Fishing and Hunting Workers
45-3031.00Rank #767 of 923 scored · top 83% by substitution
Hunt, trap, catch, or gather wild animals or aquatic animals and plants. May use nets, traps, or other equipment. May haul catch onto ship or other vessel.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution16
Exposure9
Augmentation28
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
29 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%9
panel mean rating 1.4/5 → substitution pressure 9/100
Technical feasibility todayw 20%9
panel mean rating 1.4/5 → substitution pressure 9/100
Cost vs. human wagew 15%10
panel mean rating 1.4/5 → substitution pressure 10/100
Adoption barriersw 20%inverted — strong barriers lower the score44
panel mean rating 3.2/5 (barrier strength) → substitution pressure 44/100
Sector adoption velocityw 10%5
panel mean rating 1.2/5 → substitution pressure 5/100
Task breakdown (29 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.
Compute positions and plot courses on charts to navigate vessels, using instruments such as compasses, sextants, and charts.
64CI 45–84 · exposure 59 · augmentation 100 · importance 4.2/5 · click for rater detail
Compute positions and plot courses on charts to navigate vessels, using instruments such as compasses, sextants, and charts.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Electronic navigation systems are nearly universal on commercial fishing vessels today; even small operators use GPS and electronic chartplotters, reflecting rapid and deep sectoral adoption over the past two decades. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Small-scale fishing and hunting operations are a low-digitization sector; while GPS is common, more advanced automated route optimization or AI-driven navigation adoption is slower than in tech-forward industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-powered navigation tools (real-time weather integration, collision avoidance, route optimization) significantly enhance human navigator productivity by automating calculations and flagging hazards, while the licensed operator retains decision-making authority. |
| Augmentation potential | claude-sonnet-5 | 5/5 | GPS, chartplotters, and route-optimization software massively augment a navigator's speed and accuracy while the human remains in control of the vessel and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Modern GPS and electronic navigation systems can compute positions and plot courses automatically, but fishing vessels often operate in variable conditions requiring real-time judgment about safety, regulations, and catch-related deviations that current AI cannot fully replicate end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 4/5 | Modern GPS/chartplotter and autopilot systems already automate position computation and course plotting far more accurately than manual sextant/compass work, meeting the time-saving bar for most vessels.It's largely software-driven navigation, though final integration with vessel operation still needs a human. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Maritime regulations require a licensed captain or navigator to be responsible for vessel navigation and safety, and most fishing operations maintain human oversight of all navigation decisions despite automation, creating meaningful organizational and legal friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requires manual sextant navigation on most fishing vessels; electronic aids are standard, though some regulatory backup requirements (e.g., carrying charts) and safety liability create minor friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | GPS and chartplotting hardware represent a one-time capital cost that is orders of magnitude cheaper than continuous human labor for position computation and course plotting over a vessel's operational lifetime. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Chartplotter/GPS hardware and software are inexpensive one-time or low-cost subscription tools compared to the ongoing labor cost of manual celestial/compass navigation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Electronic navigation systems (GPS, chartplotters) exist and perform reliably in production, but they are tools that require human operators to interpret charts, manage exceptions, and make navigation decisions rather than autonomous AI systems that replace the navigational task entirely. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Marine GPS chartplotters, AIS, and integrated navigation suites are mature, widely deployed products used routinely on commercial and recreational fishing vessels today. |
Oversee the purchase of supplies, gear, and equipment.
56CI 33–79 · exposure 58 · augmentation 63 · importance 3.6/5 · click for rater detail
Oversee the purchase of supplies, gear, and equipment.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Fishing and hunting are traditionally low-digitization, laggard sectors with many small firms; while larger operations may use basic procurement systems, deep AI-driven automation adoption in this sector remains limited compared to professional services or finance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting operations are typically small, physical, low-digitization businesses with minimal AI tool adoption for back-office purchasing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist human purchasing decisions by automatically flagging supplier options, comparing prices and quality metrics, managing inventory forecasts, and preparing recommendations, significantly boosting the human's decision-making speed and thoroughness. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help with price comparisons, inventory tracking, and reordering reminders, providing useful assistance while the human retains decision-making authority. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Purchasing decisions for supplies and equipment can be largely automated through AI systems that identify needs, compare vendors, manage inventory, and place orders with minimal human intervention, achieving substantial time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Purchasing decisions require judgment about specific gear needs, seasonal conditions, and vendor relationships that go beyond simple procurement; AI can assist but not fully replace oversight of this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or regulatory barriers to automating routine purchasing; however, some organizational friction may arise from preference for human vendor relationships or negotiation, and small operations may lack integration infrastructure. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for purchasing decisions, though small business owners often prefer personal control and relationships with suppliers, creating mild organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven procurement systems have low marginal costs per transaction and require minimal oversight once configured, making them significantly cheaper than dedicated human purchasing agents per unit of purchasing output. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | General e-commerce and inventory tools offer some cost savings, but human oversight is still needed for quality judgment, vendor negotiation, and equipment-specific expertise, keeping AI cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Procurement automation platforms and e-procurement systems are deployed in production across many industries; they reliably handle supplier management, order placement, and tracking, though integration with specialized fishing/hunting equipment vendors may require some setup. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Procurement software and AI-assisted purchasing tools exist broadly, but no deployed product specifically manages fishing/hunting gear oversight reliably in production for this niche context. |
Locate fish, using fish-finding equipment.
37CI 25–49 · exposure 38 · augmentation 75 · importance 4.5/5 · click for rater detail
Locate fish, using fish-finding equipment.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Commercial fishing remains a low-digitization, geographically dispersed sector dominated by small and medium-sized operations; adoption of AI-enhanced fish-finding is slow and concentrated in industrial-scale fisheries. Most fishing workers still rely on traditional equipment, experience, and manual interpretation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Commercial fishing is a low-digitization, physically dominated sector where advanced AI adoption is slow compared to office/professional sectors, though basic sonar tech is already widespread. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted fish-finding (real-time data overlays, pattern detection from sonar) can substantially augment a fisher's ability to identify productive locations faster and with higher confidence. Fish-finders with AI assistance are actively used to amplify human expertise rather than replace it, improving both speed and success rates. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Modern fish-finding sonar, GPS chartplotters, and AI-enhanced fish/species identification significantly boost a fisher's ability to locate fish efficiently, meaningfully raising productivity while human effort remains central. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Fish-finding equipment (sonar, GPS) produces data that AI can process to identify potential fishing locations, but locating fish still requires interpretation of equipment readings, knowledge of fish behavior/seasonality, and real-time decision-making. AI could automate roughly half the task—data processing and initial site recommendation—but the full workflow requires human judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | Fish-finding equipment (sonar/GPS) already assists but interpreting readings and integrating with vessel maneuvering, weather, and species knowledge still requires human judgment; AI adds analytics but doesn't fully replace the physical/decision task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Fishing licenses, territorial rights, and sustainability regulations mean that the decision to fish and where to fish often requires human legal accountability and knowledge of local regulations. Additionally, equipment familiarization and integration with existing vessel systems create organizational friction for adopting automated locating systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically for using electronic fish-finding aids, though maritime licensing for vessel operation exists; the technology itself faces little regulatory obstruction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Fish-finding equipment and AI integration require capital investment in hardware and software, plus computational overhead. For small-scale and artisanal fishing (common in this occupation), the per-task cost of AI systems remains higher than the wages of an experienced fisher using traditional equipment and knowledge. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sonar/AI-enhanced fish-finders are relatively cheap hardware add-ons, but they don't replace the human worker's overall labor cost, wages, or vessel operation, so net cost savings versus a full worker are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Fish-finder devices with computer-vision or ML-assisted analysis exist in commercial fishing contexts, but these are narrow-scope products with material limitations in accuracy and adaptability across different water conditions, fish species, and equipment types. Reliable end-to-end automation is not yet standard practice in production fishing operations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Consumer and commercial fish-finder sonar with basic AI-assisted fish detection exists, but these are decision-support tools, not autonomous location-and-catch systems deployed at scale. |
Sort, pack, and store catch in holds with salt and ice.
25CI 15–35 · exposure 13 · augmentation 13 · importance 4.2/5 · click for rater detail
Sort, pack, and store catch in holds with salt and ice.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Fishing is a labor-intensive, capital-constrained, and geographically dispersed industry with slow digitization; adoption of automation in processing is incremental and concentrated in large industrial operations, not representative of the broader sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI/robotics adoption for onboard catch handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI/robotics could assist with task guidance or monitoring (e.g., optimal packing algorithms, ice dosage recommendations), but current systems offer limited practical augmentation for the core manual handling work performed by fishing workers. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance to workers physically sorting, packing, and icing fish in vessel holds. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While mechanical sorting, packing, and ice/salt application are theoretically automatable, the task involves handling fragile, variable-geometry biological material in wet, hazardous environments—current AI systems lack the robust manipulation dexterity and environmental robustness to perform this end-to-end without significant human oversight and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task on a moving vessel involving handling variable-sized catch, salt, and ice storage—no off-the-shelf AI/robotic system performs this end-to-end today.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal barriers—no licensing requirement to sort or pack catch—but significant practical friction exists: vessel space constraints, salt and ice logistics, need for human quality judgment on catch condition, and organizational inertia toward traditional labor keep substitution slow. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but the harsh, unstructured maritime environment and physical dexterity needed create strong practical barriers to automation, though not regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems for fish handling are capital-intensive and require infrastructure investment, making per-task costs competitive only at scale; most fishing operations employ low-wage seasonal labor, keeping human-equivalent costs below current automation investment for small to mid-sized operations. |
| 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 human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrow automation exists (e.g., industrial fish sorting machines in processing plants), but these operate under controlled conditions with pre-handled catch, not on-vessel holds with unpredictable, moving inventory; no deployed general-purpose system reliably handles the full task complexity today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that sorts, packs, and stores fish catch in ship holds; this remains outside current robotics deployment in commercial fishing. |
Track animals by checking for signs such as droppings or destruction of vegetation.
21CI 10–33 · exposure 13 · augmentation 25 · importance 4.1/5 · click for rater detail
Track animals by checking for signs such as droppings or destruction of vegetation.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting sectors remain largely traditional and low-digitization. Adoption of autonomous tracking systems is negligible; operations still rely on experienced human trackers and scouts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically embodied sector with minimal AI adoption for field tracking tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging potential animal signs in image data or suggesting search patterns, but the core task of physical tracking and interpreting contextual field signs remains deeply human-dependent and offers limited augmentation upside. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some tools like trail cameras, GPS apps, and image recognition apps can assist in identifying species or patterns, but they provide only marginal support to the core tracking skill. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While computer vision could identify some animal signs in images or video, tracking animals in the field requires real-time navigation, adaptive search patterns in varied terrain, and distinguishing subtle environmental cues—tasks that current AI struggles with end-to-end. Field deployment of autonomous systems capable of matching a human tracker's effectiveness and time savings remains impractical. |
| Task automatability | claude-sonnet-5 | 1/5 | Tracking animals via physical sign interpretation in variable outdoor terrain requires embodied perception, mobility, and experiential judgment that no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | This task does not require explicit licensing, but domain expertise, unpredictable field conditions, and customer preference for experienced human judgment create meaningful friction. Liability for missed animal signs or poor tracking decisions also discourages full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, but physical terrain access, liability for missed detection, and lack of technology substitutes create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current systems requiring camera drones, GPS, specialized terrain-navigation hardware, and significant integration/oversight costs would exceed the wage burden of a human tracker, especially in remote areas where these workers operate. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based approach (e.g., drones, sensors) would add cost on top of, not replace, human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs animal tracking by signs in real-world field conditions at scale. Computer vision systems can identify some patterns in controlled settings, but integrating this with terrain navigation and adaptive search strategies for live field tracking remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously tracks wildlife by reading physical field signs; camera traps and image classifiers exist but do not replace active field tracking. |
Interpret weather and vessel conditions to determine appropriate responses.
19CI 14–25 · exposure 17 · augmentation 50 · importance 4.5/5 · click for rater detail
Interpret weather and vessel conditions to determine appropriate responses.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Fishing and hunting sectors are traditionally low-digitization, small-operator dominated industries with older vessels and equipment. Adoption of AI-driven decision systems is nascent; most operators rely on experience and basic weather services rather than integrated AI platforms. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing and hunting are low-digitization, physically dispersed sectors with minimal AI agent deployment in decision-making roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by surfacing real-time weather patterns, vessel sensor anomalies, and historical response data to the human decision-maker. Such augmentation improves situational awareness and response time without replacing the skipper's judgment and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered weather forecasts, marine condition apps, and predictive analytics meaningfully assist crew decision-making even though the final judgment remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can process weather data and vessel sensor inputs, interpreting conditions to determine nuanced safety responses requires real-time judgment, local knowledge, and accountability for life-and-death decisions. Current systems can flag alerts but cannot autonomously decide appropriate operational responses at the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time, embodied perception of vessel motion, weather, sea state, and immediate physical decision-making in a dynamic maritime environment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime safety regulations, insurance requirements, and liability law typically require a licensed captain or qualified officer to make operational decisions affecting vessel and crew safety. Legal and regulatory frameworks create hard barriers to autonomous substitution of this judgment. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical maritime decisions carry significant liability and often regulatory/licensing requirements (captain responsibility), creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing integrated vessel monitoring, weather APIs, and AI analysis requires significant infrastructure and ongoing oversight. The cost per decision rivals or exceeds the wage of an experienced deck supervisor who performs this task as part of routine work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Weather data services are cheap, but replacing the human judgment loop entirely would require sensors, integration, and liability coverage that make full automation costlier than retaining the worker for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Weather forecasting APIs and vessel monitoring systems exist, but deployed products do not reliably make autonomous operational decisions for fishing/hunting safety. Systems assist with data display but humans retain decision authority; no mature production system performs this interpretation task end-to-end. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Weather forecasting and marine advisory products exist and are used as inputs, but no deployed product autonomously interprets combined vessel and weather conditions to determine operational responses on board. |
Wash decks, conveyors, knives, and other equipment, using brushes, detergents, and water.
19CI 15–24 · exposure 8 · augmentation 0 · importance 4.1/5 · click for rater detail
Wash decks, conveyors, knives, and other equipment, using brushes, detergents, and water.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are low-digitization, small-firm-dominated sectors with slow capital investment cycles and resistance to automation in traditional maritime operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI/robotics adoption for shipboard sanitation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for a task fundamentally dependent on physical effort and manual dexterity; there is no human-in-the-loop productivity boost available from current systems. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for manual deck and equipment washing tasks. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Washing decks and equipment involves physical manipulation of tools and varied surfaces in wet, unstructured environments. Current AI systems lack the embodied robotics to reliably handle brushes, detergents, and water application at scale, though targeted spray systems exist only for narrow, fixed layouts. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning task requiring manual dexterity and mobility on a boat or vessel deck; no off-the-shelf AI or robotic system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory licensing or authorization barriers exist for this manual task, though maritime workplace safety standards and vessel design constraints create some friction to robotic integration. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for cleaning, but the physical, variable, wet marine environment and equipment handling create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized cleaning robots capable of working in marine environments remain expensive to acquire, maintain, and integrate, far exceeding the cost of low-wage deck labor in fishing industries. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this task, so any hypothetical automation would require expensive custom robotics far costlier than a human worker performing manual cleaning. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs general deck and equipment washing on fishing vessels or processing lines in production. Specialized cleaning robots exist for controlled industrial settings but not for the dynamic, salt-water, and equipment-variation context of fishing operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist for automated washing of fishing vessel decks, conveyors, and knives; this remains purely manual work in the industry. |
Remove catches from fishing equipment and measure them to ensure compliance with legal size.
19CI 5–33 · exposure 13 · augmentation 38 · importance 4.6/5 · click for rater detail
Remove catches from fishing equipment and measure them to ensure compliance with legal size.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Commercial fishing remains a physical, low-digitization sector with many small operators, offshore equipment constraints, and regulatory resistance to full automation without human verification. Adoption of AI-based measurement systems is minimal in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI/robotics adoption for hands-on catch handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted measurement tools (automated sizing displays, real-time compliance alerts) can meaningfully assist workers in identifying and sorting undersized catch faster, though workers must still perform extraction and final validation manually. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-powered image recognition tools could assist with species identification or size estimation via camera systems, but the physical removal and precise measurement still require human action and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Removing catches from equipment requires dexterous physical manipulation in unstructured environments (boats, nets, moving fish), which current robotics struggle with at scale. Measuring for legal compliance is rule-based but the prior step—reliable catch extraction—presents a significant bottleneck that prevents end-to-end automation meeting the 50% time-saving threshold today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to handle live/dead catch, remove from nets/lines/traps, and measure it—no off-the-shelf AI system can perform this physical action end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal compliance with size regulations creates liability asymmetry: a mismeasurement resulting in illegal undersized catches sold carries regulatory and financial penalties that incentivize human verification or sign-off. Many jurisdictions legally require documented human inspection for catch reporting. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this handling task, but practical barriers include harsh marine environments, catch variability, and vessel space constraints that make automation impractical rather than illegal. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized hardware (underwater cameras, robotic arms, integration into fishing vessels) and the need for robust waterproof systems make the all-in cost of deploying AI for this task comparable to or higher than the marginal wage of fishing workers in most current deployments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic solution capable of handling variable catch types and measuring them would require expensive specialized hardware, far exceeding the cost of a human worker doing this task manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision for fish size measurement has been researched and some fishing-specific measurement tools exist, reliable end-to-end systems that handle the variability of catch extraction and consistent measurement in real fishing operations remain limited. Few deployed production systems handle both steps reliably without human oversight or high error rates. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical extraction of catch from fishing gear and measurement; this remains an unsolved robotics problem in unstructured marine environments. |
Maintain engines, fishing gear, and other on-board equipment and perform minor repairs.
19CI 5–33 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail
Maintain engines, fishing gear, and other on-board equipment and perform minor repairs.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are low-digitization, small-firm-dominated sectors with limited uptake of advanced automation. Adoption of even diagnostic AI tools remains minimal; production deployment of maintenance automation is essentially non-existent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI or robotics adoption for maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered diagnostic systems can assist technicians by identifying faults and recommending repairs, raising efficiency in troubleshooting. However, the physical execution component limits augmentation impact to diagnosis and planning phases. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer some assistance via diagnostic apps, repair manuals, or troubleshooting guidance, but this provides only marginal support for the hands-on repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Engine maintenance and repair require physical manipulation in real-world environments with high variability. While diagnostic assistance could be AI-automated, hands-on work like replacing parts, adjusting valves, and troubleshooting complex mechanical/electrical systems remain beyond current AI autonomous capability. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical maintenance and repair of engines and fishing gear requires hands-on manipulation, diagnosis, and dexterity that current AI systems cannot perform without embodied robotics, which are not deployed in this context. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers include the requirement for skilled technicians to sign off on safety-critical repairs (liability), regulatory oversight of vessel equipment maintenance, and the inherent need for physical presence on-board in remote maritime environments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical, on-board, often remote marine environment creates strong practical barriers to any non-human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI diagnostic tools are relatively cheap, but the core hands-on repair work still requires skilled human labor. The loaded cost of a trained technician remains lower than deploying specialized robotics or remote telepresence systems for this physically demanding task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-based approach (e.g., robotics) would be far more expensive than a human worker performing routine repairs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed autonomous systems reliably perform on-board engine maintenance and repair. Diagnostic tools exist, but actual execution requires human technicians; robotics for this domain are still research-stage and not in production use on fishing vessels. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical maintenance or minor repairs of marine engines or fishing equipment; this remains firmly in the domain of human manual labor. |
Transport fish to processing plants or to buyers.
16CI 5–26 · exposure 5 · augmentation 38 · importance 4.1/5 · click for rater detail
Transport fish to processing plants or to buyers.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Commercial transportation is adopting logistics software and route optimization, but full autonomous fleet deployment remains minimal outside pilot projects; most fishing transport still relies on traditional human drivers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physical-labor sector with minimal AI adoption for logistics and transport tasks specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Route optimization, real-time weather alerts, and load-management software can assist drivers in planning and efficiency, though the core driving task remains human-controlled and the augmentation is primarily informational. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, scheduling, or inventory tracking to support this logistics task, but does not materially transform the physical transport itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Transporting fish to processing plants requires physical logistics in dynamic environments (vehicles, roads, weather, handling perishables), which current AI cannot perform end-to-end without human supervision and control of the vehicle itself. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical transport of fish via boat or vehicle to processing plants requires driving/piloting and physical logistics that current AI cannot perform end-to-end; autonomous vehicle/vessel tech is not deployed for this use case., |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Transportation of goods is heavily regulated by commercial licensing requirements, food safety regulations, and liability law; a licensed commercial driver must legally operate the vehicle, and food handling regulations impose specific compliance requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing uniquely protects this task, but physical/logistical realities (driving, loading, vessel operation) and lack of autonomous transport infrastructure create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous vehicle technology remains expensive, and logistics software only partially addresses the full cost of transport including vehicle depreciation, maintenance, and regulatory compliance, making current AI solutions not yet cheaper than human drivers overall. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical transport task, so AI cost is not comparable; human labor and standard vehicles remain the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While route optimization and logistics software exist, the actual operation of vehicles and management of temperature-sensitive perishable cargo at scale still requires human drivers; no deployed system operates these routes fully autonomously in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously transports fish catches to buyers or processing plants; this remains a manual logistics and driving task performed by humans or conventional vehicles. |
Scrape fat, blubber, or flesh from skin sides of pelts with knives or hand scrapers.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.7/5 · click for rater detail
Scrape fat, blubber, or flesh from skin sides of pelts with knives or hand scrapers.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The fishing and hunting processing sector is a low-digitization, small-firm-dominated industry with minimal technology adoption; automation of manual pelt processing has seen negligible uptake. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing/hunting/trapping and hide processing are low-digitization, physically manual trades with essentially no AI or robotics adoption trend. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully assist a human physically scraping pelts; this is a purely manual task where automation or tool improvement (better scrapers) would be the only productivity lever, not AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to the physical act of scraping fat and flesh from pelts. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual manipulation of fragile materials (pelts) using hand tools with fine motor control and tactile feedback to avoid damaging the skin. Current AI/robotics lacks the dexterity, real-time sensory adaptation, and failure recovery needed to perform this consistently without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine motor, tactile physical task requiring dexterous handling of irregular pelts and variable material thickness; no AI system or robot performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal licensing requirements, the manual, craft-like nature of the task and the variability of pelts create organizational friction; small-scale hunters and processors have minimal automation infrastructure. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier requires a human specifically, but the physical dexterity and variability of pelts create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Developing a specialized robotic system capable of handling delicate pelts with the required precision would involve significant R&D, hardware, and integration costs that far exceed the wage cost of manual labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute, so any hypothetical automation would require costly bespoke robotics far exceeding the low wage cost of manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial systems reliably perform pelt scraping autonomously in production environments. This task remains manual across the fishing/hunting industry; no products demonstrate end-to-end automation of this specific operation at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist for pelt fleshing/scraping; this remains a manual craft/trade skill with no commercial automation solution in production. |
Patrol trap lines or nets to inspect settings, remove catch, and reset or relocate traps.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.6/5 · click for rater detail
Patrol trap lines or nets to inspect settings, remove catch, and reset or relocate traps.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This occupation operates in rural, outdoor, low-digitization sectors with small enterprises and high reliance on embodied work; adoption of any automation is near-zero and unlikely to accelerate absent major robotics breakthroughs. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically demanding sector with essentially no AI/robotic adoption for this kind of fieldwork. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for trap patrol, catch removal, and relocation; the task is inherently physical and contextual, and current tools (GPS, monitoring sensors) provide only marginal support to the core work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with route planning, catch logging, or predictive placement via apps and sensors, but offers minimal assistance to the core physical patrol-and-reset actions. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical navigation through natural terrain, identifying trap conditions, manually handling catch, and making contextual decisions about trap relocation. Current AI lacks embodied robotics at scale and real-world dexterity for these operations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence in remote outdoor terrain or water to locate, inspect, and manually reset physical trapping equipment, which no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no direct licensing barriers to automation, the task occurs in remote natural environments with low technology infrastructure and strong cultural/occupational continuity, creating natural friction to adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier per se, but the physical, remote, variable-terrain nature of the task and need for dexterous manipulation of animals/gear create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and operational cost of any robotics capable of traversing natural terrain and performing trap-handling tasks would far exceed the wage of a skilled fishing or hunting worker for this seasonal or part-time work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic solution exists for this physical task at any cost, so a human remains the only option and is cheaper than any theoretical automation attempt. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously patrol trap lines in outdoor environments, inspect physical trap settings, remove and handle catch, or relocate equipment in unstructured terrain. This requires integrated robotics, navigation, and environmental perception that does not exist in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that patrol trap lines or nets, remove catch, and reset traps; this remains purely a manual field task. |
Connect accessories such as floats, weights, flags, lights, or markers to nets, lines, or traps.
14CI 5–24 · exposure 8 · augmentation 13 · importance 4.1/5 · click for rater detail
Connect accessories such as floats, weights, flags, lights, or markers to nets, lines, or traps.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing is a low-digitization, traditionally manual sector with small operators and limited capital for automation investment. Adoption of advanced automation in fishing remains minimal compared to information and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting are low-digitization, physically dispersed occupations with minimal AI or robotics adoption for hands-on gear assembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance on this task; automated guides or AR visualization of proper knot/connection techniques could provide marginal help, but the core work remains fundamentally manual and dependent on tactile feedback and environmental conditions. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of attaching floats, weights, or markers to fishing/trapping equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some preparatory steps (sorting, organizing) could be partially automated, the task requires dexterous physical manipulation in variable conditions (water, weather, different equipment types). Current robotic systems lack the flexibility and wet-environment durability to reliably connect varied accessories to nets and traps at scale, making meaningful end-to-end automation implausible today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical rigging task requiring hand-eye coordination on boats or in fields; no AI system can perform this physical manipulation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical task performance in harsh marine environments requires human judgment about water conditions, equipment integrity, and safe rigging—factors that create inherent friction toward automation. Fishing traditions and the craft knowledge embedded in proper rigging also create organizational and cultural barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific task, but the physical, outdoor, variable-terrain nature of the work creates practical barriers to automation beyond regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware investment in specialized marine robotics, plus integration and maintenance costs, would far exceed the loaded wage of a fishing worker performing manual assembly, making AI/automation significantly more expensive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so any hypothetical robotic solution would be far costlier than a human worker performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products exist that perform this specific task of connecting fishing accessories in real-world conditions. Specialized underwater or marine robotics are research-stage and not in production use for this purpose. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that attaches gear accessories to nets, lines, or traps; this remains purely a manual fishing/hunting task. |
Load and unload vessel equipment and supplies, by hand or using hoisting equipment.
14CI 5–24 · exposure 8 · augmentation 13 · importance 3.4/5 · click for rater detail
Load and unload vessel equipment and supplies, by hand or using hoisting equipment.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting operations are predominantly small-scale, physically remote, low-digitization sectors with minimal AI investment. These industries operate in structurally constrained environments (boats, open water) where adoption barriers and capital constraints limit mechanization velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physical-labor-heavy sector with minimal AI or robotics adoption for cargo handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with inventory tracking or optimizing load sequences, but the hands-on manipulation task itself—balancing loads on a moving deck, securing equipment in variable weather—remains fundamentally dependent on human physical presence and real-time judgment. Augmentation potential is limited by the task's core manual and environmental demands. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no meaningful assistance to a worker physically loading or hoisting equipment on a vessel. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Loading and unloading vessel equipment requires physical manipulation in variable maritime environments with irregular layouts and dynamic constraints. While some cargo handling in highly controlled warehouse settings can be partially automated, the on-deck variability, safety-critical positioning, and need to adapt to vessel motion make end-to-end automation without human oversight infeasible with current systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual/hoisting task in variable marine environments requiring dexterity and situational judgment; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime operations are heavily regulated with strict safety and liability requirements; equipment must be loaded/unloaded by competent crew familiar with vessel-specific procedures and ocean conditions. Regulatory frameworks require human judgment and accountability for cargo security and crew safety. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but physical environment constraints (boats, docks, variable cargo) and safety/liability concerns create moderate practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotics capable of operating on moving vessels with variable cargo would cost orders of magnitude more than hiring fishing/hunting workers, and integration costs with existing vessel infrastructure remain prohibitive. Labor in these sectors is relatively inexpensive, making automation economically unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative to compare costs against; human labor remains the only functional option, making AI more expensive or simply unavailable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No current deployed AI system reliably performs autonomous loading/unloading of vessel equipment in real maritime operations. Existing port automation is limited to container stacking in fixed infrastructure; fishing vessels and hunting operations involve unstructured, moving platforms with diverse cargo types. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product loads/unloads fishing vessel equipment in production; this remains a human physical labor task with no commercial automation solution. |
Steer vessels and operate navigational instruments.
14CI 5–23 · exposure 13 · augmentation 50 · importance 4.8/5 · click for rater detail
Steer vessels and operate navigational instruments.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are among the least digitized, most geographically dispersed, and smallest-scale commercial sectors; few firms have capital or infrastructure for advanced maritime automation. Adoption remains negligible in operational fleets. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI adoption in vessel operation; autonomous marine navigation remains niche and experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | GPS, electronic charting, and autopilot do meaningfully assist vessel operators by reducing fatigue and improving plotting accuracy, but these are mature tools decades old rather than emerging AI-driven augmentation. Modern AI-powered navigation aids (predictive weather, route optimization) offer modest additional gains for skilled operators. |
| Augmentation potential | claude-sonnet-5 | 3/5 | GPS, sonar, AIS, and automated navigation aids already assist human operators in course-plotting and hazard detection, meaningfully supporting but not replacing the steering task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While GPS and autopilot systems can handle routing and basic steering, the task requires real-time environmental sensing, collision avoidance in dynamic conditions, and adaptation to weather—capabilities current maritime AI lacks for independent operation. Manual oversight remains essential for safety-critical decisions in open water. |
| Task automatability | claude-sonnet-5 | 1/5 | Steering fishing vessels in dynamic marine environments requires continuous sensory judgment, adaptation to weather and wildlife behavior, and physical presence aboard; no off-the-shelf system replaces this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime law requires licensed captains to command vessels; regulatory frameworks (SOLAS, flag-state rules) mandate human crews for commercial fishing vessels. Liability for autonomous grounding, collision, or loss of life creates strong legal and insurance barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Maritime safety regulations, licensing requirements for vessel operators, and liability for at-sea accidents create strong barriers to full automation of vessel steering. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of advanced navigation AI, sensors, and oversight infrastructure is costly and requires marine-grade hardware. For small-to-medium fishing vessels (the dominant market), this remains significantly more expensive than maintaining a human crew per task-output hour. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Retrofitting or deploying autonomous navigation systems for small fishing vessels is far more costly than a human operator, especially at small-scale/independent fishing operation scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Maritime automation exists for routine operations (autopilot, GPS plotting), but no deployed system reliably performs autonomous vessel steering and navigation end-to-end in commercial fishing/hunting contexts with acceptable safety margins. Autonomous shipping remains mostly pilot projects, not production-grade fleet deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Autonomous vessel navigation exists only in limited research/military/commercial shipping trials, not in commercial fishing/hunting operations where this task occurs. |
Attach nets, slings, hooks, blades, or lifting devices to cables, booms, hoists, or dredges.
12CI 5–19 · exposure 8 · augmentation 13 · importance 3.8/5 · click for rater detail
Attach nets, slings, hooks, blades, or lifting devices to cables, booms, hoists, or dredges.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are among the least digitized, most physically constrained sectors with high operational uncertainty. Automation adoption is minimal; workers remain the standard for cable and attachment work on active vessels and in field conditions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically demanding sector with minimal AI/robotics adoption for manual rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist through real-time safety monitoring, cable tension alerts, or pre-task planning visualization, but the core manipulation task—physically attaching nets and securing connections—offers limited room for meaningful human-AI collaboration in practice. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a worker physically attaching nets or hooks to cables and hoists in the field. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires precise physical manipulation in variable outdoor conditions (weather, water, moving equipment) and involves securing safety-critical connections. While components could theoretically be handled by specialized robotics, current general-purpose AI systems cannot reliably perform the end-to-end attachment sequence with the dexterity, error tolerance, and environmental adaptation needed. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical rigging task performed on boats or in the field requiring manual dexterity, strength, and judgment about equipment and weather; no AI system can physically attach gear to cables or booms. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task has strong adoption barriers: maritime labor regulations, safety certification requirements for deck work and equipment handling, union presence in commercial fishing, and the legal requirement that qualified crew members be physically present to ensure vessel and equipment safety. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically, but physical environment, safety risk, and equipment variability create practical friction against automation beyond mere software substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of marine cable attachment would be significantly more expensive to purchase, maintain, and deploy than hiring a fishing worker, especially given the low wage baseline and task frequency variability in this sector. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical task, so any comparison would require robotics far beyond current cost-effective deployment, making AI far more expensive or simply unavailable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs this physical task in production fishing or hunting operations. Autonomous dredging and marine robotics remain research or narrowly specialized domains; general-purpose task automation is not available in the market. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical attachment task; it remains entirely manual and situational, requiring hands-on handling of nets, hooks, and lifting devices. |
Maintain and repair trapping equipment.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail
Maintain and repair trapping equipment.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting sectors are characterized by small, distributed, physically-based operations with low digitization and minimal AI adoption. Equipment maintenance remains firmly in the domain of manual, worker-performed tasks with no trend toward automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically-oriented sector with minimal AI or robotics adoption for equipment maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While diagnostic AI (e.g., image analysis to identify wear patterns) could marginally assist decision-making, current AI offers limited practical support for this inherently manual task, and most value remains with the worker's direct hands-on expertise and judgment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no current assistance for physically maintaining or repairing traps, nets, or related field equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Maintaining and repairing trapping equipment involves hands-on inspection, diagnosis of wear/damage, and manual assembly or reassembly of mechanical components in field conditions. Current AI systems lack the embodied manipulation, real-time sensory feedback, and adaptive problem-solving needed to perform this end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical repair task (fixing traps, nets, snares) requiring dexterity and hands-on manipulation of hardware, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task is inherently human-contact and hands-on work requiring presence in the field and physical interaction with equipment. The nature of the work and lack of regulatory incentive to automate creates substantial practical friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for equipment repair itself, but the physical, outdoor, unstructured nature of the work is a strong practical barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying a robotic system capable of diagnosing and repairing diverse trapping equipment in field settings would far exceed the modest wages of a fishing/hunting worker performing this maintenance task themselves. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-based alternative, so any AI approach (e.g., robotics) would be far more expensive than a human doing simple manual repairs with hand tools. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform physical repair and maintenance of mechanical trapping equipment in real-world conditions today. This task requires physical manipulation, material-specific judgment, and environmental adaptation that current systems cannot execute. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product exists that maintains or repairs trapping equipment in real-world hunting/trapping operations. |
Put fishing equipment into the water and anchor or tow equipment, according to the fishing method used.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Put fishing equipment into the water and anchor or tow equipment, according to the fishing method used.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting is a traditional, geographically dispersed, and low-digitization sector with minimal AI adoption. Current industry practices rely on human crews, and adoption of autonomous systems remains largely experimental rather than mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on gear handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or equipment monitoring via sensors, but the core task of physically deploying and anchoring equipment offers limited augmentation because human judgment and physical presence are central to safe, effective execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, sonar/fish-finding, or weather routing, but offers little direct assistance with the physical act of deploying and anchoring gear. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of equipment in variable water and weather conditions, precise deployment based on real-time conditions, and navigation judgment that current AI systems cannot perform end-to-end without human oversight. Robotic systems exist in controlled environments but not as general-purpose solutions for this task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task involving deploying and anchoring nets, traps, or lines in variable marine/aquatic environments, requiring dexterity and physical strength that no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations, safety requirements, and licensing of commercial fishing vessels create significant legal and operational barriers. Additionally, the unpredictable physical environment and requirement for real-time judgment create practical friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation of gear deployment, but the physical environment (boats, weather, water) creates practical barriers to non-human actors performing this safely. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of autonomous fishing vessels and equipment, plus integration and maintenance, substantially exceeds the loaded cost of hiring fishing workers, making AI uneconomical for this specific task at present. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive specialized marine robotics far costlier than human labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous deployment and anchoring of fishing equipment in open-water conditions. While some research into autonomous vessels exists, production systems handling the full range of fishing methods and environmental variability are not operationally deployed. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product autonomously handles the full range of fishing gear deployment (nets, pots, lines, trawls) across varying weather and water conditions; this remains research-stage at best. |
Skin quarry, using knives, and stretch pelts on frames to be cured.
10CI 5–15 · exposure 0 · augmentation 0 · importance 4.1/5 · click for rater detail
Skin quarry, using knives, and stretch pelts on frames to be cured.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are low-digitization, tradition-bound sectors with small workforce clusters and minimal technology adoption; no measurable AI or automation displacement is occurring in pelt processing. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically remote occupation with essentially no AI/robotics adoption for tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a hunter or trapper performing manual skinning and stretching; the task is purely physical craft with no obvious augmentation pathway. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical acts of skinning and stretching pelts, which are manual craft tasks with no digital or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Skinning and pelt stretching requires dexterous manipulation of irregular animal material with precise knife work and spatial judgment that current robotics and AI cannot reliably perform end-to-end in uncontrolled conditions. No deployed AI system can match human speed and quality for this task. |
| Task automatability | claude-sonnet-5 | 1/5 | Skinning quarry and stretching pelts requires fine motor dexterity, tactile feedback, and adaptive handling of irregular biological material that current robotics and AI cannot perform; no AI system can execute this physical task at all. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves direct animal handling and physical processing that typically requires on-site human presence and judgment; cultural and practical norms around hunting/trapping favor skilled craftspeople, and liability concerns around automated knife work on unpredictable material create significant friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this specific task, but the physical dexterity and craft nature of pelt preparation create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized automation for this task would require custom robotic arms, vision systems, and integration—far exceeding the cost of a skilled worker performing the task manually. The irregular nature of pelts makes standardized automation economically unfeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic substitute exists, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human trapper or skinner performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably performs animal skinning and pelt stretching; this remains entirely manual and requires skilled human labor. Robotics research exists but is not deployed in production hunting/trapping operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI or robotic products that skin animals and stretch pelts on frames; this remains entirely a manual craft skill performed by humans. |
Release quarry from traps or nets and transfer to cages.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Release quarry from traps or nets and transfer to cages.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are typically low-digitization, physically distributed sectors with small enterprises. Adoption of advanced robotics or AI for live animal handling is negligible in these industries today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically demanding sector with minimal AI/robotics adoption for hands-on animal handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotic systems offer no meaningful assistance in live animal capture, release, or cage transfer tasks; the domain requires embodied expertise and real-time physical control that augmentation tools do not meaningfully enhance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for the physical act of releasing and transferring live quarry. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of live animals in unpredictable conditions, real-time spatial reasoning in 3D environments, and animal handling expertise. Current AI systems cannot reliably perform the sensorimotor coordination and adaptive judgment needed end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task involving live animals, unpredictable movement, and dexterous handling in outdoor/aquatic environments that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Animal welfare regulations, licensing requirements for hunting and fishing operations, and liability concerns around improper animal handling create meaningful regulatory and legal barriers to full automation without human oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical unpredictability of live animals and equipment creates practical friction rather than legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of live animal handling and transfer would be prohibitively expensive relative to a human worker performing this task, especially when amortized across the irregular and context-dependent nature of the work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this physical task, so the human worker remains far cheaper than any hypothetical automation system requiring custom robotics. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task reliably in production. The task involves live animal welfare, precise physical control, and outdoor variability that exceed current robotic and AI capabilities in real-world fishing/hunting contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product exists that reliably releases quarry from traps/nets and transfers to cages in commercial fishing/hunting operations. |
Teach or guide individuals or groups unfamiliar with specific hunting methods or types of prey.
9CI 5–14 · exposure 5 · augmentation 38 · importance 3.7/5 · click for rater detail
Teach or guide individuals or groups unfamiliar with specific hunting methods or types of prey.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hunting and fishing are rural, outdoor, low-digitization sectors with strong tradition and minimal AI adoption; pilot programs are rare and production use of AI for live instruction is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Outdoor recreation and guiding services are a low-digitization, physically-oriented sector with minimal AI adoption in actual field operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating preparatory materials, video tutorials, or pre-hunting briefings that a human instructor then builds on, moderately enhancing the learning pipeline without replacing the guide. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with pre-trip educational materials, species identification apps, or planning guidance, but offers little assistance during the actual in-field teaching and guiding activity. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time interaction, safety judgment, and adaptive teaching based on individual learner needs and environmental conditions—domains where AI systems cannot reliably replace human instruction, especially in high-risk outdoor settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires in-person, physical demonstration, real-time terrain navigation, safety supervision, and hands-on skill transfer that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Hunting instruction involves liability, safety certification requirements in many jurisdictions, and strong preference for human mentorship; legal and organizational friction heavily protects this task from automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Guiding often requires licensing/certification, liability insurance, safety responsibility for weapons and wildlife, and direct human presence, creating strong structural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Producing AI-generated instructional content is cheap, but integrating it into live teaching and oversight remains labor-intensive; human guides are still far more cost-effective for actual hands-on instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this outdoor, physical guiding task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can produce static instructional content or videos, no deployed product reliably teaches hunting methods with the adaptive, contextual, and safety-critical guidance that real learners require in the field. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product guides live hunting expeditions or teaches physical hunting skills in the field; this remains entirely human-delivered. |
Select, bait, and set traps, and lay poison along trails, according to species, size, habits, and environs of birds or animals and reasons for trapping them.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Select, bait, and set traps, and lay poison along trails, according to species, size, habits, and environs of birds or animals and reasons for trapping them.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Wildlife and pest control remain low-digitization sectors with high dependence on field expertise, outdoor conditions, and regulatory oversight; adoption of AI or automation in this domain is minimal and slower than information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically dependent sector with essentially no AI/robotic adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist in identifying animal species or suggesting trapping strategies via image recognition or knowledge systems, but the bulk of value lies in field judgment, manual setup, and regulatory compliance that remain human-centric. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with species identification, habitat mapping, or trap placement recommendations via apps, but it doesn't materially transform the physical setting of traps or poison. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time environmental assessment, species-specific decision-making in outdoor settings, physical trap setup and baiting, and adaptive placement based on animal behavior—capabilities that current AI systems cannot execute in uncontrolled natural environments without human presence. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical field task requiring travel to remote sites, manual trap setting, and handling of poisons and animals; no AI system can perform the physical actions involved.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Trapping and poisoning typically require hunting or wildlife licenses and compliance with species protection regulations; legal authority to harm or kill animals is vested in licensed individuals, creating substantial regulatory and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Trapping and poison use are often regulated by wildlife and pesticide licensing, but the barrier is more about physical execution than legal restriction alone. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The physical manipulation, outdoor navigation, and real-time judgment required make this task cheaper to perform with a human worker than with current robotics or autonomous systems deployed at scale. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical trapping labor, so any AI cost comparison is moot—human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can autonomously select trap types, bait them, set them physically, or place poison in outdoor wildlife contexts; this remains entirely manual and field-based with no meaningful automation in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product sets physical traps or lays poison in real-world environments; this remains entirely a manual, embodied task. |
Harvest marine life for human or animal consumption, using diving or dredging equipment, traps, barges, rods, reels, or tackle.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Harvest marine life for human or animal consumption, using diving or dredging equipment, traps, barges, rods, reels, or tackle.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing remains a low-digitization, physical-labor sector with minimal AI adoption. Vessels are primarily operated by humans; adoption of autonomous systems is negligible, and most fishing communities operate in laggard sectors with limited digital infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Commercial fishing is a low-digitization, physically demanding industry with minimal AI/robotic adoption for actual harvesting operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with sonar mapping and predictive modeling of fish locations, but the core task of physically harvesting marine life offers limited augmentation potential. Current tools provide some navigational and catch-prediction support, but integration remains minimal in practice. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with fish-finding sonar analysis, catch prediction, or route optimization, but offers little direct help with the physical act of harvesting itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Harvesting marine life requires physical navigation of aquatic environments, operation of specialized equipment in unpredictable conditions, and real-time decision-making about catch location and handling. Current AI cannot perform this end-to-end task; the underwater environment, equipment operation, and catch handling remain beyond robotic capability today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, outdoor harvesting task requiring boat operation, diving, gear handling, and physical capture of marine life in variable environments—no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Fishing is heavily regulated by maritime law, licensing requirements, and environmental quotas that typically require licensed human captains and crew. Legal and regulatory frameworks mandate human accountability for catch methods and conservation compliance, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no licensing requires a human specifically, physical/environmental unpredictability, vessel safety regulations, and lack of mature autonomous marine harvesting technology create substantial practical barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized autonomous underwater equipment, sensing systems, and integration significantly exceeds the loaded wage of a fishing worker. Current hardware and deployment costs make AI substitution economically unfeasible relative to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any AI/robotic system capable of physical harvesting would require expensive specialized hardware, maintenance, and supervision, far exceeding the cost of human fishing labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs the integrated task of locating, capturing, and harvesting marine life at production scale. Autonomous fishing vessels and underwater robotics exist in limited research contexts but are not commercially mature or deployed in actual fishing operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI/robotic products that autonomously harvest fish or marine life at commercial scale using traps, dredges, or tackle; this remains research-stage robotics at best. |
Travel on foot, by vehicle, or by equipment such as boats, snowmobiles, helicopters, snowshoes, or skis to reach hunting areas.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Travel on foot, by vehicle, or by equipment such as boats, snowmobiles, helicopters, snowshoes, or skis to reach hunting areas.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hunting and fishing occur in low-digitization, physically decentralized sectors with strong tradition and regulatory oversight. Automation adoption is minimal; workers remain essential for safe navigation and legal compliance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hunting/fishing is a low-digitization, physically remote sector with essentially no AI/autonomous vehicle adoption for this kind of travel. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with route planning (maps, weather forecasts) or vehicle diagnostics, but most of this task—actual navigation, terrain assessment, equipment handling—requires direct human control and cannot be meaningfully augmented by AI today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | GPS navigation, weather apps, and route-planning tools offer some assistance in planning travel, but do not materially transform the physical act of traveling to remote hunting areas. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical navigation through terrain and operation of vehicles/equipment in variable outdoor conditions. Current AI cannot operate a body in the physical world or pilot vehicles autonomously across unstructured hunting terrain without human control. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical traversal task requiring bodily presence in remote outdoor terrain; no AI system can perform the actual travel itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and safety barriers exist: hunting regulations typically require a licensed human operator for vehicles (boats, snowmobiles, helicopters), liability falls on the operator, and terrain navigation demands human judgment and accountability in hazardous conditions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically, but physical environment unpredictability, safety liability, and lack of autonomous vehicle infrastructure in wilderness create real friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of such autonomous navigation would require expensive hardware, continuous mapping, and extensive safety infrastructure—far exceeding the cost of a human worker operating existing vehicles and equipment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative to compare cost against; a human (or eventually autonomous vehicle) must physically make this journey. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task end-to-end. Autonomous vehicles exist in narrow domains (highways, controlled paths) but not in the wilderness, forest, snow, or water environments required for hunting expeditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human physically traveling by boat, snowmobile, or skis to reach hunting areas; this remains purely a human/vehicle-operation task. |
Obtain permission from landowners to hunt or trap on their land.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Obtain permission from landowners to hunt or trap on their land.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hunting and fishing work is a physically dispersed, traditional sector with low digital transformation. Adoption of automated permission systems is minimal because the task inherently requires human-to-human legal negotiation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hunting and trapping is a low-digitization, physically embedded occupation with minimal AI adoption for interpersonal access negotiations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with identifying potential landowners or drafting permission request templates, but the core task of obtaining actual consent requires direct human interaction and cannot be meaningfully augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help draft permission request letters or track landowner contact information, but it offers little help with the core interpersonal negotiation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires negotiation, relationship-building, and obtaining explicit human consent from specific individuals—capabilities that current AI cannot reliably perform autonomously. Legal authority to grant hunting/trapping rights must come from the human landowner themselves, not from an AI system. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-world interpersonal negotiation, trust-building, and physical presence to identify and contact landowners, which AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Landowner permission is a hard legal and contractual barrier: only the authorized landowner can grant hunting/trapping rights, and this explicit human decision-making cannot be legally delegated to an AI agent. Authorization must come from the human directly. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in a formal sense, landowner permission requires personal relationships, trust, and often local reputation, creating strong social/organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A human worker can contact landowners directly through established networks and relationships, often at minimal cost. Implementing an AI system to handle this would require legal, compliance, and integration infrastructure that would exceed the cost of human outreach. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so any AI cost would be additive rather than a cheaper replacement for human negotiation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably obtains landowner permission for hunting or trapping rights. This would require autonomous legal negotiation and decision-making from humans, which no existing system does in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product handles landowner permission negotiations for hunting/trapping access; this remains a purely human interpersonal task. |
Obtain required approvals for using poisons or traps, and notify persons in areas where traps and poison are set.
4CI 0–9 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Obtain required approvals for using poisons or traps, and notify persons in areas where traps and poison are set.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting operations are small, dispersed, and operate in low-digitization sectors with minimal automation infrastructure; regulatory compliance work in these domains shows laggard adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically-oriented sector with minimal AI adoption for regulatory and field notification tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by drafting notification templates or summarizing regulatory requirements, but the core task—obtaining approvals and delivering notifications—must remain human-driven, limiting substantive augmentation value. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by drafting permit applications, tracking regulatory requirements, and generating notification materials, though the core approval and notification acts remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires obtaining official regulatory approvals and direct notification to affected persons, both of which fundamentally depend on human agency, legal authority, and interpersonal communication that current AI cannot independently execute. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, legal filing with specific agencies, and direct in-person or local notification of people in an area, none of which current AI can execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard legal barriers exist: regulatory approvals require authorized human officials to grant permission, and liability for poison/trap use falls on identified, legally responsible persons who must personally file notifications. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Obtaining approvals for poisons/traps typically requires a licensed or authorized individual to interact with regulatory bodies and take legal responsibility, creating a strong barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves regulatory compliance and human notification that require licensed personnel to sign off; automating it would save minimal cost since a qualified human must remain the legal responsible party. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could help draft permit applications cheaply, but the actual filing, follow-up, and notification of area residents still require human labor, limiting overall savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently obtain legal approvals or reliably notify affected persons across varied jurisdictional and communication contexts; these are fundamentally administrative and compliance-driven tasks requiring human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product handles physical permit acquisition or on-site community notification for wildlife control operations. |
Direct fishing or hunting operations, and supervise crew members.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Direct fishing or hunting operations, and supervise crew members.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting sectors are laggards in digital adoption, operate in remote/physical environments with limited connectivity, and have minimal track record of AI integration or agent deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting are low-digitization, physically-based industries with minimal AI adoption for operational leadership roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with navigation, weather forecasting, or catch analysis, but offers limited productivity gain for core supervisory and operational direction tasks that remain heavily dependent on human judgment and presence. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route planning, weather/catch data analysis, or communication logistics, but offers little help with the core supervisory and directive leadership task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing operations and supervising crew requires real-time environmental judgment, safety decisions, crew coordination, and adaptive problem-solving in dynamic outdoor conditions—tasks that demand human expertise and accountability that current AI cannot reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing operations and supervising crew in dynamic, physical, outdoor environments requires real-time judgment, leadership, and physical presence that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal, safety, and liability barriers protect this role: fishing/hunting operations require licensed operators, safety regulations mandate human supervision of crew, and insurance and liability frameworks explicitly require accountable human leadership. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Crew supervision often involves safety responsibility, licensing (e.g., captain's license), and legal accountability for crew and vessel, creating strong human-in-charge requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is inherently human-dependent (physical presence, crew management, legal responsibility); AI has no path to cost advantage since a human supervisor remains mandatory. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory/leadership function, so AI cost comparison is not applicable and the human remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously direct fishing or hunting expeditions or manage crew supervision; this requires physical presence, authority, and legal/safety accountability that only licensed humans can provide. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages fishing/hunting crews or field operations; this remains far outside current commercial AI applications. |
Kill or stun trapped quarry, using clubs, poisons, guns, or drowning methods.
3CI 0–5 · exposure 0 · augmentation 0 · importance 4.5/5 · click for rater detail
Kill or stun trapped quarry, using clubs, poisons, guns, or drowning methods.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Fishing and hunting are traditional industries with low digitization, small workforce, and strong cultural preference for human skill. Adoption of automation in this sector has been minimal and faces active resistance from stakeholders and regulators. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Fishing and hunting is a low-digitization, physically remote sector with essentially no AI adoption for this specific lethal/physical task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance in the actual killing or stunning of quarry; the task is fundamentally manual and embodied. AI might assist with tracking or locating prey, but not the core lethal action itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI provides no meaningful real-time assistance for the physical act of killing or stunning trapped animals in the field. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Killing or stunning trapped quarry requires physical manipulation of weapons and animals in real-world environments with unpredictable conditions. Current AI systems cannot operate robotic arms reliably enough to handle guns safely, respond to animal movement, or manage the precise force needed in wet or uncontrolled outdoor settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, real-world action requiring presence in remote outdoor environments and direct manipulation of live animals; no AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Significant legal and regulatory barriers exist: hunting and fishing are heavily regulated by state/federal agencies, licenses are required, animal welfare laws constrain methods, and liability for injury or improper kill is substantial. A human operator must typically be legally responsible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hunting/trapping methods are subject to wildlife and safety regulations, humane-treatment rules, and licensing requirements that constrain who and how this task is performed, though not strictly to AI systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automation would require custom robotic systems, specialized sensors, and ongoing maintenance—orders of magnitude more expensive than the labor cost of a skilled hunter or fisher performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-based alternative to cost-compare; a human with tools remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial or production AI system performs this task. While robotic arms exist in controlled factory settings, deploying them to stun or kill live animals in the field remains a research problem with massive liability and safety implications. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product exists that autonomously kills or stuns trapped wild quarry in field conditions. |
Participate in animal damage control, wildlife management, disease control, and research activities.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Participate in animal damage control, wildlife management, disease control, and research activities.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Wildlife and fishing sectors remain low-digitization, field-dependent industries with minimal AI adoption for core operational tasks. Automation has not penetrated these physically and environmentally contingent activities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This sector (fishing/hunting/wildlife fieldwork) has very low digitization and AI adoption; it is a physically-grounded, low-tech occupational area. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with data analysis, disease monitoring systems, or research documentation, but meaningful augmentation is limited because the task's core—live animal handling, field assessment, and real-time adaptive management—requires human presence and judgment in uncontrolled environments. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with data analysis, population modeling, disease tracking via sensors, and research documentation, but offers minimal help with the hands-on control and management activities themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires field presence, live animal handling, situational judgment in dynamic environments, and adaptive decision-making that current AI cannot perform end-to-end. No AI system today can autonomously conduct wildlife management, disease control, or research fieldwork. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence in outdoor environments, direct animal handling, trapping, culling, and field observation that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: wildlife management typically requires hunting/trapping licenses, field certifications, and government authorization. Disease control and research activities often mandate licensed personnel and direct professional accountability for outcomes. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Wildlife management and disease control often require permits, licensing, and government authorization, plus physical presence and judgment in unpredictable field conditions, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot yet perform this task, making cost comparison impossible. The human expertise, field equipment, and physical presence required remain irreplaceable by current technology. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical fieldwork, so any AI cost comparison is moot—human labor with specialized equipment remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs field-based animal damage control, wildlife management, or research activities. These require physical presence, environmental assessment, and adaptive expertise that exceed current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs field-based animal damage control or wildlife capture/management activities autonomously; this remains entirely a human physical activity. |
Related occupations — Farming, Fishing & Forestry
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.