Continuous Mining Machine Operators
47-5041.00Median wage $61,810/yr14,000 employed (US)Rank #888 of 923 scored · top 96% by substitution
Operate self-propelled mining machines that rip coal, metal and nonmetal ores, rock, stone, or sand from the mine face and load it onto conveyors, shuttle cars, or trucks in a continuous operation.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution10
Exposure6
Augmentation28
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
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%6
panel mean rating 1.2/5 → substitution pressure 6/100
Cost vs. human wagew 15%9
panel mean rating 1.4/5 → substitution pressure 9/100
Adoption barriersw 20%inverted — strong barriers lower the score19
panel mean rating 4.2/5 (barrier strength) → substitution pressure 19/100
Sector adoption velocityw 10%8
panel mean rating 1.3/5 → substitution pressure 8/100
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.
Reposition machines to make additional holes or cuts.
42CI 5–79 · exposure 41 · augmentation 38 · importance 4.5/5 · click for rater detail
Reposition machines to make additional holes or cuts.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large-scale mining operations, particularly those in developed regions and extractive firms with capital-intensive operations, are actively deploying autonomous drills and loaders; adoption is accelerating in the mineral extraction sector as equipment vendors integrate remote and autonomous capabilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a physically demanding, low-digitization sector with slow, capital-intensive adoption cycles for automation technology. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted positioning and predictive maintenance tools can help operators optimize repositioning sequences and reduce manual corrections, but the task itself is already fairly mechanical and does not substantially benefit from ongoing human-AI collaboration once automation is feasible. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-assisted guidance and semi-autonomous positioning systems exist to help operators, but they provide limited assistance rather than transformative productivity gains for this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Repositioning mining machines to make additional holes or cuts is fundamentally a physical positioning task with defined spatial targets and standard mechanical sequences, which autonomous systems and remote-control systems with positioning feedback can now execute with high precision and time savings exceeding 50% on established mining sites. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical repositioning of heavy underground mining equipment based on real-time sensory judgment of rock conditions, which off-the-shelf AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Mining regulations typically emphasize safety and environmental compliance rather than mandating human operators for specific positioning tasks; insurance and liability concerns exist but are not absolute legal blocks to automation, and many mining firms own their equipment outright, reducing licensing friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Underground mining safety regulations, equipment certification requirements, and high liability for cave-ins or equipment damage create strong barriers to full automation of machine repositioning. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Autonomous repositioning eliminates direct operator wage and reduces cycle time significantly; integration and monitoring costs are lower than continuous human operation, making the cost ratio favorable to automation by a substantial margin. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require expensive specialized robotics, sensors, and safety systems far exceeding the cost of a human operator for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Autonomous repositioning and drilling systems exist in production at some mining operations, but they operate within constrained environments and require significant setup; broader deployment faces technical variability due to site-specific geology and equipment configurations, keeping reliable production-scale systems in the middling range. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously repositions continuous mining machines in active underground production; remote-control and some autonomy exist in research/limited trials but not as reliable standard practice for this specific maneuver. |
Observe and listen to equipment operation to detect binding or stoppage of tools or other equipment malfunctions.
19CI 14–25 · exposure 17 · augmentation 50 · importance 4.3/5 · click for rater detail
Observe and listen to equipment operation to detect binding or stoppage of tools or other equipment malfunctions.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Mining is a capital-intensive, geographically dispersed, and risk-averse sector with slow digital transformation. Pilot monitoring systems exist, but production replacement of operator vigilance remains rare; adoption is laggard relative to software and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, capital-intensive, physically hazardous sector with historically slow AI/automation adoption relative to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted monitoring dashboards and alerts can help operators by flagging anomalies and reducing cognitive load, improving productivity and response time. However, the human operator remains essential for judgment and intervention, making this a useful but not transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Condition-monitoring sensors and predictive maintenance alerts can help operators detect anomalies earlier, providing meaningful but partial assistance alongside direct sensory monitoring. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI-based equipment monitoring systems can detect some malfunctions through sensor fusion and acoustic analysis, but continuous mining equipment operates in harsh, variable conditions with many subtle failure modes that current systems struggle to distinguish reliably. Full end-to-end replacement with 50% time savings is not demonstrated in mining contexts. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical presence in a mine, sensory monitoring of heavy machinery vibration/sound in a harsh underground environment, and immediate physical intervention capability that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining safety regulations and liability frameworks often require human operators on-site for real-time judgment and emergency response; legal responsibility for equipment and worker safety typically cannot be fully delegated to AI. Organizational and regulatory inertia is substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (MSHA and similar bodies) require human oversight of mining equipment operation, and liability for equipment failure or accidents underground creates strong pressure to keep a human in the loop. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Comprehensive sensor suites, edge computing, integration with existing control systems, and ongoing calibration/maintenance are capital-intensive. The loaded cost of a mining operator is low relative to total equipment value and downtime risk, making automation cost-competitive only in large, standardized operations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor retrofit, ruggedized hardware for underground conditions, and integration with mining equipment is costly relative to an operator already present and performing this as one of many concurrent duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed monitoring systems exist for industrial equipment (vibration sensors, temperature, acoustic monitoring), but they typically require dense sensor installation, calibration per machine, and suffer from false positives in the chaotic mining environment. Production reliability remains materially below human operators in field conditions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Vibration/acoustic sensor-based predictive maintenance systems exist and are deployed in some mining equipment, but full autonomous detection and response replacing operator vigilance underground is not yet standard production practice. |
Determine locations, boundaries, and depths of holes or channels to be cut.
18CI 11–25 · exposure 13 · augmentation 50 · importance 4.4/5 · click for rater detail
Determine locations, boundaries, and depths of holes or channels to be cut.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Mining remains a capital-intensive, geographically dispersed sector with slow digital adoption relative to finance or software; while some large operations pilot geological AI, production-level adoption of fully autonomous hole-location determination is minimal and slow across the industry. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Mining is a capital-intensive, physically-oriented industry with historically slower AI adoption compared to information-sector industries, though some digitization of mine planning is underway. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist mining engineers by processing seismic data, visualizing subsurface models, and recommending candidate locations, thereby accelerating preliminary analysis; however, the human expert must validate and finalize decisions given the safety and financial stakes. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted geological modeling, LIDAR scanning, and mine planning software can help operators and engineers visualize and calculate cut locations more efficiently, improving productivity while humans retain final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with mapping and spatial analysis of geological data, determining optimal drill locations and depths requires real-time sensor integration, subsurface interpretation under uncertainty, and integration with safety/equipment constraints that current AI systems cannot reliably automate end-to-end without substantial human oversight and site-specific calibration. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical assessment of a mine face, geological judgment, and integration of survey data in a dynamic underground environment that current AI systems cannot perform end-to-end without extensive human oversight and physical presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining regulations, safety compliance, and equipment specifications typically require a licensed or certified engineer to sign off on drilling plans; liability for incorrect depths or locations (risk of equipment damage, worker safety, regulatory violation) creates strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mining safety regulations, engineering sign-off requirements, and liability for structural/geological errors in underground operations create strong barriers to full automation of this judgment task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI surveying and analysis tools require significant up-front licensing, sensor infrastructure, and integration costs, while a skilled mining engineer's loaded wage for this specialized task remains competitive; the cost-benefit ratio is not yet favorable for widespread substitution. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While mine planning software can assist, the human expertise (geologist/engineer judgment plus site-specific verification) is still required, so all-in AI costs including sensors, integration, and oversight are not clearly cheaper than human determination. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI-based geological interpretation and visualization tools exist in research and limited deployment, but no mature production system reliably determines mining hole locations and depths autonomously; existing solutions require geologist or engineer validation and cannot replace the full decision chain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously determines mining cut locations, boundaries, and depths in production underground mining operations; this remains largely a research/automation-assist area tied to specific mine planning software. |
Move controls to start and regulate movement of conveyors and to start and position drill cutters or torches.
16CI 7–25 · exposure 13 · augmentation 25 · importance 4.5/5 · click for rater detail
Move controls to start and regulate movement of conveyors and to start and position drill cutters or torches.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Mining remains a capital-constrained, geographically dispersed, and safety-conservative sector with legacy equipment; digital automation adoption in mining lags information/finance sectors, with most operations still relying on trained human operators rather than autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Mining is a capital-intensive, physically demanding sector with slower digitization; some large firms pilot automated haulage/drilling but widespread production deployment for this task is limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers limited augmentation for this task; while some monitoring dashboards or predictive maintenance tools exist, they do not meaningfully enhance an operator's ability to move controls or position cutting equipment in real time. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-assisted guidance and monitoring systems help operators optimize cutting and conveyor control, but AI does not yet substantially transform this manual control task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While mechanical control of conveyors is theoretically automatable, the task requires real-time sensing, adaptive positioning of drill cutters/torches, and response to equipment variability in a safety-critical mining environment—capabilities that current off-the-shelf systems cannot reliably perform end-to-end without extensive customization and human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical machine-control task in an underground mining environment requiring real-time manipulation of heavy equipment; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations face strict safety regulations, equipment-specific certification requirements, and liability concerns around automated drill/torch positioning that could cause injury or equipment damage; regulatory bodies and insurance require human oversight and sign-off on critical equipment movements. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations, equipment certification requirements, and liability concerns around underground heavy machinery create strong barriers to full automation without human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Retrofitting mining equipment with AI-capable automation, sensor systems, and real-time control infrastructure is capital-intensive; the total cost of ownership (hardware, integration, maintenance) typically exceeds the loaded wage of a single operator for many years. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous mining equipment requires expensive sensors, ruggedized robotics, and safety systems, making it far more costly than a human operator for this narrow task today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some industrial automation and PLC systems can manage routine conveyor operation, but positioning drill cutters or torches in response to dynamic mining conditions requires sophisticated sensor fusion and adaptive control that is not reliably deployed in production mining environments at scale today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously operates continuous mining machine controls in production; some mining automation exists but is research/pilot-stage for this specific control task. |
Operate mining machines to gather coal and convey it to floors or shuttle cars.
15CI 5–25 · exposure 13 · augmentation 25 · importance 4.6/5 · click for rater detail
Operate mining machines to gather coal and convey it to floors or shuttle cars.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Mining sectors are laggards in AI/automation adoption relative to finance or software. Autonomous systems are limited to a few large-scale, well-capitalized operations; most mines continue with human operators. Adoption is slow due to capital intensity, regulatory complexity, and safety conservatism. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Coal mining is a low-digitization, physically demanding industry with slow technology adoption cycles and heavy capital/safety constraints limiting AI deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI augmentation of human operators (e.g., real-time monitoring dashboards, predictive maintenance alerts) exists in some modern mines, but meaningful productivity enhancement is limited. The task is largely manual operation requiring situational awareness; current AI tools offer incremental gains rather than transformative assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring and semi-automated controls can assist operators with precision and safety, but AI augmentation of this physical task remains limited today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Modern autonomous haul trucks and continuous mining machines exist in limited deployments, but they require specialized hardware, structured mine environments, and continuous human oversight. The task involves real-time perception of underground conditions, equipment state, and safety protocols that current general-purpose AI cannot reliably handle end-to-end without significant infrastructure investment and human fallback. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires direct physical operation of heavy mining equipment underground; no off-the-shelf AI system can perform this manipulation and material-gathering task end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and safety barriers exist: mining is heavily regulated (MSHA, provincial authorities), equipment operators typically require licenses and certifications, and liability for underground accidents is substantial. Union agreements and workforce expectations also create friction. However, no absolute legal prohibition on automation exists in most jurisdictions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Underground coal mining is heavily regulated for worker and equipment safety (e.g., MSHA), requiring certified operators and strict oversight, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous mining systems require massive capital investment in specialized hardware, software integration, and mine infrastructure. The per-task cost of operating and maintaining such systems, including fallback human operators, remains higher than employing human operators directly, especially in smaller or marginal mines. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous underground mining systems require expensive sensors, ruggedized robotics, and safety systems that currently cost far more than human-operated equipment for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While autonomous mining equipment has been piloted (e.g., Rio Tinto, BHP), these are specialized, purpose-built systems requiring custom sensors and controlled conditions. Deployment at scale across diverse mines remains rare, and reliability in unpredictable underground environments with equipment failures and safety-critical decisions remains a material challenge. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Autonomous continuous miners exist only in limited research/pilot deployments at a few mines; there is no widely deployed product reliably performing this task in production. |
Scrape or wash conveyors, using belt scrapers or belt washers, to minimize dust production.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Scrape or wash conveyors, using belt scrapers or belt washers, to minimize dust production.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a capital-intensive, physically-oriented sector with slow digitization of routine maintenance tasks. Adoption of autonomous cleaning systems in this domain is minimal and largely experimental. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotic adoption for maintenance tasks like this, lagging far behind information or professional service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI/robotics offer minimal assistance to operators performing belt scraping. Computer vision could theoretically detect dust levels to inform when cleaning is needed, but this represents marginal augmentation of a straightforward manual task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no meaningful assistance for this hands-on physical maintenance task; sensors might flag dust levels but do not aid the scraping/washing action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of belt scrapers or washers on conveyors in real-world mining environments. Current AI systems lack embodied robotics capable of reliably performing such mechanical tasks in dusty, variable industrial settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical maintenance task requiring manual manipulation of scraping/washing equipment on underground conveyor systems; no off-the-shelf AI or robotic system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations are heavily regulated (safety, occupational health, equipment certification), and human operators must monitor and maintain equipment. Liability for equipment damage or inadequate cleaning, combined with on-site safety protocols, creates strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed, underground mining safety regulations, hazardous conditions, and equipment liability create significant organizational and safety-driven friction against introducing unproven automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotics for this task would require significant capital investment, integration, and maintenance costs that far exceed the wage of a mining machine operator performing occasional scraping/washing duties. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require custom robotics for harsh underground environments, which is currently far more expensive than having a human operator perform this routine maintenance task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous conveyor scraping or washing in mining operations. While some automated cleaning systems exist in other industries, they are specialized installations, not generalizable AI solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously scrape or wash mining conveyors in production; this remains a manual maintenance activity performed by human operators underground. |
Repair, oil, and adjust machines, and change cutting teeth, using wrenches.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Repair, oil, and adjust machines, and change cutting teeth, using wrenches.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a laggard sector for automation of physical maintenance tasks due to harsh operating conditions, remote locations, equipment variability, and the need for immediate troubleshooting and safety oversight. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a highly physical, low-digitization sector with minimal AI agent adoption for hands-on equipment maintenance tasks; automation here lags far behind information-sector adoption patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI-powered diagnostics systems could assist operators in identifying maintenance needs, current augmentation is limited because the core physical execution—wrenching, oiling, tooth replacement—remains entirely manual and human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with diagnostic guidance, maintenance scheduling, or manuals lookup, but offers negligible direct assistance to the physical act of repairing and adjusting machinery with wrenches. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in unstructured underground environments, precise manual dexterity with tools, and real-time tactile feedback. Current AI systems cannot reliably perform end-to-end mechanical repair, oiling, and adjustment in mining conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical repair, oiling, and adjustment of underground mining machinery using hand tools requires manual dexterity, mobility in confined hazardous spaces, and physical manipulation that no current AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations require on-site human operators for safety-critical equipment maintenance; regulatory frameworks and union agreements typically mandate trained human technicians to perform or certify repairs and adjustments on production machinery. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the legal sense, mine safety regulations, physical hazard requirements, and equipment-specific training create meaningful organizational and safety barriers to any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotics systems capable of operating in mining environments, combined with integration and remote supervision, far exceeds the loaded wage of a skilled machine operator performing maintenance tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute performing this physical labor, so any comparison to human labor cost defaults to AI being non-viable or infinitely more expensive since no product exists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products can autonomously perform physical maintenance tasks like oiling machines, adjusting mechanisms, or changing cutting teeth in active mining environments. This remains firmly in research/robotics development stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical maintenance tasks like changing cutting teeth or wrench-based adjustments on mining machines; this remains firmly in the domain of human manual labor. |
Conduct methane gas checks to ensure breathing quality of air.
4CI 4–4 · exposure 0 · augmentation 38 · importance 4.7/5 · click for rater detail
Conduct methane gas checks to ensure breathing quality of air.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a low-digitization, highly regulated sector with strong on-site human presence requirements. Autonomous AI adoption in underground safety-critical functions remains minimal, and regulatory oversight creates substantial structural resistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a low-digitization, physically hazardous sector with slow AI adoption for safety-critical functions; automation here lags far behind information/professional service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by logging sensor data or alerting operators to anomalies, but the core task—conducting the actual check and certifying air quality—remains fundamentally human and legally-required. Augmentation potential is limited. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sensor-based monitoring systems and data dashboards can alert and assist operators in tracking methane levels continuously, improving situational awareness even though the human remains responsible for the check. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Methane gas checks require real-time sensor deployment in potentially hazardous underground environments and immediate safety decision-making based on readings. Current AI systems cannot independently perform the physical measurement, calibration, and on-site judgment needed to ensure worker safety in this safety-critical context. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence underground with certified gas detection equipment and immediate on-site judgment; current AI cannot physically perform this atmospheric safety check end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining safety regulations (MSHA, national equivalents) legally mandate that qualified personnel conduct atmospheric monitoring and gas checks before work proceeds. A licensed or trained human must sign off on air quality; automation cannot replace this regulatory requirement. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Mine safety regulations (e.g., MSHA) mandate certified personnel to conduct and verify atmospheric checks; this is a hard legal/licensing requirement with severe liability for error (explosion risk). |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Gas detection equipment and sensors are relatively inexpensive, but the task requires human operators already on-site for mining work. Replacing this with an autonomous AI system would require significant infrastructure investment with minimal cost savings over the existing human check. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Fixed sensor networks are cheap to run, but replacing the human verification and judgment role with a fully autonomous system requires costly certified hardware, redundancy, and compliance infrastructure, making all-in cost not clearly cheaper than the human task component. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs autonomous methane monitoring in active mining operations. While sensor technology and data logging exist, the integration into autonomous safety-critical decision-making in mines is not a production capability of any general AI system today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously conducts methane/air quality checks in mining as a substitute for the human role; fixed gas sensors exist but are not 'AI performing the task' in the operator's stead. |
Hang ventilation tubing and ventilation curtains to ensure that the mining face area is kept properly ventilated.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.8/5 · click for rater detail
Hang ventilation tubing and ventilation curtains to ensure that the mining face area is kept properly ventilated.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining, especially underground operations, remains a labor-intensive, low-digitization sector with slow adoption of autonomous systems due to safety criticality and regulatory constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for manual infrastructure tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to workers hanging physical tubing and curtains; the task involves tactile feedback, spatial judgment, and direct manual manipulation that augmentation tools cannot enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with monitoring airflow sensors or planning optimal ventilation curtain placement, but it offers little help with the physical act of hanging tubing and curtains. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical installation of ventilation equipment in hazardous underground mining environments, involving spatial reasoning, manual dexterity, and real-time assessment of conditions. Current AI systems cannot manipulate physical objects or navigate unstructured underground mine sites reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual installation task in a confined underground mining face requiring dexterity, spatial judgment, and adaptation to irregular rock surfaces; no AI system can perform this physical hanging of tubing and curtains today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mine safety regulations mandate human personnel on-site to perform and verify ventilation setup; liability and worker safety requirements create hard legal barriers to automation in underground mining operations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) require certified personnel to manage ventilation systems critical to preventing gas buildup and explosions, and physical access constraints in mining faces make substitution very difficult. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems have no practical deployment cost advantage here since they cannot perform the physical work; human labor remains the only viable option, making the cost ratio heavily human-favored. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven substitute performing this physical task, so any hypothetical automation (e.g., specialized robotics) would be far more expensive than a human miner performing routine installation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs physical installation of ventilation systems in mines. This remains a manual, hands-on task requiring human workers on-site in a safety-critical environment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs ventilation infrastructure underground; this remains entirely a human physical labor task with no robotic or AI substitute in production. |
Drive machines into position at working faces.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Drive machines into position at working faces.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a capital-intensive, physically isolated industry with slow digital transformation. Pilot autonomous vehicles exist at a handful of large operations, but the sector overall shows minimal production deployment of driverless mining equipment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a physically intensive, lower-digitization sector where full autonomy adoption for face positioning is still nascent, with most advances in remote-control rather than AI-driven autonomy. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Augmentation is limited; operators rely on experience, spatial awareness, and real-time judgment in hazardous underground environments. While camera systems and proximity sensors assist, AI offers minimal productivity gain on the core task of positioning machines at the face. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some assistive technologies like remote control, sensor-assisted guidance, and semi-autonomous tramming exist, but they provide limited productivity transformation for this specific positioning task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Driving physical machines to underground working faces requires real-time navigation, obstacle avoidance, and precise positioning in complex, unstructured underground environments with poor visibility and dynamic hazards. Current AI cannot reliably operate heavy machinery in these conditions autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically driving a continuous mining machine into position underground requires real-time perception of unstructured, hazardous rock faces and precise physical control that off-the-shelf AI systems cannot perform end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining operations face strict MSHA and international safety regulations that mandate human operators for machine control in active mines. Liability for equipment failure, injury, or ore loss rests on the mine operator, creating strong legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations, equipment certification requirements, and liability for underground equipment operation create strong barriers to full autonomous substitution without human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous mining vehicles remain in early pilot phases with high integration, infrastructure, and maintenance costs. The loaded cost of developing and maintaining such systems far exceeds the wage of an operator for the foreseeable future. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require expensive specialized robotics, sensors, and integration far exceeding the cost of a human operator, making AI substantially more expensive per task-equivalent today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform autonomous heavy machinery operation in active mining environments. While autonomous vehicles exist in controlled settings, underground mining presents unique challenges (radio signal loss, dust, congestion, safety-critical proximity to workers) that no production system addresses. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed commercial products that autonomously position continuous mining machines at working faces; automation in underground mining remains largely research/pilot stage or limited to remote teleoperation by humans. |
Install casings to prevent cave-ins.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Install casings to prevent cave-ins.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a capital-intensive, traditionally conservative sector with strong worker safety requirements and union presence. Adoption of autonomous systems for core underground mining tasks remains minimal, with operators still performing most hands-on structural work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically intensive sector with slow adoption of full automation for hazardous manual tasks like casing installation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While monitoring and diagnostic AI tools could assist operators in assessing ground conditions or planning casing placement, the core task of physically installing casings offers limited augmentation potential given the need for direct manual control and real-time structural assessment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven sensors and monitoring can inform decisions about roof stability and support needs, offering some assistance, but does not meaningfully change the physical installation process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Installing casings to prevent cave-ins is a physical task requiring real-time judgment about structural safety, ground conditions, and precise placement in underground environments. Current AI systems cannot reliably manipulate physical materials or operate in the hazardous, unstructured subsurface conditions this task demands. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical underground mining task requiring manual manipulation of heavy roof support/casing materials in confined, hazardous spaces; no current AI system can perform this physical installation work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining safety regulations typically require licensed, qualified human operators to perform or directly oversee critical structural tasks like casing installation. Liability for cave-ins and worker safety creates strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) impose strict certification and safety-critical procedures around ground support installation, creating strong regulatory and liability barriers to automation of this specific task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized mining equipment and human operators performing this safety-critical task cost substantially less than developing and deploying autonomous robotic systems capable of functioning reliably in underground mining environments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so the comparison defaults to AI being effectively infeasible/more costly than a human miner performing the work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform physical casing installation in mining operations. This task requires embodied robotics with sophisticated sensing and control in harsh, unpredictable underground conditions where current systems do not operate reliably. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product installs mine casings or roof supports; this remains entirely research-stage or nonexistent for full physical automation, though some mining equipment has remote/automated control features distinct from casing installation. |
Apply new technologies developed to minimize the environmental impact of coal mining.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Apply new technologies developed to minimize the environmental impact of coal mining.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Coal mining is a laggard sector for general AI adoption due to its physical, hazardous nature, regulatory constraints, and the specialized expertise required; adoption of automation in this domain remains slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Coal mining is a low-digitization, heavy-industry sector with slow technology adoption cycles, especially for physical operational tasks like this one. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist in analyzing environmental impact data or literature on new technologies, but the core task of evaluating and applying novel solutions in coal mining remains heavily dependent on human expertise and regulatory judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with data analysis, monitoring environmental sensors, or optimizing technology selection, but it offers limited direct assistance to the hands-on application of new mining technologies. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Applying novel environmental technologies requires domain expertise, decision-making about which innovations fit specific mining conditions, and hands-on technical judgment that current AI systems cannot reliably perform end-to-end without expert human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This task involves physically implementing new equipment, procedures, and mining techniques underground, which requires manual operation, physical dexterity, and site-specific judgment that AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Coal mining is heavily regulated, environmental compliance requires licensed professionals and regulatory sign-off, and deploying new technologies in active mines carries liability and safety requirements that legally mandate human expert involvement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mining is heavily regulated for safety and environmental compliance, often requiring certified operators and regulatory sign-off, creating strong barriers to full automation of this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task demands specialized mining expertise and regulatory navigation; human mining engineers and environmental specialists command significant wages that would exceed the cost of current AI assistance for this complex, judgment-heavy work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI systems cannot substitute for the physical labor and equipment operation involved, so there is no viable AI cost comparison—human operators remain necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product today reliably assesses, selects, and implements new environmental mining technologies—this requires expert judgment, regulatory knowledge, and site-specific customization beyond current automation capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates mining machinery or physically applies environmental-mitigation technologies in coal mines; this remains a human-executed operational task. |
Guide and assist crews laying track and resetting supports and blocking.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Guide and assist crews laying track and resetting supports and blocking.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a capital-intensive, conservative sector with slow digitization and strong reliance on on-site expertise and human judgment. Adoption of autonomous crew guidance is negligible in production environments today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for tasks like this, and progress is slow due to harsh operating conditions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI-powered communication or monitoring tools could provide some assistance (e.g., documenting support positions, alerting to hazards), the core task of guiding and directing crews in real time remains fundamentally dependent on a human supervisor's presence and judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors, monitoring systems, or digital mine-planning tools may assist with logistics or safety monitoring, but they offer limited direct productivity enhancement for the physical guiding and support-resetting work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time coordination of multiple crew members in hazardous underground environments, making safety decisions and adjusting physical placements on the fly. Current AI cannot navigate unstructured mines, communicate directionally with crews, or perform the embodied spatial reasoning needed to guide physical track-laying work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on coordination task in an underground mine involving guiding crew members through manual track-laying and support-resetting work; no current AI system can perform this physical guidance and manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining operations are heavily regulated, require licensed personnel for safety oversight, and entail legal liability for worker safety. A qualified human supervisor is typically mandated by law and mining regulations, creating hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations, structural integrity liability, and the need for a trained human present to supervise ground support installation create strong practical and regulatory barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of site supervision in mining would require custom hardware (robots, sensors, communication systems) and extensive integration, making the all-in cost far higher than employing an experienced human crew supervisor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI attempt would require expensive robotics/sensor infrastructure far exceeding human labor costs for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously guide mining crews through track-laying and support-resetting operations. This requires integrated perception, real-time communication, physical presence, and accountability for crew safety—well beyond current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs this specific underground physical crew-guidance and installation task; it remains firmly in the human-labor domain. |
Check the stability of roof and rib support systems before mining face areas.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Check the stability of roof and rib support systems before mining face areas.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a traditionally low-digitization sector with heavy regulatory oversight and strong safety-culture preferences for human expert judgment; adoption of automation for safety-critical structural assessment is laggard, with most operations still relying on trained human inspectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a physically intensive, low-digitization sector with slow technology adoption cycles and heavy reliance on human safety inspections. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While sensor monitoring systems can assist by logging conditions or alerting to anomalies, current AI provides minimal augmentation to the human inspector's core task of real-time structural judgment and decision-making under uncertainty in a hazardous underground environment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring systems (strain gauges, seismic sensors, LIDAR scans) can provide data to assist operators, but these are narrow supplementary tools rather than transformative productivity aids for this specific judgment-based safety task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Checking roof and rib stability requires real-time assessment of complex physical conditions underground, including visual inspection of support systems, detection of micro-fractures, and judgment about structural integrity—tasks that demand human sensory assessment, tactile feedback, and contextual safety reasoning that current AI cannot perform end-to-end in a mining environment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection of underground mine geology and structural supports in hazardous, variable conditions, which current AI systems cannot perform end-to-end without extensive sensor infrastructure and human judgment on-site.rio |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining safety regulations in most jurisdictions legally mandate that a qualified human inspector perform or sign off on roof and rib stability assessments before mining operations proceed; liability for structural failure and worker safety creates hard legal and regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Mine safety regulations (e.g., MSHA) mandate qualified personnel to inspect roof and rib support conditions before work proceeds, and liability for cave-ins/collapses is severe, creating hard legal and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI vision and sensor systems capable of underground monitoring require expensive hardware infrastructure, continuous human oversight for safety-critical decisions, and skilled personnel to interpret results—making the total cost comparable to or exceeding human inspector wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any AI-assisted monitoring still requires sensor installation, maintenance, and human verification underground, making it more costly than simply having a trained operator perform the check given current technology maturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs autonomous structural safety assessment in active mining operations; this remains a specialized domain requiring licensed human inspectors and would face substantial regulatory and liability barriers to full automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs full roof/rib stability assessment in place of a human operator; sensor-based monitoring exists but is supplementary, not a substitute for the task itself. |
Move levers to raise and lower hydraulic safety bars supporting roofs above machines until other workers complete framing.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Move levers to raise and lower hydraulic safety bars supporting roofs above machines until other workers complete framing.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a capital-intensive, heavily regulated sector with strong labor unions and deep operational conservatism around safety systems. Adoption of autonomous controls for critical roof support has been minimal despite decades of mining automation research. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a physically demanding, low-digitization sector with minimal automation of safety-critical manual controls; adoption of AI/robotics for this exact task is essentially absent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance for this task; sensor systems might provide height/pressure monitoring, but the core lever-moving operation and safety coordination remain entirely human-dependent with limited scope for meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-based monitoring or automated alerts could someday support operator awareness of roof conditions, but current AI provides negligible direct assistance to the lever operation task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical manipulation of hydraulic equipment in a dangerous, dynamic underground environment where timing and coordination with human workers are safety-critical. Current AI systems cannot reliably operate heavy machinery in unstructured mining environments or respond to unpredictable worker positioning and timing cues. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task in an underground mining environment requiring direct sensory judgment and coordination with nearby workers; no off-the-shelf AI system can perform this physical operation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining regulations and MSHA standards legally require trained human operators to control safety-critical hydraulic systems; liability for roof collapse or worker injury falls on the operator/company, creating a hard legal and safety barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Mine safety regulations (e.g., MSHA) mandate certified human operators for roof support systems given severe injury/fatality risk, making this a hard-barrier, human-must-perform task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying robust robotics capable of operating hydraulic equipment and monitoring worker safety in a mine would cost substantially more than paying a skilled operator, with significant integration and maintenance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific physical safety task, so AI cost is effectively infinite relative to a human operator today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous hydraulic safety-bar operation in active mining sites. This requires specialized robotics with advanced sensing and control in hazardous conditions—research stage only, not production-ready. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product operates hydraulic safety bar levers in coordination with human framing crews in live mining conditions; this remains far outside current robotic deployment in mines. |
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.