Derrick Operators, Oil and Gas
47-5011.00Median wage $58,620/yr10,590 employed (US)Rank #868 of 923 scored · top 94% by substitution
Rig derrick equipment and operate pumps to circulate mud or fluid through drill hole.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution11
Exposure7
Augmentation27
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
15 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%7
panel mean rating 1.3/5 → substitution pressure 7/100
Technical feasibility todayw 20%9
panel mean rating 1.3/5 → substitution pressure 9/100
Cost vs. human wagew 15%8
panel mean rating 1.3/5 → substitution pressure 8/100
Adoption barriersw 20%inverted — strong barriers lower the score23
panel mean rating 4.1/5 (barrier strength) → substitution pressure 23/100
Sector adoption velocityw 10%9
panel mean rating 1.3/5 → substitution pressure 9/100
Task breakdown (15 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.
Listen to mud pumps and check regularly for vibration and other problems to ensure that rig pumps and drilling mud systems are working properly.
46CI 30–62 · exposure 45 · augmentation 63 · importance 4.4/5 · click for rater detail
Listen to mud pumps and check regularly for vibration and other problems to ensure that rig pumps and drilling mud systems are working properly.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large integrated oil & gas operators have begun deploying remote monitoring and predictive maintenance, but adoption is still in the pilot-to-early-deployment phase rather than ubiquitous. Smaller operators and legacy rigs lag significantly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas extraction is a capital-intensive, physically-based industry with historically slower digitization and AI adoption compared to information/professional services sectors, though predictive maintenance pilots are growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven dashboards displaying real-time vibration metrics, trend analysis, and anomaly alerts substantially enhance derrick operators' ability to detect problems early and prioritize interventions, keeping the human in a supervisory but far more efficient role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sensor-based monitoring and predictive analytics can meaningfully assist operators by flagging anomalies earlier and reducing the need for constant manual listening, improving safety and efficiency while the operator remains responsible for response. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Continuous vibration and sound monitoring of industrial equipment can be substantially automated via sensors and AI-powered anomaly detection systems that perform condition monitoring and predictive maintenance. Current IoT + machine learning systems can detect equipment failures and degradation with >50% time savings compared to manual inspection rounds. |
| Task automatability | claude-sonnet-5 | 2/5 | While vibration/acoustic sensors and predictive maintenance AI exist, this task as described requires physical presence on the rig floor, tactile/auditory monitoring, and immediate physical response, limiting full end-to-end automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Oil & gas operators must maintain safety and regulatory compliance (API standards), and equipment certification often requires documented human verification. However, augmenting rather than replacing human monitoring is already normalized, reducing hard barriers to partial automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human specifically for this monitoring subtask, but safety-critical drilling operations carry liability concerns and often require human presence for immediate corrective action, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated sensor networks and edge AI for condition monitoring have become cost-competitive with periodic human inspection labor; sensor amortization and cloud inference are significantly cheaper than repeated rig personnel rounds, especially over long operational periods. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting rigs with sensor arrays, edge computing, and monitoring software involves substantial capital and integration costs that may not yet undercut the cost of an operator already present on-site performing multiple duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed condition monitoring and predictive maintenance products exist in oil & gas operations (e.g., sensor-based monitoring platforms), but they typically require human validation of alerts and integration with existing rig infrastructure. Production systems are working but still depend on human oversight and confirmation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Condition-monitoring systems using vibration sensors and acoustic analysis are deployed in some industrial settings, but comprehensive AI systems replacing an on-site derrick operator's continuous sensory monitoring of mud pumps are not standard in oil and gas operations yet. |
Prepare mud reports, and instruct crews about the handling of any chemical additives.
25CI 25–25 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail
Prepare mud reports, and instruct crews about the handling of any chemical additives.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas operations are heavily regulated, risk-averse, and slow to adopt autonomous decision-making in safety-critical processes; pilot projects exist but widespread production deployment of AI-generated mud reports and chemical instructions remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas field operations are a physically-oriented, lower-digitization sector where AI adoption for on-site crew instruction and reporting remains slow and mostly pilot-stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-generating report drafts, flagging anomalies in mud properties, and summarizing chemical safety data, meaningfully reducing clerical work and enhancing crew briefing preparation, though the human expert must validate all recommendations. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft and standardize mud reports from sensor data, aiding the operator, but does not materially assist the interpersonal instruction of crew on chemical handling. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Mud reports involve structured technical data that could be partially automated (calculations, formatting), but interpreting mud properties, deciding chemical additives, and translating requirements into crew-specific instructions require contextual judgment and real-time field decisions that current AI cannot reliably do end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Report drafting from data could be partially automated, but instructing crews on hazardous chemical handling requires physical presence, judgment, and real-time communication that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chemical handling and mud management affect wellbore integrity, safety, and regulatory compliance; derrick operators and mud engineers carry professional responsibility, and liability for incorrect instructions creates strong barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Handling chemical additives on a rig involves safety regulations, certification requirements, and liability concerns that necessitate qualified personnel for instruction and oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for report generation and data summarization are modest in cost, but the need for expert human review and modification to ensure safety and accuracy means the effective cost per fully autonomous task completion remains high relative to the time saved. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While report generation could be cheap via AI, the instructional/safety component still requires a trained human on-site, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can draft routine reports from sensor data, no deployed product reliably generates final mud reports or safety-critical chemical handling instructions that crews depend on; this remains largely manual work in production wells. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some digital mud-logging and reporting software exists with data entry assistance, but no deployed product reliably generates full mud reports or delivers safety instructions to rig crews. |
Control the viscosity and weight of the drilling fluid.
21CI 16–25 · exposure 17 · augmentation 50 · importance 4.5/5 · click for rater detail
Control the viscosity and weight of the drilling fluid.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas has adopted monitoring technology but maintains conservative practices around core drilling operations. Digital transformation is slower than in software or finance sectors, with most adoption limited to augmentation rather than replacement of human operators. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas drilling is a physically intensive, moderately digitized sector where automation is advancing slowly and unevenly, mostly through sensor-assisted monitoring rather than full task replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Real-time monitoring dashboards and automated alerts can assist operators in tracking fluid properties and flagging anomalies, improving decision speed and reducing manual sampling; however, the assistant remains secondary to human judgment on adjustments. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Real-time mud-logging analytics and predictive software can help operators anticipate needed adjustments and flag anomalies, improving decision speed and accuracy while the human still performs the physical control actions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While sensors can continuously monitor viscosity and weight, human judgment is needed to interpret complex drilling conditions, adjust parameters based on unexpected geological changes, and decide when manual intervention is required. Current AI cannot reliably replicate the full decision-making loop without significant human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical monitoring and adjustment task on drilling rigs requiring real-time sensory judgment and manual intervention with mud pumps and additives; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Drilling operations are heavily regulated by safety and environmental standards; offshore operations require licensed personnel on-site, and liability for well integrity failures creates strong legal and organizational barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical oilfield operations are subject to strict regulatory, safety, and liability requirements, and errors in mud weight/viscosity can cause blowouts, making unsupervised automation highly restricted. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Sensor systems and integration costs are substantial, and the requirement for continuous monitoring infrastructure, plus ongoing human oversight for adjustments and troubleshooting, keeps total cost comparable to or higher than a derrick operator's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor and monitoring software add cost on top of, not instead of, the derrick operator, since physical fluid handling and equipment operation still require a human present at the rig. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated monitoring systems exist for drilling fluid properties, but deployed products remain sensor-dependent with material limitations in real-time adjustment and lack the adaptive decision-making of experienced operators. Few systems operate fully autonomously in production without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some drilling automation and mud-logging sensor systems exist that monitor fluid properties and suggest adjustments, but actual control of viscosity/weight still requires a human operator on-site executing physical changes. |
Start pumps that circulate mud through drill pipes and boreholes to cool drill bits and flush out drill cuttings.
15CI 5–25 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Start pumps that circulate mud through drill pipes and boreholes to cool drill bits and flush out drill cuttings.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas operations are adopting remote monitoring and automated alerts, but full autonomous pump control remains rare in production. Many drilling sites operate older equipment with limited digital integration, and operator expertise in reading mud conditions remains valued. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction is a physical, low-digitization sector with slow AI adoption for hands-on rig floor operations compared to information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered monitoring dashboards, predictive alerts for pump failure, and real-time mud property analysis can assist operators in making faster decisions, but the core pump-starting action and situation assessment remain human-led. Augmentation is useful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-based monitoring and predictive analytics can inform pump operation decisions and flag anomalies, offering some assistance, but the core physical task itself sees limited AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Starting pumps is a discrete, repeatable mechanical action that could be automated with simple controls, but the task requires real-time monitoring of mud circulation, pressure, and flow rates to ensure safe drilling conditions. Current AI lacks the integrated sensor fusion and adaptive control needed to handle variable downhole conditions autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical equipment-operation task requiring hands-on control of pumps, mud circulation systems, and rig equipment in a hazardous field environment; current AI cannot physically start pumps or perform this manual operation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Drilling operations are heavily regulated (OSHA, API standards, environmental rules), and the derrick operator has direct responsibility for equipment integrity and crew safety. Liability asymmetry—a pump failure causing a blowout or spill—creates strong organizational and legal resistance to removing human oversight. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas drilling operations have strict safety regulations, requires physical presence, certified personnel, and involves high liability for equipment failure or blowouts, creating strong barriers to remote/AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Retrofitting derrick equipment with advanced automation and sensor systems, plus integration and maintenance, is capital-intensive relative to the hourly wage of a derrick operator. The cost of equipment failure or safety incidents further tilts against AI-only substitution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical action, so AI cost for full task substitution is effectively infinite relative to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While pump automation systems exist (SCADA, remote monitoring), they typically require human operators to initiate sequences and respond to alarms or anomalies. No production systems fully automate this task end-to-end without human oversight of mud properties, circulation pressures, and drilling parameters. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this physical task; existing rig automation systems assist with monitoring but human derrick operators still physically operate pumps and equipment. |
Inspect derricks for flaws, and clean and oil derricks to maintain proper working conditions.
9CI 5–13 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail
Inspect derricks for flaws, and clean and oil derricks to maintain proper working conditions.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While oil and gas is digitizing, the specific task of in-person derrick inspection and maintenance has seen only limited automation pilots; most operators still rely on human inspectors and maintenance crews due to safety regulations and the hands-on nature of the work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with slow AI adoption for hands-on maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide marginal assistance through remote visual analysis to flag potential problem areas before a human technician investigates, but the core manual and safety-critical elements remain firmly human-driven, limiting meaningful productivity amplification. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor data, predictive maintenance analytics, and computer vision can flag potential flaws or wear patterns to inform human inspectors, offering modest assistance rather than transformation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Inspecting derricks for flaws and performing hands-on cleaning and oiling require physical presence, real-time visual assessment of varied environmental conditions, and direct mechanical manipulation that current AI systems cannot perform. This task is fundamentally tied to on-site physical work in hazardous conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical inspection, cleaning, and lubrication of derrick equipment requires manual dexterity and on-site presence in hazardous environments that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas operations are heavily regulated by occupational safety (OSHA, API standards) and environmental agencies; derrick maintenance is tied to operator certification and liability requirements, and the task inherently requires a human physically present in a hazardous location to ensure safety compliance and legal responsibility. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, physical liability, and the need for hands-on hazard assessment in oil and gas operations create strong barriers to full automation of physical inspection and maintenance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and integration costs of remote inspection systems, plus the fallback requirement for human technicians to perform actual cleaning, oiling, and remedial work, makes AI intervention more expensive than direct human inspection and maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute performing physical maintenance tasks, so AI cost is effectively infinite relative to a human worker for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision could theoretically assist with visual inspection from video feeds, no deployed AI system reliably performs the full end-to-end task of flawed derrick assessment and maintenance in production oil and gas operations. Narrow inspection pilots may exist, but they do not replace the human operator's physical presence and judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously inspects, cleans, and oils oilfield derricks; at most, sensor-based monitoring exists as a research/pilot stage complement, not a substitute. |
Weigh clay, and mix with water and chemicals to make drilling mud, using portable mixers.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Weigh clay, and mix with water and chemicals to make drilling mud, using portable mixers.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations are capital-intensive and risk-averse in automation adoption for safety-critical processes; drilling mud preparation remains primarily manual due to the need for on-site adjustments and regulatory accountability, with minimal AI adoption to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, lower-digitization sector where AI adoption for hands-on rig tasks remains minimal and mostly limited to data analytics rather than physical task automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with recipe recommendations or real-time mixing ratio adjustments based on sensor data, but the predominant requirement for physical manipulation and hands-on material handling limits meaningful augmentation of the human operator's core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensors and monitoring systems can help track mud weight and composition parameters to guide operators, but this offers only modest assistance to the core physical mixing task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Weigh clay, mix with water and chemicals, and operate portable mixers involve physical manipulation of materials, precise measurement in dynamic field conditions, and equipment operation that requires real-time adjustments. Current AI systems cannot physically handle, weigh, or operate mixing equipment without significant bespoke robotics that are not yet deployed in this context. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task involving weighing, mixing, and handling materials with portable equipment on a drilling rig, which current AI systems cannot perform end-to-end without robotic embodiment far beyond today's deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical process in oil and gas operations, regulatory oversight of drilling fluid composition and quality, and requirement for licensed/trained personnel to ensure compliance with API and environmental standards create substantial legal and operational barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation of mud mixing, but harsh field conditions, safety protocols, and reliance on human judgment for mud consistency create practical organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware and integration costs for a robotic system capable of weighing, measuring, and mixing drilling mud in field conditions would far exceed the loaded wage of a derrick operator performing this routine task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical automation would require expensive robotics/sensor infrastructure far exceeding current human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform this end-to-end task in production. The task demands physical dexterity, on-site environmental adaptation, and real-time quality control that exceed current autonomous system capabilities in unstructured oilfield settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously weighs and mixes drilling mud on rig sites; this remains a manual/mechanically-assisted process performed by human operators. |
Inspect derricks, or order their inspection, prior to being raised or lowered.
6CI 0–13 · exposure 5 · augmentation 38 · importance 4.5/5 · click for rater detail
Inspect derricks, or order their inspection, prior to being raised or lowered.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas operations are capital-intensive and safety-critical but digitization of inspection workflows is still in early stages; most sites rely on human inspectors following established procedures rather than AI-driven systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with slow AI adoption for hands-on safety-critical inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Computer vision tools can assist inspectors by flagging potential wear, corrosion, or damage for closer examination, reducing the time spent on visual scanning, though human judgment and final sign-off remain essential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled sensors, drones, or checklists could assist in flagging anomalies or documenting inspections, but they play a minor supportive role rather than transforming the core physical inspection process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical on-site inspection of complex industrial equipment and real-time safety judgment about structural integrity before critical operations. Current AI systems cannot autonomously perform the hands-on inspection, assess subtle defects, or make the safety decisions required without human presence and expertise. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical inspection of heavy equipment for structural integrity and safety requires hands-on visual and tactile assessment in an outdoor industrial setting, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Regulatory frameworks (OSHA, API standards) and industry liability requirements mandate that qualified, licensed personnel inspect and certify derrick safety before operation. Legal and safety accountability for derrick integrity create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations (OSHA, industry standards) typically require qualified personnel to inspect and certify rigging equipment before operation, creating strong liability and regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The setup, integration, and ongoing oversight costs for reliable vision systems combined with the need for a qualified human inspector to validate findings would likely match or exceed the cost of direct human inspection by a trained derrick operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute performing this physical safety inspection, so any AI cost consideration is moot relative to the human doing the actual inspection. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision AI can assist with image analysis of derricks, no deployed product reliably performs full autonomous inspection and sign-off at scale. Some computer vision tools exist for asset monitoring, but production systems still depend heavily on human inspectors to evaluate safety-critical findings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously inspects derricks pre-raising/lowering; some sensor-based structural monitoring exists in research/pilot form but not as a substitute for this task. |
Repair pumps, mud tanks, and related equipment.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Repair pumps, mud tanks, and related equipment.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations remain heavily physical and manual; adoption of automation for field repairs is minimal, with the sector lagging in digitization and robotic deployment compared to information and manufacturing sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field maintenance is a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on equipment repair. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with remote diagnostic monitoring and predictive maintenance recommendations, but the core repair work remains human-dependent, limiting the productivity multiplier effect. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with predictive maintenance alerts or diagnostic guidance, but offers little direct help with the physical repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Repairing physical equipment requires on-site hands-on work, precise mechanical diagnosis, and tool operation that current AI systems cannot perform. AI cannot navigate, assess damage visually in situ, manipulate tools, or execute repairs in real-world conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical diagnosis and repair of pumps and mud tanks requires manual dexterity, tool use, and fieldwork in hazardous environments that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations, worker licensing requirements, liability for equipment failure and workplace injury, and the need for real-time human judgment in hazardous oil and gas environments create strong legal and operational barriers to automated repair. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing mandates a human perform this repair, safety regulations, equipment liability, and the hazardous physical environment create significant practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized repair robots capable of this work far exceeds the loaded wage of a trained derrick operator, making automation economically infeasible at current technology levels. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for hands-on mechanical repair, so any AI cost comparison is moot—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously repair pumps, mud tanks, or related oil and gas equipment. This task demands embodied robotics and real-world mechanical action that does not exist in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical repair of oilfield equipment; robotics for this specific maintenance task remain research-stage at best. |
Guide lengths of pipe into and out of elevators.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Guide lengths of pipe into and out of elevators.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas automation remains slow in physical operations; derrick work is labor-intensive, site-specific, and heavily regulated. No measurable production-scale displacement by AI or robotics is evident in this skilled trades segment. |
| 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 hands-on rig floor tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with monitoring pipe inventory, detecting pipe dimensions via computer vision, or logging operations; however, the core task of guiding pipe in real time benefits minimally from current AI, as human perception and reaction remain essential for safety. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of guiding pipe into elevators; sensor-based monitoring may exist elsewhere but not for this specific manual action. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time spatial coordination in a high-risk, dynamic physical environment where pipe dimensions, weight, and positioning vary. Current AI systems cannot autonomously manipulate or precisely position heavy industrial equipment without human oversight and manual control. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity and real-time force feedback on a drilling rig; no current AI or robotic system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and safety barriers exist: OSHA requires trained, certified derrick operators to oversee critical lifting operations; liability and catastrophic error costs (dropped pipe, worker injury) create legal liability asymmetry; and human presence is mandated in safety-critical roles on rigs. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas rig operations have strict safety regulations, physical hazard exposure, and require trained personnel on-site, creating strong organizational and regulatory barriers to automation of this specific manual task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Derrick operators earn substantial loaded wages ($70k–$100k+), and the cost of autonomous systems capable of this task—including sensors, robotics, integration, and redundancy for safety—would exceed the wage cost per unit task for many years. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed at scale, so any hypothetical automation would require expensive specialized robotics far costlier than current human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems reliably perform this task in production oil and gas operations. The task demands physical manipulation, real-time environmental adaptation, and safety-critical judgment that existing robotics or AI agents do not meet at industry standard. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product guides pipe into elevators on a rig floor; this remains a manual, physically demanding task performed by human derrick operators. |
String cables through pulleys and blocks.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
String cables through pulleys and blocks.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations remain highly physical and site-specific with strong regulatory oversight and union-represented workforces. Adoption of automation for skilled derrick work has been minimal, with industry focusing on larger-scale mechanization rather than AI-driven robotics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction field operations are a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with planning cable layouts or monitoring cable tension through sensors, but these augmentations are peripheral to the core task of physically stringing cables. Current assistance capabilities are limited and not widely deployed in derrick operations. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of stringing cables through pulleys and blocks; this is purely manual mechanical work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of cables in a complex, three-dimensional environment with precise positioning through mechanical components. Current AI systems lack embodied robotics capable of reliably performing fine motor manipulation of cables in outdoor, high-risk oil and gas settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical rigging task requiring dexterity, strength, and hazard awareness on an oil rig; no current AI system or robot performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, OSHA compliance, and industry standards require qualified human operators to perform critical rigging tasks on active derricks. Liability concerns and the hazardous nature of the work create strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rig safety regulations, physical dexterity requirements, and liability for equipment/personnel safety mean this must be performed by trained, often certified personnel on-site. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized robotic system capable of performing this task would cost orders of magnitude more than the loaded wage of a derrick operator, and would require extensive customization and maintenance for this specific application. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at any cost for this physical task, so AI is not cheaper—it's not a functioning alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs cable stringing through pulleys and blocks in derrick operations. This remains fundamentally a physical task requiring dexterity, situational awareness, and real-time adaptation that industrial robotics have not yet solved at production scale in this domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs cable stringing through pulleys/blocks in oilfield settings; this remains a human manual labor task. |
Steady pipes during connection to or disconnection from drill or casing strings.
5CI 5–5 · exposure 0 · augmentation 13 · importance 3.8/5 · click for rater detail
Steady pipes during connection to or disconnection from drill or casing strings.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations remain heavily dependent on skilled manual labor and human judgment in physically hazardous environments. Automation adoption in drilling operations is slow, with most investment in monitoring and data analysis rather than autonomous equipment manipulation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction field operations are a low-digitization, physically demanding sector with minimal AI/robotics adoption for hands-on drilling floor tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist via real-time monitoring and alerts (pipe stress, vibration anomalies), but the core physical task of steadying pipes during connection cannot be meaningfully augmented by current AI without direct equipment control, which is not mature in this domain. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of steadying pipe during connection or disconnection. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Steadying pipes during connection/disconnection requires real-time physical manipulation in a dynamic, high-risk environment with heavy equipment. Current AI systems lack the embodied robotics, force feedback, and autonomous dexterity to perform this safety-critical mechanical task reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands-on manipulation of heavy pipe during connection/disconnection on a rig floor; no AI system can perform this physical action. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, liability exposure for equipment damage or worker injury, and industry standards require certified human operators to oversee and perform critical pipe-handling operations. The high consequence of failure creates strong regulatory and insurance barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, physical risk to workers and equipment, and the need for trained personnel on rig floors create strong organizational and safety-driven barriers to any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robotic solution capable of this task would require significant capital investment (hundreds of thousands to millions), custom engineering, and maintenance, far exceeding the cost of a derrick operator's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute performing this physical task, so cost comparison favors the human worker by default since the alternative (specialized robotics) is far more capital-intensive than current wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform autonomous pipe-steadying in drilling operations. This task demands specialized heavy equipment control, precise positioning in confined spaces, and immediate response to vibration and load changes—capabilities not present in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this physical steadying task; automation here would require robotics, not AI software, and no such robotic system is in production use for this specific task. |
Set and bolt crown blocks to posts at tops of derricks.
3CI 0–5 · exposure 0 · augmentation 0 · importance 4.4/5 · click for rater detail
Set and bolt crown blocks to posts at tops of derricks.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations remain relatively non-digitized in field assembly tasks; adoption of autonomous systems for derrick work is minimal and constrained by regulatory, safety, and technical barriers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically demanding sector with minimal AI/robotic adoption for manual rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers minimal assistance for physical crown block installation; tools such as torque guides or digital checklists provide only marginal support compared to the skilled judgment required. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this physical bolting and rigging task at height; at most, planning software might schedule maintenance but not aid execution. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Setting and bolting crown blocks to derrick posts is a physical assembly task requiring spatial reasoning, precise positioning at height, and fastener torque verification—capabilities current AI systems cannot perform end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical rigging task requiring climbing derricks, manual positioning of heavy equipment, and bolting at height—no current AI system or robot performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has strong legal and safety barriers: OSHA regulations, industry certifications, and liability requirements mandate that qualified human workers perform and certify high-altitude structural work on derricks. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, fall-protection requirements, and heavy equipment liability mean this task requires trained, certified personnel with strict oversight, though not a formal licensing barrier like law or medicine. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Fully autonomous robotic systems capable of setting crown blocks at derrick heights would be extremely capital-intensive and specialized, making them far more expensive than deploying a skilled derrick operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom heavy-lift robotics far exceeding human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform high-altitude structural assembly and bolting tasks in oil and gas environments; this remains work requiring human technicians on-site. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs or bolts crown blocks; this remains a manual oilfield task performed by skilled derrick hands. |
Position and align derrick elements, using harnesses and platform climbing devices.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Position and align derrick elements, using harnesses and platform climbing devices.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas field operations remain highly hands-on and resistant to automation due to regulatory requirements, safety liability, remote locations, and the physical heterogeneity of worksites. Adoption of autonomous systems for this task is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction is a low-digitization, physically intensive sector with minimal AI/robotic adoption for hands-on field tasks like derrick climbing and positioning. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance in positioning and aligning physical derrick elements on-site; the task is entirely dependent on the operator's embodied presence, spatial reasoning, and real-time safety judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with monitoring, scheduling, or safety alerts but offers negligible direct assistance to the physical act of climbing and positioning derrick elements. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in a hazardous 3D environment at height, using specialized safety equipment and making real-time spatial judgments. Current AI systems cannot operate the harnesses, climbing devices, or position structural elements in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on task requiring climbing, positioning heavy equipment, and using safety harnesses in a hazardous outdoor environment—no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Occupational safety regulations (OSHA, API standards) require licensed, trained human operators for high-risk derrick work, with explicit liability assigned to the responsible human. Safety sign-off and in-person competency are legally mandated. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas field operations involve strict safety regulations, certification requirements for working at heights, and liability concerns that require trained human personnel to perform this physically hazardous task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics or autonomous systems capable of this task would be prohibitively expensive compared to a trained derrick operator's loaded wage, with substantial integration and liability overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative performing this physical task, so cost comparison favors the human worker entirely; specialized robotics for this would be far more expensive than human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously position and align derrick elements while safely using climbing harnesses and platform devices. This requires embodied robotics in an unstructured, safety-critical environment where no production systems exist. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical derrick positioning and climbing tasks; this remains firmly in the domain of human physical labor with no robotic substitutes in production. |
Supervise crew members, and provide assistance in training them.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Supervise crew members, and provide assistance in training them.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas is a laggard sector for AI adoption in core operations; physical hazard environments, regulatory constraints, and cultural reliance on human expertise slow substitution of supervisory roles. No evidence of AI replacing derrick supervisors in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with minimal AI adoption for on-site crew supervision and training. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by scheduling training, generating compliance documents, or logging crew performance, but augmentation is limited because supervision and training rely on direct human judgment, real-time feedback, and mentoring relationships that AI cannot meaningfully enhance without a human supervisor in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, tracking training records, or generating training materials, but offers minimal assistance for the core supervisory and hands-on training work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Supervising crew members and training them requires real-time judgment, interpersonal responsiveness, safety assessment, and adaptive communication that current AI systems cannot perform autonomously in a high-hazard environment. These tasks demand human presence, authority, and accountability that cannot be delegated to AI end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising crew and hands-on training on an oil rig requires physical presence, real-time judgment, and interpersonal leadership that current AI cannot perform end-to-end.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated by OSHA, API, and environmental authorities that impose strict requirements for on-site human supervision, safety accountability, and direct training by qualified personnel. A licensed, responsible human must legally supervise crew and sign off on safety-critical training. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, certification requirements, and liability for supervising hazardous rig operations create strong barriers against replacing a human supervisor with automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Derrick operators commanding supervisory roles have high loaded wages ($100k+/year with benefits); AI systems cannot replicate the cost structure of a single supervisor across a shift, and oversight of any AI system would add cost rather than replace the supervisor's salary. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for a supervisory/training role in this physical, high-risk environment, so AI cost comparison is not meaningful; human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably supervises oil and gas crews or delivers hands-on safety training in production settings. While AI can draft training materials or log observations, the core supervisory and mentoring functions require human judgment and legal responsibility that deployed systems do not address. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product supervises field crews or delivers hands-on physical safety training on rigs today; this remains firmly a human role. |
Clamp holding fixtures on ends of hoisting cables.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Clamp holding fixtures on ends of hoisting cables.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations are capital-intensive and risk-averse; offshore and field-based physical tasks see minimal AI/automation adoption relative to office work. Adoption of robotic systems for derrick operations remains marginal despite decades of mechanization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas extraction field operations are a low-digitization, physical-labor-intensive sector with minimal AI/robotic adoption for manual rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist through real-time monitoring, predictive maintenance alerts, or safety compliance checking, but the core physical clamping action offers limited scope for augmentation while keeping a human in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful assistance for the physical act of clamping fixtures onto hoisting cables; sensors or monitoring tools do not materially change this hands-on task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment in a dynamic offshore/onsite environment with precise positioning and safety-critical outcomes. Current AI systems lack the embodied dexterity, environmental awareness, and real-time force-feedback capabilities needed to safely clamp fixtures on cable ends. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical dexterity task requiring hands-on manipulation of heavy rigging equipment in a hazardous field environment; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations have strict safety regulations, liability frameworks, and often require licensed/certified personnel to perform critical rigging and cable-handling tasks. The safety-critical nature and legal/insurance requirements create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical rigging work on oil rigs is subject to strict occupational safety regulation and liability concerns, requiring trained personnel, though not necessarily a specific professional license. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Developing or deploying automation for this task would require custom robotics, integration, and safety certification—orders of magnitude more expensive than a trained derrick operator performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI or robotic substitute exists for this physical task, so the AI cost is effectively infinite relative to a human worker performing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs this task in production oil and gas settings. While specialized robotics exist for some industrial tasks, the combination of cable handling, fixture clamping, and variable field conditions is not solved by any commercial system in routine use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed robotic or AI products performing cable clamping on oil rigs; this remains purely a human manual task in production settings today. |
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.