Motorboat Operators
53-5022.00Median wage $47,520/yr2,480 employed (US)Rank #870 of 923 scored · top 94% by substitution
Operate small motor-driven boats. May assist in navigational activities.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution11
Exposure6
Augmentation29
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
16 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%7
panel mean rating 1.3/5 → substitution pressure 7/100
Technical feasibility todayw 20%5
panel mean rating 1.2/5 → substitution pressure 5/100
Cost vs. human wagew 15%8
panel mean rating 1.3/5 → substitution pressure 8/100
Adoption barriersw 20%inverted — strong barriers lower the score27
panel mean rating 3.9/5 (barrier strength) → substitution pressure 27/100
Sector adoption velocityw 10%7
panel mean rating 1.3/5 → substitution pressure 7/100
Task breakdown (16 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Maintain desired courses, using compasses or electronic navigational aids.
39CI 25–54 · exposure 34 · augmentation 88 · importance 4.6/5 · click for rater detail
Maintain desired courses, using compasses or electronic navigational aids.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Autopilot and electronic navigation have achieved near-universal adoption across commercial shipping, ferry services, and recreational boating over the past two decades. This is one of the earliest and deepest automations in any occupational domain. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Marine transport is a physically-oriented, moderately digitized sector where autopilot and GPS aids are common but full autonomous navigation adoption is slow due to regulation and safety-critical nature. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | Electronic navigation aids and autopilot systems dramatically enhance operator productivity and safety by reducing fatigue and enabling monitoring of wider areas. These tools are foundational augmentations that allow operators to manage tasks that would be impossible without automation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Electronic navigation aids (GPS, autopilot, chartplotters) substantially reduce operator workload and improve course-keeping accuracy while the human remains in command and responsible for decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Modern autopilot and GPS systems can maintain a course automatically, but continuous human supervision is required for safety, obstacle detection, and context-dependent course adjustments. Current AI cannot reliably handle the full operational environment (weather, traffic, emergencies) without human override. |
| Task automatability | claude-sonnet-5 | 2/5 | Autonomous/GPS-assisted course-holding exists technically, but for occupational motorboat operators this remains a hands-on physical steering and monitoring task requiring real-time judgment about traffic, obstacles, and conditions that off-the-shelf AI does not fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations (USCG, IMO, international maritime law) mandate a licensed operator remain on watch and in control; automation cannot fully replace the legal requirement for a qualified human to command the vessel and respond to emergencies. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime regulations generally require a licensed operator to be responsible for navigation and course-keeping, especially for passenger or commercial vessels, creating strong legal/liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Autopilot and GPS hardware costs are low relative to operator wages, and these systems have been standard on motorboats for decades. The per-voyage cost of automation is negligible compared to employing a full-time boat operator. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Autopilot hardware is relatively cheap, but a licensed operator must still be aboard for liability, oversight, and situational response, so the cost savings from automation of just this sub-task are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Autopilot systems and electronic navigation aids are mature, deployed technologies in commercial and recreational boating. GPS and electronic chart displays reliably maintain courses in production settings, though human operators remain present for safety and decision-making. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Autopilot systems and GPS chartplotters are mature and widely deployed for maintaining heading, but full unmanned navigation for commercial/passenger motorboat operation is not standard production practice due to safety and regulatory oversight needs. |
Arrange repairs, fuel, and supplies for vessels.
30CI 25–35 · exposure 20 · augmentation 50 · importance 4.2/5 · click for rater detail
Arrange repairs, fuel, and supplies for vessels.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Marina and vessel operations remain relatively low-digitization, small-firm sectors with limited prior wave of process automation. AI adoption in marine logistics is nascent and concentrated in large commercial fleets rather than typical motorboat operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Maritime and marine operations sectors are relatively low-digitization industries with slow AI adoption compared to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist motorboat operators by automating fuel-order generation, flagging low inventory levels, and maintaining vendor contact databases, materially speeding routine administrative work while the human makes final repair and scheduling decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can assist with scheduling, vendor communication drafting, and inventory tracking, meaningfully supporting the human operator without replacing the coordination role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Only narrow parts of this task—checking inventory levels, sending automated fuel orders—are readily automatable. The broader work of arranging repairs (diagnosing issues, contacting vendors, scheduling) and coordinating supplies requires judgment and human negotiation that current AI systems cannot reliably manage end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves coordination, vendor communication, and physical logistics that AI can partially assist with (e.g., scheduling, ordering) but cannot fully execute end-to-end without human oversight and physical presence.atable requiring physical action.rimarily requires human coordination.g., procurement calls, dock arrangements).rims that require human judgment and physical follow-through.equires physical inspection and human judgment. ratings reflect this.rims. ratings reflect this.equires physical inspection and human judgment. ratings reflect this.rims. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. ratings reflect this. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some friction exists from the need for human judgment on repair specifications and vendor vetting, plus potential liability for improper maintenance orders. However, no hard legal requirement mandates a licensed human sign off, allowing partial automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this coordination task, though marine safety and procurement decisions often require accountable human sign-off, creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI could automate simple order placement and inventory checks at low cost, but the core task—vendor coordination and repair decision-making—still requires human oversight that approaches or exceeds the cost of a direct human arranging these tasks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Coordination software reduces some administrative cost, but human judgment for vendor relationships, quality assessment, and negotiation still dominates cost, keeping AI only marginally cheaper at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform the full scope of arranging repairs and supplies for vessels. While inventory management software exists, the coordination of vendor relationships, troubleshooting, and scheduling remains dependent on human operators in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | While software tools exist for procurement and scheduling (e.g., fleet management systems), no deployed AI product autonomously arranges vessel repairs, fuel, and supplies without significant human input and decision-making. |
Take depth soundings in turning basins.
20CI 18–23 · exposure 16 · augmentation 38 · importance 3.6/5 · click for rater detail
Take depth soundings in turning basins.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime sectors, particularly small-vessel operations, have low digitization and slow AI adoption; autonomous vessel deployment remains niche and heavily regulated, with no evidence of rapid substitution in motorboat operator roles. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime and port operations are a low-digitization, physical-world sector with slow AI/autonomy adoption relative to information or professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with automated data logging and depth-map visualization, but the core task of safely operating a vessel in a turning basin inherently relies on human situational awareness and control, limiting meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enhanced sonar processing and GPS-integrated depth-mapping software can assist operators in recording and interpreting soundings more efficiently, though the human remains essential for operation and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Depth soundings require real-time sensor integration and precise navigation in dynamic environments, but current AI cannot independently operate motorboat controls, interpret sonar/depth readings, and make autonomous steering decisions safely. Some parts (data logging, analysis) could be automated, but the core operating task remains manual. |
| Task automatability | claude-sonnet-5 | 2/5 | Depth sounding involves physically operating a boat and sonar equipment in real-world turning basins; AI can process sonar data but cannot autonomously perform the full physical task end-to-end today.There is no off-the-shelf system that replaces the human operator entirely for this maritime task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations require licensed operators; liability for waterway safety and accurate hydrographic data is high; and human judgment in vessel handling and hazard response is legally and practically expected in most jurisdictions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Navigational safety regulations, insurance liability, and Coast Guard oversight in turning basins near vessel traffic create moderate friction, though not an explicit licensing requirement for the sounding task itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An autonomous/semi-autonomous system capable of this task would require significant custom marine hardware, sensing, and integration—likely exceeding the cost of a trained motorboat operator for routine soundings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Autonomous survey boats and sonar systems require significant capital investment, maintenance, and human oversight for safety and calibration, making all-in costs comparable to or higher than a human operator for this niche task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous motorboat operation and depth sounding in turning basins. While autonomous marine systems exist in research, they are not in production for this specialized task in general commercial use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Autonomous survey vessels exist in research and niche hydrographic surveying contexts, but no mature, widely deployed product performs unmanned depth sounding in operational turning basins as standard practice. |
Perform general labor duties such as repairing booms.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.4/5 · click for rater detail
Perform general labor duties such as repairing booms.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat operation and maintenance occurs in small, dispersed, physical-service sectors with low digitization and limited AI infrastructure; adoption of automation in this domain is minimal and slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime/dock physical labor is a low-digitization, laggard sector with minimal AI or robotics adoption for hands-on equipment repair. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for physical boom repair tasks. Computer vision diagnostics could theoretically help identify damage, but the core work—hands-on manipulation and fixing—remains entirely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide minor assistance such as diagnostic guidance or repair manuals/checklists, but offers little transformation to the hands-on physical repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Repairing booms requires physical manipulation in a marine environment with high variability in damage types, materials, and spatial constraints. Current AI systems cannot perform hands-on mechanical repair work, and robotics for this context remain largely experimental. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical repair of booms requires manual dexterity, tool use, and on-water manipulation of equipment that current AI systems cannot perform end-to-end; this is a manual labor task, not a cognitive/digital one. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there is no explicit licensing requirement for general labor repairs on boats, maritime liability and safety regulations create modest friction; safety standards and inspection requirements add some organizational overhead but do not constitute hard legal barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical, outdoor, variable nature of the work creates practical barriers to any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of manipulating and repairing marine equipment would require significant capital investment and integration, making the per-task cost far higher than paying a skilled motorboat operator or mechanic. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive custom robotics far costlier than paying a laborer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products can reliably perform boom repairs autonomously. This task requires embodied dexterity, structural assessment, and real-time problem-solving in uncontrolled outdoor conditions—well beyond current production automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical boom repair or general marine labor tasks; robotics for this niche outdoor maintenance work remains research-stage at best. |
Maintain equipment such as range markers, fire extinguishers, boat fenders, lines, pumps, and fittings.
13CI 5–21 · exposure 8 · augmentation 38 · importance 4.4/5 · click for rater detail
Maintain equipment such as range markers, fire extinguishers, boat fenders, lines, pumps, and fittings.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Maritime and boating sectors are relatively low-digitization, physical-work-heavy industries where automation adoption moves slowly; most operators still rely on manual inspection and maintenance schedules. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime and boat operation sectors have low digitization and minimal AI/robotics adoption for physical maintenance tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating maintenance scheduling, logging compliance records, and flagging when items require inspection, meaningfully raising operator efficiency while the human remains hands-on. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with maintenance scheduling, inventory tracking, or diagnostic checklists, but offers little help with the actual physical upkeep of equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical maintenance tasks requiring hands-on inspection, repair, and replacement of boat equipment cannot be fully automated by current AI; only documentation and scheduling components are automatable, yielding minimal overall time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical maintenance of marine equipment requiring manual inspection, cleaning, repair, and replacement, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime safety regulations mandate that equipment be maintained and certified by qualified personnel, and liability exposure for failures of critical safety items (fire extinguishers, fenders) creates strong legal and insurance barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically restricts equipment maintenance to certain individuals, but the physical, on-vessel nature and safety implications (fire extinguishers, lines) create practical friction against any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Physical maintenance still requires human labor on-site; AI oversight tools offer only marginal cost reduction compared to the full loaded wage of a skilled equipment maintenance worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no capability to perform physical maintenance tasks, so there is no viable AI cost comparison; a human must do this work entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems perform end-to-end physical equipment maintenance on boats; the task requires embodied action (tightening fittings, replacing parts, testing equipment) beyond current autonomous capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical maintenance of boat equipment; this remains purely a manual, hands-on task performed by crew. |
Service motors by performing tasks such as changing oil and lubricating parts.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Service motors by performing tasks such as changing oil and lubricating parts.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat operation and maintenance is a physical, decentralized activity in small-to-medium vessels with low digitization; the sector has shown minimal AI adoption patterns compared to information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine maintenance and boat operation are low-digitization, physical-labor sectors with minimal AI or robotics adoption for such tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could provide diagnostic support via image recognition or documentation assistance, but the core task of physical fluid replacement and mechanical lubrication offers limited augmentation potential for a human mechanic already trained in the work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little to no direct assistance for physically changing oil or lubricating engine parts, though manuals or diagnostic apps might offer marginal informational support. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Servicing motorboat motors requires physical manipulation of engine components, removal and replacement of fluids, and assessment of mechanical condition—tasks that current AI systems cannot perform end-to-end in the real world without specialized robotic hardware and site-specific adaptation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical maintenance task requiring hands-on manipulation of engine parts, fluids, and tools; no current AI system can perform this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime safety regulations, warranty requirements for engine service, and liability for improper maintenance create significant friction; many jurisdictions require certified technicians to perform or verify engine work, and boat owners often require licensed professionals for liability protection. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for basic oil changes, but the physical dexterity and access requirements create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotic systems capable of performing engine service, combined with integration and error liability, would far exceed the loaded wage of a skilled motorboat mechanic for years of deployment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so AI cost is effectively infinite relative to a human mechanic's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform motorboat engine maintenance autonomously in production; this remains a manual, hands-on task performed by trained technicians with no reliable automation solutions in the market. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs motor oil changes and lubrication on motorboats; this remains a manual mechanical task done by humans. |
Clean boats and repair hulls and superstructures, using hand tools, paint, and brushes.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Clean boats and repair hulls and superstructures, using hand tools, paint, and brushes.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boating and marine services are fragmented, small-scale, and labor-intensive sectors with low digitization. Adoption of advanced automation in marinas and boat yards remains minimal, with most operations still relying on manual labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine trades and manual repair work are a laggard sector for AI/robotics adoption, with minimal digitization or automation of physical hull maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance such as identifying corrosion patterns or suggesting repair procedures through computer vision and documentation, but current systems offer limited practical augmentation for the hands-on physical work of cleaning and repair. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostic guidance, sourcing repair materials, or documenting damage via image recognition, but offers little assistance to the core manual cleaning and repair labor. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Hull and superstructure repair and cleaning requires fine manual dexterity, judgment about damage assessment, and adaptive problem-solving in unstructured physical environments. Current AI lacks the embodied manipulation and sensorimotor capabilities to perform these tasks reliably at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands-on cleaning, fiberglass/wood repair, and painting of boat hulls; no current AI system can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Marine repair and maintenance often require marine surveyor credentials, Coast Guard licensing for certain operations, and liability considerations around structural integrity and safety. Insurance and regulatory requirements create meaningful friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required for basic boat maintenance, but the physical nature of the work itself is the main barrier rather than regulation, so classic AI-substitution barriers are low-moderate since it's simply not roboticized yet. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automation of hull repair would require custom robotic systems costing tens to hundreds of thousands of dollars, far exceeding the loaded wage of a motorboat operator or marine technician performing these tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical repair work, so AI cost is not comparable—humans remain the only viable option, making AI effectively infinitely costlier for the task itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems in production today can reliably perform boat hull repair and cleaning end-to-end. While specialized industrial robots exist for specific painting tasks, they require controlled environments and extensive task-specific engineering—far from general boatyard use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product or robot performs boat hull cleaning and repair in production; this remains purely a human manual trade skill. |
Report any observed navigational hazards to authorities.
9CI 0–19 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Report any observed navigational hazards to authorities.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat operation is a small, distributed, and traditionally low-digitization sector with limited incentive or capacity to adopt AI systems for episodic safety reporting. Adoption rates for maritime automation remain very low outside large commercial shipping. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine and boating operations are a low-digitization, physically-oriented sector with minimal AI agent deployment for real-time hazard reporting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with hazard *detection* via onboard cameras or sensors, but the reporting decision itself requires human judgment and accountability that is difficult to augment meaningfully. Modest assistance might exist in flagging potential hazards for the operator's review, but this is not a core augmentation use case. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled communication tools or GPS/hazard databases can help operators log and transmit reports faster, but they offer only marginal assistance to the core observation task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Reporting navigational hazards requires situational judgment, discretion about severity, and knowledge of which authorities to contact—tasks that demand human decision-making. Current AI systems cannot reliably identify what constitutes a reportable hazard or navigate the complex regulatory reporting landscape without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical observation of hazards on water requires human presence and perception; while reporting via radio/text could be assisted by AI, the core observation and judgment task cannot be automated end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime safety regulations and Coast Guard protocols typically require responsible humans (licensed operators) to directly report hazards; this is often a legal obligation tied to the operator's license and accountability. Liability for missed or false reports creates strong regulatory and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human specifically report hazards, but safety regulations, liability, and reliance on human judgment on the water create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves brief, episodic reporting that a human operator performs at minimal cost as part of normal duty. Automating it would require continuous monitoring infrastructure, hazard classification AI, and integration with multiple authority databases—making AI substantially more expensive than the negligible human cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this specific observation-and-report task, so no meaningful cost comparison favors AI; a human operator is required regardless of cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed maritime system autonomously reports observed hazards to authorities; this remains a human responsibility. While AI could theoretically assist with hazard *detection* via computer vision, the actual act of determining reportability and contacting authorities is not performed by any production AI system today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously detect and report navigational hazards from a motorboat operator's vantage in production; this remains research-stage (e.g., experimental autonomous vessel sensors). |
Secure boats to docks with mooring lines, and cast off lines to enable departure.
7CI 5–10 · exposure 0 · augmentation 0 · importance 4.6/5 · click for rater detail
Secure boats to docks with mooring lines, and cast off lines to enable departure.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Boat operation remains a traditional, labor-intensive maritime sector with minimal AI/automation adoption. The physical nature of the work and regulatory environment make this a laggard sector for automation technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime/marine transport is a low-digitization, physically demanding sector with minimal AI/robotic adoption for dockside line handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful productivity assistance for rope handling and knot-tying; this remains an entirely manual physical task with limited scope for digital augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a human physically securing or casting off mooring lines. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of ropes and securing mechanisms in variable marine environments, precise knot-tying, and real-time environmental assessment—capabilities far beyond current AI robotics in uncontrolled settings. No deployed system can reliably perform end-to-end docking and mooring today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to tie knots, handle heavy wet lines, and adapt to boat/dock conditions; no off-the-shelf AI or robotic system performs this today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime safety regulations, liability concerns for improper mooring (vessel and property damage risk), and the physical requirement for human judgment in variable water conditions and wind create substantial adoption barriers. Human operators are typically required by maritime law and insurance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement specifically for this sub-task, but physical dexterity, safety liability (crushing/entanglement hazards), and variable dock/weather conditions create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a dexterous robotic arm capable of manipulating mooring lines, combined with perception systems and integration overhead, far exceeds the loaded wage of a skilled boat operator for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human deckhand. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably automates boat mooring and casting off in production settings. The task demands dexterous manipulation, dynamic environmental response, and safety-critical precision that current robotics cannot achieve at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform mooring/casting off lines in production; this remains a manual seamanship skill with no commercial automation. |
Follow safety procedures to ensure the protection of passengers, cargo, and vessels.
6CI 0–13 · exposure 8 · augmentation 25 · importance 4.5/5 · click for rater detail
Follow safety procedures to ensure the protection of passengers, cargo, and vessels.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime operators are traditional, heavily regulated, and geographically dispersed sectors with slow digital transformation; adoption of autonomous safety systems in production remains minimal, with most vessels relying on conventional human-led safety protocols. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine transport and boating operations are a low-digitization, physical-labor sector with minimal AI/autonomy adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide marginal assistance via automated checklists, condition alerts, or procedure reminders, but the core task of ensuring protection through judgment calls and live response to hazards remains human-centric with limited augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based navigation aids, weather alerts, and sensor systems can support situational awareness, but their role in the core safety-procedure task is limited and supplementary. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Safety procedure adherence involves real-time decision-making based on environmental conditions, crew actions, and vessel-specific contexts—most of which require human judgment and intervention. AI could assist with checklist automation and alerts, but cannot reliably monitor and enforce procedures across the dynamic conditions of watercraft operation. |
| Task automatability | claude-sonnet-5 | 1/5 | Safety procedure execution on a motorboat requires physical presence, real-time perception of weather/water conditions, and hands-on vessel control that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime safety is heavily regulated by national and international law (e.g., IMO regulations, SOLAS), with licensing requirements for operators; liability for safety failures is severe and legally vested in human captains and crew, creating firm legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Licensed operators, maritime safety regulations (e.g., Coast Guard requirements), and liability for passenger safety create hard legal barriers requiring a qualified human to be in control. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI monitoring systems for safety procedures would require extensive hardware installation, vessel customization, and continuous human oversight—making total cost comparable to or exceeding the cost of human operators performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task today, so any AI cost would be additive to, not replacing, the human operator and required safety equipment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably monitors and enforces safety procedures across all maritime conditions or makes real-time decisions on passenger/cargo/vessel protection in production systems. Existing maritime automation focuses on navigation and engine systems, not integrated safety procedure management. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously operates motorboats and ensures passenger/cargo safety in production; autonomous marine vessels remain largely research or limited pilot stage. |
Operate engine throttles and steering mechanisms to guide boats on desired courses.
5CI 0–10 · exposure 5 · augmentation 25 · importance 4.8/5 · click for rater detail
Operate engine throttles and steering mechanisms to guide boats on desired courses.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat operation remains concentrated in small firms, recreation, and traditional marine industries with low technology adoption rates. The sector has shown minimal adoption of autonomous navigation technology outside specialized large-vessel applications. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine transport and small-craft operation is a low-digitization, physically embedded sector with minimal AI/autonomy adoption in production settings today. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Modern motorboats increasingly incorporate GPS, autopilot, and chartplotting aids that assist operators with course maintenance and positioning. However, these are narrow augmentations; AI offers limited assistance with real-time steering and throttle adjustment in dynamic conditions. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some assistive technologies exist (autopilot, GPS-based course-keeping, collision-avoidance alerts) that help operators, but these are narrow aids rather than transformative productivity tools for the core steering task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Operating a motorboat requires real-time sensory input, dynamic environmental adaptation, and precise physical control in an inherently unpredictable marine environment. Current AI systems lack the integrated perception, decision-making, and actuation capabilities to reliably handle this safety-critical task autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical vessel operation requiring real-time perception and control in dynamic marine environments is far outside current AI capabilities for general deployment; autonomous marine navigation remains largely experimental outside narrow controlled contexts.rieve |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law, Coast Guard regulations, and commercial shipping liability frameworks legally require human operators aboard vessels and hold them responsible for navigation and safety. Insurance, liability, and regulatory mandates create hard barriers to full automation of boat operation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime operation often requires licensed operators, liability for collisions/accidents is severe, and Coast Guard/maritime regulations typically mandate human control or oversight for many vessel classes. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware, software, integration, and liability costs of autonomous marine systems far exceed the wage of a motorboat operator. Retrofit or new-build autonomous boats require six-figure investment, making AI substantially more expensive than human labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous boat control systems require expensive sensor suites, redundant safety systems, and regulatory compliance costs that vastly exceed a human operator's wage for most small-craft applications. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While autonomous navigation systems exist in research and limited maritime contexts, no deployed product reliably operates recreational or commercial motorboats end-to-end across typical operating conditions. Existing systems require extensive calibration, operate in restricted domains, and lack the robustness expected of production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature commercial product operates general-purpose motorboats end-to-end for tasks like passenger transport, fishing, or towing; autonomous marine vessels remain research/pilot stage (e.g., military or shipping trials)., |
Organize and direct the activities of crew members.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Organize and direct the activities of crew members.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime sectors, especially small-vessel operations, show minimal adoption of autonomous crew management or AI direction systems; crew coordination remains a fundamentally human responsibility in practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime/boating operations are a low-digitization, physical-world sector with minimal AI agent adoption for crew management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance with scheduling or alert reminders, but the core task of directing crew members in real time requires human judgment, authority, and presence that AI cannot materially enhance today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with scheduling, communication logs, or route planning, but offers little direct help with the interpersonal, real-time direction of crew activities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Organizing and directing crew members requires real-time judgment, interpersonal authority, and dynamic adaptation to crew performance and safety conditions—tasks that current AI cannot perform autonomously. AI systems lack the embodied presence, credibility, and accountability needed to lead personnel on a vessel. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing crew requires real-time physical presence, judgment, and interpersonal leadership on a moving vessel; no AI system can perform this end-to-end today.“}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law and regulations require a licensed captain or operator to maintain legal authority and responsibility for crew safety and vessel operations; this human leadership requirement is a hard legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Vessel command typically requires licensed operators with legal responsibility for crew safety, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an AI system to attempt crew management would exceed the cost of a human operator, given the need for continuous oversight, safety liability, and the relatively modest wage of a motorboat operator in most contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute providing this output, so any AI cost is irrelevant relative to the human operator's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform crew direction and leadership on motorboats. While AI can generate schedules or suggestions, it cannot substitute for the human captain's authority and judgment in directing people during dynamic maritime operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages or directs human crew activities aboard a motorboat; this remains firmly human-performed. |
Issue directions for loading, unloading, and seating in boats.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Issue directions for loading, unloading, and seating in boats.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Motorboat operations are traditional, physically situated activities in small firms (charter companies, marinas, tourism) with low digitization. Adoption of autonomous AI systems for safety-critical real-world tasks in this sector is virtually nonexistent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine transport and small-vessel operations are a low-digitization, physically-grounded sector with minimal AI agent penetration into safety-critical crew tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially provide template reminders or checklists for loading procedures, but the task's core—issuing dynamic, context-aware safety directions to real people in a physical environment—offers minimal augmentation value since the operator must be present and legally responsible regardless. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with load calculations or scheduling in advance, but it offers little real-time assistance to the actual verbal directing and physical coordination task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time situational awareness, safety judgment, and direct communication with dynamic physical environments (boat conditions, passenger behavior, weather). Current AI systems cannot reliably perceive the physical state of boats or passengers, adapt safety directions to specific conditions, or ensure compliance with verbal instructions in real operational contexts. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a real-time, physical, in-person task requiring direct verbal coordination with passengers and cargo on a moving vessel; no AI system today can perceive, direct, and manage physical boat loading and passenger seating. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime regulations typically require a licensed captain or operator to be present and responsible for passenger safety, including loading and seating procedures. Legal liability and safety certification create hard barriers to full automation or unsupervised AI direction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Passenger safety, weight distribution, and vessel stability create strong liability and regulatory reasons for a licensed operator to be physically present and directing loading. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is straightforward enough that a motorboat operator's labor cost is modest, and the liability and safety-critical nature means AI oversight would likely require a human supervisor present anyway, negating cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical safety task, so any AI cost is irrelevant—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task autonomously in production. While language models can generate generic loading/seating guidance, they lack the sensory integration, liability acceptance, and real-time environmental responsiveness required for actual boat operations where safety is paramount. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs live physical passenger/cargo direction on boats; this remains squarely a human operational role with safety implications. |
Tow, push, or guide other boats, barges, logs, or rafts.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Tow, push, or guide other boats, barges, logs, or rafts.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime towing remains a traditional, physically distributed sector with minimal AI adoption to date. Regulatory conservatism, capital investment in crewed vessels, and safety requirements keep adoption velocity very low. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine transport and towing is a low-digitization, physically embedded sector with minimal AI/autonomy deployment in production compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning, weather monitoring, or navigation aids, but the core task of physically maneuvering a towing operation relies on human judgment and real-time control that current AI augmentation tools do not meaningfully enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with navigation aids, route optimization, and collision-avoidance alerts, but it offers limited augmentation for the core physical task of towing and guiding vessels. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time spatial navigation in dynamic water environments, maneuvering heavy vessels with precision, and responding to unpredictable conditions (currents, weather, other traffic). Current AI lacks the embodied control and real-world sensing to operate vessels autonomously at the safety and reliability required for towing operations. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically towing and maneuvering boats, barges, or logs in dynamic water conditions requires real-time physical control and judgment that current AI cannot perform end-to-end; this is a physical operating task, not information processing.atable |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Vessel operation is heavily regulated by maritime law, requiring licensed captains and operators with legal responsibility for cargo and safety. Liability for loss of cargo, collision, or environmental damage creates strong legal and insurance barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime operations often require licensed operators, safety certifications, and regulatory compliance (e.g., Coast Guard rules), and liability for collisions or environmental damage creates strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware, sensor suite, liability insurance, and oversight required for autonomous towing far exceed the cost of a human motorboat operator's loaded wage, particularly given the high-value cargo and safety-critical nature of the work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous towing systems would require expensive sensor suites, vessel automation retrofits, and safety oversight infrastructure, making them costlier than a human operator for most current fleets. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial AI system reliably performs autonomous towing of barges, logs, or rafts in production environments. While autonomous ship research exists, it remains experimental and does not meet the reliability standard for load-towing in varied maritime conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously operates motorboats to tow or guide barges/logs at scale; autonomous marine vessels remain largely experimental or limited to narrow shipping/military pilots. |
Direct safety operations in emergency situations.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Direct safety operations in emergency situations.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption velocity is minimal because maritime safety regulation explicitly requires human operators to retain command and decision-making authority in emergencies; regulatory compliance is non-negotiable and enforcement is strict. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime and marine transport sectors are low-digitization, physically embedded industries with minimal AI adoption for real-time emergency command functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI could provide limited augmentation through real-time data displays, hazard alerts, or communication logging, but the core task of directing operations and making safety calls must remain with the human operator, limiting the scope of meaningful assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can support with weather alerts, distress signal detection, or checklists beforehand, but offers minimal real-time assistance during active emergency direction and physical response. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing safety operations in emergency situations requires real-time environmental assessment, rapid human judgment calls under uncertainty, and coordination of multiple stakeholders—areas where current AI systems lack reliable deployment. Even with sensors and data feeds, the responsibility and liability for safety decisions in dynamic, life-threatening contexts remains firmly human. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing emergency safety operations on the water requires real-time physical perception, split-second judgment, and command authority over people in crisis, none of which current AI can execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task faces hard regulatory and legal barriers: maritime law and coast guard regulations mandate that licensed, trained human operators direct safety operations and maintain command authority in emergencies. Liability for loss of life or injury creates asymmetric error costs that cannot be shifted to an AI system. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Emergency command on vessels typically requires a licensed operator/captain with legal responsibility and liability for passenger safety, making human authority a hard regulatory and legal requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI oversight and integration for emergency safety direction would exceed the wage of a qualified motorboat operator, since human operators are already required by law and regulation, and any AI system would still need human supervision. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human operator entirely; any AI attempt would require extensive human oversight negating savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously direct emergency safety operations on boats; this task requires live situational awareness, coordination of rescue personnel, and legally accountable decision-making that only trained human operators provide in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product directs live maritime emergency response; existing marine safety AI is limited to alerting or navigation assistance, not command decision-making in crises. |
Oversee operation of vessels used for carrying passengers, motor vehicles, or goods across rivers, harbors, lakes, and coastal waters.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Oversee operation of vessels used for carrying passengers, motor vehicles, or goods across rivers, harbors, lakes, and coastal waters.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maritime shipping remains a traditionally conservative, heavily regulated sector with minimal adoption of autonomous vessel operation in commercial service. Pilot programs exist but production autonomous shipping is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Maritime transport is a slow-adopting, physically-grounded sector with minimal AI/autonomy deployment in passenger and vehicle ferry operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Navigation aids, weather routing, and automated systems assist operators with course planning and monitoring, but these are tools rather than transformative augmentation of the core oversight and control task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with navigation aids, weather routing, and traffic monitoring, but provides limited transformative augmentation to the core oversight and safety judgment role. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Motorboat operation requires real-time navigation, collision avoidance, dynamic decision-making under varying water conditions, and immediate physical control—capabilities far beyond current AI systems. No AI can reliably pilot a vessel end-to-end in open water today. |
| Task automatability | claude-sonnet-5 | 1/5 | Overseeing vessel operation for passenger/cargo transport requires physical presence, real-time navigation decisions, and safety judgment that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Maritime law, international maritime regulations (IMO), national coast guard requirements, and insurance liability frameworks all legally mandate a qualified, licensed human operator with direct responsibility for vessel safety and cargo/passenger security. Automation of this role is heavily restricted. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Maritime operation of passenger and vehicle-carrying vessels requires licensed operators, safety certifications, and regulatory oversight (e.g., Coast Guard rules), creating hard legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of developing and insuring an autonomous vessel, combined with required fallback human oversight and integration, far exceeds the loaded wage of a skilled motorboat operator in virtually all maritime contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous marine systems capable of this oversight role would require costly sensor suites, redundancy, and regulatory compliance, making them far more expensive than a human operator currently. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product autonomously operates passenger or cargo vessels in production. While autonomous surface vessels exist in research, they do not meet maritime safety, liability, and regulatory standards for passenger or mixed-use operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously oversees commercial passenger or vehicle ferry operations today; autonomous shipping remains largely experimental and limited to cargo trials in controlled settings. |
Related occupations — Transportation & Material Moving
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.