Helpers--Extraction Workers
47-5081.00Median wage $47,730/yr6,700 employed (US)Rank #824 of 923 scored · top 89% by substitution
Help extraction craft workers, such as earth drillers, blasters and explosives workers, derrick operators, and mining machine operators, by performing duties requiring less skill. Duties include supplying equipment or cleaning work area.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution14
Exposure5
Augmentation22
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
14 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%5
panel mean rating 1.2/5 → substitution pressure 5/100
Technical feasibility todayw 20%3
panel mean rating 1.1/5 → substitution pressure 3/100
Cost vs. human wagew 15%4
panel mean rating 1.1/5 → substitution pressure 4/100
Adoption barriersw 20%inverted — strong barriers lower the score52
panel mean rating 2.9/5 (barrier strength) → substitution pressure 52/100
Sector adoption velocityw 10%4
panel mean rating 1.1/5 → substitution pressure 4/100
Task breakdown (14 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.
Observe and monitor equipment operation during the extraction process to detect any problems.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Observe and monitor equipment operation during the extraction process to detect any problems.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Extraction sectors remain relatively low-digitization, fragmented (many small operators), and conservative on safety automation; pilot programs exist but production-scale AI-only monitoring remains rare and adoption is slow relative to IT/finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Extraction industries are physical, safety-critical, and have historically slower digitization and AI adoption compared to information-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Real-time alerts from sensor systems and AI-flagged anomalies can assist human monitors by reducing fatigue and highlighting edge cases for inspection, but the assistance is partial—human judgment remains essential for safe interpretation and response. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sensor dashboards, predictive alerts, and monitoring software can meaningfully assist helpers in detecting equipment issues earlier, even though a human remains essential for on-site judgment and response. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can recognize some equipment anomalies via visual inspection of static images or simple sensor data, but real-time continuous monitoring for subtle failures, contextual problem detection, and safe intervention decisions require human judgment and environmental awareness that AI systems cannot reliably automate end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | While sensor-based monitoring and anomaly detection systems exist, this task involves physical presence, situational awareness of a dynamic worksite, and hands-on verification that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations in extraction industries (mining, oil/gas) typically require a licensed or certified human operator to monitor critical equipment; liability and injury-cost exposure mean automated systems must be legally signed off by qualified personnel, creating hard adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety regulations in extraction industries (mining, oil/gas) often require human oversight and on-site personnel for hazard detection, creating moderate regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of reliable AI monitoring (hardware sensors, compute, model maintenance, human oversight) typically costs as much as or more than employing a monitoring worker, especially when accounting for the residual error rate and legal liability. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying comprehensive sensor arrays, monitoring software, and integration for physical extraction equipment is capital-intensive compared to a helper's wage, though it may pay off at large scale over time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Vision-based monitoring systems and sensor analytics exist in early deployment, but they struggle with the variable, unstructured environments of extraction sites and produce false positives and missed detections at rates that require human verification; no mature production system reliably replaces human monitoring at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some extraction sites use IoT sensors and predictive maintenance software, but these are narrow-scope tools supplementing rather than replacing human observational monitoring in the field. |
Dig trenches.
20CI 10–30 · exposure 8 · augmentation 25 · importance 3.4/5 · click for rater detail
Dig trenches.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Construction and extraction remain lower-digitization, fragmented sectors with high labor availability and conservative equipment adoption patterns; autonomous trenching pilots are rare, and most firms continue labor-intensive methods. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and construction-adjacent physical labor sectors show minimal AI/software adoption for manual digging tasks; mechanization exists but is unrelated to AI adoption trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI augmentation for trenching is limited; operator-assisted machines (e.g., GPS-guided excavators) modestly improve precision, but the core task remains largely manual equipment operation without substantial AI-driven productivity gain. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, mapping, GPS guidance, or utility-line detection to support trench digging, but offers little direct assistance to the physical digging act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI-equipped robots lack the environmental adaptability, obstacle detection, and precision needed for trenching in varied terrain at scale, though autonomous excavators show promise in narrow, pre-mapped conditions. Meaningful automation would require persistent human oversight for site-specific hazards, soil conditions, and obstacle management. |
| Task automatability | claude-sonnet-5 | 1/5 | Digging trenches is a physical manual labor task requiring real-world manipulation of soil, tools, and machinery in variable terrain; no AI system (as opposed to specialized robotics/machinery, which is a separate technology) can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Trenching is subject to significant safety regulations (OSHA, soil shoring requirements) and site-specific hazard assessments that currently require licensed professionals; liability and digging-permit authority create moderate friction, though no strict legal bar to machine operation exists. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing uniquely protects this task from automation, but physical, safety, and terrain-variability barriers make substitution by current technology difficult regardless of legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Equipment cost, maintenance, operator labor, and integration overhead for semi-autonomous trenching systems remain comparable to or exceed manual labor wages, especially for small-to-medium jobsites where setup cannot be amortized over large volumes. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical labor task, so AI inference cost is not a comparable input; any automation would require capital-intensive robotic excavation equipment, not AI software. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous trenching end-to-end in production. Experimental robotics exist but remain in pilot/research phases with heavy operator control; excavation automation is not yet a proven, deployable system in real-world jobsites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product digs trenches; this remains a physical excavation task performed by humans and human-operated machinery, not an AI/software capability. |
Organize materials to prepare for use.
19CI 15–24 · exposure 8 · augmentation 13 · importance 3.9/5 · click for rater detail
Organize materials to prepare for use.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction industries, where most of these helpers work, are relatively low-digital, capital-intensive sectors with slow technology adoption cycles. Manual material handling remains the norm, and adoption of automation for organizational tasks lags far behind information-sector digitization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries (mining, oil/gas) are among the least digitized sectors with minimal AI-driven physical automation deployed for basic material organization tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance via computer vision to identify materials or suggest organization schemes, but the physical dexterity and contextual judgment required means AI remains a minor support tool rather than a productivity transformer for this task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little direct assistance for the physical act of organizing materials on an extraction site, as this is a manual, hands-on task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Organizing and preparing physical materials requires spatial reasoning, object handling, and contextual judgment about what 'preparation' means in a specific work environment. Current AI cannot autonomously perform end-to-end physical organization tasks; at best, AI could help plan organization workflows (small fraction of the task) but human workers remain essential for actual material movement and arrangement. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task at extraction sites requiring mobility, manipulation, and judgment about physical objects in unstructured outdoor environments, which current AI systems cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there is no strict licensing requirement for material organization, physical safety regulations, site-specific hazards, and the unstructured nature of extraction sites create moderate friction. Human presence is often required for safety and environmental compliance rather than as a formal legal mandate. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but physical site conditions, safety protocols, and lack of automation infrastructure create practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying robotics or specialized systems to organize physical extraction materials would far exceed the wage of entry-level extraction helpers. Integration, maintenance, and customization for variable material types makes AI much more expensive than human labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this physical task, so the comparison defaults to AI being more expensive or simply unavailable relative to low-wage manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous material organization and preparation in extraction or industrial settings. Robotics in this domain remain highly specialized, expensive, and limited to narrow scenarios; general-purpose physical organization by AI is not production-ready. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes physical extraction materials on-site; this remains a manual labor task with no robotic substitution at production scale in this industry. |
Clean up work areas and remove debris after extraction activities are complete.
19CI 15–24 · exposure 8 · augmentation 0 · importance 3.8/5 · click for rater detail
Clean up work areas and remove debris after extraction activities are complete.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction is a laggard sector for physical automation, with limited digitization and low capital investment in robotics relative to labor-intensive operations. Adoption remains pilot-level, not production-scale. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries (mining, oil and gas) are among the least digitized and slowest to adopt AI/robotics for physical site tasks, with adoption concentrated in monitoring/analytics rather than physical labor. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer minimal productivity assistance for physical cleanup tasks; debris removal and site clearing require embodied action, not information processing or decision support that AI augmentation could meaningfully provide. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little direct assistance for physical debris clearing and site cleanup, which is manual and equipment-based rather than cognitive or data-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cleanup and debris removal involve unstructured physical environments with variable obstacles, making end-to-end automation infeasible for current robots. While autonomous systems can handle some structured tasks, the 50% time-saving threshold requires reliable perception, manipulation, and navigation in unpredictable extraction sites—capabilities not yet deployable at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical debris removal and site cleanup in extraction settings requires mobile manipulation, judgment about hazardous materials, and navigation of uneven terrain that current AI/robotics cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Workplace safety regulations and site liability create some friction, but no hard legal requirement mandates human cleanup. However, site-specific hazards and insurance concerns add organizational friction to automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for cleanup, though safety regulations (OSHA, MSHA) around extraction sites impose procedural requirements that indirectly favor trained human workers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying autonomous cleanup systems (robots, integration, maintenance) in extraction sites far exceeds the wage cost of human helpers. Capital and operational overhead make this economically infeasible compared to low-wage labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical labor task, so any hypothetical robotic system would be far more expensive than a human laborer given current robotics costs and immaturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mature commercial product reliably performs comprehensive cleanup and debris removal in extraction environments today. Experimental robotics exist but are research-stage, with poor generalization to varied site conditions and debris types. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs post-extraction site cleanup; this remains firmly in the domain of manual labor with heavy equipment operated by humans. |
Drive moving equipment to transport materials and parts to excavation sites.
18CI 5–30 · exposure 13 · augmentation 25 · importance 4.1/5 · click for rater detail
Drive moving equipment to transport materials and parts to excavation sites.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Excavation and construction remain low-digitization, decentralized sectors with heterogeneous sites; autonomous adoption is largely pilot-stage in niche scenarios (large mines, quarries) and negligible in typical construction and extraction operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and construction sectors have historically low digitization and AI adoption for physical operations tasks, with automation limited to a few large-scale mining operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with route planning and predictive maintenance, but the core driving task relies on real-time perception and judgment in unstructured environments where current AI provides limited productivity gains while the human remains fully in control. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some GPS/route optimization and equipment telematics can assist route planning and monitoring, but this offers only marginal assistance to the core physical driving task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Autonomous vehicles exist but require significant infrastructure, mapping, and regulatory approval; current deployment in excavation sites with unstructured terrain and dynamic conditions is minimal. The task involves complex site navigation and material handling that falls short of the 50% time-saving threshold with deployed systems today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically driving moving equipment over rough terrain to transport materials to excavation sites requires embodied physical control that current AI systems cannot perform end-to-end; this is a physical task, not information processing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Transportation and material handling have significant regulatory and liability constraints; operators must meet licensing requirements and insurance rules, and safety liability for autonomous systems on active excavation sites remains legally unclear, creating organizational and legal friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like a CDL in all cases, safety regulations, liability for accidents near workers and equipment, and site-specific hazard requirements create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous vehicle systems are capital-intensive and require ongoing maintenance, mapping updates, and remote oversight; total cost per transport run often exceeds the wage of a human driver when accounting for integration and liability costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous vehicle systems for off-road, unstructured excavation sites require expensive sensor suites, site mapping, and safety oversight, making them costlier than a human driver for this scale of operation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous heavy vehicles operate in controlled settings (mining, dedicated routes) but reliable production systems for general excavation site transport remain limited. Material handling and site-specific routing challenges create material error rates in real-world deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously drives heavy equipment carrying materials to extraction/excavation sites in production; autonomous haul trucks exist only in narrow, controlled mining contexts, not general extraction site operations. |
Unload materials, devices, and machine parts, using hand tools.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Unload materials, devices, and machine parts, using hand tools.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and material handling sectors remain largely non-digitized and continue to rely on human labor for unloading tasks. Adoption of automation in these sectors is slow, with AI/robotics penetration minimal compared to information and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries are low-digitization, physically intensive sectors with minimal AI/robotics adoption for manual material handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI tools offer minimal assistance to a human unloading materials with hand tools; the task is fundamentally physical and does not benefit meaningfully from AI-augmented decision-making or information retrieval in situ. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance for the physical act of unloading materials with hand tools; there is no software or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of objects in unstructured environments using hand tools—a core challenge for current robotic systems. While specialized industrial robots exist for structured material handling, general-purpose AI systems cannot reliably perform this task end-to-end with 50% time savings today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring mobility, dexterity, and handling of heavy or awkward materials with hand tools; no current AI system can perform this end-to-end without robotic embodiment far beyond today's deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are few formal regulatory or licensing barriers to automating material unloading, but adoption is limited by technical feasibility and cost—not by legal or organizational gatekeeping. Physical task requirements create practical friction rather than hard institutional barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier specifically blocks automation, but physical site conditions, safety requirements, and variable material handling create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of deploying capable robotic systems, plus integration and maintenance, far exceeds the loaded wage of extraction helpers in most regions. Current solutions are not economically competitive with human labor for this unstructured task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed for this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost for this job. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs general unloading of diverse materials and machine parts with hand tools in typical extraction sites. This remains primarily a research domain for robotics; production systems are either narrowly scoped or require extensive task-specific engineering. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs unloading of extraction-site materials and machine parts using hand tools; this remains outside current robotics/AI product capability in unstructured field environments. |
Collect and examine geological matter, using hand tools and testing devices.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Collect and examine geological matter, using hand tools and testing devices.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and mining sectors show lagging AI adoption; field work automation remains limited to narrow tasks like remote monitoring rather than hands-on collection and examination. Digitization is uneven and capital deployment remains focused on traditional labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries are physically intensive, low-digitization sectors with minimal AI/robotic adoption for hands-on field sample collection and testing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with post-collection analysis (e.g., spectroscopy data interpretation, sample cataloging) but offers minimal augmentation during field collection and real-time geological examination requiring tactile feedback and spatial judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based analysis tools (e.g., image recognition, geochemical data software) can help interpret test results after collection, but the core collection and manual testing steps remain unassisted. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires field presence, physical manipulation of geological specimens with hand tools, and on-site examination using specialized testing devices. Current AI cannot operate physical tools in unstructured field environments or make real-time judgments about geological matter quality and characteristics. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical field task requiring sample collection with hand tools and hands-on testing of geological matter; no current AI system can perform this end-to-end without embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are few hard legal barriers to automation, workplace safety regulations, union agreements in some sectors, and the necessity of human expertise for field judgment create moderate adoption friction. Physical site access and specialized tool operation remain human-dominated. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier specifically prevents automation, but the physical, outdoor, variable-terrain nature of the work creates strong practical barriers to robotic substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI solutions capable of field robotics, geological analysis automation, and on-site inspection remain extremely expensive relative to hiring extraction worker helpers at typical wage rates. The infrastructure required far exceeds loaded labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical collection and hands-on testing, so AI cost comparison is essentially moot; humans remain the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously perform hands-on geological collection and field examination with hand tools and testing devices. This is fundamentally a physical task requiring embodied presence and dexterity in mining or extraction sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously collects and examines geological samples in the field; this remains a manual, embodied task performed by human workers. |
Clean and prepare sites for excavation or boring.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Clean and prepare sites for excavation or boring.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and construction sectors have traditionally lagged in automation adoption due to site variability, regulatory complexity, and the physical, unstructured nature of the work environment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and construction site labor is a low-digitization, physically intensive sector with minimal AI/robotic adoption for this type of site prep work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in site mapping and planning via drone reconnaissance or digital modeling, but most of the manual cleaning and preparation work cannot meaningfully benefit from AI augmentation tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, mapping, or site surveying via drones/software, but it offers little direct help to the physical act of clearing and preparing a site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Site cleaning and preparation requires physical manipulation of terrain, debris, and equipment in highly variable outdoor environments. Current AI systems cannot perform the mechanical and sensorimotor work needed end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical labor task requiring manual clearing, moving materials, and operating hand tools on uneven terrain; no current AI system can perform this physical work end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal licensing barriers for site preparation itself, OSHA regulations, site safety protocols, and liability concerns create meaningful adoption friction that slows automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically prevents automation, but site safety regulations, insurance/liability for excavation work, and the unstructured physical environment create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized site preparation equipment and autonomous systems capable of this work would require significant capital investment and oversight, making them substantially more expensive than deploying human workers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical robotic solution would be far more expensive than a human laborer given current robotics costs and limitations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform site cleaning and preparation for excavation. The task demands robust field robotics and autonomous heavy equipment, which are not yet in production use at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product exists that clears and prepares extraction/boring sites; this remains purely manual field labor with occasional heavy equipment operated by humans. |
Provide assistance to extraction craft workers, such as earth drillers and derrick operators.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Provide assistance to extraction craft workers, such as earth drillers and derrick operators.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction is a traditionally low-digitization, physical sector with slow automation adoption; human helpers remain the cost-effective and legally compliant choice for on-site support functions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries (oil, gas, mining) are low-digitization, physically intensive sectors with minimal AI/robotics adoption for manual labor tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While wearable AI or mobile monitoring could marginally improve safety awareness or task sequencing, there is limited scope for meaningful AI assistance to a human helper whose primary role is physical, in-person support and safety vigilance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with scheduling, safety monitoring, or predictive maintenance alerts, but offers little direct augmentation to the physical assistance work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical coordination, hazard awareness, and dynamic response to equipment operators in active extraction environments. Current AI systems cannot provide embodied assistance on job sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical assistance (fetching tools, moving materials, positioning equipment) at extraction sites, which requires physical manipulation and mobility current AI/robotics cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: OSHA regulations mandate human supervision and safety protocols on extraction sites; liability for equipment operation and worker safety typically requires licensed/responsible human oversight; union agreements often specify staffing ratios. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier for the helper role itself, but physical site safety requirements and OSHA-type regulations create some friction against unproven automation solutions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Human helpers are paid low wages in extraction; deploying robotics or autonomous systems for site assistance would cost far more than the human labor it replaces. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI solution (e.g., robotics) would be far more costly than an entry-level helper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can reliably perform on-site helper functions for extraction workers; this task fundamentally requires physical presence and real-time interaction with heavy machinery and personnel. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs general physical helper duties on drilling rigs or extraction sites; this remains firmly a human physical labor role. |
Set up and adjust equipment used to excavate geological materials.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Set up and adjust equipment used to excavate geological materials.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and mining sectors have historically slow digital transformation and limited AI adoption; heavy reliance on on-site physical expertise, regulatory conservatism, and capital constraints limit velocity of automation in this labor category. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and mining industries are among the least digitized sectors with low AI adoption for physical manual labor tasks like equipment setup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal augmentation for on-site equipment setup; remote monitoring and diagnostic tools exist but do not meaningfully enhance the productivity of workers actually performing physical configuration and adjustment tasks. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with diagnostic checklists, sensor-based monitoring, or equipment manuals, but core physical setup and adjustment tasks receive minimal direct AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical setup and adjustment of excavation equipment on-site, involving mechanical calibration, positioning, and real-time environmental assessment that current AI cannot perform end-to-end without human supervision and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy extraction equipment on-site, which is entirely outside the capability of current AI systems lacking robotic embodiment for this domain. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist due to safety regulations in extraction industries, worker compensation and liability requirements, MSHA/similar oversight of equipment operation, and the legal requirement for licensed/certified personnel to operate mining and excavation equipment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no licensing specifically mandates a human for equipment setup, safety regulations, site liability, and the physical nature of the work create strong organizational and practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task involves specialized physical labor in hazardous extraction environments where human wage costs are lower than the capital and integration costs of autonomous robotic systems capable of safe, reliable setup and adjustment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost is not comparable; human labor remains the only option and thus cheaper by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously set up and adjust physical excavation equipment in the field; this remains firmly in the domain of human operators and technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously set up or adjust excavation equipment; this remains a purely physical, manual task performed by workers on site. |
Repair and maintain automotive and drilling equipment, using hand tools.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Repair and maintain automotive and drilling equipment, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and automotive equipment repair remains a hands-on physical task in low-digitization, field-based settings with minimal AI adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and field mechanical maintenance is a low-digitization, physically intensive sector with minimal AI/robotics adoption in production settings today. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist in diagnostics (fault prediction, documentation) or procedural guidance, but such tools are not yet widely deployed in extraction/automotive repair contexts and offer only modest productivity gains. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can offer minor assistance such as diagnostic guidance, repair manuals, or troubleshooting suggestions via mobile apps, but it doesn't meaningfully transform the hands-on repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Repairing and maintaining drilling and automotive equipment requires physical manipulation in unstructured environments, precise sensorimotor coordination with hand tools, and real-time problem diagnosis. Current AI has no capability to perform these physical tasks end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical repair and maintenance of drilling/automotive equipment requires manual dexterity, mobility, and adaptive physical manipulation that no current AI system can perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Heavy equipment repair and maintenance often requires licensed technicians, carries liability risks (equipment failure endangers workers), and may be subject to safety and regulatory oversight that mandates human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically blocks automation, but safety regulations, jobsite liability, and the physical/hazardous work environment create meaningful friction against deploying autonomous systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this physical task, so the cost ratio is not meaningful; a human must do the work, making AI irrelevant to labor cost comparison. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven physical substitute for this labor, so any AI solution would require expensive robotics with no cost advantage over human helpers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products can reliably perform hands-on equipment repair and maintenance. This remains a purely human task in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs hands-on mechanical repair of drilling equipment; robotics for unstructured mechanical repair remain research-stage or highly narrow lab demonstrations. |
Dismantle extracting and boring equipment used for excavation, using hand tools.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Dismantle extracting and boring equipment used for excavation, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and mining sectors have low digitization rates, rely heavily on field expertise, and face geographic and operational constraints that slow AI adoption. No measurable displacement of dismantling work by automated systems is evident in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries are physical, low-digitization sectors with minimal AI/robotics adoption for hands-on mechanical disassembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with equipment documentation, damage mapping, or procedural guidance via visual inspection and knowledge systems, but the physical and safety-critical nature of hand-tool dismantling limits meaningful augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with digital manuals, diagnostics, or scheduling around this task, but offers little direct assistance to the physical hand-tool dismantling process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Dismantling heavy extracting and boring equipment requires fine-grained spatial reasoning, dexterity, force calibration, and real-time physical adaptation to variable equipment states. Current AI systems have no robotic embodiment or perception capability to reliably perform this end-to-end task in an outdoor, unstructured environment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy machinery components in variable field conditions, which current AI systems and robots cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Work-site safety regulations, equipment certification, liability for improper dismantling, and the requirement for certified workers to handle hazardous materials and high-value equipment create significant legal and organizational barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, equipment liability, and physical workplace hazards create practical friction against any automated approach. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The hardware and sensing infrastructure required to enable even partial automation (robotic arms, environmental perception, safety systems) would substantially exceed the loaded labor cost of a skilled extraction helper performing manual dismantling. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic system for this task, so any AI-based solution would be far more costly than a human worker, if achievable at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can autonomously dismantle extraction equipment using hand tools. This task requires physical manipulation in unpredictable field conditions, which remains beyond the scope of reliable, production-ready AI systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical dismantling of extraction/boring equipment; this remains firmly in the domain of human manual labor. |
Load materials into well holes or into equipment, using hand tools.
7CI 5–10 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Load materials into well holes or into equipment, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and mining are traditionally conservative, physically dispersed sectors with limited prior AI adoption. Adoption of automated material handling in these fields remains minimal and slow, constrained by capital intensity and regulatory hurdles. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction industries are physical, low-digitization sectors with minimal AI/robotic adoption for manual field labor tasks, consistent with laggard-sector patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal augmentation for hand-tool loading tasks; perhaps simple monitoring or scheduling aids could help, but the core task is manual physical work where AI assistance does not meaningfully enhance human productivity or safety in field conditions. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to a worker physically loading materials into well holes with hand tools; there is no cognitive or planning component AI could meaningfully augment here. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of materials into well holes or equipment in typically unstructured, varied environments. Current AI systems lack the embodied robotics capability to reliably perform load-bearing work at field scale, especially in variable geological or equipment contexts. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring materials handling in outdoor, unstructured extraction sites; current AI systems (including robotics) cannot perform this dexterous, situational task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: OSHA and mining regulations mandate safe loading practices and worker presence; liability for unsafe automated loading in extraction sites is high; and the unstructured field environment creates engineering and legal friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically bars automation, but safety regulations, site hazards, and liability for well-site operations create meaningful friction against introducing untested automated equipment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Bespoke robotic systems capable of field deployment for material loading would require substantial capital expenditure, integration, and maintenance far exceeding the low wages typical for helper positions in extraction work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human laborer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this material-loading task in real extraction operations. Heavy-duty industrial robots exist for structured warehouse loading, but well-site loading involves irregular access, varied material types, and safety constraints that current systems do not handle in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs autonomous loading of materials into well holes using hand tools; this remains far outside current robotic manipulation capabilities in unstructured field environments. |
Signal workers to start geological material extraction or boring.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Signal workers to start geological material extraction or boring.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Extraction and mining sectors are relatively low in digitization and automation adoption for on-site helper tasks; most operations still rely on traditional human coordination and signaling practices. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extraction and mining sectors are physical, low-digitization industries with minimal AI agent adoption for on-site operational coordination tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minimal assistance—perhaps analyzing site conditions or recommending timing—but the core signaling task remains human-dependent due to real-time coordination and safety requirements. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled communication tools or sensors could support situational awareness, but they offer limited direct assistance to the core act of human signaling in real time. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time coordination with human workers on an active extraction site, involving safety-critical decisions and physical coordination that current AI systems cannot execute autonomously without on-site robotic infrastructure. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, real-time coordination task on extraction sites requiring presence and situational awareness; no current AI system can perform this signaling function end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations and liability frameworks mandate human oversight of extraction operations; workers must be directly supervised, and signaling is often a safety-critical function requiring a certified or licensed human present on-site. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations in mining/extraction environments typically require human oversight and communication protocols, and error costs (accidents, equipment damage) are high, creating strong organizational and regulatory friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating this task would require specialized robotics or autonomous signaling systems that are far more expensive than the loaded wage of a helper worker who currently performs it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute product for this task, so any AI-based alternative would require costly sensor/robotic infrastructure exceeding human labor costs today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs this task end-to-end; it requires embodied presence on an active worksite, visual assessment of conditions, and direct communication with equipment operators—capabilities not yet productized at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform on-site human signaling for extraction crews; this remains a manual, physically-present task. |
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.