Commercial Divers

49-9092.00
Median wage $72,990/yr3,450 employed (US)Rank #841 of 923 scored · top 91% by substitution

Work below surface of water, using surface-supplied air or scuba equipment to inspect, repair, remove, or install equipment and structures. May use a variety of power and hand tools, such as drills, sledgehammers, torches, and welding equipment. May conduct tests or experiments, rig explosives, or photograph structures or marine life.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution13
Exposure11
Augmentation36

Substitution — the headline: capability discounted by cost, barriers and adoption.

Exposure — technical capability alone, regardless of whether anyone deploys it.

Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.

Tasks on the substitution scale

23 rated tasks, binned by substitution score.

Position among all scored occupations

Distribution of 923 occupation scores; the marker is this occupation.

Tasks with substitution ≥ 70

0%

Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.

Why this score

The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.

Task automatabilityw 35%11

panel mean rating 1.4/5 → substitution pressure 11/100

Technical feasibility todayw 20%11

panel mean rating 1.4/5 → substitution pressure 11/100

Cost vs. human wagew 15%12

panel mean rating 1.5/5 → substitution pressure 12/100

Adoption barriersw 20%inverted — strong barriers lower the score20

panel mean rating 4.2/5 (barrier strength) → substitution pressure 20/100

Sector adoption velocityw 10%9

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

Task breakdown (23 tasks)

Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.

Obtain information about diving tasks and environmental conditions.

60

CI 28–92 · exposure 62 · augmentation 75 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510014/5Maritime and diving operations increasingly rely on automated weather services, dive-site apps, and integrated data platforms. Professional dive operators and commercial dive companies have adopted digital information systems widely, and this information-retrieval task fits naturally into existing digitized workflows.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a small, highly physical, low-digitization sector with minimal AI adoption in its workflows.
Augmentation potentialclaude-haiku-4-5-202510015/5AI substantially augments human divers and dive planners by instantly aggregating real-time conditions, tide tables, temperature, visibility forecasts, and hazard warnings—tasks that previously required manual consultation of multiple sources. The human divemaster remains in control of final go/no-go decisions while benefiting from faster, more comprehensive decision support.
Augmentation potentialclaude-sonnet-53/5AI can usefully aggregate weather, tide, and marine condition data to assist divers in planning, though it doesn't replace on-site information gathering.
Task automatabilityclaude-haiku-4-5-202510015/5Obtaining information about diving tasks and environmental conditions is primarily a data retrieval and synthesis task. Current AI systems can access real-time weather data, water condition databases, tidal charts, dive site information, and safety briefing materials, then aggregate and summarize this information in seconds—far exceeding 50% time savings at equal or better quality than manual research.
Task automatabilityclaude-sonnet-52/5Gathering dive task specs and environmental data (weather, currents, water conditions, job scope) requires physical site assessment, communication with clients, and situational judgment that current AI cannot fully replicate end-to-end.'
Adoption barriersclaude-haiku-4-5-202510012/5Although dive operations may prefer a human divemaster to personally brief the team for accountability and custom judgment, obtaining the underlying environmental data has no legal requirement for a licensed human to collect it. Organizational preference and oversight practices create some friction, but not hard barriers.
Adoption barriersclaude-sonnet-53/5No strict licensing barrier for information-gathering itself, but safety-critical decision-making downstream creates institutional caution against relying solely on AI-sourced data.
Cost vs. human wageclaude-haiku-4-5-202510015/5AI-based information retrieval costs pennies per query (API calls to weather, oceanographic, and dive-condition databases) versus human labor hours spent gathering, calling harbormasters, checking multiple sources, and compiling briefings. The cost ratio heavily favors automation.
Cost vs. human wageclaude-sonnet-52/5While weather/data aggregation tools are cheap, the human diver still must integrate site-specific job details and safety judgment, so AI alone doesn't yet replace the labor cost.
Technical feasibility todayclaude-haiku-4-5-202510015/5Deployed products today reliably perform weather lookup, environmental data retrieval, and condition synthesis at scale. Systems like web-integrated AI agents, weather APIs, and oceanographic data platforms have mature integrations that diver operations and dive shops already use in production.
Technical feasibility todayclaude-sonnet-52/5Some products exist for aggregating marine weather/tide data digitally, but no deployed system autonomously gathers full dive task and environmental context reliably in production.

Communicate with workers on the surface while underwater, using signal lines or telephones.

37

CI 5–69 · exposure 45 · augmentation 50 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Commercial diving remains a specialized, safety-critical sector with slow digitization and strong resistance to full automation of safety-critical communications; most operations still rely on human communication specialists despite available automation technology.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a physical, low-digitization trade with minimal AI adoption for core underwater operational communication tasks.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can significantly augment the surface communication worker by automating routine signal interpretation, logging all communications, detecting anomalies in diver status, and providing real-time transcription and alerts, while the human remains responsible for critical decisions and emergency response.
Augmentation potentialclaude-sonnet-52/5AI could assist with transcription, translation, or monitoring of communication logs and telemetry data topside, but offers little direct assistance to the diver's real-time communication task underwater.
Task automatabilityclaude-haiku-4-5-202510015/5Surface-to-underwater communication via signal lines or telephones is fundamentally a data transmission task that can be fully automated today using acoustic modems, hydrophones, and pre-programmed response protocols; AI can reliably encode, transmit, receive, and interpret standardized diving signals and voice communications with significant time and error reduction.
Task automatabilityclaude-sonnet-51/5This requires a physical human diver underwater performing physical labor while communicating; AI cannot perform the underwater physical presence or communication act itself, only potentially relay or transcribe messages.
Adoption barriersclaude-haiku-4-5-202510014/5Regulatory standards and industry safety protocols mandate human oversight and decision-making in diving operations; communication records must often be certified by licensed divers or safety officers, creating a legal requirement for human sign-off on critical communications.
Adoption barriersclaude-sonnet-54/5Commercial diving involves strict safety regulations, certification requirements, and life-safety communication protocols that mandate human divers and topside personnel, creating strong regulatory and safety barriers.
Cost vs. human wageclaude-haiku-4-5-202510014/5AI-driven underwater communication systems (acoustic modems, automated transcription, signal logging) have dramatically lower marginal cost per communication cycle compared to the loaded wage of a surface communication specialist, particularly for routine status checks and protocol-based exchanges.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this task, so cost comparison favors the human by default since automation isn't viable.
Technical feasibility todayclaude-haiku-4-5-202510014/5Underwater acoustic communication systems and automated signal interpretation are deployed in commercial diving operations and marine robotics, though reliability can degrade in challenging acoustic environments; mature products exist in production but occasional signal loss or noise interference requires human oversight.
Technical feasibility todayclaude-sonnet-51/5No deployed product replaces the diver's real-time verbal/signal communication role underwater; this is inherently tied to a human being physically present underwater.

Take appropriate safety precautions, such as monitoring dive lengths and depths and registering with authorities before diving expeditions begin.

36

CI 0–73 · exposure 45 · augmentation 50 · importance 4.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Commercial diving is a physically specialized, low-digitization sector with small operator bases and high regulatory fragmentation. Adoption of autonomous safety-monitoring systems lags information and finance sectors; many operations still rely on manual logs and human oversight.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a physical, highly regulated, low-digitization occupation with minimal AI adoption for safety-critical field operations.
Augmentation potentialclaude-haiku-4-5-202510014/5AI-driven monitoring systems dramatically assist divers and supervisors by real-time alerting, automated depth/time logging, and compliance reminders, raising safety oversight productivity while humans retain decision-making authority on mission-critical risks.
Augmentation potentialclaude-sonnet-52/5Software and sensors can help log dive depth/time data and flag risk thresholds, offering minor assistance, but the core safety judgment and regulatory registration remain manual.
Task automatabilityclaude-haiku-4-5-202510015/5Safety monitoring (dive times, depths) and registration are primarily data-entry and rule-checking tasks that current AI systems can fully automate with compliance checklists and logging. Automated dive computers already monitor depths/times; registration filing and verification are straightforward administrative workflows that meet the 50%-time-saving threshold with off-the-shelf tools.
Task automatabilityclaude-sonnet-51/5This requires physical presence, real-time judgment during a dive, and coordination with authorities before a physical expedition; no AI system can perform the actual precaution-taking and registration process end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Regulatory bodies may require human accountability and sign-off on safety protocols before diving; liability frameworks often mandate that a qualified human (dive master or supervisor) certifies compliance, creating a legal barrier to full automation.
Adoption barriersclaude-sonnet-55/5Commercial diving safety protocols are governed by strict regulatory and certification requirements (e.g., OSHA, dive authority registration) requiring licensed, accountable humans to perform and sign off on safety precautions.
Cost vs. human wageclaude-haiku-4-5-202510015/5Automated monitoring and registration cost pennies per dive once systems are deployed, versus the labor cost of manual pre-dive checks and administrative filing by personnel at loaded rates, achieving over 10x cost advantage.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this task, so no cost comparison favors AI; a human diver and dive supervisor remain necessary at full cost.
Technical feasibility todayclaude-haiku-4-5-202510014/5Deployed AI systems (workflow automation, compliance software, electronic logging systems) reliably handle registration and monitoring documentation in diving operations. However, human judgment around ambient conditions and final sign-off may introduce minor gaps, preventing a perfect 5.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product manages diver safety monitoring or dive registration in production; at best software tools log dive computer data but the compliance/safety task itself is human-performed.

Install, inspect, clean, or repair piping or valves.

26

CI 5–47 · exposure 33 · augmentation 38 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Underwater inspection and robotics are growing in oil & gas and marine sectors, but adoption remains concentrated in large operators with capital-intensive infrastructure. Small and mid-sized diving operations lag significantly, and fully autonomous repair remains limited.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a physical, low-digitization trade with minimal AI/robotic adoption in production settings for hands-on underwater repair tasks.
Augmentation potentialclaude-haiku-4-5-202510013/5Robotic systems can usefully assist divers by handling reconnaissance, pre-inspection mapping, and some cleaning tasks, allowing human divers to focus on high-skill repair work. However, the deeply integrated physical and perceptual demands limit how much divers' productivity rises while remaining in direct control.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, imagery analysis from ROV footage, or predictive maintenance scheduling, but offers little direct augmentation to the physical act of diving and manual repair.
Task automatabilityclaude-haiku-4-5-202510014/5The task involves hands-on manipulation and perception in a highly constrained underwater environment. AI could automate inspection (via autonomous drones with computer vision) and potentially cleaning with robotic systems, though valve installation and repair require dexterous manipulation and real-time problem-solving that current systems struggle with underwater. Realistic time savings likely exceed 50% for inspection and cleaning components.
Task automatabilityclaude-sonnet-51/5This requires physical underwater manipulation of piping and valves, a manual dexterity task in an unstructured hazardous environment that current AI systems cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Significant barriers include safety certification requirements for underwater work, liability concerns if automation fails in critical infrastructure, regulatory approval for autonomous systems in marine environments, and the specialized expertise required to supervise automated underwater operations.
Adoption barriersclaude-sonnet-54/5Underwater industrial work often requires certified divers meeting safety and liability standards, with regulatory oversight for pressure vessel and pipeline work, creating substantial barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Commercial diver wages are high (~$60-90k+ with hazard pay), but underwater robotic systems, their maintenance, specialized boats, and operators represent substantial capital and operational costs that may not achieve cost parity with human divers in many scenarios.
Cost vs. human wageclaude-sonnet-51/5Specialized underwater robotic manipulation systems capable of this work are far more expensive to develop, deploy, and maintain than employing a commercial diver for the task.
Technical feasibility todayclaude-haiku-4-5-202510013/5Autonomous underwater vehicles for inspection and cleaning are deployed in industry, but with material limitations in murky water, complex valve geometry, and repair tasks requiring adaptive decision-making. Robotic arms for underwater repair exist in research/specialized settings but lack the reliability and autonomy for general production deployment.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously performs underwater pipe/valve installation, inspection, or repair; ROVs assist but require human piloting and decision-making, not AI-driven autonomous manipulation.

Operate underwater video, sonar, recording, or related equipment to investigate underwater structures or marine life.

16

CI 11–21 · exposure 5 · augmentation 50 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of autonomous underwater systems is slow and concentrated in niche sectors (deep-water oil & gas, military, research). Most commercial diving work remains human-executed; ROV technology adoption has been gradual and is not displacing divers at scale in the mainstream market.
Sector adoption velocityclaude-sonnet-52/5The commercial diving sector is a physical, non-digitized industry with slow AI/robotics adoption, though offshore energy and marine inspection segments have adopted ROVs/AUVs at moderate pace.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-powered video enhancement, sonar data analysis, and real-time mapping can assist divers in interpreting underwater findings and planning dives, but the diver remains the primary decision-maker and operator performing the core task.
Augmentation potentialclaude-sonnet-53/5AI-enhanced image/sonar analysis and object recognition can help divers or remote operators interpret underwater video and sonar data faster, improving productivity in the data-review portion of the task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical presence underwater, real-time equipment manipulation in a dynamic and hazardous environment, and adaptive decision-making based on complex underwater conditions. Current AI systems cannot remotely operate or substitute for the embodied expertise needed to safely deploy and manage specialized underwater equipment.
Task automatabilityclaude-sonnet-51/5Physically operating equipment underwater in a hostile environment requires human or specialized robotic presence; current general-purpose AI cannot perform the physical task of deploying and manipulating diving equipment underwater.
Adoption barriersclaude-haiku-4-5-202510014/5Diving operations are heavily regulated by maritime and occupational safety authorities; a licensed commercial diver is often legally required to conduct underwater work, certify findings, and assume liability for equipment and safety. Organizational and regulatory frameworks strongly favor human oversight.
Adoption barriersclaude-sonnet-53/5Commercial diving often requires certified professionals for safety and legal reasons, and physical underwater work has inherent risk and liability considerations that create moderate barriers to pure automation, though ROVs are increasingly accepted as substitutes for some inspection work.
Cost vs. human wageclaude-haiku-4-5-202510012/5Specialized underwater robotic systems are extremely costly to purchase, maintain, and operate, and still require human expertise to deploy and interpret. The all-in cost remains comparable to or higher than hiring commercial divers for most operations.
Cost vs. human wageclaude-sonnet-52/5Deploying ROVs/AUVs can be cost-effective in some contexts, but for many jobs the capital cost of underwater robotics and required expertise makes it comparable to or more expensive than human divers for smaller or complex jobs.
Technical feasibility todayclaude-haiku-4-5-202510012/5While autonomous underwater vehicles (AUVs) with cameras and sonar exist, they are specialized, expensive research/military tools with significant operational limitations, not general deployed products that can perform this task reliably across the range of scenarios commercial divers handle. Most production systems require human operator involvement.
Technical feasibility todayclaude-sonnet-51/5While underwater ROVs and AUVs exist, they are specialized robotics products, not general AI systems, and commercial diving still relies heavily on human divers for many inspection tasks requiring dexterity and judgment.

Carry out non-destructive testing, such as tests for cracks on the legs of oil rigs at sea.

14

CI 11–16 · exposure 5 · augmentation 50 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Offshore oil and gas sectors are capital-intensive, risk-averse, and geographically dispersed; ROV adoption has been gradual and remains limited to visual inspection in many cases. The sector's conservative safety culture and regulatory environment slow AI-driven automation compared to information or professional services sectors.
Sector adoption velocityclaude-sonnet-52/5Offshore oil and gas is a slow-adopting, highly regulated, physically demanding sector where ROV/AUV inspection is growing but full replacement of divers with autonomous AI systems remains limited and gradual.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-assisted visual analysis and defect detection on ROV feeds can help human operators prioritize areas of concern and improve inspection efficiency. However, the underwater environment and the need for real-time physical judgment limit the scope of augmentation to image interpretation rather than full task restructuring.
Augmentation potentialclaude-sonnet-53/5AI-enhanced image/signal analysis and ROV-assisted inspection tools can help identify anomalies and cracks faster, augmenting diver/inspector judgment even though the core physical task remains human-performed.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical underwater presence, dexterity, and real-time sensory judgment in a hazardous, unstructured deep-water environment. Current AI systems cannot autonomously conduct underwater non-destructive testing with the reliability and adaptability demanded by safety-critical offshore operations.
Task automatabilityclaude-sonnet-51/5This requires physical underwater presence and manipulation of NDT equipment on submerged structures, which no current AI system can perform end-to-end; only the physical robotics/manipulation aspect could theoretically help, but that is not deployed as autonomous replacement.
Adoption barriersclaude-haiku-4-5-202510014/5Regulatory requirements (offshore drilling permits, classification society rules, liability for structural integrity certification) often mandate that qualified human divers or certified offshore inspectors sign off on critical test results. Insurance and legal liability for missed cracks on safety-critical infrastructure create strong contractual and regulatory barriers to full automation.
Adoption barriersclaude-sonnet-54/5Safety-critical structural inspections on oil rigs are subject to strict regulatory and certification requirements, often mandating qualified inspectors/divers to validate results, creating significant liability and compliance barriers.
Cost vs. human wageclaude-haiku-4-5-202510012/5Commercial divers command high wages (often $200–400+/hour including benefits and hazard premium), but autonomous underwater inspection systems are capital-intensive, specialized equipment with high deployment costs. The all-in cost of AI-driven inspection per task equivalent currently remains competitive with human divers rather than substantially cheaper.
Cost vs. human wageclaude-sonnet-52/5Underwater robotic/ROV inspection systems exist but require expensive specialized hardware, vessel support, and skilled operators, so all-in costs remain comparable to or higher than diver costs in many cases despite some labor savings.
Technical feasibility todayclaude-haiku-4-5-202510012/5While ROV (remotely operated vehicle) inspection systems with cameras exist in industry, they typically require skilled human operators to interpret imagery and direct testing procedures. No fully autonomous underwater NDE system demonstrably performs this task end-to-end without human supervision in production settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously performs underwater NDT inspection and diagnosis on offshore rig legs without human divers or ROV pilots directing and interpreting results in production settings.

Inspect the condition of underwater steel or wood structures.

13

CI 5–21 · exposure 9 · augmentation 38 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Diving and offshore operations are capital-intensive, safety-critical, and geographically dispersed; adoption of AI-driven autonomous inspection remains pilot-stage with no significant measured displacement of commercial divers in production settings.
Sector adoption velocityclaude-sonnet-52/5Commercial diving is a low-digitization, physically demanding trade; ROV/AI adoption is growing in select high-value niches like offshore energy but remains slow and uneven across the broader sector.
Augmentation potentialclaude-haiku-4-5-202510012/5Underwater cameras and sonar visualization assist divers during inspection, but the diver remains the primary agent; AI has not yet demonstrated capability to meaningfully augment the cognitive and physical work of structure evaluation at depth.
Augmentation potentialclaude-sonnet-53/5AI-enhanced imaging, sonar analysis, and ROV-captured footage analysis can help divers and engineers better document and assess structural conditions, improving efficiency and consistency without replacing the underwater task itself.
Task automatabilityclaude-haiku-4-5-202510011/5Underwater structure inspection requires physical presence at depth, real-time adaptation to changing conditions, and nuanced human judgment about structural integrity that current AI cannot perform remotely or autonomously with sufficient reliability to replace the diver.
Task automatabilityclaude-sonnet-51/5This requires physical presence underwater to visually and tactilely inspect structures, which current AI systems cannot perform end-to-end; ROVs with AI vision assist but don't replace the diver's role.
Adoption barriersclaude-haiku-4-5-202510015/5Diving operations are heavily regulated by OSHA and IMCA; liability for structural assessment errors is high and typically requires licensed, certified human divers who are legally responsible for inspection findings.
Adoption barriersclaude-sonnet-53/5No strict licensing mandate requires a human diver by law for all inspections, but liability, certification standards (e.g., ADCI, engineering sign-off), and physical access constraints create real friction against pure automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Commercial diver labor is already cost-competitive with underwater ROV operations including pilot, equipment, and mobilization; autonomous underwater inspection does not yet achieve significantly lower total cost.
Cost vs. human wageclaude-sonnet-52/5ROV/AI inspection systems have high capital and deployment costs, and often still require skilled operators and divers for verification, making all-in costs comparable to or higher than direct diver work in many jobs.
Technical feasibility todayclaude-haiku-4-5-202510012/5While underwater drones with cameras exist, they lack the tactile feedback, fine motor control, and adaptive decision-making that divers provide; no deployed product reliably performs full structural inspection at the quality and thoroughness a commercial diver delivers.
Technical feasibility todayclaude-sonnet-52/5AI-assisted underwater ROVs/drones with computer vision exist for structural inspection in some industrial contexts (offshore oil rigs, dams), but they are not a mature substitute for diver judgment on wood/steel condition assessment across most commercial diving jobs.

Take test samples or photographs to assess the condition of vessels or structures.

13

CI 5–21 · exposure 13 · augmentation 50 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While ROV (remotely operated vehicle) adoption is growing in offshore industries, human divers remain the standard for complex, variable inspection tasks; automation remains limited to narrow, well-defined scenarios.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and underwater inspection is a physical, low-digitization sector with minimal AI/robotic adoption for autonomous data collection at scale.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist divers by pre-analyzing video streams to flag anomalies or guide sampling location selection, improving efficiency and safety without replacing the diver's physical presence and judgment.
Augmentation potentialclaude-sonnet-53/5AI can assist with analyzing collected photographs or sensor data for structural defects and anomaly detection, improving the post-collection assessment phase even though the collection itself is unaffected.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can analyze photographs to assess structural condition, the task requires positioning cameras/sampling equipment in hazardous underwater environments, which demands physical presence and real-time decision-making that current AI cannot perform autonomously. Image analysis alone represents only a fraction of the full task.
Task automatabilityclaude-sonnet-51/5This requires a human diver physically entering underwater environments to collect samples or photographs; current AI cannot perform the physical underwater collection task itself.
Adoption barriersclaude-haiku-4-5-202510014/5Offshore regulations, maritime law, and insurance requirements often mandate human-certified divers to conduct physical inspections and collect samples for legal liability and quality assurance purposes.
Adoption barriersclaude-sonnet-54/5Commercial diving requires certification, safety protocols, and liability considerations for underwater structural inspections, creating strong barriers to non-human performance of the physical task.
Cost vs. human wageclaude-haiku-4-5-202510011/5The capital cost of underwater robotics, specialized sensors, integration, and human oversight rivals or exceeds the cost of employing skilled commercial divers for sample collection and initial assessment.
Cost vs. human wageclaude-sonnet-51/5AI cannot substitute for the physical diving and sampling labor, so there is no meaningful AI cost comparison for the core physical act; underwater robotics remain expensive and human-supervised.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI image analysis tools exist and can identify some structural defects, but deployed systems lack the accuracy and reliability required for critical infrastructure assessment in complex underwater conditions where lighting, silt, and angles vary unpredictably.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product replaces the physical act of diving to collect samples or images; ROVs exist but are separate equipment operated by humans, not autonomous AI systems performing this end-to-end.

Inspect and test docks, ships, buoyage systems, plant intakes or outflows, or underwater pipelines, cables, or sewers, using closed circuit television, still photography, and testing equipment.

12

CI 7–16 · exposure 5 · augmentation 50 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While ROV technology has improved, adoption remains modest and incremental in commercial diving. Sectors like offshore oil and marine infrastructure have piloted automation but continue to rely heavily on human divers due to operational complexity, unpredictability, and regulatory conservatism around critical infrastructure assessment.
Sector adoption velocityclaude-sonnet-52/5Underwater infrastructure inspection is a physical, low-digitization niche; ROV and drone adoption is growing but full replacement of divers remains slow and sector-specific.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-assisted image analysis and automated anomaly detection in CCTV footage can help divers interpret findings more efficiently, and real-time data visualization can enhance situational awareness. However, augmentation is limited by the fundamental requirement for human decision-making about inspection priorities and underwater navigation.
Augmentation potentialclaude-sonnet-53/5AI-powered image/video analysis (e.g., automated defect detection in CCTV footage) can help divers and inspectors process and interpret visual data faster, improving productivity on the analysis portion of the task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical presence underwater in hazardous environments to operate specialized inspection equipment and interpret real-time sensor data, which current AI systems cannot perform. The integration of remote CCTV operation, equipment calibration, and environmental hazard response demands human judgment and physical intervention that autonomous systems are not deployed to execute today.
Task automatabilityclaude-sonnet-51/5This task requires physical presence underwater to operate cameras and testing equipment, place sensors, and navigate hazardous environments—no AI system can perform the physical inspection itself.
Adoption barriersclaude-haiku-4-5-202510014/5Underwater inspection involves strict occupational safety regulations, liability exposure for infrastructure damage, and legal requirements that qualified personnel certify inspection findings. Many regulatory frameworks and client contracts require licensed divers to perform or validate critical infrastructure inspections, creating hard barriers to full automation.
Adoption barriersclaude-sonnet-54/5Commercial diving requires certification, safety protocols, and liability considerations for underwater work in industrial/maritime settings, creating strong barriers to full automation even though remote ROVs are increasingly used.
Cost vs. human wageclaude-haiku-4-5-202510012/5Underwater ROVs and remote inspection systems are capital-intensive and require specialized operators, technicians, and support vessels. The combined cost of equipment, maintenance, trained personnel, and integration is often comparable to or higher than hiring experienced commercial divers for inspection work.
Cost vs. human wageclaude-sonnet-51/5AI cannot replace the physical diving and manipulation work; any AI-assisted image analysis is a small fraction of total task cost dominated by diver labor, equipment, and safety support.
Technical feasibility todayclaude-haiku-4-5-202510012/5While remotely operated underwater drones with cameras exist, they still require skilled human operators on-site to navigate, adjust equipment, and make real-time decisions about what to inspect. No fully autonomous system reliably conducts comprehensive underwater infrastructure inspections without continuous human supervision and control.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously conducts underwater dives to inspect infrastructure; ROVs assist but require human divers or operators for many tasks, especially in complex or hazardous conditions.

Perform activities related to underwater search and rescue, salvage, recovery, or cleanup operations.

8

CI 5–11 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI/robotic systems in commercial diving remains limited; while ROVs are used in specific offshore oil/gas and inspection niches, the broader commercial diving sector (rescue, salvage, cleanup) continues to rely on human divers, with slow capital investment in automation.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine salvage are physical, low-digitization sectors with minimal AI/autonomous system adoption in actual field operations.
Augmentation potentialclaude-haiku-4-5-202510012/5AI assists marginally through sonar mapping, pre-dive planning visualization, and remote monitoring, but the core underwater work remains human-dependent; augmentation is limited because the physical and cognitive demands of the task cannot be substantially offloaded while a diver remains in the loop.
Augmentation potentialclaude-sonnet-53/5Sonar imaging, AI-assisted object detection, and ROV-based mapping can help divers locate targets faster and improve situational awareness, though the physical task remains human-performed.
Task automatabilityclaude-haiku-4-5-202510011/5This task fundamentally requires physical underwater presence, manual dexterity, and real-time decision-making in a hazardous, unpredictable environment that current AI cannot replicate. Robots exist for specific underwater tasks, but they cannot autonomously perform the full range of search, rescue, salvage, recovery, and cleanup operations that human divers execute.
Task automatabilityclaude-sonnet-51/5Underwater search, rescue, salvage and cleanup require physical presence, manipulation of tools, and adaptive judgment in hazardous environments that no current AI system can perform end-to-end without a human diver or ROV pilot.
Adoption barriersclaude-haiku-4-5-202510014/5Rescue and salvage operations in many jurisdictions are governed by maritime law, certification requirements, and liability frameworks that mandate human professional responsibility; safety regulations and union contracts in some sectors also legally require certified divers for certain activities.
Adoption barriersclaude-sonnet-54/5Safety-critical rescue and salvage work often involves regulatory diving certifications, liability concerns, and situations requiring human judgment and legal accountability, creating strong barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Specialized underwater robotics and autonomous systems are extremely capital-intensive and limited in scope compared to commercial diver services; the all-in cost of deployment, integration, and specialized equipment remains very high relative to trained human divers for most operations.
Cost vs. human wageclaude-sonnet-51/5Specialized underwater robotics and human oversight required for safe operation are expensive relative to a diver's wage, and equipment cannot fully replace human labor for these tasks.
Technical feasibility todayclaude-haiku-4-5-202510011/5While remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) exist for specialized inspection tasks, no deployed AI system can reliably perform complex underwater search and rescue or salvage operations end-to-end, particularly in dynamic rescue scenarios requiring human judgment and physical manipulation.
Technical feasibility todayclaude-sonnet-51/5While ROVs and sonar-assisted search tools exist, they are operated by human crews and do not autonomously perform rescue or salvage operations reliably in production without human divers and operators.

Remove rubbish or pollution from the sea.

7

CI 5–10 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Adoption of autonomous systems in commercial diving and marine pollution removal is extremely slow; the sector remains physically-rooted and human-dependent, with legacy practices, regulatory inertia, and high fault costs discouraging automation trials.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine environmental services are a low-digitization, physical-labor sector with minimal AI/robotic adoption in production settings.
Augmentation potentialclaude-haiku-4-5-202510012/5AI and remote-operated systems can assist divers by providing real-time imaging, hazard detection, and mapping, but augmentation is limited because the core task requires human dexterity, situational judgment, and adaptive problem-solving in variable underwater conditions that current systems cannot meaningfully enhance.
Augmentation potentialclaude-sonnet-52/5AI can assist with mapping pollution hotspots, route planning, or image analysis from sonar/video, but offers little direct help with the hands-on physical removal task itself.
Task automatabilityclaude-haiku-4-5-202510011/5Removing rubbish or pollution from the sea requires physical manipulation of objects underwater in unstructured, variable environments—tasks beyond current AI capabilities. Autonomous underwater vehicles and robots exist for specific, controlled tasks, but cannot autonomously navigate debris fields, identify relevant pollution, and selectively remove it with the flexibility and judgment a human diver applies.
Task automatabilityclaude-sonnet-51/5This is a physical underwater manipulation task requiring diving skill, spatial awareness, and dexterity in unpredictable marine environments; no AI system can perform the physical removal itself.
Adoption barriersclaude-haiku-4-5-202510014/5Strong regulatory and safety barriers apply: commercial diving is heavily licensed and liability-laden, environmental remediation is subject to regulatory oversight, and autonomous systems underwater face unresolved legal and insurance frameworks that limit deployment without human oversight and sign-off.
Adoption barriersclaude-sonnet-53/5No licensing barrier prevents robotic substitution in principle, but safety, environmental regulation, and the physical unpredictability of marine sites create real operational friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Current autonomous underwater systems capable of any debris handling are extremely expensive to acquire, deploy, and maintain, with high per-mission costs that far exceed the wage of a commercial diver for equivalent cleanup work.
Cost vs. human wageclaude-sonnet-51/5Underwater robotic systems capable of debris removal require expensive specialized hardware, maintenance, and human oversight, making them costlier than a diver's labor for most current use cases.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system performs end-to-end underwater pollution removal at commercial scale or reliability. Research prototypes and ROVs exist for narrow tasks (e.g., specific pipeline inspection), but autonomous pollution cleanup remains largely experimental and human-supervised.
Technical feasibility todayclaude-sonnet-51/5There are no deployed products that autonomously dive and remove debris from the sea at meaningful scale; underwater cleanup robots remain experimental/pilot stage, not mature commercial products replacing divers.

Check and maintain diving equipment, such as helmets, masks, air tanks, harnesses, or gauges.

7

CI 0–14 · exposure 8 · augmentation 25 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving operates in highly regulated, physically demanding environments with strong safety compliance cultures. Adoption of AI-driven automation in this niche, high-liability domain remains minimal; organizations continue to rely on trained human divers for equipment checks.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a highly physical, low-digitization trade with minimal AI integration into equipment maintenance workflows.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could provide marginal assistance (e.g., checklist reminders, image-based visual wear detection) but diving equipment maintenance is inherently tactile and safety-critical, limiting the scope for augmentation without human oversight and final judgment.
Augmentation potentialclaude-sonnet-52/5AI could assist with maintenance scheduling, logging inspection records, or flagging anomalies via sensor data, but it does not meaningfully enhance the physical inspection process itself.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could assist in inspections via image analysis of equipment (e.g., detecting corrosion or wear), the physical manipulation, hands-on testing, and real-time pressure/integrity assessment required for diving equipment maintenance cannot be automated end-to-end by current systems. This task requires tactile assessment and judgment that falls short of 50% time savings at equal quality today.
Task automatabilityclaude-sonnet-51/5This is a hands-on physical inspection and maintenance task requiring manual dexterity, tactile checks, and physical manipulation of equipment that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Diving equipment maintenance and certification is heavily regulated by maritime and occupational safety authorities (OSHA, DNV, IMCA standards), and critical safety inspections must be performed and signed off by certified, licensed divers. Legal and liability requirements create hard barriers to automation.
Adoption barriersclaude-sonnet-54/5Diving equipment safety is subject to strict certification and inspection standards, and errors carry life-threatening consequences, creating strong liability and regulatory barriers to any non-human process.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of robots or AI systems capable of handling delicate, high-stakes equipment inspection and maintenance (with full liability coverage for safety-critical failure) far exceeds the loaded wage of a commercial diver performing these checks themselves.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this physical task, so AI cost is not comparable—human labor remains the only option.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs complete diving equipment maintenance and checking in production. Computer vision for visual inspection exists, but the critical safety elements—pressure testing, seal integrity verification, functional operation of regulators—require specialized physical interaction that is not mature or deployable at scale.
Technical feasibility todayclaude-sonnet-51/5No deployed product exists that autonomously inspects, maintains, or repairs diving equipment; this remains purely a manual technical task.

Perform offshore oil or gas exploration or extraction duties, such as conducting underwater surveys or repairing and maintaining drilling rigs or platforms.

6

CI 0–11 · exposure 0 · augmentation 50 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5While ROV technology is in use in offshore oil and gas, adoption of autonomous systems for diving tasks remains limited; most deep-water maintenance still depends on human divers or closely supervised teleoperation. Regulatory conservatism and technical constraints slow automation uptake.
Sector adoption velocityclaude-sonnet-51/5Offshore oil and gas is a highly physical, low-digitization sector with minimal AI/robotic displacement of hands-on diving tasks to date.
Augmentation potentialclaude-haiku-4-5-202510013/5ROVs and underwater sensors can assist divers by surveying sites before human descent, providing real-time data feeds, and handling some non-critical tasks, improving situational awareness and reducing dangerous bottom time. However, human divers remain essential for complex problem-solving and hands-on interventions.
Augmentation potentialclaude-sonnet-53/5AI-enhanced sonar, imaging analysis, and ROV-assisted surveys can help divers plan and assess conditions before physical work, providing moderate assistance without replacing the physical task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical presence underwater, manipulation of heavy equipment in a hazardous environment, and real-time decision-making about structural integrity and safety. Current AI systems cannot deploy to underwater depths, work in pressurized environments, or perform the hands-on repairs and maintenance that define commercial diving.
Task automatabilityclaude-sonnet-51/5This requires physical underwater manipulation, welding, inspection, and repair in hazardous offshore environments—tasks requiring embodied physical labor that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Offshore oil and gas operations are heavily regulated by government bodies (OSHA, DNV, etc.); commercial divers must be licensed and certified by law, and liability for subsea work rests with qualified personnel. No automation exemption exists from these legal and safety requirements.
Adoption barriersclaude-sonnet-54/5Commercial diving requires certification, safety regulations, and liability considerations; offshore platforms have strict regulatory oversight requiring qualified personnel for safety-critical repairs.
Cost vs. human wageclaude-haiku-4-5-202510012/5Specialized underwater robotics and ROV systems are extremely expensive to acquire, operate, and maintain, often exceeding the cost of trained human divers. Even with automation, human divers remain necessary for complex, unexpected repairs and site assessment.
Cost vs. human wageclaude-sonnet-51/5Underwater robotic systems capable of complex repair work are far more expensive than human divers to develop, deploy, and maintain, with no cost advantage today.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system or robotic product reliably performs the full spectrum of commercial diving tasks independently—surveys, repairs, and maintenance in deep-water, high-pressure conditions with the adaptability required. Remotely operated vehicles (ROVs) exist but require continuous human piloting and oversight; they are not autonomous agents.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs offshore diving repair or extraction duties; ROVs and AUVs assist with some survey work but human divers remain essential for hands-on repair tasks.

Remove obstructions from strainers or marine railway or launching ways, using pneumatic or power hand tools.

5

CI 0–10 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving remains a traditional, human-dependent sector with slow technological adoption. While ROVs are used for inspection, autonomous intervention tools have not meaningfully displaced human divers, and regulatory and safety culture strongly favors human operators.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine maintenance are low-digitization, physically demanding trades with minimal AI/robotic adoption for manipulation tasks in the field today.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist via underwater imaging and obstruction detection prior to a diver's work, but the core physical task of tool operation underwater offers limited augmentation potential because the human diver must remain fully engaged for safety and precision in this inherently manual, hazardous work.
Augmentation potentialclaude-sonnet-51/5AI offers essentially no meaningful assistance to a diver physically removing obstructions with power tools underwater; there is no digital interface for AI to augment this manual task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of tools underwater or in marine environments, precise navigation in three dimensions, real-time sensory feedback, and adaptive problem-solving in hazardous conditions. Current AI systems lack embodied presence, dexterity, and the ability to operate safely in high-pressure underwater settings.
Task automatabilityclaude-sonnet-51/5This is a physical underwater manipulation task requiring in-person diving, tool handling, and adaptive force feedback in murky, hazardous conditions that no current AI or robotic system can perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving is a licensed profession with strict regulatory oversight (OSHA, IMCA standards), mandatory human certification, and legal liability requirements. Maritime safety law requires qualified human divers to perform or directly oversee underwater work, creating hard legal and safety barriers to automation.
Adoption barriersclaude-sonnet-53/5While not licensed like some professions, commercial diving requires certification, safety protocols, and physical presence underwater, creating practical barriers to remote or automated substitution, though not strict legal sign-off requirements.
Cost vs. human wageclaude-haiku-4-5-202510011/5Underwater robotics capable of this task are extremely expensive (hundreds of thousands to millions), require specialized maintenance and operators, and still underperform human divers in cost-effectiveness per task completed in diverse marine conditions.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this task at any comparable cost; specialized underwater manipulator robots would be far more costly than a diver's labor for this occasional task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI or robotic system currently performs underwater obstruction removal from strainers or marine railways at commercial scale with human-equivalent reliability. Specialized underwater robotics exist for inspection but not autonomous tool-based intervention in these specific maritime contexts.
Technical feasibility todayclaude-sonnet-51/5No deployed commercial product autonomously removes obstructions from strainers or marine railways using hand tools; underwater manipulation robotics remain research-stage for such unstructured tasks.

Descend into water with the aid of diver helpers, using scuba gear or diving suits.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5This task exists in a sector (offshore, marine, underwater operations) with minimal AI adoption. The physical and regulatory constraints mean automation velocity is near zero.
Sector adoption velocityclaude-sonnet-51/5Commercial diving is a physically demanding, low-digitization trade with minimal AI adoption; the sector shows little to no movement toward AI-driven task replacement for the physical descent itself.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could provide minor assistance through pre-dive planning tools, safety monitoring, or data analysis, but cannot meaningfully augment the core act of descending and operating underwater. The diver remains entirely dependent on their own embodied skills.
Augmentation potentialclaude-sonnet-52/5AI can assist with dive planning, safety monitoring, or data logging support topside, but offers no direct assistance to the physical act of descending underwater.
Task automatabilityclaude-haiku-4-5-202510011/5Descending into water with scuba gear or diving suits requires physical presence in a harsh, unpredictable underwater environment. No current AI system can physically perform this task or replace the embodied presence and real-time sensorimotor control a human diver must maintain.
Task automatabilityclaude-sonnet-51/5This is a physical task requiring a human body to descend underwater; no AI system can substitute for the physical act of diving, though underwater robots/ROVs are a separate technology, not 'AI performing this task' via automation of human labor in the current form.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving is a regulated profession requiring licensed, trained human divers who must physically be present underwater. Regulatory requirements, safety standards, and liability frameworks mandate human divers perform this task—no human can be replaced by automation.
Adoption barriersclaude-sonnet-54/5Commercial diving requires certification, safety protocols, and physical presence; liability and safety regulations strongly favor human divers or purpose-built ROV systems rather than AI-driven substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI systems cannot perform this task at all, making cost comparison moot. The task fundamentally requires human physical presence, which AI cannot substitute.
Cost vs. human wageclaude-sonnet-51/5There is no AI-based cost comparator for this physical task; specialized underwater robotic alternatives are typically far more expensive than a human diver for many commercial diving jobs, especially at shallow/medium complexity.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product performs underwater descent with scuba or diving suit operation. Underwater robotics exist but serve different purposes and do not replace the human diver role or perform this specific task in commercial diving operations.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs human diving descent; ROVs and AUVs exist as alternative hardware solutions but do not represent AI automating the diver's physical task.

Repair ships, bridge foundations, or other structures below the water line, using caulk, bolts, and hand tools.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving remains a traditional, physically-present sector with high barriers to digital transformation; adoption of AI-driven solutions is minimal due to specialized expertise, regulatory constraints, and the inherent physicality of the work.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine construction are low-digitization, physically demanding sectors with minimal AI/robotics adoption for actual manipulation tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5Limited augmentation potential: AI could assist with pre-dive planning, structural analysis, or post-repair documentation, but the core underwater repair task requires autonomous human dexterity and cannot be meaningfully augmented by current AI systems.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, structural assessment via sonar/imaging analysis, or documentation, but offers little direct assistance during the physical repair process itself.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires underwater manipulation of physical structures in a submerged environment with high safety risks, heavy machinery, and real-time environmental adaptation—capabilities well beyond current AI/robotics in uncontrolled marine settings.
Task automatabilityclaude-sonnet-51/5This requires physical manipulation underwater in hazardous, variable conditions—no current AI system or robot can perform freeform structural repair with caulk, bolts, and hand tools autonomously.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving is heavily regulated by maritime authorities (e.g., OSHA, IMCA) and requires certified human divers to legally perform and sign off on safety-critical repairs; liability and regulatory compliance create hard barriers to automation.
Adoption barriersclaude-sonnet-54/5Certification (commercial diving licenses), safety regulations, liability for structural failure, and physical presence requirements create strong barriers against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Current subsea robotics cost orders of magnitude more than a trained diver's operational cost, including the need for specialized support vessels, training, and integration infrastructure.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this task at any cost, so AI is not cheaper—human divers remain the only functional option.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs subsea structural repair with hand tools in variable underwater conditions; specialized ROVs exist but are limited to observation and simple mechanical tasks, not complex repair work.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs autonomous underwater structural repair; remotely operated vehicles exist for inspection but not for skilled manual repair work requiring dexterity and judgment.

Recover objects by placing rigging around sunken objects, hooking rigging to crane lines, and operating winches, derricks, or cranes to raise objects.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The commercial diving sector is small, highly specialized, and physically constrained; adoption of automation is virtually non-existent because the task requires human presence in dangerous underwater environments and cannot be delegated to general-purpose AI or robots at scale.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine salvage are low-digitization, physically demanding sectors with minimal AI/robotic adoption for complex manipulation tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with planning (modeling crane loads, rigging geometry), but the diver must physically execute the task underwater; augmentation is minimal because the core work—underwater placement and manipulation—cannot be meaningfully assisted by AI in real-time while the human remains in control.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, sonar/imaging analysis, or ROV-based site surveys to inform divers, but offers little direct assistance to the physical rigging and hookup process itself.
Task automatabilityclaude-haiku-4-5-202510011/5This task is fundamentally physical and requires underwater work in hazardous, variable environments—placing rigging, hooking lines, and operating heavy equipment in deep water. Current AI systems cannot physically manipulate objects underwater or operate cranes from a submerged position; the task is beyond any automated system today.
Task automatabilityclaude-sonnet-51/5This requires physical underwater manipulation, rigging attachment, and real-time judgment in a hazardous fluid environment—no AI system can perform the physical dexterity and underwater work involved.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving is heavily regulated by OSHA, the Coast Guard, and international maritime law; dive operations require licensed, certified divers who must legally supervise and execute recovery work. Liability for underwater accidents and equipment failure creates hard barriers to automation.
Adoption barriersclaude-sonnet-54/5Commercial diving often requires certification, safety regulations, and liability considerations around underwater salvage work, though not a strict licensing mandate for the specific task as with medical/legal fields.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized diving equipment, safety infrastructure, and the high-skill wages of commercial divers (often $50k–$100k+ annually) far exceed the cost of any remotely operated system in use today; the capital and training investment to automate this is orders of magnitude higher than human labor.
Cost vs. human wageclaude-sonnet-51/5Specialized ROV/robotic systems capable of complex rigging manipulation would be far more expensive to develop, deploy, and maintain than employing a commercial diver for this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product performs underwater object recovery and rigging work. The combination of deep-water operation, precise physical manipulation, and real-time environmental adaptation (currents, visibility, water pressure) remains research-stage only.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs underwater rigging and hookup of sunken objects; ROVs assist with inspection but cannot autonomously handle complex rigging tasks reliably at scale.

Cut and weld steel, using underwater welding equipment, jigs, and supports.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving is a niche, capital-intensive, low-digitization sector with minimal AI adoption; subsea construction and maintenance remain heavily dependent on human expertise and physical presence.
Sector adoption velocityclaude-sonnet-51/5Underwater construction/marine services is a low-digitization, physical-labor sector with minimal AI adoption; automation here lags far behind information-based industries.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with pre-dive planning, weld simulation, or post-dive inspection analysis, but offers limited real-time assistance during the actual underwater task execution where human judgment and adaptation are paramount.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, inspection imagery analysis, or ROV-assisted monitoring, but offers little direct augmentation to the physical welding/cutting act itself.
Task automatabilityclaude-haiku-4-5-202510011/5Underwater welding requires precise manual dexterity, real-time sensory feedback, and adaptive problem-solving in a hostile, high-pressure environment with equipment failure modes that current AI cannot manage remotely or autonomously.
Task automatabilityclaude-sonnet-51/5Underwater welding requires physical manipulation, tactile feedback, and real-time adaptation to turbid, dynamic conditions that no current AI system can perform end-to-end; this is a physical trade skill, not a cognitive/digital task.
Adoption barriersclaude-haiku-4-5-202510015/5Underwater work is inherently human-contact-dependent and involves extreme physical and safety requirements; regulatory frameworks and liability concerns strongly protect this task, as no autonomous substitute is viable.
Adoption barriersclaude-sonnet-54/5Commercial diving/underwater welding requires certification (e.g., ADCI), safety regulation, and liability considerations given life-threatening hypoxia/decompression risks, creating strong barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized underwater welding robots, if they existed at scale, would be far more expensive to deploy, maintain, and operate than a human commercial diver for the foreseeable future.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-driven substitute; specialized ROV welding systems are extremely expensive capital equipment requiring human oversight, making them costlier than a diver for most jobs, not cheaper.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system can perform underwater welding end-to-end; this requires specialized robotics in extreme conditions, which remains in research and limited prototype stages without proven reliability in production settings.
Technical feasibility todayclaude-sonnet-51/5No deployed AI or robotic product autonomously performs underwater welding/cutting in commercial settings; specialized underwater welding robots exist only in narrow research/industrial pilot contexts, not as generally available AI products.

Install pilings or footings for piers or bridges.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving occurs in specialized, geographically dispersed marine and infrastructure sectors with minimal digitization. Adoption of underwater automation remains nascent, limited to inspection tasks in a few large projects, not production deployment of installation work.
Sector adoption velocityclaude-sonnet-51/5Marine construction and commercial diving are physical, low-digitization sectors with minimal AI or robotics adoption for actual installation work.
Augmentation potentialclaude-haiku-4-5-202510012/5AI tools could assist with planning, 3D modeling, or pre-dive analysis, but the underwater installation work itself is difficult to augment remotely. Current AI applications for divers remain marginal and do not meaningfully enhance productivity on the core installation task.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, structural calculations, or ROV-based site surveys before/after the dive, but offers little real-time assistance during the physical installation itself.
Task automatabilityclaude-haiku-4-5-202510011/5Installing pilings or footings for piers or bridges requires underwater construction work, manual manipulation of heavy equipment in harsh aquatic environments, real-time problem-solving, and precise positioning in 3D space. Current AI and robotics cannot operate reliably in these conditions to perform the full task end-to-end.
Task automatabilityclaude-sonnet-51/5This is a physical underwater construction task requiring manual manipulation of heavy materials, rigging, and precise placement in variable underwater conditions; no AI system can perform this end-to-end today.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving work is heavily regulated by OSHA, IMCA, and state diving boards; the work environment (underwater, high-pressure, hazardous) creates strict liability frameworks; and bridge and pier construction typically requires licensed professional engineers to sign off on structural work, creating hard legal and safety barriers to automation.
Adoption barriersclaude-sonnet-54/5Commercial diving for construction requires certification, safety regulation compliance, and physical presence underwater in hazardous conditions, creating strong licensing and liability barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized commercial divers command high wages ($200k+ annually for experienced workers) and require minimal overhead per task compared to the cost of developing, deploying, and maintaining underwater automation systems capable of structural installation work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for this task, so any AI-based approach (e.g., advanced robotics) would be far more expensive than a certified commercial diver's labor given current technology costs.
Technical feasibility todayclaude-haiku-4-5-202510011/5While experimental underwater robots exist for inspection, no deployed commercial products reliably perform the complete installation of pilings or footings for bridges. The task demands dexterity, situational awareness, and adaptability that deployed systems do not yet achieve in production environments.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs underwater piling/footing installation; underwater construction remains a manual diving trade with only remote-operated vehicle assistance for inspection, not installation.

Salvage wrecked ships or their cargo, using pneumatic power velocity and hydraulic tools and explosive charges, when necessary.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial salvage is a niche, geographically dispersed, capital-intensive sector with limited digital infrastructure and slow technology adoption. Operations remain dependent on specialist human expertise and regulatory compliance, with minimal evidence of AI-driven displacement in production.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and marine salvage are physical, low-digitization sectors with minimal AI/robotic adoption for actual task execution, only slow uptake of ROVs for support functions.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with wreck mapping, structural analysis, or explosive charge planning via pre-dive visualization, but the core physical task—operating tools, managing risk, responding to underwater hazards—remains fundamentally human-dependent with limited augmentation upside.
Augmentation potentialclaude-sonnet-52/5Sonar mapping, ROV-assisted surveying, and planning software can help divers assess wrecks and plan salvage operations, but this offers only partial assistance to the core physical task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation underwater in unpredictable, hazardous environments—placing explosive charges, operating heavy hydraulic tools, and navigating wreckage. Current AI has no embodied capability to perform deep-water salvage operations end-to-end; the task is fundamentally dependent on human judgment, dexterity, and safety in extreme conditions.
Task automatabilityclaude-sonnet-51/5This requires physical presence underwater to operate tools, place explosives, and manipulate wreckage in unpredictable conditions—no AI system can perform the physical salvage work itself today.
Adoption barriersclaude-haiku-4-5-202510015/5Salvage diving is heavily regulated by maritime and occupational safety authorities; explosives use requires licensing and authorization. A licensed, qualified human diver must legally perform or sign off on salvage operations, and liability for wreck disturbance and environmental impact creates hard legal barriers to full automation.
Adoption barriersclaude-sonnet-54/5Heavy regulatory oversight (explosives handling, maritime salvage law, safety certification) and physical/liability risks create strong barriers to full automation of this task.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized commercial diving equipment, safety systems, and trained personnel command high loaded costs. Current underwater automation (ROVs, autonomous systems) for salvage remains expensive, fragile, and requires extensive human oversight—making all-in AI cost comparable to or higher than skilled divers.
Cost vs. human wageclaude-sonnet-51/5Specialized underwater robotics with manipulation and explosive-handling capability would be far more expensive to develop, deploy, and maintain than employing a commercial diver for this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI system or robotic product reliably performs autonomous ship wreck salvage with explosives and heavy tool operation in production settings. While ROVs exist for underwater inspection, autonomous end-to-end salvage of wrecked cargo remains research-stage and highly bespoke.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical underwater salvage operations; remote-operated vehicles exist for inspection but not for autonomous execution of full-scale wreck/cargo salvage with tools and explosives.

Set or guide placement of pilings or sandbags to provide support for structures, such as docks, bridges, cofferdams, or platforms.

3

CI 0–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Maritime and underwater construction remain highly traditional sectors with minimal AI adoption; physical, safety-critical underwater work resists automation and requires human judgment in unpredictable subsea environments.
Sector adoption velocityclaude-sonnet-51/5Marine construction and commercial diving are physical, low-digitization sectors showing minimal AI/robotic adoption for hands-on underwater tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI might assist with pre-dive planning or post-dive documentation, but provides minimal real-time assistance during the actual underwater placement task, which remains primarily human-dependent.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, sonar/imaging analysis, or positioning guidance topside, but offers little direct augmentation to the physical underwater placement action itself.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires underwater placement of physical structures in real-time, with precise positioning and environmental judgment. Current AI cannot operate autonomous underwater vehicles reliably for such structural placement, nor can it handle the dynamic underwater conditions and real-time adjustments needed.
Task automatabilityclaude-sonnet-51/5This is a physical underwater manipulation task requiring in-water judgment, dexterity, and real-time adaptation to currents/visibility that no current AI system (robotic or software) can perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Strict maritime and occupational safety regulations require licensed commercial divers for underwater structural work; liability for structural failure is high and requires certified human expertise and sign-off.
Adoption barriersclaude-sonnet-54/5Commercial diving is often subject to safety regulations, certification requirements, and structural engineering sign-off, and physical underwater work carries high liability for errors affecting structural integrity.
Cost vs. human wageclaude-haiku-4-5-202510011/5Underwater robotics and AI systems capable of autonomous placement would cost significantly more than deploying a trained commercial diver, including equipment, maintenance, and oversight costs.
Cost vs. human wageclaude-sonnet-51/5Specialized underwater manipulation robotics with sufficient reliability would be far more expensive to develop, deploy, and maintain than employing a commercial diver for this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product performs autonomous underwater structural placement reliably. While ROVs exist, they require human operation and do not constitute AI autonomous performance of this task.
Technical feasibility todayclaude-sonnet-51/5There are no deployed products where AI autonomously sets or guides piling/sandbag placement underwater in commercial diving operations; underwater construction robotics remain research-stage or human-teleoperated.

Supervise or train other divers, including hobby divers.

0

CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving is a conservative, safety-critical, highly regulated sector with strong institutional and legal resistance to removing human supervisors. Adoption of AI for diver supervision is negligible and structurally unlikely given the life-safety stakes.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and dive training is a highly physical, low-digitization trade with minimal AI adoption in the supervisory/training function itself.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with pre-dive briefing materials, record-keeping, e-learning modules for theoretical components, or monitoring equipment logs, but these are peripheral to the core supervisory and real-time training role. Augmentation potential is limited to administrative and educational support.
Augmentation potentialclaude-sonnet-52/5AI could help with training materials, dive planning software, or logging, but offers limited assistance to the core in-water supervisory and instructional task.
Task automatabilityclaude-haiku-4-5-202510011/5Supervising and training divers requires real-time judgment, dynamic risk assessment, emergency response coordination, and personalized instruction—tasks deeply dependent on human presence, situational awareness, and accountability. Current AI cannot operate autonomously in underwater environments or provide the legal/safety responsibility inherent in diver supervision.
Task automatabilityclaude-sonnet-51/5Supervising and training divers requires physical presence, hands-on demonstration, real-time safety oversight, and interpersonal instruction underwater or poolside, none of which current AI can perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Diver supervision and training are heavily regulated by OSHA, ANDI, PADI, and other certification bodies, and legal liability for underwater accidents rests on the qualified human supervisor. Regulatory frameworks explicitly require a licensed/certified human to supervise and sign off on training.
Adoption barriersclaude-sonnet-55/5Dive instruction and supervision typically require certified human instructors (e.g., PADI/NAUI) for liability, safety, and legal reasons, making this a hard-barrier task requiring licensed human sign-off and physical presence.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI systems cannot reduce the cost of diver supervision—a skilled human supervisor or instructor is a non-substitutable safety requirement. The liability and regulatory burden mean human oversight remains essential and full cost cannot be avoided.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for physical dive supervision/training, so the human is the only cost-effective option and AI does not compete on cost.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product can independently supervise or train divers today. While simulation and e-learning tools exist, they lack the real-time responsiveness, environmental adaptation, and liability oversight required for actual diver supervision in production diving operations.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product supervises or trains divers in physical settings; this remains entirely a human instructor role with certification requirements.

Drill holes in rock and rig explosives for underwater demolitions.

0

CI 0–0 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Commercial diving is a small, physically-grounded sector with minimal digitization. Adoption of AI/robotics is slow and limited to inspection and retrieval tasks, not safety-critical demolition work.
Sector adoption velocityclaude-sonnet-51/5Commercial diving and underwater demolition is a highly physical, low-digitization niche field with minimal AI/robotics adoption in production settings.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with pre-dive planning (surveying, route mapping) or post-dive analysis, but the core task—physically drilling holes and rigging explosives underwater—offers limited scope for real-time AI augmentation of the diver's in-water actions.
Augmentation potentialclaude-sonnet-52/5AI can assist with planning, simulation, and demolition calculations beforehand, but offers little real-time assistance during the actual underwater drilling and rigging execution.
Task automatabilityclaude-haiku-4-5-202510011/5Underwater drilling and explosive rigging demand real-time physical manipulation in a harsh, variable environment (pressure, currents, visibility). Current AI cannot operate underwater robotic systems with the precision, adaptability, and safety margins required for this high-consequence task.
Task automatabilityclaude-sonnet-51/5This is a physical underwater manipulation task requiring dexterity, real-time sensory feedback, and safe handling of explosives in a hazardous fluid environment; no AI system today can perform the physical drilling and rigging itself.
Adoption barriersclaude-haiku-4-5-202510015/5Commercial diving and explosives handling are heavily licensed professions with strict regulatory oversight (OSHA, ATF, maritime law). A certified diver must legally perform and sign off on the work, creating a hard legal barrier to full automation.
Adoption barriersclaude-sonnet-55/5Handling explosives underwater requires licensed, certified commercial divers with specialized training, strict safety regulation, and legal liability, making human authorization essentially mandatory.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized underwater equipment, human expertise in diving and explosives handling, and safety protocols remain far cheaper and more reliable than developing and maintaining autonomous underwater systems capable of this work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this task at any comparable cost; specialized underwater robotic systems that could attempt this would be far more expensive than a trained diver, if they existed at all.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial AI system performs end-to-end underwater drilling and explosive placement. While ROVs exist for inspection, autonomous operation at the precision and safety level needed for demolition is not established in production use.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs underwater drilling and explosive rigging autonomously; underwater manipulation robots for demolition remain research/prototype stage, not production tools used by divers.

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.