Camera and Photographic Equipment Repairers
49-9061.00Median wage $52,720/yr1,650 employed (US)Rank #570 of 923 scored · top 62% by substitution
Repair and adjust cameras and photographic equipment, including commercial video and motion picture camera equipment.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution24
Exposure18
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
15 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
7%
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%20
panel mean rating 1.8/5 → substitution pressure 20/100
Technical feasibility todayw 20%14
panel mean rating 1.6/5 → substitution pressure 14/100
Cost vs. human wagew 15%16
panel mean rating 1.7/5 → substitution pressure 16/100
Adoption barriersw 20%inverted — strong barriers lower the score57
panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100
Sector adoption velocityw 10%5
panel mean rating 1.2/5 → substitution pressure 5/100
Task breakdown (15 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.
Requisition parts or materials.
74CI 70–79 · exposure 75 · augmentation 63 · importance 4.1/5 · click for rater detail
Requisition parts or materials.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Repair and manufacturing sectors have been integrating automated requisitioning and procurement into ERP systems for years; adoption is already widespread in organized shops and is accelerating with AI-driven procurement agents. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Camera/photographic equipment repair is a small, niche, low-digitization trade where such automation is less commonly adopted compared to large-scale industries, though generic inventory software exists. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting part alternatives, cross-referencing specifications, and flagging stock shortages, but since full automation is feasible, augmentation is less central than direct automation in this task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based inventory tools can flag low stock, suggest reorder quantities, and auto-generate purchase orders, meaningfully reducing the administrative burden on the technician. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Requisitioning parts or materials is highly routine and codifiable. AI systems can parse inventory systems, match part numbers to equipment specifications, check stock levels, generate purchase orders, and submit requisitions with minimal human intervention, easily exceeding 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Requisitioning parts is a structured, rule-based procurement task (checking inventory, ordering from suppliers, tracking part numbers) that AI-integrated inventory/procurement systems can handle with minimal human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; requisitioning is an administrative task. Some organizations may require human authorization for approval workflows or cost thresholds, but these are policy checkpoints rather than hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or safety-critical sign-off is required for ordering parts; the main friction is integration with existing shop workflows and supplier systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | API-driven AI requisitioning costs pennies per request in inference and integration overhead, whereas a human requisitioner costs $20–50 per hour loaded; the cost advantage is orders of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated procurement systems cost a small fraction of the labor time a technician would spend manually checking stock and placing orders, especially at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature ERP and inventory management systems with API integration already perform programmatic requisitioning at scale in manufacturing and repair shops. Current AI agents can interface with these systems reliably, though some edge cases or vendor-specific systems may require fallback human review. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Procurement and inventory management software with automated reordering, supplier integration, and predictive stocking is widely deployed across repair and maintenance industries today. |
Record test data and document fabrication techniques on reports.
49CI 45–52 · exposure 45 · augmentation 63 · importance 3.3/5 · click for rater detail
Record test data and document fabrication techniques on reports.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Small repair shops and equipment service sectors show slower digitization and AI adoption compared to enterprise manufacturing; most shops still rely on manual or legacy documentation systems with limited automation investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera/photographic equipment repair is a small, niche manual trade with low digitization and no evidence of AI agent adoption in this specific documentation workflow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist technicians by auto-populating templates from test data, suggesting documentation structure, and organizing notes into formal reports while the technician verifies and refines content, substantially raising documentation productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help repairers draft and standardize report language and organize test data into readable documentation, saving time on write-up even though the technical testing remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate structured reports and documentation of fabrication processes from images or text descriptions with moderate setup, but requires human validation of technical accuracy and test data interpretation. The task involves significant structured data entry and documentation that could achieve time savings, though quality assurance is necessary. |
| Task automatability | claude-sonnet-5 | 3/5 | Recording structured test data and describing standardized fabrication techniques in reports is largely templated text generation, which current AI can draft well, though it requires accurate input of specific measurements and observations from the repair process.of the repairer. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal barriers exist, though some technical documentation may require technician sign-off for warranty/certification purposes, and organizational workflows may prefer human-verified records. Customer expectations and liability considerations create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for report writing itself, though accuracy of technical documentation matters for quality control and warranty purposes, creating some liability-driven caution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered documentation tools cost roughly comparable to a technician's hourly wage when accounting for integration, oversight, and error correction—neither clearly dominant, with cost-benefit depending on volume and accuracy requirements. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Once data is captured, AI drafting of reports is cheap, but the overall workflow still requires a technician to run tests and input results, limiting overall savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for automated report generation and technical documentation (OCR, form-filling, template-based systems), but they have material limitations in parsing handwritten notes, interpreting specialized equipment measurements, and ensuring technical correctness in specialized optical/mechanical contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No widely deployed product specifically integrates with camera repair test rigs to auto-generate these reports; general documentation assistants exist but aren't tailored or validated for this niche technical domain. |
Read and interpret engineering drawings, diagrams, instructions, or specifications to determine needed repairs, fabrication method, and operation sequence.
29CI 23–35 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Read and interpret engineering drawings, diagrams, instructions, or specifications to determine needed repairs, fabrication method, and operation sequence.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Equipment repair sectors are traditionally conservative and geographically fragmented, with small shops and individual technicians dominating. Digital tool adoption is slower than in information-intensive fields, and there is no measurable production deployment of AI for autonomous repair interpretation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera/photographic equipment repair is a niche, low-digitization trade with minimal reported AI tool adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automatically extracting and summarizing drawing information, flagging key specifications, or suggesting similar past repairs, reducing the technician's manual review time. However, the final interpretation and decision remain human-centered, making it a moderate augmentation tool rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help technicians by summarizing manuals, translating technical jargon, or flagging likely fault points from diagrams, offering moderate assistance while the technician still executes the physical diagnosis and repair. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract text and identify basic components from engineering drawings via OCR and vision systems, interpreting complex diagrams to determine repair methods and sequencing requires understanding context, material properties, and failure modes that current AI struggles with reliably. Significant human oversight remains necessary for safety-critical decisions. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision-language models can interpret some diagrams and text, but reliably extracting precise repair specs and translating them into a physical operation sequence for specialized camera equipment requires hands-on judgment beyond current systems.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety and liability concerns are substantial: incorrect repair interpretation on optical or precision equipment can cause equipment damage or user injury. Equipment manufacturers often require certified technicians to sign off on repair decisions, creating legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical dexterity and troubleshooting judgment tied to interpretation somewhat protect the task from full substitution, though not strongly regulated. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI document analysis (vision APIs, OCR) is inexpensive per instance, but the integration, training on domain-specific drawings, and mandatory human review to catch errors makes the total cost comparable to or higher than a technician reviewing the same materials directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI reading assistance is cheap, the actual value-add here is limited without integration into repair workflows, so overall cost savings versus a technician's time are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems reliably perform end-to-end interpretation of technical drawings for repair determination. Vision models can extract drawing content and label components, but diagnosing needed repairs from specifications requires domain expertise and real-world judgment that deployed products lack at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There are no deployed products specifically used in the field to read camera repair schematics and generate actionable repair/fabrication sequences at production reliability. |
Examine cameras, equipment, processed film, or laboratory reports to diagnose malfunction, using work aids and specifications.
28CI 23–33 · exposure 20 · augmentation 50 · importance 4.0/5 · click for rater detail
Examine cameras, equipment, processed film, or laboratory reports to diagnose malfunction, using work aids and specifications.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a small, specialized, and declining sector with limited digitization and low investment in automation. The economic scale and market size do not incentivize rapid AI deployment, and adoption remains minimal in practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera repair is a small, low-digitization niche trade with minimal reported AI tool adoption or investment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered image analysis and defect detection could assist technicians by highlighting visible anomalies or suggesting common failure modes based on equipment type and symptom patterns, moderately improving diagnostic speed and consistency. However, the core judgment and reasoning required remain largely human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help technicians by retrieving repair manuals, cross-referencing error codes, or summarizing diagnostic specifications, moderately speeding up the diagnostic research portion of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Diagnosing equipment malfunctions requires visual inspection, tactile assessment, and contextual interpretation of failure modes that are difficult for current AI to handle end-to-end without significant human guidance. While AI can classify defects in images with some accuracy, the integration of multiple diagnostic signals (equipment state, reports, specifications) and the ability to formulate a complete repair strategy remains beyond reliable automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Diagnosis of mechanical/electronic camera faults requires physical inspection, disassembly, and hands-on testing that current AI cannot perform; AI can assist with reference lookup but not the core diagnostic act. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are moderate friction points: technicians may require informal certification or shop-specific training, and errors in diagnosis directly impact customer satisfaction and warranty liability. However, no hard legal or licensing requirement mandates that a certified human must sign off on the diagnosis itself, so barriers are not insurmountable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for camera repair, but physical manipulation and specialized diagnostic equipment access create practical barriers to remote/AI automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI vision and diagnostic systems remain relatively expensive to implement, train, and maintain for this specialized domain, while the human technician performing this task is typically paid at moderate rates. The cost-benefit does not yet favor automation across the board. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Without a viable automated end-to-end solution, any AI assistance (e.g., manual lookup) adds marginal cost savings but doesn't replace the technician's labor, so cost ratio remains close to human-comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed computer vision systems can detect some visible defects in images, but production-grade diagnostic tools for camera repair remain scarce and unreliable in real shop environments. Most deployed products are narrow (e.g., scratch detection) rather than comprehensive diagnostic systems, and they lack the robustness needed for consistent, safe deployment in repair workflows. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously diagnose physical camera or optical equipment malfunctions in production; this remains a manual, hands-on repair task. |
Measure parts to verify specified dimensions or settings, such as camera shutter speed or light meter reading accuracy, using measuring instruments.
26CI 19–33 · exposure 20 · augmentation 25 · importance 4.0/5 · click for rater detail
Measure parts to verify specified dimensions or settings, such as camera shutter speed or light meter reading accuracy, using measuring instruments.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a shrinking, physically-embedded sector with limited digitization. Adoption of automated measurement systems in small independent repair shops remains minimal, with most work still performed by experienced technicians using manual instruments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera and photographic equipment repair is a small, physical, low-digitization trade with minimal AI adoption pressure or investment in automating specialized diagnostic/repair equipment tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide marginal assistance through computer vision-based reference images or automated calibration checking via image analysis, but the hands-on measurement task itself requires direct physical interaction that AI cannot meaningfully augment while the technician remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with interpreting measurement data, flagging out-of-spec readings, or providing repair guidance, but does not meaningfully transform the physical measurement process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with image analysis and some measurement interpretation from photos, physically measuring camera parts with precision instruments (calipers, optical benches, light meters) requires hands-on manipulation. Current robotic systems lack the dexterity and environmental adaptation for reliable, autonomous precision measurement of diverse camera components. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of measuring instruments and hands-on interaction with camera hardware, which current AI systems cannot perform without robotic embodiment; some data interpretation could be assisted but the core physical measurement task cannot be automated end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Camera repair shops operate as small, physically-anchored businesses with established technician workflows. There is no legal licensing requirement, but organizational friction around integrating precision robotic systems and customer preference for human expertise create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific task, but the physical nature of repair work using specialized calibration tools creates practical barriers to automation beyond simple software replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The specialized equipment (precision calipers, light meters, optical benches) and integration costs for an automated measurement system would be substantial relative to a technician's hourly wage for this task, which is narrowly defined and often performed as part of a broader repair workflow. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Without robotic automation capable of handling delicate camera equipment and precision instruments, AI cannot substitute for the human technician, making the human the only viable option and thus cheaper in practice than any AI-robotic hybrid solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform autonomous precision measurement of camera parts end-to-end. Computer vision can identify components and verify some settings from images, but actual dimensional measurement with instruments and shutter speed/light meter calibration verification remain primarily human tasks in production repair shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical measurement of camera shutter speeds or light meter accuracy autonomously; this remains a manual technician task requiring specialized test equipment and physical dexterity. |
Lay out reference points and dimensions on parts or metal stock to be machined, using precision measuring instruments.
26CI 19–33 · exposure 20 · augmentation 38 · importance 2.8/5 · click for rater detail
Lay out reference points and dimensions on parts or metal stock to be machined, using precision measuring instruments.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair and small-scale precision machining remain highly specialized, low-digitization craft sectors with small firm sizes; adoption of automation in these contexts is slow and confined to high-volume industrial manufacturers rather than repair shops. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera/photographic equipment repair is a small, low-digitization physical trade with minimal AI/robotic adoption reported, unlike sectors such as finance or information services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision systems could help technicians verify measurements and suggest reference points, improving accuracy and reducing measurement time; however, the human must still physically execute the layout, limiting the productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with calculating dimensions or generating layout templates from CAD data, but it offers little help with the actual physical marking and instrument use central to this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Layout of reference points and dimensions requires spatial reasoning and precision measurement interpretation, but the physical act of marking involves manual dexterity in a three-dimensional workspace that current AI vision systems struggle to execute end-to-end. AI can recognize and measure but cannot reliably manipulate tools or mark materials without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a precise physical manipulation task requiring hands-on use of measuring instruments on physical stock; current AI cannot perform the physical layout work, only assist with planning or calculations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety and quality assurance in precision metalwork create some organizational friction and oversight requirements, but no hard legal or licensing barriers prevent automation. Customer preference for human inspection and error liability asymmetry (incorrect layout ruins expensive parts) moderate adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the task requires physical dexterity and precision tool handling in a specialized repair context, creating practical (not legal) barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The manual precision labor required, combined with the specialized equipment (coordinate measurement machines, marking tools) and any robotic integration, means total cost is likely comparable to or exceeds a skilled technician's hourly rate for typical repair shop volumes. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based approach would require added robotic hardware and integration costs far exceeding the cost of a skilled technician performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While vision systems can identify parts and read precision instruments, no deployed product reliably executes the full workflow of laying out reference points on physical metal stock autonomously. Robotic systems exist for some machining contexts but are narrow in scope and typically require extensive setup for each part geometry. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical layout marking on metal stock using precision instruments; this remains a manual machinist/repairer skill with no robotic or AI product doing this reliably in production for this niche trade. |
Test equipment performance, focus of lens system, diaphragm alignment, lens mounts, or film transport, using precision gauges.
19CI 5–33 · exposure 13 · augmentation 38 · importance 4.4/5 · click for rater detail
Test equipment performance, focus of lens system, diaphragm alignment, lens mounts, or film transport, using precision gauges.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera and photographic equipment repair is a specialized, declining craft industry with small shops, low digital infrastructure, and minimal AI adoption; most repairers work independently or in small teams with little capital for automation investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera repair is a small, low-digitization niche trade with minimal AI adoption and no visible movement toward automating physical equipment testing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered visual inspection tools could assist technicians by flagging alignment issues or automating gauge reading, but the core task of testing and judgment requires the human technician in the loop to adjust, verify, and certify the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help repairers look up specifications, diagnostic procedures, or interpret gauge readings via reference tools, but it doesn't materially transform the hands-on testing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems could theoretically analyze photographs of precision gauges and lens alignment, the task requires hands-on physical manipulation of delicate optical equipment, real-time tactile feedback, and judgment calls about mechanical tolerances that current AI systems cannot perform end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of precision gauges on optical and mechanical camera components, which current AI systems cannot perform without a robotic embodiment that doesn't exist for this niche task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Repair of camera equipment often requires manufacturer certification and warranty liability; errors in alignment or focus calibration directly affect customer product quality and carry legal/reputational cost, creating strong incentives for human expert sign-off on precision adjustments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task demands specialized physical dexterity and tacit mechanical knowledge that create strong practical barriers to automation, though not regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The specialized hardware (precision gauges, optical benches), ongoing integration with testing equipment, and high error costs in optical repair mean that current AI inspection systems are typically more expensive than the skilled technician labor they might supplement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative to perform this physical testing, so any AI solution would require costly custom robotics far exceeding technician wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision can detect some alignment issues from images, but no deployed product reliably performs the full suite of mechanical testing (focus testing, diaphragm alignment, lens mount inspection, film transport verification) with the precision required for repair sign-off without human technician validation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical diagnostic testing of camera lens focus, diaphragm alignment, or film transport mechanisms; this remains a manual skilled-trade activity. |
Clean and lubricate cameras and polish camera lenses, using cleaning materials and work aids.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.3/5 · click for rater detail
Clean and lubricate cameras and polish camera lenses, using cleaning materials and work aids.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a small, specialized sector with relatively low digital infrastructure and limited capital for advanced automation. Adoption of AI-driven robotics remains minimal, typical of physical, hands-on trades with modest firm sizes. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera repair is a small, low-digitization craft trade with minimal AI/robotics adoption and no visible trend toward automation of physical repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by identifying lens defects via image analysis or recommending cleaning protocols, but the core manual work—gentle cleaning and polishing—remains primarily human-executed. Augmentation potential is limited because the task is inherently tactile and judgment-light. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostics, repair manuals, or sourcing parts information, but offers little direct assistance for the physical cleaning and polishing steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-driven robotic systems could theoretically perform cleaning and lubrication, this task requires precise tactile feedback, judgment about surface condition, and careful handling of delicate optical components. Current general-purpose AI systems lack the embodied dexterity and real-time sensory integration needed for reliable end-to-end automation at 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor physical task requiring hands-on manipulation of delicate optical and mechanical parts; no current AI system can perform physical cleaning, lubrication, or polishing.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Camera repair requires some technical expertise and customer trust in equipment handling, but no hard legal licensing barrier exists. Organizational adoption would face friction around equipment investment, skill requirements, and customer preference for human craftsmanship, but not regulatory prohibition. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the physical dexterity, variability of camera models, and risk of damaging optics create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems capable of this work would require significant capital investment, integration, and maintenance, making the all-in cost per task substantially higher than a skilled technician's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require specialized robotics far more costly than a technician's labor for this low-volume, high-precision task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs full cleaning, lubrication, and lens polishing of cameras autonomously today. Specialized robotics research exists but has not reached production deployment in camera repair shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs camera cleaning/lubrication/lens polishing in production; this remains purely manual craft work. |
Assemble aircraft cameras, still or motion picture cameras, photographic equipment, or frames, using diagrams, blueprints, bench machines, hand tools, or power tools.
17CI 10–24 · exposure 8 · augmentation 25 · importance 3.7/5 · click for rater detail
Assemble aircraft cameras, still or motion picture cameras, photographic equipment, or frames, using diagrams, blueprints, bench machines, hand tools, or power tools.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair and assembly is a specialized, low-volume sector with predominantly small shops and craftspeople. Digitization and AI adoption in this niche trade is minimal; production automation exists in only the largest factories. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera and photographic equipment repair is a niche, low-digitization manufacturing/repair trade with minimal AI or robotics adoption reported in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted visual inspection and step-by-step guidance through AR overlays could help technicians, but the core assembly task is manual and tactile. Current tools offer limited augmentation beyond traditional blueprint displays and quality checklists. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with reading diagrams, generating assembly instructions, or diagnosing issues via image recognition, but it offers little help with the physical manipulation central to this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Assembly of precision camera equipment requires spatial reasoning, dexterity, and real-time visual inspection to align components correctly. Current AI vision systems can guide assembly steps, but the end-to-end physical manipulation, quality verification, and problem-solving when tolerances are tight remain firmly in the human domain; no AI system achieves 50% time savings on full-task assembly today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical assembly task requiring dexterity, fine motor control, and manipulation of small mechanical/optical parts using hand tools and machines—far beyond current AI capabilities without embodied robotics. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Aircraft camera assembly may have regulatory oversight (FAA certification), and quality assurance typically requires human sign-off on critical components. Customer trust in hand-assembled precision optics remains a minor friction factor, but no strict licensing prohibition on automation exists. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but physical embodiment and precision handling of specialized equipment create substantial practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized precision assembly robots and vision systems are expensive (six figures per setup) with high integration costs, whereas skilled camera repair technicians command moderate wages. The all-in cost of current automation exceeds human labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation solution for this physical assembly task, so any attempted robotic solution would be far more costly than employing a skilled technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end camera or aircraft equipment assembly. Robotics for precision assembly exist in narrow, controlled factory settings, but general-purpose assembly of diverse photographic equipment with diagrams and blueprints is still research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product performs precision camera or optical equipment assembly at scale; such tasks remain research-stage for robotics manipulation of delicate components. |
Install electrical assemblies and wiring in aircraft camera housings and memory cards or film in cameras, following blueprints and using hand tools and soldering equipment.
16CI 5–28 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Install electrical assemblies and wiring in aircraft camera housings and memory cards or film in cameras, following blueprints and using hand tools and soldering equipment.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera and photographic equipment repair is a specialized, low-digitization, small-firm sector with minimal public investment in automation. Most shops remain traditional and manual; there is no evidence of meaningful AI or robotic adoption in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera and photographic equipment repair is a small, specialized manual trade with low digitization and minimal reported AI/robotics adoption for physical assembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with blueprint interpretation or defect detection (via image analysis), but the core task—soldering and hand assembly—remains primarily manual. Augmentation potential is limited because spatial reasoning, tool manipulation, and real-time quality control are still human-centric. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with blueprint interpretation or diagnostic guidance, but offers minimal help with the core physical soldering and installation work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task combines electrical assembly (soldering, wiring) with physical installation in constrained camera housings, requiring spatial reasoning, precision hand coordination, and real-time quality assessment. Current AI systems cannot reliably perform end-to-end soldering or camera assembly with the precision and error-checking required, though robotic assistance on isolated sub-tasks (wire cutting, blueprint reading) is emerging. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of small electrical components, soldering, and precise installation of hardware into camera housings—tasks requiring fine motor skills and dexterity that current AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Aircraft camera systems fall under aerospace certification and quality assurance requirements; electrical work often requires licensed technicians and documented traceability. Liability asymmetry is high—assembly defects can cascade into mission-critical failures, creating strong regulatory and organizational friction against full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing is required, the physical nature of the work, need for precision with blueprints, and specialized equipment create practical barriers to automation beyond regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized equipment (soldering robots, vision systems, handling fixtures) for aircraft-grade assembly work is capital-intensive and requires significant setup per housing variant. Loaded human wages for skilled technicians are often lower than the amortized cost of automation for this precision, low-volume, high-variability work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical assembly and soldering work, so any AI-based approach would require robotic hardware far more expensive than human labor for this specialized, low-volume task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some industrial robots can perform soldering in controlled environments, but none reliably install wiring and assemblies into aircraft camera housings—specialized, low-volume equipment with variable geometries and hand-tool operations. No production systems handle the full task with the quality assurance aerospace applications demand. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that perform physical soldering, wiring installation, or hardware assembly in specialized camera equipment; this remains firmly in the domain of human manual labor. |
Recommend design changes or upgrades of microfilming, film-developing, or photographic equipment.
16CI 5–28 · exposure 8 · augmentation 38 · importance 2.3/5 · click for rater detail
Recommend design changes or upgrades of microfilming, film-developing, or photographic equipment.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair and photographic equipment modification operates in a niche, low-digitization sector with small firms and specialized craftspeople—sectors historically slow to adopt AI automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is a small, declining niche (legacy photographic/microfilm equipment repair) with minimal digitization or AI tool adoption reported in this specific trade. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by retrieving prior design specifications or similar equipment data, but the creative and technical judgment required to recommend viable improvements means human expertise remains central and augmentation is limited. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft technical reports, research relevant materials/specs, or brainstorm design improvement ideas, providing moderate assistance to a technician's proposal process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires deep domain expertise in photographic engineering, understanding of customer needs, and creative problem-solving to propose design improvements. Current AI systems cannot reliably synthesize novel equipment designs or upgrades that meet real engineering and market constraints. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires deep hands-on technical knowledge of mechanical/optical systems combined with practical repair experience; AI can assist with drafting suggestions but cannot independently generate valid, field-tested design recommendations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Equipment manufacturers typically rely on licensed engineers and technical specialists to approve design changes; liability concerns around equipment modifications and industry standards create substantial adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement exists, but organizational trust and reliance on hands-on technical validation create moderate friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An expert camera repairer's specialized knowledge and judgment command high value relative to the cost of running inference; the task demands years of accumulated expertise that AI cannot yet replicate at equivalent quality. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform this reliably alone, cost comparisons favor the human expert who has direct equipment experience; using AI would require significant human oversight anyway. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products currently perform end-to-end design recommendation and upgrade specification for specialized photographic equipment in production environments. This requires domain-specific engineering knowledge beyond general AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously recommends equipment design changes for niche photographic/microfilm hardware; this remains a specialist human judgment task. |
Disassemble equipment to gain access to defect, using hand tools.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail
Disassemble equipment to gain access to defect, using hand tools.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a declining, small-scale sector with minimal digital infrastructure and investment in automation. Adoption of physical manipulation AI in repair shops is effectively non-existent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Equipment repair is a low-digitization, physical trade with minimal AI/robotics adoption for hands-on disassembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for the core physical disassembly activity. While AI might help document or catalog parts, the hands-on mechanical work requires direct human capability. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance or repair manuals/instructions prior to disassembly, but offers little help with the physical act of disassembly itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Disassembling delicate photographic equipment using hand tools requires fine motor control, spatial reasoning, and real-time problem-solving in response to physical feedback. Current AI systems lack embodied manipulation capabilities and cannot reliably operate physical tools in unstructured real-world conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical disassembly of camera equipment requires fine motor manipulation, tactile feedback, and adaptive handling of varied mechanisms that no current AI system or robot can perform reliably.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements, liability concerns and the requirement for human judgment to identify defects after disassembly create modest friction. The task itself does not mandate human sign-off, but downstream work typically does. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for equipment repair, but physical embodiment and equipment variability create strong practical barriers to automation, though not regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this task would cost tens of thousands of dollars in capital, maintenance, and programming, far exceeding the loaded wage of a skilled repair technician performing routine disassembly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical automation (specialized robotics) would be far more costly than a technician's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs physical disassembly of camera equipment using hand tools. Robotic arms exist for structured industrial tasks but lack the dexterity and adaptability needed for precision disassembly of varied camera models. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical disassembly of photographic equipment; this remains firmly in the domain of human manual dexterity and robotics research at best. |
Fabricate or modify defective electronic, electrical, or mechanical components, using bench lathe, milling machine, shaper, grinder, or precision hand tools, according to specifications.
14CI 5–24 · exposure 13 · augmentation 25 · importance 3.5/5 · click for rater detail
Fabricate or modify defective electronic, electrical, or mechanical components, using bench lathe, milling machine, shaper, grinder, or precision hand tools, according to specifications.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera and photographic equipment repair is a small, specialized, low-digitization sector dominated by independent repair shops and small firms. Adoption of AI-driven or robotic fabrication in this niche is minimal and moving slowly, with most shops relying on skilled human technicians. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Equipment repair trades involving manual machining are a low-digitization, physical craft sector with minimal AI/robotics adoption for this specific fabrication work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with design optimization or simulation of component specifications, but provides limited real-time help during the hands-on fabrication and modification process itself. A human technician remains essential for tool setup, sensory inspection, and adaptive problem-solving. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with CAD design suggestions, specification lookup, or diagnostic guidance, but offers little direct assistance for the physical machining and fabrication process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can design and simulate component modifications, physically fabricating or modifying parts requires robotic manipulation with sub-millimeter precision and real-time sensory feedback. Current robotic systems lack the dexterity and adaptive capability to reliably operate bench lathes, milling machines, or hand tools on defective components without extensive task-specific setup and frequent human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical fabrication task requiring precision machining tools and manual dexterity; no current AI system can operate a lathe or mill to fabricate custom parts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical manipulation of precision tools and machines, combined with the need to inspect and adapt to defective components in real time, creates substantial barriers to full automation. Safety regulations around machinery operation, quality assurance requirements, and the craft nature of precision repair work all discourage substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically applies, but physical presence, tool operation, and precision craftsmanship create strong practical barriers to any remote or software-only substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic setups for component fabrication and modification are extremely capital-intensive and require specialized integration. For a low-volume, high-variability task like camera equipment repair, the equipment cost far exceeds the loaded wage of a skilled technician. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost is effectively infinite relative to a human machinist/repairer doing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI systems reliably perform end-to-end fabrication or modification of electronic, electrical, or mechanical components using traditional machine tools. While industrial robotics exist, they are narrowly task-specific and require extensive programming; no general solution scales across the variety of components camera repair entails. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs autonomous machining/fabrication of custom electronic or mechanical repair components; this remains squarely in the domain of skilled human technicians. |
Calibrate and verify accuracy of light meters, shutter diaphragm operation, or lens carriers, using timing instruments.
14CI 10–19 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Calibrate and verify accuracy of light meters, shutter diaphragm operation, or lens carriers, using timing instruments.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a niche, fragmented industry dominated by small independent shops and service centers with minimal digitization and low investment in automation. The sector lags in AI/robotic adoption and operates in physical, hands-on environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera repair is a niche, low-digitization trade with minimal AI adoption pressure and no evidence of AI-driven displacement in this physical craft occupation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide marginal assistance by automating data logging from timing instruments and flagging out-of-spec measurements, but the core calibration work—physical adjustment and verification—remains human-dependent. The augmentation is limited to analysis and documentation rather than direct task transformation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with diagnostic data logging or reference lookups for calibration specs, but offers minimal help with the actual physical calibration and verification process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | The task involves precision mechanical and optical calibration that requires physical manipulation, real-time feedback, and contextual judgment about equipment state. While AI could assist in analysis and interpretation of measurement data, end-to-end automation—from setup through hands-on adjustment to verification—remains beyond current robotic or AI capabilities in unstructured repair environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of precision optical/mechanical equipment with hands-on calibration and testing using specialized timing instruments, which is beyond current AI capabilities without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Equipment manufacturers often require certified technicians to perform calibration work to maintain warranty and liability coverage, though this is not a universal hard legal requirement like medical or legal licensing. Customer trust in human expertise and the specialized nature of the work create organizational friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically applies, but the need for hands-on physical manipulation of delicate equipment and specialized timing instruments creates a natural barrier to remote/software-only automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of precision robotic systems capable of handling delicate optical equipment calibration, combined with integration and maintenance overhead, far exceeds the loaded wage of a skilled camera repair technician for this specialized work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical calibration work, so the human technician remains the only cost-effective option for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs precision calibration of camera optical and mechanical systems independently. This task requires specialized hardware (timing instruments), fine motor control, and real-world variation handling that exists only in research prototypes, not production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical calibration and repair of camera mechanisms; this remains a manual technician task requiring dexterity and specialized diagnostic tools. |
Adjust cameras, photographic mechanisms, or equipment such as range and view finders, shutters, light meters, or lens systems, using hand tools.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Adjust cameras, photographic mechanisms, or equipment such as range and view finders, shutters, light meters, or lens systems, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Camera repair is a niche, declining sector with predominantly small independent shops, low digitization, and minimal venture interest in automation. Adoption of AI-driven solutions in this space has been negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Camera/photographic equipment repair is a small, low-digitization physical trade with minimal AI or robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by providing diagnostic images or reference guides, but the core task—physical adjustment with hand tools—offers limited opportunity for meaningful augmentation. AI vision might help identify what needs adjustment, but execution remains human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostic reference lookup or repair documentation, but offers little assistance for the actual hands-on mechanical adjustment work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of delicate mechanical and optical components using hand tools in a three-dimensional space. Current AI systems lack embodied robotics with the dexterity, tactile feedback, and real-time adaptation needed to safely adjust camera mechanisms without causing damage. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of precision mechanical and optical components with hand tools, a manual dexterity task current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: customers typically require a human technician to sign off on repairs for warranty and liability purposes, and there is strong customer preference for human expertise on valuable equipment. Many jurisdictions also expect professional credentials for equipment repair. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the task demands specialized tactile skill, tooling, and equipment access that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this work would be extremely expensive to procure, integrate, and maintain, far exceeding the cost of a skilled human repair technician's labor for this type of work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the all-in cost of an AI solution for physical repair is effectively higher than paying a skilled technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs fine mechanical adjustment of camera equipment autonomously. While robotic arms exist in research, none are in production for camera repair, and the task requires visual inspection, judgment about proper alignment, and iterative adjustment that current systems cannot do reliably. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs fine mechanical adjustment of camera internals; this remains a specialized human craft skill. |
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.