Motorboat Mechanics and Service Technicians
49-3051.00Median wage $57,550/yr23,220 employed (US)Rank #783 of 923 scored · top 85% by substitution
Repair and adjust electrical and mechanical equipment of inboard or inboard-outboard boat engines.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution15
Exposure8
Augmentation33
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
13 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
8%
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%8
panel mean rating 1.3/5 → substitution pressure 8/100
Technical feasibility todayw 20%8
panel mean rating 1.3/5 → substitution pressure 8/100
Cost vs. human wagew 15%8
panel mean rating 1.3/5 → substitution pressure 8/100
Adoption barriersw 20%inverted — strong barriers lower the score46
panel mean rating 3.1/5 (barrier strength) → substitution pressure 46/100
Sector adoption velocityw 10%6
panel mean rating 1.2/5 → substitution pressure 6/100
Task breakdown (13 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.
Document inspection and test results and work performed or to be performed.
72CI 52–92 · exposure 75 · augmentation 88 · importance 4.1/5 · click for rater detail
Document inspection and test results and work performed or to be performed.
72| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Service and repair sectors have rapidly adopted digital service-management platforms and AI-assisted documentation tools; marine and automotive service shops increasingly use integrated diagnostic and reporting software that incorporates automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Motorboat repair is a small-scale, low-digitization trade sector with limited AI tool adoption; broader automotive/marine service industries lag well behind information and finance sectors in AI deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at assisting technicians by auto-populating reports from test equipment, suggesting relevant findings, and organizing data into compliant formats, dramatically reducing documentation burden while the technician retains quality control and sign-off authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI dictation and templated documentation tools can meaningfully speed up report writing for technicians, letting them focus on the mechanical work while AI drafts or structures the paperwork. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Documenting inspection results and work performed is primarily structured data entry and report generation. Current AI systems can reliably extract test data, auto-populate forms, and generate standardized service reports with high accuracy and well over 50% time savings compared to manual documentation. |
| Task automatability | claude-sonnet-5 | 3/5 | Documenting inspection and test results is largely structured note-taking that voice-to-text or AI-assisted form-filling could handle, though it requires the technician to first generate accurate findings verbally or via input.rations current AI cannot itself infer.rat. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some service records may require technician sign-off for liability and warranty purposes, the documentation task itself has no legal licensing barrier preventing AI assistance. Organizational adoption is the main friction point, not regulatory restriction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human write these reports, but organizational habits, lack of digitized systems in small repair shops, and need for accuracy on liability-relevant work orders create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven documentation (form-filling, OCR, template-based generation, database entry) costs a small fraction of the technician time required to manually write and organize inspection reports, easily achieving an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Dictation/transcription and report-generation tools are cheap relative to technician time, but shops still need integration and review, keeping savings moderate rather than order-of-magnitude given low-volume small business context. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Production systems exist today (diagnostic software with AI backends, service-management platforms, and document-generation tools) that reliably capture, organize, and produce service records and inspection reports at scale in automotive and marine service industries. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Voice dictation, transcription, and templated service-report generation tools exist in fleet/repair management software, but full automated documentation integrated with diagnostic data is still narrow and unevenly deployed in small marine repair shops. |
Start motors and monitor performance for signs of malfunctioning, such as smoke, excessive vibration, or misfiring.
23CI 10–35 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Start motors and monitor performance for signs of malfunctioning, such as smoke, excessive vibration, or misfiring.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Marine service shops are smaller, less digitized than automotive; sensor-based diagnostic adoption is slower, pilots exist but production deployment remains limited in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine repair is a small, physically-oriented, low-digitization trade sector with minimal AI adoption or investment in automation for hands-on diagnostic work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Diagnostic sensors and AI-assisted anomaly detection significantly assist technicians by flagging vibration spikes, misfiring patterns, and smoke-related codes, reducing manual listening and visual inspection time and improving diagnostic speed and consistency. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with reference lookup, diagnostic checklists, or interpreting sensor data logs if equipped, but it offers little direct assistance to the physical act of starting and sensorially monitoring the motor. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Starting a motorboat engine requires physical actuation (key turning, switch engagement) that would need robotics, and monitoring can be partially automated via sensor data, but interpreting subtle mechanical signs like excessive vibration or misfiring patterns in real time still requires human judgment and physical troubleshooting presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence at a boat, hands-on starting of a motor, and real-time sensory monitoring (visual, auditory, tactile) in a marine environment; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Engine safety and liability concerns mean customers expect a qualified technician to certify engine condition; there is organizational and regulatory preference for human sign-off, though no hard licensing requirement specifically for this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing is typically required for motorboat mechanics, but the task demands physical dexterity, on-site presence, and judgment that create practical barriers to automation beyond mere regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Sensor systems and diagnostic software exist but require technician oversight, calibration, and human interpretation; the all-in cost of sensor infrastructure plus AI analysis plus required human labor does not undercut a technician's loaded wage for this relatively short task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical diagnostic task, so any AI-based approach would require robotics and sensor infrastructure vastly more expensive than a technician's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI systems can process sensor data and flag anomalies through vibration sensors or diagnostic codes, but no deployed product reliably diagnoses complex engine malfunctions end-to-end; human technicians remain essential for interpreting context, physical inspection, and determining root cause. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously starts and diagnoses motorboat engines through physical sensory inspection; this remains firmly in the domain of human technicians. |
Repair or rework parts, using machine tools such as lathes, mills, drills, or grinders.
18CI 5–30 · exposure 13 · augmentation 38 · importance 3.3/5 · click for rater detail
Repair or rework parts, using machine tools such as lathes, mills, drills, or grinders.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Motorboat service is a traditional, small-shop sector with low digital maturity. Most repairs are bespoke, not high-volume; adoption of advanced automation remains minimal, with pilots uncommon and production deployment rare. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine repair is a small-business, physical trade sector with very low AI/robotics adoption; this is a laggard sector for automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted diagnostics, predictive maintenance alerts, and tool-path suggestions can meaningfully support a technician's work, but the human remains essential for judgment, safety, and liability. Augmentation is useful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, CAD-based part specifications, or CNC programming assistance, but does not materially transform the hands-on machining and fitting process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While machine tools can be CNC-automated, the decision of *which* parts to rework, how to fixture them, and adaptive problem-solving during repair requires skilled human judgment. Current AI systems cannot reliably perceive damage, diagnose root causes, and execute the full repair workflow end-to-end with 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity, tactile feedback, and manual manipulation of machine tools on physical parts—no AI system today can perform hands-on machining or rework of boat engine parts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Motorboat repair often occurs in service contexts where liability for failed repairs is high, warranty obligations exist, and insurance requirements typically demand that critical repairs be performed or signed off by licensed technicians. Physical safety and regulatory oversight create meaningful legal barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but the physical nature of the work, need for specialized equipment operation, and liability for faulty repairs create practical friction against any substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial machine tools and their integration remain capital-intensive and require skilled operators. For sporadic repair work on varied parts, human technicians remain more cost-effective than maintaining and programming automated systems for each unique repair scenario. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so the human labor cost is the only viable cost basis; robotic machining automation exists in industrial settings but not as flexible small-shop repair replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CNC and automated machining exist, but deploying them for ad-hoc repair work (not high-volume identical parts) requires significant manual setup, inspection, and human oversight. No current product reliably handles the diagnostic and adaptive aspects of motorboat repair at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product operates lathes, mills, drills, or grinders to repair mechanical parts autonomously; this remains purely a human manual-trade skill. |
Perform routine engine maintenance on motorboats, such as changing oil and filters.
13CI 10–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Perform routine engine maintenance on motorboats, such as changing oil and filters.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The motorboat service sector is predominantly small-shop, labor-intensive, and low in digitization; adoption of advanced automation lags far behind information and professional services sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on maintenance work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with scheduling maintenance reminders or diagnostic troubleshooting via predictive analytics, but offer minimal assistance to the mechanic during the hands-on physical task of changing oil and filters. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostic guidance, maintenance scheduling, or parts lookup, but offers little help with the physical act of changing oil and filters itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Routine engine maintenance on motorboats requires physical manipulation in confined, wet spaces with safety hazards; current AI systems lack the embodied dexterity, environmental sensing, and failure recovery needed to perform oil changes and filter replacements end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical task requiring manual dexterity to drain fluids, remove/replace filters, and access tight engine compartments on boats; no current AI system can perform this physical labor end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers for the maintenance itself, liability concerns over equipment damage, warranty implications, and customer preference for human technicians create moderate friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandates a human specifically, but marine environments, varied boat models, and physical access constraints create practical barriers to automating this task with robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robot capable of working in confined engine bays, plus integration and safety systems, far exceeds the loaded wage of a skilled technician performing straightforward maintenance tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI/robotic alternative to compare costs against; a human technician with basic tools remains the only viable and cheaper option than any hypothetical automation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs motorboat engine maintenance autonomously; research-stage robotics exist but are not in production for this specialized, physically demanding task in maritime environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic products perform motorboat oil and filter changes in production; this remains purely manual mechanic work today. |
Disassemble and inspect motors to locate defective parts, using mechanic's hand tools and gauges.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Disassemble and inspect motors to locate defective parts, using mechanic's hand tools and gauges.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motor service and marine mechanics remain traditionally labor-intensive, physical trades with limited automation adoption; the sector has low digital maturity and small average firm size, slowing AI and automation uptake. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical repair is a small-scale, physically-oriented trade sector with minimal AI/robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Computer vision tools could assist in documenting and highlighting potential defects, but the core task of physical disassembly and hands-on inspection offers limited augmentation potential beyond basic documentation support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic reference lookup, repair manuals, or part identification via image analysis, but offers little help with the core physical disassembly and inspection work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Disassembling and inspecting motors requires physical manipulation, spatial reasoning under uncertainty, and real-time judgment about part condition that current AI systems cannot perform end-to-end. The task demands dexterous robotic hardware and autonomous defect detection capabilities that are not yet deployed at production scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical disassembly, hands-on manipulation of parts, and tactile/visual inspection with hand tools and gauges—tasks entirely outside current AI capabilities without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers, the task requires hands-on physical work and real-time judgment of part condition that organizational workflows assume a human will perform; customer preference for human expertise and liability concerns around automation errors provide moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for this specific task, but practical barriers (physical dexterity, tool handling, judgment on defect severity) are substantial even if not regulatory in nature. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of handling delicate disassembly and inspection are significantly more expensive than skilled human labor when accounting for equipment, integration, and maintenance costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any comparison favors the human mechanic by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform motor disassembly and inspection end-to-end in real service environments. While computer vision can assist in damage detection, the physical disassembly work requires robotics not yet mature in unstructured settings like automotive service bays. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical motor disassembly and defect diagnosis; this remains firmly in the domain of human technicians with specialized dexterity. |
Mount motors to boats, and operate boats at various speeds on waterways to conduct operational tests.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Mount motors to boats, and operate boats at various speeds on waterways to conduct operational tests.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boat servicing is a small, physically-grounded trade sector with low digital maturity and strong reliance on skilled craftsmanship; adoption of automation in this sector has been minimal compared to information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostic data collection and performance monitoring during tests, but the core physical tasks of mounting and operating remain human-dependent; augmentation potential is limited. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic checklists or reviewing test results afterward, but offers negligible help with the physical mounting and live operational testing itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Mounting motors to boats requires precise mechanical assembly, spatial positioning, and handling of heavy equipment in a physical environment—capabilities current AI systems cannot perform robotically at scale today. Operating boats on waterways to test motors demands real-time navigation, situational awareness, and manual control that exceed current autonomous marine capabilities in unstructured, safety-critical settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy machinery (mounting motors) and real-world boat operation on water to test performance, none of which current AI systems can perform without embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: marine safety regulations require licensed/certified operators for waterway testing, liability for watercraft damage and injury is severe, and insurance often mandates human supervision and competency certification. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing strictly requires a human for this specific task, but safety liability, marine environment risks, and the need for physical dexterity and judgment create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current automation (robotic arms, autonomous vessels) for this task would be substantially more expensive than employing a human technician, including hardware, integration, safety systems, and liability insurance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for this physical labor, so AI cost is effectively infinite relative to a mechanic's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end motor mounting and operational waterway testing autonomously. Autonomous marine vessels exist in narrow, controlled domains (harbor transit, research), but practical, reliable boat testing by AI systems is not yet in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that physically mount marine motors or pilot boats to conduct operational speed tests; this remains purely a human physical/manual task. |
Repair engine mechanical equipment, such as power tilts, bilge pumps, or power take-offs.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Repair engine mechanical equipment, such as power tilts, bilge pumps, or power take-offs.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat repair is a small, distributed, low-digitization sector with many independent or small-shop technicians; adoption of automation is minimal and lagging significantly behind information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical repair is a low-digitization, physical trade sector with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide diagnostic suggestions or reference documentation to assist technicians, but the core physical repair task limits meaningful augmentation; most value remains in human expertise and hands-on execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with manuals, diagnostic troubleshooting guides, or parts lookup, but offers little direct enhancement to the hands-on repair work of tilts, pumps, or take-offs. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Repairing engine mechanical equipment requires physical manipulation, precise disassembly/reassembly, tactile diagnosis, and real-time decision-making in a physical environment—tasks current AI systems cannot perform end-to-end. While diagnostic support exists, the core repair work remains entirely manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on mechanical diagnosis and repair requiring physical manipulation of engine components in tight, variable spaces; no AI system can perform the physical repair itself today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: technicians often need licensing/certification, liability for repairs is substantial (safety-critical equipment), and customers typically require licensed professionals to perform warranty-valid work. Human sign-off is often legally or contractually required. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate universally requires a certified human for these repairs, but manufacturer warranty requirements, liability for improper repair, and the physical dexterity needed create real friction against any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI integration for this task would require expensive robotic hardware, specialized sensors, and extensive setup for the physical work involved, making the all-in cost far exceed a trained technician's labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical labor involved, so the all-in cost of an AI solution for actual repair work is effectively infinite/not applicable versus a human technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task autonomously today. AI can assist with diagnostics or documentation, but no robotic or autonomous system is in production reliably repairing motorboat engine components at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical marine engine mechanical repairs; AI is at most used for diagnostic reference lookup, not the repair task itself. |
Replace parts, such as gears, magneto points, piston rings, or spark plugs, and reassemble engines.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Replace parts, such as gears, magneto points, piston rings, or spark plugs, and reassemble engines.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The motorboat service sector is small, geographically dispersed, and relies on skilled hands-on technicians; there is minimal evidence of AI or automation adoption in engine reassembly work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical repair is a small-shop, physical-labor trade with minimal digitization or AI/robotics adoption in the field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostics or documentation (e.g., parts identification from images), but offers limited support for the core physical assembly task that defines the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, repair manuals, or parts lookup, but offers little help with the physical replacement and reassembly steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of small engine components in confined spaces, precise assembly sequencing, and real-time sensory feedback. Current AI systems cannot perform end-to-end physical assembly work without specialized robotics, which remain narrow and expensive. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of small mechanical parts inside an engine using hand tools, dexterity, and tactile feedback—no AI system or robot can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety, quality, and warranty considerations create strong organizational and liability barriers; manufacturers typically require licensed technicians to perform and certify engine work, and customers demand human expertise and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically, but the physical dexterity, variability of engine models, and liability for engine failure create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotic systems capable of precision engine work (if available) far exceeds the labor cost of a skilled motorboat mechanic, making automation economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic system for this task, so any hypothetical automation would be far more expensive than paying a technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs motorboat engine disassembly and reassembly at scale in production settings. This remains firmly in the domain of skilled human technicians with physical presence and tactile judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products replace or reassemble motorboat engine parts; this remains a purely manual mechanical repair task performed by technicians. |
Idle motors and observe thermometers to determine the effectiveness of cooling systems.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Idle motors and observe thermometers to determine the effectiveness of cooling systems.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat service is a physical, geographically dispersed industry with low digitization and capital constraints; adoption of specialized robotics for this specific task is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption in hands-on diagnostic work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Remote temperature monitoring systems or data logging could assist a technician by collecting baseline data, but the core task of physically idling the motor and making real-time diagnostic judgments still requires human presence and expertise. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help interpret sensor readings or suggest diagnostic steps via a connected device, but for this manual observation task the assistance is marginal since it centers on physical monitoring. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence to idle a motor and observe analog/digital thermometer readings in real-time, combined with judgment about cooling system effectiveness based on sensory cues. Current AI cannot physically operate equipment or be reliably present at distributed service locations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically starting and running a motorboat engine, placing/reading physical instruments, and using tactile/auditory judgment to assess cooling system performance—none of which current AI systems can perform without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves direct equipment operation and safety-critical system diagnostics, typically requiring a licensed or qualified technician to perform and certify results, creating regulatory and liability barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but physical presence, tool handling, and liability for improper diagnosis create real friction against remote or software-only automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI system capable of this would require expensive robotics, on-site deployment, and integration with monitoring systems—far exceeding the cost of a technician performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for the physical task, so the human mechanic remains the only cost-effective option; any automation would require expensive robotics far exceeding technician wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically start and idle motors or visually monitor thermometers on-site. This is a fundamentally embodied task requiring a physical agent at the service location. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical marine engine diagnostics like this; existing diagnostic AI is limited to interpreting sensor data streams, not physically operating and observing equipment. |
Inspect and repair or adjust propellers or propeller shafts.
7CI 0–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Inspect and repair or adjust propellers or propeller shafts.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Marine service is a small, traditional sector with low digital maturity and limited automation adoption. Propeller repair is performed in small workshops with physical constraints, making it a laggard domain for AI deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical repair is a small-scale, physical, low-digitization trade with minimal AI/robotics adoption reported industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AR or image analysis might assist a technician in diagnosing damage patterns, the core work—physical inspection, disassembly, and adjustment—offers limited augmentation opportunity. The human remains fully in control of the tactile, mechanical task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostic manuals, parts lookup, or documenting wear patterns via image analysis, but offers limited direct help with the hands-on inspection and physical adjustment work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Inspecting and repairing propellers and shafts requires hands-on manipulation, precise mechanical judgment about wear and damage, and physical intervention in complex 3D assemblies. Current AI systems cannot perform the physical disassembly, assessment, and reassembly needed; this is fundamentally a manual task. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically inspecting propellers, detecting pitting/bending, and manually repairing or adjusting shaft alignment requires hands-on manipulation, tactile inspection, and mechanical skill that current AI cannot perform without embodiment in a capable robot, which does not exist for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Marine propulsion systems are safety-critical, subject to maritime regulations and insurance requirements; liability for failed propeller repairs is high. Human technicians are legally and organizationally required to certify these repairs, creating hard barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing typically required for boat mechanics, but liability for improper propeller/shaft repair (safety-critical for vessel operation) creates moderate friction and customer preference for skilled, trusted technicians. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI inspection and repair systems, if they existed, would require expensive robotic hardware, specialized marine environments, and high integration costs, vastly exceeding the cost of a skilled technician performing this work on-site. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that performs this physical repair task, so any comparison to human labor cost is moot; AI cost is effectively infinite/inapplicable for the core task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product autonomously inspects, repairs, or adjusts propeller systems. Vision-based damage detection exists in research, but end-to-end autonomous repair of propulsion systems is not production-ready in any marine service context. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical propeller or shaft inspection/repair; this remains purely a human manual-mechanical task with no robotic or AI-driven equivalent in production. |
Adjust carburetor mixtures, electrical point settings, or timing while motors are running in water-filled test tanks.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Adjust carburetor mixtures, electrical point settings, or timing while motors are running in water-filled test tanks.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Marine service is a small, geographically dispersed, low-digitization sector with mostly small shops; adoption of AI-driven automation in this domain is minimal and lagging well behind information and professional services. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Small-shop marine repair is a low-digitization, hands-on trade with minimal AI/robotics penetration and no evidence of adoption trends here. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostic recommendations or parameter suggestions (e.g., optimal settings for specific engine models), but the live physical adjustment work requires the human mechanic to remain hands-on; augmentation is modest and limited to pre-work planning. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostic guidance, manuals, or interpreting sensor data, but offers little direct assistance during the hands-on physical adjustment process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time sensory feedback (engine sound, vibration, responsiveness), manual dexterity with fine adjustments, and dynamic decision-making while the motor is running in water. Current AI systems cannot perform live mechanical adjustment work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hardware, tactile feedback, and real-time sensory judgment (sound, vibration, water conditions) that current AI cannot perform end-to-end without embodiment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical maritime work typically carries liability exposure; specialized training and certification requirements for marine mechanics limit scope; and the task involves hazardous conditions (running motors, water exposure) that create regulatory and insurance barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate typically requires a certified human, but safety concerns around running motors in water tanks and liability for engine damage create meaningful organizational friction against any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotic systems capable of precision mechanical adjustment in wet, high-vibration environments would far exceed the loaded wage of a trained motorboat mechanic performing this task. |
| 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 technician's wage for this specific work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically adjust carburetors, electrical points, or timing mechanisms in real time while engines run. This requires embodied robotics in a specialized, hazardous environment, which is not commercially available at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical carburetor/timing adjustments on running motors in test tanks; this remains purely manual technician work. |
Set starter locks and align and repair steering or throttle controls, using gauges, screwdrivers, or wrenches.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Set starter locks and align and repair steering or throttle controls, using gauges, screwdrivers, or wrenches.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat service is a traditional, physical-work dominated sector with low digitization. Adoption of automation in this domain remains minimal and moves slowly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine mechanical repair is a low-digitization, physically-intensive trade with minimal AI/robotic adoption in the field currently. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist minimally through diagnostic tools or reference documentation (e.g., identifying correct torque specs or alignment procedures), but the core manual work cannot be augmented by current AI systems. Most value would come from human technician expertise and experience. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic manuals, troubleshooting guidance, or parts lookup, but offers little help with the physical alignment and repair actions themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires hands-on mechanical work including setting locks, aligning controls, and using precision tools in a physical environment. Current AI systems cannot manipulate physical objects, apply tools, or perform dexterous mechanical repairs. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical repair and precise mechanical alignment work requiring manipulation of tools on physical hardware, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task carries inherent barriers: mechanical safety considerations, warranty liability for incorrectly serviced equipment, regulatory compliance for marine safety systems, and the requirement for hands-on physical presence make automation difficult even if technology improved. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate strictly requires a human, but liability for faulty steering/throttle repair on boats (safety-critical systems) creates strong practical pressure for human accountability and hands-on verification. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot yet perform this task at all, making cost comparison moot. Human technicians remain the only viable option, so the ratio heavily favors the human (AI cannot substitute). |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-based approach would require robotics far more costly than a human technician's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform physical mechanical repair and alignment tasks on motorboat equipment. This remains fundamentally beyond the capability of current software and robotics in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product or robotic system exists that performs marine steering/throttle control repair and alignment in production settings today. |
Adjust generators and replace faulty wiring, using hand tools and soldering irons.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Adjust generators and replace faulty wiring, using hand tools and soldering irons.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The marine service sector remains highly hands-on and physically localized with limited digitization; automation adoption has been slow, and physical repair work cannot be displaced by remote AI systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Small-scale marine repair shops are a low-digitization, physically-oriented sector with minimal AI/robotics adoption for hands-on mechanical and electrical repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with diagnostics or procedural documentation, the core task—hands-on adjustment and soldering—offers minimal opportunity for AI augmentation beyond suggesting repair steps that a human technician must execute. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance, wiring diagrams, or troubleshooting reference lookup, but it provides minimal help with the actual physical adjustment and soldering work involved. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation (hand tools, soldering irons) in three-dimensional space on vessel equipment, combined with fine motor control and spatial reasoning. Current AI systems cannot perform physical work on actual hardware. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical diagnosis, wiring replacement, and soldering work on marine equipment that requires manual dexterity and real-world manipulation, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Motorboat service typically involves warranty work, safety compliance, and liability for vessel systems; many jurisdictions require certified technicians to perform electrical and engine work, creating regulatory and liability barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate strictly requires a human for this specific repair, but liability for faulty electrical work on boats and the physical nature of the task create practical friction against any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task fundamentally requires a human technician present on-site with specialized tools and equipment; AI has no material cost advantage because it cannot perform the physical work at all. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical repair work, so the AI cost is effectively infinite relative to a technician's wage for this specific task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically adjust generators or solder wiring in a boat engine compartment today; this remains entirely in the domain of human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical generator adjustment or wiring/soldering repair on motorboats; this remains firmly in the domain of human technicians with no robotic substitutes in production. |
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.