specialized high-volume industrial facilities.
Sector adoption velocityclaude-sonnet-51/5Entertainment/venue technical trades are a low-digitization, physical-labor sector with minimal AI or robotics adoption for equipment maintenance tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist by identifying which lamps have burned out or which filters are damaged through image analysis or predictive maintenance, but the hands-on execution remains technician-dependent, limiting augmentation gains.
Augmentation potentialclaude-sonnet-52/5AI could assist with diagnostics, maintenance scheduling, or identifying faulty equipment via sensor data, but offers little help with the actual physical repair action itself.
Task automatabilityclaude-haiku-4-5-202510012/5Replacing lamps or color filters involves physical manipulation in varied spatial configurations and requires dexterity that current general-purpose AI systems lack. While the cognitive component (identifying what needs replacement) is automatable, the physical execution remains beyond current robotic capabilities in most real-world lighting setups.
Task automatabilityclaude-sonnet-51/5This is a hands-on physical task requiring manual dexterity to access fixtures, remove and replace lamps/filters, and handle equipment on rigs or stages; no AI system can perform this physical manipulation.
Adoption barriersclaude-haiku-4-5-202510012/5While there is no strict licensing requirement for lamp replacement, workplace safety regulations, height/access requirements, and the need for visual inspection and judgment create moderate organizational friction against full automation.
Adoption barriersclaude-sonnet-53/5While not licensed work, it often occurs in time-sensitive live production/venue contexts requiring physical presence, safety awareness (electrical, rigging), and hands-on trust, creating moderate practical barriers to any automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying, integrating, and maintaining a robotic system capable of this task far exceeds the loaded wage of a technician performing routine lamp and filter changes, which are low-labor-cost activities.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-based alternative to compare costs against; a human technician remains the only means of physically executing this repair task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs physical lamp replacement or filter installation at production scale today. Specialized robotics exist only in narrow, pre-engineered environments and are not standard in lighting maintenance workflows.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical lighting equipment repair; this remains purely a manual, in-person task with no robotic or AI-driven substitute in production use.

Disassemble and store equipment after performances.

15

CI 15–15 · exposure 0 · augmentation 25 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The entertainment and live-event sectors have low automation rates for physical post-show tasks; this remains predominantly manual labor with minimal AI/robotic deployment.
Sector adoption velocityclaude-sonnet-51/5Live event and entertainment technical crews are a low-digitization, physically-oriented sector with minimal AI/robotics adoption for manual equipment handling tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with inventory tracking or documentation of stored equipment via computer vision, but offers minimal productivity boost for the core manipulation and physical storage work itself.
Augmentation potentialclaude-sonnet-52/5AI could help with inventory tracking, checklists, or optimal packing/storage planning, but offers little direct assistance with the physical disassembly and stowing itself.
Task automatabilityclaude-haiku-4-5-202510011/5Disassembling and storing equipment involves manipulating physical objects in varied configurations, which current AI systems cannot perform autonomously. This requires embodied manipulation skills and spatial reasoning that are not yet deployable at scale.
Task automatabilityclaude-sonnet-51/5Physically disassembling and packing lighting rigs, cables, and fixtures requires manual dexterity, spatial judgment, and mobility that current AI systems cannot perform end-to-end.'
Adoption barriersclaude-haiku-4-5-202510012/5While there is no strict licensing requirement, liability concerns around equipment damage and safety (especially with expensive or fragile lighting gear) present some organizational friction to automation adoption.
Adoption barriersclaude-sonnet-52/5No licensing requirement exists, but physical handling of expensive, fragile, and safety-sensitive equipment in varied venues creates practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Acquiring and maintaining robotic systems capable of safe equipment handling would cost far more than the loaded wage of a lighting technician performing this task.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute, so the human labor cost remains the only practical option, making AI comparatively more expensive or simply unavailable.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs general equipment disassembly and storage in real theater or venue environments. While robotic arms exist in research, they lack the dexterity and adaptability needed for diverse lighting rigs.
Technical feasibility todayclaude-sonnet-51/5No deployed robotic or AI product handles teardown and storage of stage lighting equipment in production venues today; this remains purely manual work.

Assess safety of wiring or equipment set-up to determine the risk of fire or electrical shock.

11

CI 0–23 · exposure 13 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting technician and electrical safety roles operate in unions, regulated environments, and small-to-medium firms with minimal digital transformation. Adoption of AI automation is essentially nonexistent in this sector, reflecting both regulatory barriers and the physical, hands-on nature of the work.
Sector adoption velocityclaude-sonnet-51/5Physical, on-site technical trades like lighting/rigging show minimal AI adoption for hands-on safety inspection tasks; this is a low-digitization, hardware-centric field.
Augmentation potentialclaude-haiku-4-5-202510013/5AI vision tools could usefully assist technicians by highlighting potential defects or anomalies on equipment (e.g., flagging corroded terminals or frayed wire), speeding inspection, but the human technician remains essential for final judgment and regulatory sign-off.
Augmentation potentialclaude-sonnet-52/5AI could help by referencing safety codes, checklists, or flagging documented issues, but it cannot meaningfully assist with the physical inspection itself.
Task automatabilityclaude-haiku-4-5-202510012/5While AI vision systems can detect some obvious hazards (exposed wires, damaged insulation), assessing electrical safety requires real-time testing equipment, knowledge of code compliance, and judgment about equipment interaction—tasks that demand physical measurement and contextual expertise. Current AI lacks the integration to perform end-to-end safety assessment with 50% time savings.
Task automatabilityclaude-sonnet-51/5This requires physical inspection of live equipment, cabling, and rigging in real environments to detect hazards; current AI cannot physically assess wiring or set-up conditions end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Safety assessments of electrical equipment are heavily regulated and often require licensed electricians to sign off on compliance with fire and electrical codes. Legal liability for fire or shock incidents creates asymmetric error costs that effectively lock this task to credentialed professionals.
Adoption barriersclaude-sonnet-54/5Electrical and fire safety inspection carries liability and often falls under safety codes/certifications requiring qualified personnel, creating strong practical and sometimes regulatory barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of deploying AI inspection systems (specialized cameras, integration, liability oversight) combined with the need for human verification makes this more expensive than direct human inspection for now. The specialized equipment and setup required offsets any inference savings.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing the physical inspection, so any AI cost comparison is moot—human presence is mandatory, making AI not cheaper for the actual task.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably performs complete electrical safety assessment today. Some computer vision systems can flag visual defects, but genuine safety evaluation requires circuit testing, voltage measurement, and regulatory compliance verification—areas where AI products lack production reliability and scope.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical electrical/fire safety inspection of stage or set lighting rigs autonomously; this remains a hands-on human task.

Set up and focus light fixtures to meet requirements of television, theater, concerts, or other productions.

11

CI 10–13 · exposure 0 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Live entertainment and production sectors are slower to digitize core operational tasks; lighting setup remains highly specialized, manual work with minimal AI adoption in production pipelines today.
Sector adoption velocityclaude-sonnet-52/5Live production and entertainment industries have been slower to adopt AI for physical crew work compared to office-based knowledge sectors, though some automated lighting control adoption exists.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with lighting design visualization or pre-planning via simulation tools, but current systems offer limited real-time assistance for the physical setup and focus task itself; augmentation potential is narrow.
Augmentation potentialclaude-sonnet-53/5AI-assisted lighting design software and pre-visualization tools can help plan fixture placement and settings, improving efficiency in the planning phase even though physical execution remains manual.
Task automatabilityclaude-haiku-4-5-202510011/5Setting up and focusing light fixtures requires spatial reasoning, fine motor control, and real-time adjustment based on subjective aesthetic judgment and production-specific constraints. Current AI systems cannot physically manipulate equipment or make nuanced decisions about lighting focus and positioning in complex 3D environments.
Task automatabilityclaude-sonnet-51/5This is a physical task requiring hands-on rigging, aiming, and adjustment of lighting fixtures in real venues, which current AI systems cannot perform without robotic embodiment.
Adoption barriersclaude-haiku-4-5-202510013/5While not legally licensed, lighting setup requires physical presence on set, safety certifications in some venues, and close collaboration with directors and technical teams, creating moderate organizational and practical friction to full automation.
Adoption barriersclaude-sonnet-53/5No licensing requirement exists, but physical access, safety concerns (working at heights, rigging), and on-site coordination create practical friction against remote or automated substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying robotic systems capable of physical light-fixture manipulation, integration, and safety validation would far exceed the loaded wage of a trained lighting technician for the foreseeable future.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for the physical labor involved, so any comparison favors the human technician who can actually perform the task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product can autonomously set up, position, and focus physical light fixtures in production environments. This task requires embodied manipulation, environmental adaptation, and artistic judgment that current robotics and vision systems do not handle reliably.
Technical feasibility todayclaude-sonnet-51/5No deployed products physically set up and focus lighting fixtures; automated lighting control software exists but does not replace physical installation and focusing work.

Consult with lighting director or production staff to determine lighting requirements.

9

CI 5–13 · exposure 0 · augmentation 38 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Entertainment and live production remain labor-intensive, human-centered sectors with limited AI adoption in creative roles; consultation and collaborative decision-making are not areas where measurable automation is occurring.
Sector adoption velocityclaude-sonnet-52/5Film/TV/live production sectors are adopting AI for post-production and planning tools but face-to-face creative consultation tasks show slow uptake.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist by summarizing past lighting designs, proposing reference images, or drafting technical specs after human consultants reach consensus, but it cannot yet meaningfully participate in the creative dialogue itself.
Augmentation potentialclaude-sonnet-53/5AI tools can help pre-visualize lighting setups, generate mood boards, or simulate lighting plans that inform the consultation, aiding but not replacing the human dialogue.
Task automatabilityclaude-haiku-4-5-202510011/5Consultation requires nuanced understanding of creative vision, production constraints, and real-time collaborative decision-making—areas where current AI lacks genuine negotiation and creative judgment capabilities. The task inherently depends on human stakeholders articulating and refining requirements through dialogue.
Task automatabilityclaude-sonnet-51/5This is a collaborative, judgment-heavy conversation about artistic vision and technical constraints that requires real-time interpersonal negotiation and creative interpretation not achievable by current AI end-to-end.'
Adoption barriersclaude-haiku-4-5-202510014/5The task intrinsically requires human communication, judgment about creative intent, and sign-off by human authorities (lighting director/production staff); organizational workflows and professional practice standards strongly favor human stakeholders in these consultations.
Adoption barriersclaude-sonnet-53/5No licensing requirement, but strong organizational and creative-collaboration friction plus need for in-person trust and shared artistic vision limit substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI systems capable of even partial consultation would require significant specialized training and integration overhead, whereas a lighting technician's time on this task is already embedded in their labor cost and requires no additional infrastructure.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this consultation, so cost comparison favors the human entirely.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably conducts substantive creative consultations with production teams or synthesizes production requirements into actionable lighting specifications. This remains a human-to-human interaction in professional practice.
Technical feasibility todayclaude-sonnet-51/5No deployed product replaces the consultative dialogue between lighting technicians and directors; this remains a human-to-human creative discussion.

Patch or wire lights to dimmers or other electronic consoles.

7

CI 0–15 · exposure 0 · augmentation 25 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting technician work occurs in physical, on-site environments with low digitization. No measurable AI or robotic adoption for electrical wiring tasks has occurred in theaters, studios, or event venues.
Sector adoption velocityclaude-sonnet-51/5Live event and theatrical lighting work is a low-digitization, physically manual sector with little evidence of AI-driven automation of the physical wiring task itself.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with planning wiring diagrams or console configurations pre-deployment, but offers minimal real-time assistance for the hands-on patching, soldering, and physical installation work that dominates this task.
Augmentation potentialclaude-sonnet-52/5Software-based lighting control consoles and patch software can help technicians plan and verify patches faster, but the physical wiring step itself receives minimal AI assistance.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires precise physical manipulation of electrical components in real-world environments, decisions about wiring safety and code compliance, and on-site troubleshooting. Current AI systems cannot autonomously perform electrical work that demands physical dexterity, spatial reasoning, and live electrical hazard management.
Task automatabilityclaude-sonnet-51/5This requires physical handling of cables, connectors, and equipment on-site, which current AI systems cannot perform without robotic embodiment that doesn't exist for this task today.
Adoption barriersclaude-haiku-4-5-202510015/5Electrical work is heavily regulated; licensed electricians must often sign off on installations, and liability for faulty wiring creates strong legal and insurance barriers to full automation. Building codes and safety standards mandate human oversight and accountability.
Adoption barriersclaude-sonnet-52/5No licensing requirement specifically bars automation, but the physical nature of the task and need for on-site electrical safety awareness create practical friction against any substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of handling electrical wiring safely would be prohibitively expensive compared to a skilled lighting technician's wage, particularly given the low-volume, site-specific nature of each patching job.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute for the physical labor involved, so the human remains the only option and cost comparison is not applicable in AI's favor.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI products autonomously perform electrical wiring or patching tasks. This requires specialized robotic systems for physical manipulation and electrical safety oversight that do not exist in production for general lighting environments.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical patching or wiring of lighting equipment; this remains entirely manual, hands-on work in production and event environments.

Visit and assess structural and electrical layout of locations before setting up lighting equipment.

7

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Lighting and event production remain largely traditional, manual sectors with strong reliance on experienced technicians for site assessment. Digitization and AI adoption in this space has been minimal and pilot-stage at best.
Sector adoption velocityclaude-sonnet-51/5Film/production and event lighting sectors have low digitization for this specific physical inspection task, with no evidence of AI displacing human site assessments.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist by processing pre-collected photos or helping document findings, but the core assessment task—visiting a location and evaluating its suitability for lighting—requires human judgment, proprietary knowledge, and real-time decision-making on site.
Augmentation potentialclaude-sonnet-52/5AI could assist with reviewing blueprints, checking electrical codes, or planning logistics beforehand, but offers minimal help with the core on-site physical assessment.
Task automatabilityclaude-haiku-4-5-202510011/5This task fundamentally requires in-person physical assessment of a specific location's structural and electrical characteristics. Current AI systems cannot navigate physical spaces, evaluate real-world electrical safety conditions, or perform on-site inspections without human presence.
Task automatabilityclaude-sonnet-51/5This requires physical presence at a location to visually and physically inspect structural and electrical layouts, which current AI systems cannot perform end-to-end.'
Adoption barriersclaude-haiku-4-5-202510015/5Strong regulatory and safety barriers apply: electrical work and safety assessments are licensed professional domain areas in most jurisdictions, and liability for incorrect assessments creates legal requirements for human sign-off and professional certification.
Adoption barriersclaude-sonnet-53/5While not formally licensed, safety concerns around electrical assessment and on-site judgment create practical barriers to full automation, though not hard legal requirements.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying robots or autonomous systems capable of safe electrical inspection, combined with required oversight and liability concerns, far exceeds the wage cost of sending a trained technician to visit a site in person.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this physical assessment, so the human remains the only cost-effective option.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can analyze images or blueprints, no deployed system reliably performs autonomous site assessment and electrical evaluation in production environments. Some use cases might exist for processing photos of a space, but this represents a narrow subset and requires human follow-up verification.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously visits physical sites and assesses electrical/structural conditions for lighting setup; this remains a human, on-site task.

Install electrical cables or wire fixtures.

5

CI 5–5 · exposure 0 · augmentation 25 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The construction and lighting industries remain largely low-digitization, site-based sectors with limited AI adoption. Manual installation by skilled workers remains the dominant practice with minimal AI displacement.
Sector adoption velocityclaude-sonnet-51/5Physical trades like electrical and lighting installation show minimal AI/robotic adoption compared to digitized information-sector tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with planning cable routes or generating compliance documentation, but the core physical task of installing cables and fixtures offers minimal augmentation opportunity for the human technician in the loop.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, diagramming, or troubleshooting circuit layouts, but offers little direct help with the hands-on physical act of installing cables or fixtures.
Task automatabilityclaude-haiku-4-5-202510011/5This task involves physical manipulation in three-dimensional space—routing cables, securing fixtures, handling precise electrical connections—which current AI systems cannot perform end-to-end. While robots exist in research settings, no generally available AI system can autonomously install electrical cables or fixtures at production scale.
Task automatabilityclaude-sonnet-51/5Physical installation of cables and fixtures requires manual dexterity, spatial navigation, and on-site adaptation that current AI systems and robots cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Electrical installation is subject to strict building codes, licensing requirements, and liability frameworks that mandate licensed electricians or trained technicians perform or sign off on the work. Safety and legal requirements create substantial adoption barriers.
Adoption barriersclaude-sonnet-54/5Electrical work is often subject to licensing, code compliance, and safety regulations, and physical installation requires certified human presence for liability and safety reasons.
Cost vs. human wageclaude-haiku-4-5-202510011/5The capital cost of robotic systems capable of cable installation, plus integration and ongoing maintenance, far exceeds the loaded wage of a skilled lighting technician for this work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-driven substitute for physical wiring work, so any comparison favors the human technician who can actually perform the task at standard cost.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product performs this task reliably in real-world conditions. The task requires dexterity, spatial reasoning, code compliance verification, and adaptation to varied site conditions that remain beyond current deployed automation.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously installs electrical cabling or lighting fixtures in real work environments; this remains firmly in the domain of skilled human labor.

Set up scaffolding or cranes to assist with setting up of lighting equipment.

3

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Physical construction and event technology sectors have minimal AI adoption for equipment setup tasks. The industry remains labor-dependent with slow mechanization rates for skilled manual work.
Sector adoption velocityclaude-sonnet-51/5Physical rigging and stagecraft trades show minimal AI/robotic adoption; this is a low-digitization, hands-on physical labor sector.
Augmentation potentialclaude-haiku-4-5-202510011/5AI offers no meaningful assistance in the hands-on physical setup of scaffolding or crane operation. The task is inherently manual and real-time, with no complementary software or information-layer augmentation applicable.
Augmentation potentialclaude-sonnet-52/5AI could assist with planning logistics, load calculations, or safety checklists, but offers little direct help with the physical act of erecting scaffolding or operating cranes.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of heavy equipment in real-world environments with significant safety constraints. Current AI systems lack embodied capabilities to operate scaffolding or cranes, which demand real-time spatial reasoning, load management, and regulatory compliance in situ.
Task automatabilityclaude-sonnet-51/5This is a physical rigging and setup task requiring manual assembly of scaffolding/crane equipment and precise physical handling; no current AI system can perform this end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Heavy equipment operation is heavily regulated by OSHA and local authorities; qualified operators must be licensed, certified, and legally responsible for safety. Only licensed humans can legally operate cranes and erect scaffolding.
Adoption barriersclaude-sonnet-54/5Scaffolding and crane operation typically require certified operators, safety training, and compliance with occupational safety regulations, creating strong legal and liability barriers to non-human performance.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of autonomous robotic systems capable of scaffolding setup vastly exceeds the loaded wage of skilled technicians. Human labor remains far cheaper for this physical task.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for this physical task, so AI cost is effectively infinite relative to human labor for performing the actual work.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product can autonomously set up scaffolding or operate cranes in production today. This requires specialized robotics with real-world manipulation and environmental sensing capabilities that do not exist at commercial scale.
Technical feasibility todayclaude-sonnet-51/5No deployed AI or robotic product performs scaffold/crane setup for lighting rigs in production; this remains a manual, skilled-labor task.

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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.