Roustabouts, Oil and Gas
47-5071.00Median wage $46,960/yr44,970 employed (US)Rank #892 of 923 scored · top 97% by substitution
Assemble or repair oil field equipment using hand and power tools. Perform other tasks as needed.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution9
Exposure1
Augmentation19
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
13 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
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%1
panel mean rating 1.0/5 → substitution pressure 1/100
Technical feasibility todayw 20%0
panel mean rating 1.0/5 → substitution pressure 0/100
Cost vs. human wagew 15%1
panel mean rating 1.0/5 → substitution pressure 1/100
Adoption barriersw 20%inverted — strong barriers lower the score42
panel mean rating 3.3/5 (barrier strength) → substitution pressure 42/100
Sector adoption velocityw 10%3
panel mean rating 1.1/5 → substitution pressure 3/100
Task breakdown (13 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Bolt together pump and engine parts.
19CI 10–28 · exposure 8 · augmentation 25 · importance 3.4/5 · click for rater detail
Bolt together pump and engine parts.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas field operations remain labor-intensive and geographically dispersed; adoption of field automation in assembly work is slow and concentrated only in the largest offshore/onshore integrated operations, well below adoption rates in software or information sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a low-digitization, physically demanding sector with minimal AI/robotics adoption for manual assembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited scope for AI assistance—workers could use computer vision for alignment hints or pressure sensors for bolt-torque guidance, but these are narrow enhancements on a fundamentally hands-on physical task that demands human judgment and feel. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with digital work instructions, torque specs, or predictive maintenance guidance, but offers little direct assistance to the physical bolting task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Bolting together pump and engine parts requires precise spatial reasoning, dexterity, and real-time adjustment in a physical environment. While robotic systems exist in controlled factory settings, the oil and gas field context involves variable positioning, vibration, access constraints, and ad-hoc assembly that current mobile AI agents cannot handle autonomously at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | Bolting together pump and engine parts requires physical dexterity, mobility, and manipulation in an unstructured oilfield environment that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal licensing barriers exist for the task itself, though OSHA regulations apply to work practices generally. The primary barriers are technical (current robot limitations) rather than legal or organizational. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, equipment liability, and the physical/hazardous nature of oilfield work create real friction against unproven automated systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying capable manipulation robots, training, integration, and field maintenance costs substantially exceed a roustabout's loaded wage for this specific task. Automation cost-per-task is not yet favorable outside high-volume manufacturing. |
| 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 be far costlier than a human worker today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs general pump and engine assembly in oil and gas field conditions. Specialized industrial robots exist for structured manufacturing lines, but not for the variable, on-site assembly tasks a roustabout encounters in operational field settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product reliably performs assembly of pump/engine parts in oilfield settings; this remains research-stage robotics at best. |
Clean trucks used in the fields.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.3/5 · click for rater detail
Clean trucks used in the fields.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas field services remain among the least-automated sectors due to harsh physical environments, capital constraints in many operations, and reliance on flexible manual labor to handle varied, unpredictable tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a low-digitization, physically intensive sector with minimal AI/robotics adoption for manual tasks like vehicle cleaning. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for truck cleaning—there is no established augmentation pathway through tools like computer vision or robotic arms at the typical roustabout operation scale. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this manual physical cleaning task, as it involves no cognitive or data-processing component AI tools could enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning trucks requires physical manipulation in unstructured outdoor environments, including scrubbing, spraying, and accessing various surfaces. Current AI and robotics cannot reliably perform this task end-to-end without human oversight or assistance. |
| Task automatability | claude-sonnet-5 | 1/5 | Cleaning trucks in oilfield settings is a manual physical task requiring mobility, dexterity, and handling of equipment in outdoor/rugged environments; no AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers specific to truck cleaning itself, though safety and environmental regulations around oil and gas operations create some friction to automation deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates a human, but physical unstructured environments and equipment costs create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous cleaning systems capable of field deployment would require significant capital investment, maintenance, and oversight—far exceeding the low hourly wage of a roustabout performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute, so any hypothetical automation (specialized robotics) would be far more costly than a low-wage laborer performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercially deployed systems can autonomously clean trucks in oil and gas field conditions. While industrial cleaning robots exist in controlled settings, none operate reliably in the variable, mud-heavy environments typical of field operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic products clean oilfield trucks in production; general-purpose cleaning robots remain research-stage or limited to structured indoor environments. |
Dig drainage ditches around wells and storage tanks.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.0/5 · click for rater detail
Dig drainage ditches around wells and storage tanks.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas roustabout work remains dominated by manual labor in geographically dispersed, lower-digitization operations. Adoption of automation in these roles has been minimal, with sector-wide digital transformation lagging knowledge work and urban logistics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a low-digitization, physically demanding sector with minimal AI/robotics adoption for manual excavation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful AI assistance technology for planning or executing drainage ditch digging. The task is straightforward physical labor with minimal decision complexity that does not benefit from AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI provides essentially no assistance for the physical act of digging ditches, though it might help with site planning or scheduling unrelated to the manual task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Digging drainage ditches requires dynamic physical manipulation in unstructured outdoor environments with variable soil conditions. Current AI and robotics cannot reliably perform excavation work end-to-end with the precision, adaptation, and safety standards required for oil and gas operations. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical excavation task in outdoor oilfield terrain requiring manual or heavy-equipment digging; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no hard legal licensing barriers, the physical demands and safety-critical nature of working around wells and storage tanks create moderate adoption friction. OSHA oversight and site-specific safety protocols add some friction, though not prohibitive ones. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for ditch digging, but safety regulations, site permitting, and environmental compliance around well sites create some procedural friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of autonomous excavation equipment and the technical integration required far exceed the loaded wage of manual laborers performing this straightforward, low-skill task. Specialized machinery for this niche application is economically unviable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative, so AI cost is effectively infinite relative to human/equipment labor for this physical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products perform autonomous ditch digging in oil and gas settings. While some specialized construction robots exist for limited scenarios, none operate reliably at production scale in the complex field conditions (varying terrain, obstacles, utility locations) typical of this work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously dig drainage ditches around wellsites; this remains manual labor or human-operated machinery work. |
Dig holes, set forms, and mix and pour concrete into forms to make foundations for wood or steel derricks.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Dig holes, set forms, and mix and pour concrete into forms to make foundations for wood or steel derricks.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas roustabout work remains in laggard sectors for AI/robotics adoption: low digital maturity, scattered small work crews, harsh field conditions, and strong regulatory constraints limit pilot deployments. Production adoption of autonomous concrete foundation work is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field construction is a low-digitization, physically demanding sector with minimal AI/robotic adoption for manual foundation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI and robotic systems offer minimal direct assistance to a human roustabout performing digging, form-setting, and concrete pouring. Digital site planning or equipment monitoring tools have limited bearing on the core physical labor of this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning, scheduling, or specifications via software tools, but offers little direct assistance to the physical digging, form-setting, and pouring activities themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in unstructured outdoor environments (digging, form-setting, pouring concrete), precise spatial reasoning in variable ground conditions, and real-time adaptation—capabilities far beyond current autonomous systems. No AI or robotics system can reliably perform all sub-tasks end-to-end with the dexterity and safety margins required on active oil/gas sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical construction task involving excavation, formwork, and concrete pouring in outdoor oilfield conditions; no current AI system can perform manual labor or physical manipulation end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas work is heavily regulated for safety and site liability; insurance, OSHA compliance, and certification requirements for foundation work create organizational friction. The task also inherently requires on-site human presence and decision-making for safety and quality assurance in hazardous environments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical nature of the task itself is the primary barrier rather than regulatory or liability concerns; robotics for this remains immature and site conditions vary widely. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized construction robotics remain capital-intensive and require extensive setup and supervision on site. The all-in cost (equipment, programming, operator oversight, maintenance) far exceeds the wage cost of a skilled roustabout performing this outdoor, variable task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical labor task, so AI cost is effectively infinite relative to human labor cost for this specific work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While specialized concrete-pouring or excavation robots exist in controlled industrial settings, none reliably perform the full workflow (dig holes, set forms, mix concrete, pour into custom forms) in the field conditions typical of derrick foundation work. No deployed commercial product performs this integrated task at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs digging, formwork setting, or concrete pouring; this remains purely manual/heavy-equipment-assisted human labor. |
Cut down and remove trees and brush to clear drill sites, to reduce fire hazards, and to make way for roads to sites.
10CI 5–15 · exposure 0 · augmentation 13 · importance 2.9/5 · click for rater detail
Cut down and remove trees and brush to clear drill sites, to reduce fire hazards, and to make way for roads to sites.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas field operations remain among the most resistant to automation in physical domains; remote well sites offer poor digital infrastructure, and skilled roustabouts are valued for adaptive physical work in unstructured environments with limited adoption pressure. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor and land-clearing work are low-digitization, physically demanding sectors with minimal AI/robotic adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to human roustabouts performing this task; remote sensing could inform planning but does not meaningfully augment the core physical work of cutting and removing vegetation on site. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of cutting trees and clearing brush; planning tools like GIS mapping are tangential rather than integral to this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physically cutting down and removing trees and brush in outdoor environments—work demanding manipulation, navigation of terrain, and real-time environmental assessment that current AI systems cannot perform end-to-end. No deployed automation achieves meaningful time savings at equal quality for this fundamentally physical task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical outdoor manual labor task involving chainsaws, brush clearing, and land clearing equipment that requires physical presence and dexterity current AI systems cannot provide end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory barriers exist around land clearing, fire code compliance, environmental permitting, and workplace safety certification. Human operators must ensure compliance with site-specific regulations, environmental protections, and safety protocols that cannot be delegated to autonomous systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically prevents automation, but the physical, unstructured outdoor environment and safety requirements around heavy machinery and terrain create practical friction against remote or autonomous operation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized forestry equipment and skilled labor remain significantly cheaper than the theoretical cost of building, deploying, and maintaining autonomous tree-removal robots with safety redundancy for hazardous environments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-based substitute for this physical task, so any comparison would require expensive robotic hardware far exceeding the cost of human labor for this purpose. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably performs autonomous tree-felling and brush removal at scale. This requires robotics integration, environmental safety compliance, and physical dexterity far beyond current deployed systems in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs tree cutting and land clearing autonomously; this remains within the domain of human operators using power tools and heavy equipment, sometimes with robotic/mechanized assistance but not AI-driven autonomy. |
Supply equipment to rig floors as requested and provide assistance to roughnecks.
9CI 5–13 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail
Supply equipment to rig floors as requested and provide assistance to roughnecks.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas is a capital-intensive but physically constrained sector where adoption of autonomous systems has been slow; most rig operations remain heavily dependent on human labor despite high costs. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with minimal AI/robotic adoption for manual rig floor labor. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI-enabled inventory management or predictive maintenance systems could help, the core task of physical supply delivery and on-site assistance offers limited opportunity for meaningful AI augmentation of the roustabout's role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with logistics scheduling or equipment tracking, but offers little direct augmentation to the physical act of supplying equipment and assisting roughnecks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical supply delivery and real-time assistance on active rig floors, requiring navigation of hazardous environments, manual handling, and dynamic responsiveness to roughneck requests—capabilities far beyond current AI or robotic systems in unstructured offshore conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical labor task requiring manual handling of heavy equipment on an oil rig floor in real time, well outside current AI/robotics capability for general deployment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas operations face significant regulatory oversight, safety certification requirements, and strict liability rules around equipment handling and worker safety; automation would need regulatory approval and human oversight remains legally mandated. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, hazardous environment protocols, and physical unpredictability of rig operations create substantial practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotics and automation capable of operating in harsh rig conditions are prohibitively expensive and limited in versatility compared to the loaded wage of a roustabout. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic substitute exists at scale, so any hypothetical automation would require expensive custom robotics far costlier than human labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems reliably operate end-to-end in oil and gas rig environments to supply equipment and provide on-site assistance; this remains in the domain of human workers. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical equipment supply and manual assistance to roughnecks on rig floors; this remains human physical labor. |
Unscrew or tighten pipes, casing, tubing, and pump rods, using hand and power wrenches and tongs.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.9/5 · click for rater detail
Unscrew or tighten pipes, casing, tubing, and pump rods, using hand and power wrenches and tongs.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The oil and gas sector, particularly upstream field operations, remains highly conservative in automation adoption and relies on proven manual methods. Digitization of wellsite operations is slow, and physical robotic deployment remains rare despite industry interest. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a low-digitization, physically intensive sector with minimal AI/robotics adoption for manual rig tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal direct augmentation for hand-wrench and tongs work; perhaps computer vision for bolt torque verification or pressure monitoring could assist inspection, but the core physical task leaves little room for meaningful AI-assisted productivity gain. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no direct assistance to a worker physically tightening or unscrewing pipes and rods with hand and power tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of heavy equipment in variable field conditions, with real-time torque control and tactile feedback. Current AI and robotic systems cannot reliably handle the dexterity, strength modulation, and environmental adaptation needed for consistent, safe pipe assembly work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, strength, and judgment in unpredictable oilfield conditions; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas operations involve significant liability, safety certification requirements, and regulatory oversight of equipment handling. Workers must often be trained and licensed; automation solutions face pressure from safety inspection regimes, insurance requirements, and union labor agreements in many jurisdictions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed work per se, oilfield safety regulations, liability for wellsite accidents, and the physical/hazardous nature of the environment create substantial practical and organizational barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of this work (custom hydraulic or electric manipulators) is orders of magnitude higher than the wage cost of a trained roustabout, with integration and maintenance overhead offsetting any labor savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific manual task, so any comparison would require expensive specialized robotic rig equipment with high capital and integration costs relative to a roustabout's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this task end-to-end in oil and gas operations. While some specialized robotics exist in controlled lab settings, field deployment on real wellsites at scale does not exist in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs manual pipe/tubing connection and disconnection with wrenches and tongs; this remains a research-stage robotics problem at best, with automated pipe handling systems being specialized rig equipment rather than general AI. |
Dismantle and repair oil field machinery, boilers, and steam engine parts, using hand tools and power tools.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Dismantle and repair oil field machinery, boilers, and steam engine parts, using hand tools and power tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas is a traditional, physically grounded sector with slow digital transformation. Adoption of AI for physical field tasks remains minimal; most automation in the sector focuses on monitoring and drilling, not mobile machinery repair by autonomous agents. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a physically intensive, low-digitization sector with minimal AI/robotics adoption for hands-on mechanical repair work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostics (sensor data analysis, fault prediction) or documentation, but the core mechanical task of disassembly and hands-on repair offers limited room for AI augmentation of the human operator's core productivity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, repair manuals, or predictive maintenance scheduling, but offers little direct help with the physical dismantling and repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Dismantling and repairing oil field machinery requires physical manipulation in unstructured outdoor/industrial environments, dexterity with hand and power tools, real-time troubleshooting, and contextual judgment. Current AI systems cannot perform end-to-end mechanical repair work on physical equipment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy machinery in a hazardous field environment; no current AI system can perform hands-on disassembly and repair of mechanical equipment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas operations are heavily regulated; physical safety and equipment integrity require qualified, licensed personnel to perform or certify repairs. Insurance, liability, and industry standards create strong institutional barriers to full automation of critical equipment repair. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing mandates a human specifically for this task, safety regulations, physical dexterity requirements, and liability for equipment failure create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized industrial robots, integration, maintenance, and remote operation supervision would far exceed the loaded wage of a roustabout for this hands-on field work, particularly given the non-standard nature of machinery encountered. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical labor task, so any AI-based approach would be far costlier or simply infeasible compared to a human roustabout. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform physical disassembly and repair of oil field machinery in production settings. Vision-guided robotics for this domain remain experimental and lack the adaptability required for diverse, worn equipment encountered in oil and gas fields. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs unstructured mechanical dismantling and repair of oilfield equipment; this remains firmly in the domain of skilled manual labor. |
Move pipes to and from trucks, using truck winches and motorized lifts, or by hand.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.4/5 · click for rater detail
Move pipes to and from trucks, using truck winches and motorized lifts, or by hand.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas roustabout work remains one of the least digitized, most labor-intensive occupations; automation adoption in this sector is slow and limited to confined, high-value tasks. No evidence of meaningful AI or robotic displacement in typical field operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a physically demanding, low-digitization sector with minimal AI/robotics adoption for manual rigging and pipe handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential; wearable assistance or predictive maintenance tools might help roustabouts avoid injury, but AI cannot meaningfully assist in the core physical act of moving pipes. The task is fundamentally hands-on with little room for AI-augmented human productivity gains. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of moving pipes with winches or by hand; this is not a cognitive or data-processing task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy pipes in outdoor, unstructured environments with variable positioning and safety constraints that current AI systems cannot perform end-to-end. Robotics for pipe handling in oil/gas fields exist only in early-stage research; no deployed autonomous system can reliably move pipes to/from trucks without human supervision. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring manual labor and operation of heavy equipment in outdoor oilfield settings; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, OSHA requirements, and liability frameworks heavily constrain automation of manual pipe handling in hazardous oil/gas environments. Workers must be physically present and certified; regulatory and insurance barriers make substitution difficult regardless of technical feasibility. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, physical site conditions, and liability for heavy equipment operation create real friction against unproven automated systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of pipe handling (large industrial arms, mobile platforms) require millions in capital, integration, and maintenance—far exceeding the loaded wage of a roustabout ($50–70k/year all-in), making human labor economically dominant for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so any AI cost comparison is moot; human labor with mechanical winches remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production system today can autonomously perform this physical task at scale. While robotic lifting and manipulation systems exist in controlled manufacturing settings, none demonstrate reliable, safe operation in the dynamic, outdoor oil/gas environment required here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product moves pipes on oilfield sites; this remains purely a human/mechanical winch and physical labor task. |
Clean up spilled oil by bailing it into barrels.
7CI 0–15 · exposure 0 · augmentation 13 · importance 3.2/5 · click for rater detail
Clean up spilled oil by bailing it into barrels.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations remain physically constrained with high regulatory oversight; despite digitization in some operations, spill response remains a manual, on-demand activity with minimal automation adoption in practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a physically intensive, low-digitization sector with minimal AI/robotics adoption for manual cleanup tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist via spill detection sensors or predictive tools, but the core task of physically bailing oil requires human judgment, safety protocols, and regulatory compliance that limit assistive AI value on the job site itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance to the physical act of bailing spilled oil into barrels; this is a manual task with no cognitive or planning component AI could enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in unstructured outdoor environments (spill sites) with variable oil distribution, containment, and waste handling—capabilities far beyond current robotic systems deployed at scale. No current AI system can autonomously perform the full end-to-end cleanup with meaningful time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task requiring dexterity, judgment about spill conditions, and physical bailing motion in hazardous outdoor environments; no current AI system can perform this physical action. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil spill response is heavily regulated under environmental law (Clean Water Act, state regulations) requiring human-certified operators and documented chain-of-custody for waste; human presence and licensing are legally mandated for this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physically hazardous, unstructured environment (spilled oil, uneven terrain, safety gear) creates practical barriers to any current automated system, robotic or AI-driven. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized equipment, human oversight, and integration costs for spill response robotics exceed the labor cost of human roustabouts, particularly given the low volume and high variability of spill events. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so AI cost is effectively infinite/inapplicable compared to human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably performs autonomous oil spill cleanup via bailing and barreling in production settings. Robotic solutions for oil recovery exist only in limited research or specialized contexts, not as deployable industrial systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs oil spill bailing into barrels; this remains a manual task done by human workers with basic tools. |
Walk flow lines to locate leaks, using electronic detectors and by making visual inspections, and repair the leaks.
6CI 0–13 · exposure 0 · augmentation 38 · importance 3.5/5 · click for rater detail
Walk flow lines to locate leaks, using electronic detectors and by making visual inspections, and repair the leaks.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas is moderately digital but conservative on field operations. Drone inspection pilots exist but full autonomous leak detection and repair adoption remains rare; most work is still manual or minimally augmented. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a low-digitization, physically intensive sector with minimal AI-driven task displacement in hands-on field roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Electronic leak detectors and visual aids already exist and are routinely used, providing baseline augmentation. AI-enhanced detection (e.g., predictive analytics on sensor data) could assist technicians marginally, but the physical inspection and repair work itself offers limited room for AI assistance while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Electronic leak detectors and sensor analytics with AI-enhanced pattern recognition can help workers pinpoint likely leak locations faster, improving inspection efficiency even though repair remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical navigation across pipelines, operation of hand-held detection equipment in variable outdoor conditions, and hands-on repair work. Current AI systems cannot physically traverse infrastructure, operate detectors, or perform manual repairs without hardware not yet deployable at scale in oil/gas field conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Requires physical presence in outdoor field terrain to walk lines, visually inspect, operate handheld detectors, and physically repair leaks—no AI system can perform the physical inspection or repair. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated (OSHA, EPA, industry safety standards); leak repair typically requires licensed technicians and on-site certification. Safety liability and legal responsibility for pipeline integrity create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, hazardous environment protocols, and physical dexterity needs create practical friction against remote/AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform the core physical work (walking, detecting, repairing), so the comparison is inapplicable. Any inspection drone would require human technician oversight and manual repair regardless, keeping total cost above human labor for the task as stated. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical walking, detection, and repair, so AI cost is not comparable—human labor remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously walk flow lines, locate leaks via electronic detectors, and perform repairs in production environments. Drone-based inspection exists but does not replace the full task, and repair still requires human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously walks pipeline routes and performs physical leak repairs; fixed sensors and drones exist for monitoring but not for the full walk-and-repair task. |
Lay gas and oil pipelines.
6CI 5–7 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Lay gas and oil pipelines.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas construction remains labor-intensive and geographically dispersed, with limited digitization and slow adoption of advanced automation. Pilot projects exist but production-scale displacement is minimal compared to information and professional services sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field labor is a physically intensive, low-digitization sector with minimal AI/robotic adoption for manual construction tasks like pipe-laying. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with route planning, material tracking, and inspection documentation, but offers limited direct productivity boost to the core physical act of laying pipe in real terrain and complex field conditions. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, route optimization, and monitoring via sensors or software, but offers little direct assistance to the physical act of laying pipe. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Laying gas and oil pipelines requires physical installation in variable outdoor environments, precise spatial positioning, and real-time problem-solving in trenches and across terrain. Current AI and robotics cannot perform this end-to-end with comparable speed and safety. |
| Task automatability | claude-sonnet-5 | 1/5 | Laying pipelines requires heavy physical labor, equipment operation, and manipulation of pipe segments in outdoor terrain, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pipeline work is highly regulated by federal and state authorities (DOT, OSHA); licensed professionals often must supervise or certify work. Safety liability, environmental compliance, and the physical hazard make fully autonomous substitution both legally restricted and organizationally friction-laden. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pipeline construction is subject to safety regulations, certifications for equipment operators, and physical liability concerns, creating substantial barriers to any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic and AI-augmented pipeline systems remain capital-intensive and require significant setup, maintenance, and human oversight, making the all-in cost exceed that of trained roustabouts for this heavy, outdoor labor task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for physical pipeline laying, so cost comparison favors human/mechanical labor by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems or robots reliably perform full pipeline laying in production. While some specialized construction robots exist, they lack the adaptability, judgment, and integrated safety protocols needed for complex pipeline installation work at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically lays oil and gas pipelines; this remains entirely a human/machine-operator task with no autonomous system in production. |
Guide cranes to move loads about decks.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Guide cranes to move loads about decks.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas platforms are capital-intensive, highly regulated, and conservative in safety adoption. Deck crane guidance remains almost entirely human-supervised in production; AI adoption for this task is negligible, and regulatory environment slows exploration of automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction and physical deck operations are a low-digitization, laggard sector with minimal AI/robotics adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Augmentation is limited because the task is already tightly coupled to real-time human decision-making with a qualified operator. Simple AI aids (e.g., load visualization overlays) might assist marginally, but the core judgment and communication role remains unassisted by current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based load monitoring or camera-assisted visibility tools could marginally help operators, but no significant AI-driven productivity transformation exists for this task today. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Guiding cranes to move loads about decks requires real-time spatial awareness, visual coordination with heavy equipment operators, and judgment about load stability in dynamic conditions. Current AI systems cannot reliably perceive deck environments, communicate intent to operators, or supervise dynamic load placement with the safety margins required—no end-to-end automation is feasible. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, safety-critical task requiring real-time spatial judgment, hand signaling, and coordination in dynamic outdoor environments; current AI cannot perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations face strict safety regulations, insurance requirements, and liability rules that effectively require human personnel to direct crane operations and approve load movements. Regulatory frameworks and accident-cost asymmetry create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy safety regulation, liability concerns for crane operations, and OSHA/maritime rules around signaling and rigging create strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of developing, deploying, and insuring autonomous crane guidance systems, plus the oversight infrastructure, far exceeds the wage of a skilled roustabout who performs this task directly. Human labor remains dramatically cheaper for this physically-embedded, liability-sensitive role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require expensive sensor arrays, robotics, and redundant safety systems, making it costlier than a human roustabout for the foreseeable near term. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system in production can autonomously guide crane operations on oil platforms. While computer vision and robotics exist, integrating them into live deck crane guidance with liability acceptance does not occur in practice; this remains a human-supervised task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously guides crane loads on oil and gas decks; crane automation exists only in narrow, controlled industrial settings like ports, not this context. |
Related occupations — Construction & Extraction
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.