Roof Bolters, Mining
47-5043.00Median wage $78,540/yr2,160 employed (US)Rank #914 of 923 scored · top 99% by substitution
Operate machinery to install roof support bolts in underground mine.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution7
Exposure4
Augmentation25
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%4
panel mean rating 1.1/5 → substitution pressure 4/100
Cost vs. human wagew 15%4
panel mean rating 1.1/5 → substitution pressure 4/100
Adoption barriersw 20%inverted — strong barriers lower the score18
panel mean rating 4.3/5 (barrier strength) → substitution pressure 18/100
Sector adoption velocityw 10%3
panel mean rating 1.1/5 → substitution pressure 3/100
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.
Position bolting machines, and insert drill bits into chucks.
15CI 5–25 · exposure 13 · augmentation 25 · importance 4.9/5 · click for rater detail
Position bolting machines, and insert drill bits into chucks.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Mining is a traditionally conservative, capital-intensive sector with slow digital transformation. While large operators pilot automation, production-level deployment of bolting machine positioning remains limited; small and mid-size mining operations lag significantly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on equipment operation tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI currently offers limited assistance for positioning bolting machines and drill bit insertion—these are primarily manual, spatial tasks. Vision aids or remote monitoring could provide marginal help, but transformative productivity augmentation is not yet demonstrated in production. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-assisted or semi-automated positioning aids may exist in modern mining equipment, but AI provides minimal meaningful assistance to the core physical task of positioning and bit insertion. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Positioning bolting machines requires spatial reasoning, real-time adjustments, and interaction with physical equipment in variable underground mining conditions. While drill bit insertion into chucks is mechanically simple, the full task—positioning for safety and precision in dynamic mining environments—remains beyond reliable end-to-end automation today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy underground mining equipment in a hazardous environment; no current AI or robotic system can perform this end-to-end positioning and drill bit insertion task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining automation faces stringent regulatory oversight (safety standards, equipment certification), liability concerns in hazardous environments, and union/worker agreements. Roof bolting is safety-critical; regulators and operators require human judgment and accountability, creating strong legal and organizational barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Underground mining safety regulations, MSHA requirements, and the physical/mechanical nature of equipment operation in hazardous roof-fall-prone environments create strong barriers to non-human operation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotics and vision systems for mining environments are expensive to develop, deploy, and maintain. The hardware, integration, and safety oversight costs remain substantially higher than the wages of roof bolters performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed at scale, so any hypothetical automation would require expensive specialized robotics far exceeding current human labor costs for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform autonomous positioning of bolting machines in mining conditions. Research-stage robotics exist, but production systems handling the spatial precision and environmental variability of underground mining are not in operational use at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously position roof bolting machines and load drill bits in active underground mines; this remains a manual, physically demanding operation. |
Force bolts into holes, using hydraulic mechanisms of self-propelled bolting machines.
14CI 5–23 · exposure 13 · augmentation 38 · importance 4.9/5 · click for rater detail
Force bolts into holes, using hydraulic mechanisms of self-propelled bolting machines.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a traditionally slow-adopter sector with strong regulatory oversight and union presence. Adoption of autonomous bolting machines is minimal; most operations rely on existing semi-automated equipment and human operators, reflecting laggard automation patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for this specific task; automation here lags far behind office/professional service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted positioning guidance, real-time hole detection via computer vision, and predictive alerts about equipment status could meaningfully assist roof bolters in performing the task faster and safer, though the human remains essential for decision-making and safety oversight. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some machine-assisted controls and sensors can help operators monitor drilling parameters and rock stability, but this offers only modest, narrow productivity gains rather than transformative assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While the hydraulic bolt-forcing mechanism itself is already automated in self-propelled bolting machines, the task requires positioning the machine at correct locations, detecting hole alignment, and handling edge cases in underground mining conditions. Current AI cannot reliably perform the full end-to-end task (positioning, alignment detection, obstacle avoidance) in the variable underground environment at 50%+ time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical operation of heavy underground mining equipment requiring perception, force control, and adaptation to unpredictable rock conditions; current AI cannot perform this end-to-end task.atabases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations have strong safety regulations, insurance requirements, and union considerations around autonomous equipment in underground work. The task involves high-risk environments where regulatory approval and liability concerns create substantial friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mining safety regulations, certification requirements for operating heavy equipment underground, and high liability for roof collapse create strong barriers to full automation without extensive regulatory and engineering validation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Retrofitting mining equipment with autonomous bolting capabilities, including sensors, controls, and integration, likely costs more than the loaded wage of a roof bolter. Maintenance and downtime for complex automated systems further increase total cost relative to human labor in this sector. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing this task, so any comparison to human labor cost is moot; specialized robotics for this would be far more costly than a human operator currently. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs autonomous bolt-forcing in active mining operations. While some mining equipment has automation features, fully autonomous positioning and bolt insertion in underground mines faces significant technical and operational barriers; most systems remain research or pilot stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI-driven robotic system autonomously operates roof bolting machines in production underground mines today; automation efforts remain limited to assisted control, not full task substitution. |
Perform tests to determine if methane gas is present.
11CI 0–23 · exposure 13 · augmentation 50 · importance 4.9/5 · click for rater detail
Perform tests to determine if methane gas is present.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Mines have adopted automated continuous gas monitoring systems in many operations, but routine pre-work and spot checks still rely on human operators performing manual tests; adoption is mixed and incremental rather than rapid wholesale replacement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically hazardous sector with minimal AI agent deployment for safety-critical sensing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven automated gas monitoring systems substantially assist human roof bolters by providing continuous real-time alerts, trend data, and flagged hazard zones, allowing workers to make faster and better-informed decisions about where and when to work safely. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor data can be logged and monitored via digital dashboards or IoT systems to alert workers, offering some assistance, but the core testing and hands-on verification remain manual and safety-critical. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While gas detection instruments can be automated to collect readings, the task requires physical sampling in specific mine locations, judgment about where to test, and interpretation of results in real-time operational context—elements that current AI systems cannot fully execute end-to-end without human oversight and repositioning of equipment. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically operating gas detection equipment underground and responding to hazardous conditions in real time; no AI system can perform the physical sensing and mine-specific judgment involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining regulations (e.g., MSHA standards) typically require a qualified human to conduct atmospheric testing and certify safety conditions; liability for missed gas hazards falls on responsible personnel, 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 personnel to perform gas testing and safety checks underground, making this a hard, legally-mandated human task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated gas detection equipment costs are significant and require ongoing maintenance, calibration, and human supervision; total cost-per-test is comparable to or exceeds the marginal cost of a trained worker performing the test with existing handheld detectors. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-only substitute for this task; the cost comparison is not applicable since a human with certified equipment is still required for the physical test and safety judgment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Gas detectors and monitoring systems exist and are widely used in mines, but they require human operators to position them, interpret readings in context, respond to alerts, and make safety decisions; no autonomous system reliably performs the full task of determining methane presence as a standalone deployed product. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently performs methane testing in mining; fixed sensors and handheld detectors exist but require human operation, interpretation, and physical presence. |
Drill test holes and test bolts for specified tension, using torque wrenches.
11CI 0–23 · exposure 13 · augmentation 25 · importance 4.8/5 · click for rater detail
Drill test holes and test bolts for specified tension, using torque wrenches.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a laggard sector for advanced automation, with most operations relying on manual labor and traditional equipment. Roof bolting is physically hazardous and digitization adoption is slow; pilots are rare and production deployment of autonomous systems is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotic adoption for hands-on structural safety tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered measurement aids (automated torque logging, tension monitoring dashboards) offer modest assistance with documentation and verification, but the core physical task of drilling and torque testing remains manual and human-controlled, limiting augmentation impact. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-based monitoring and data logging can assist in tracking bolt tension trends over time, but the core drilling and physical testing process gets little direct AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Testing bolts with torque wrenches requires precise physical manipulation in a mining environment with variable, unstructured conditions. While AI-controlled robotic systems could theoretically perform drilling and torque testing, current deployed systems cannot reliably handle the dynamic underground setting, bolt variability, and safety-critical verification that this task demands, leaving only partial automation possible. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical underground mining task requiring manipulation of drilling equipment and torque wrenches in a hazardous environment; no AI system today can perform the physical drilling and testing operations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations are heavily regulated (MSHA, local mining codes) and bolt integrity is safety-critical; licensed personnel or engineer sign-off is often legally required for structural verification. Liability and error cost asymmetry are high, and operators typically prefer human accountability in safety-sensitive underground work. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Mine safety regulations (MSHA) require certified personnel to perform roof bolting and support verification, and physical presence underground with specialized equipment is legally and physically required. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic systems capable of underground bolt testing are capital-intensive and require ongoing maintenance, integration, and operator oversight. The loaded wage of a roof bolter is moderate, making the AI system cost-per-task comparable to or higher than human performance when amortized across typical mine operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable—human labor with specialized equipment remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Few production systems exist for autonomous bolt testing in underground mining. Robotics companies have prototypes for controlled environments, but reliable deployment in actual mine shafts—with dust, uneven surfaces, and safety requirements—remains limited to research and pilot projects, not mainstream operational use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product autonomously drills test holes and tension-tests roof bolts in mines; this remains a manual, human-performed safety-critical task. |
Perform safety checks on equipment before operating.
7CI 0–14 · exposure 8 · augmentation 25 · importance 4.9/5 · click for rater detail
Perform safety checks on equipment before operating.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a traditionally low-digitization, physical-asset sector with strong regulatory oversight and conservative adoption patterns; autonomous pre-operation inspections are not yet adopted in production at meaningful scale. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for hands-on equipment inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered visual inspection tools or checklists could assist in documenting conditions, but the core task—hands-on verification and human accountability for worker safety—limits meaningful productivity gains without fundamentally changing the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors and IoT-based condition monitoring can flag anomalies to assist the bolter, but they only marginally supplement the manual, tactile safety check process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in analyzing visual inspections of equipment conditions, the task requires hands-on tactile verification (checking bolt tightness, wear, structural integrity) and judgment calls that must account for equipment-specific standards—this cannot be fully automated end-to-end today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical inspection of mining equipment requires hands-on manipulation, visual and tactile checks in a hazardous underground environment that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining regulations and liability frameworks legally require a qualified, human operator to certify equipment safety before use; this creates a hard regulatory barrier that prevents substitution regardless of technical capability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) mandate certified human operators to conduct pre-operational safety checks, creating strong regulatory and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI vision systems for equipment inspection still require human setup, calibration, and verification; the total cost of implementing and maintaining such systems exceeds the labor cost of a single safety inspection by a trained bolter. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical inspection, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous system reliably performs full pre-operation safety checks on mining equipment in production environments; this task requires human presence, tactile inspection, and accountability that current AI products do not demonstrate at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical pre-operation safety checks on roof bolting equipment; sensor-based monitoring exists but does not replace the manual inspection task. |
Perform ventilation tasks, such as hanging ventilation curtains and tubes.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.9/5 · click for rater detail
Perform ventilation tasks, such as hanging ventilation curtains and tubes.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a capital-intensive, physically-grounded sector with slow automation adoption for skilled trades work; underground operations present harsh conditions that have resisted robotic substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotic adoption for manual installation tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital tools could assist with ventilation planning or monitoring, but the physical task of hanging curtains and tubes offers minimal scope for AI augmentation during execution itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers negligible assistance for physically hanging curtains and tubes in mine shafts; this is a hands-on manual task with no meaningful cognitive or planning component AI can enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Ventilation task installation requires physical manipulation in complex underground mine environments with variable geometry, height constraints, and safety considerations. Current AI and robotics cannot reliably perform this hands-on assembly work end-to-end in real mine conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring installation of curtains and tubes in confined, hazardous underground mining environments, far beyond current robotic or AI capability.value |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining safety regulations require certified human workers to install and verify ventilation systems, and the physical hazards of underground work create liability and authorization barriers that legally necessitate trained human presence and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) require certified personnel to perform ventilation control tasks in hazardous underground settings, creating strong regulatory and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of such physical tasks would require expensive custom robotics, installation, and maintenance that would far exceed the loaded wage of a roof bolter performing routine ventilation setup. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system to compare costs against; human labor remains the only functional option, making AI substitution currently far more expensive or impossible. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can autonomously hang ventilation curtains and tubes in active mine settings. This is physically demanding manual work that remains entirely human-performed in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical ventilation installation in mines; this remains entirely research-stage or nonexistent for this specific manual labor. |
Install various types of bolts, including truss, glue, and resin bolts, traversing entire ceiling spans.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Install various types of bolts, including truss, glue, and resin bolts, traversing entire ceiling spans.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a laggard sector in AI adoption due to remote locations, asset specificity, strong unions, safety-critical nature, and the technical difficulty of automating unstructured physical tasks in hazardous underground environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a physically intensive, low-digitization sector with slow adoption of autonomous systems for hazardous manual tasks like roof bolting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with planning bolt placement or monitoring structural conditions remotely, but the core physical installation and traversal task leaves limited room for augmentation while a human performs the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-based and semi-automated bolting rigs can assist with precision and monitoring, but AI-driven cognitive assistance for this specific physical task is minimal. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physically traversing roof ceilings and manually installing mechanical components in an unstructured underground environment. Current AI lacks the embodied dexterity, spatial reasoning in variable mining conditions, and real-time problem-solving needed to install bolts safely and reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, safety-critical underground mining task requiring manual manipulation of heavy machinery and materials in confined, hazardous spaces; no current AI system can perform the physical installation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining safety regulations and worker compensation frameworks typically mandate qualified human inspection and certification of critical safety infrastructure like roof support systems. Liability and regulatory requirements create legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) require certified personnel for roof support work, and liability for roof collapse is severe, creating strong regulatory and safety barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any hypothetical robotic system capable of this task would require substantial custom engineering, maintenance, and operator oversight in hazardous underground conditions, making it far more expensive than the loaded wage of a roof bolter. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for this physical task, so any 'AI' cost is not comparable to replacing the human miner; equipment automation costs (robotics) far exceed current labor costs for partial gains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems currently perform underground roof bolting at scale in production mining operations. While some mining automation exists, it is limited to surface or highly structured tasks; underground bolt installation in variable geological conditions remains human-performed. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously installs roof bolts in mines; automated bolting rigs exist as machinery but still require human operators for setup, judgment, and adaptation to geological conditions. |
Rotate chucks to turn bolts and open expansion heads against rock formations.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Rotate chucks to turn bolts and open expansion heads against rock formations.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a capital-intensive, physically isolated sector with slow digitization; roof bolting is among the most hazardous and specialized tasks, with little evidence of automation adoption in current production operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a low-digitization, physically demanding sector with slow automation adoption, especially for underground manual equipment operation tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While some sensor aids or positioning guides might marginally assist human bolters, the physical precision and tactile feedback required in real-time rock contact limits meaningful augmentation to remote monitoring or planning phases rather than the core bolting operation itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring or automated torque feedback systems could assist operators, but current AI offers minimal direct augmentation to this specific physical bolting action. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in a underground mining environment with irregular rock formations, demanding real-time tactile feedback and adaptive positioning that current robots cannot reliably perform at scale in unstructured mines. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring a machine operator to control a roof bolting rig in a mine, involving tactile feedback and real-time judgment of rock conditions; no current AI/robotics system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations face strict safety regulations and liability concerns; human roof bolters often work under union agreements and licensing/certification requirements, and the task occurs in hazardous underground environments where substitution faces both regulatory and safety scrutiny. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Underground mine safety regulations, certification requirements for equipment operators, and severe liability/safety consequences of failure create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized mining robots capable of roof bolting would require significant capital investment and site-specific integration, making them substantially more expensive than the hourly wage of trained roof bolters. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute performing this specific action at any cost, so AI is not cheaper than a human operator for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems currently perform roof bolting reliably in production mining environments; this remains a specialized manual skill requiring human judgment and physical dexterity in hazardous, variable conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously operates roof bolting chucks against variable rock formations; this remains manual heavy-equipment operation in underground mining. |
Remove drill bits from chucks after drilling holes, and insert bolts into chucks.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.6/5 · click for rater detail
Remove drill bits from chucks after drilling holes, and insert bolts into chucks.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a capital-intensive, geographically dispersed, and safety-critical sector with slow digital transformation and strong union presence. Automation of on-site manual labor like roof bolting remains rare; most mechanization focuses on ore extraction and haulage, not task-level substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for manual equipment tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could theoretically provide guidance (e.g., positioning cues, bolt torque specifications via heads-up display), but the immediate feedback and spatial precision required for safe chuck management offer limited augmentation potential over current tool design and worker training. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this fine manual task of swapping drill bits and inserting bolts into chucks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves precise physical manipulation of small drill bits and bolts in a mining environment with hazardous conditions. Current AI systems lack the embodied dexterity, real-time sensorimotor control, and situational awareness needed to reliably perform these manual operations in the field, even partially. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task in an underground mining environment requiring dexterity, force sensing, and mobility that current AI systems and robotics cannot perform reliably or safely at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining operations are heavily regulated with strict safety oversight; workers performing bolting tasks must be trained and certified, and liability for bolting failures (structural collapse risk) falls on qualified personnel. Regulatory bodies and mining companies prioritize human accountability in load-bearing work. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) impose strict requirements on roof bolting operations and equipment handling, and physical hazards underground create strong liability and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The upfront capital cost of a purpose-built robotic system with sufficient dexterity, environmental hardening for mining, and failsafes would far exceed the loaded wage of a roof bolter. Operating and maintaining such a system in a mining context would remain expensive relative to direct labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific mechanical task, so any hypothetical automation would require expensive specialized robotics far exceeding human labor cost for this narrow motion. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems perform this physical task reliably in production mining environments. While laboratory robotics can manipulate small objects, they require controlled settings and extensive setup—far from the spontaneity and variability of an active mining operation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously swaps drill bits and inserts bolts in chucks on roof bolting machines; this remains outside current robotics deployment in mining. |
Tighten ends of anchored truss bolts, using turnbuckles.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Tighten ends of anchored truss bolts, using turnbuckles.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a physical, capital-intensive sector with slow digital adoption. Roof bolting remains a core manual safety task, and operational inertia and hazardous working conditions limit rapid automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mining is a low-digitization, physically intensive sector with minimal AI/robotics penetration into core underground manual tasks like bolt tightening. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance on this task; perhaps tool-condition monitoring or predictive maintenance scheduling could marginally help, but the core bolting operation itself does not benefit meaningfully from AI augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with monitoring bolt tension data or predictive maintenance scheduling, but offers negligible direct assistance to the physical tightening action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in a confined, underground mining environment where precise torque application and manual dexterity are essential. Current AI and robotics cannot reliably operate turnbuckles in these conditions at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of mining equipment in underground hazardous conditions—no current AI or robotic system can perform this dexterous, safety-critical physical task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining safety regulations and liability concerns create meaningful barriers; a human must inspect and sign off on bolt integrity, and customer/regulatory preference for human-verified critical safety work is strong in mining operations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (MSHA) require certified personnel for roof support work, and the physical, hazardous underground environment creates strong practical and regulatory barriers against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robotic system capable of tightening bolts in underground mining conditions would require substantial capital investment, maintenance, and skilled operators—far exceeding the cost of a trained roof bolter's labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed for this task, so the human remains the only cost-effective option; specialized mining robotics for this exact task don't exist commercially. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this specific manual bolting task reliably in active mining environments today. Specialized mining robotics exist but are not standard, and this particular task remains manual. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform automated truss bolt tightening with turnbuckles in mining environments; this remains manual mechanical work performed by skilled workers. |
Drill bolt holes into roofs at specified distances from ribs or adjacent bolts.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.9/5 · click for rater detail
Drill bolt holes into roofs at specified distances from ribs or adjacent bolts.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining is a capital-intensive, traditionally conservative sector with slow adoption of autonomous drilling systems. Most roof bolting remains manual; automation projects are experimental and limited to a handful of advanced operations, not widespread production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically extreme sector with minimal AI agent adoption; automation here trends toward mechanization, not AI-driven autonomy. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted guidance (e.g., drilling-position recommendations, rib-distance calculations from survey data) could provide minor support, but the physical drilling task itself offers limited scope for meaningful human-AI collaboration in typical mining workflows. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-assisted or semi-automated bolting machinery can aid precision and safety monitoring, but general AI systems offer little direct augmentation to the physical drilling task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical drilling in a dynamic, hazardous underground mining environment with precise positioning relative to existing roof structures. Current AI and robotics cannot reliably execute this end-to-end in real mining conditions, which involve unstable rock, variable geology, and safety-critical placement. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical drilling task requiring precise manipulation of heavy equipment in confined, hazardous underground environments; current AI systems cannot perform this physical operation end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining safety regulations, worker compensation law, and mining codes typically require licensed, trained human personnel to perform roof support installation due to the life-safety implications of failures. Liability and regulatory requirements for autonomous systems in this context are substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (MSHA) require trained, certified operators for roof bolting due to severe injury/fatality risk from roof falls, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized drilling equipment, robotic systems capable of precise underground operation, and the infrastructure needed for autonomous mining equipment would far exceed the loaded wage of a roof bolter, with high capital and integration costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing this task, so cost comparison favors human/mechanized operation by default; any robotic alternative would require massive capital investment exceeding human labor costs today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous roof bolting in active mining operations today. While some research exists on mining automation, production systems do not exist that can drill and place bolts at specified distances in the varied, hazardous conditions of actual mine roofs. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously drills roof bolt holes in mining; automated bolting rigs exist as mechanized equipment but not as AI-driven autonomous systems in production. |
Pull down loose rock that cannot be supported.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.9/5 · click for rater detail
Pull down loose rock that cannot be supported.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a physical, low-digitization sector with strong union presence and safety-first culture; automation adoption is slow and heavily constrained by regulatory oversight and hazard management requirements. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotics adoption for direct hazard remediation tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While sensors or monitoring systems might assist in identifying loose rock, the core task of judgment and physical removal is fundamentally human-centered; AI plays only a marginal supportive role, if any. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors and AI-based ground monitoring can help flag unstable rock zones, offering some situational awareness assistance, but do not meaningfully change the physical execution of pulling down loose rock. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in an unstructured, variable underground environment with safety-critical decisions about which rock can vs. cannot be supported. Current AI systems cannot operate robotic equipment reliably in such hazardous, unpredictable conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, safety-critical manual task performed in hazardous underground mine conditions requiring direct physical manipulation and situational judgment; no AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining safety regulations require licensed, trained personnel to assess and work with roof stability; liability for collapse or worker injury is severe and legally assigned to qualified humans, creating hard regulatory and liability barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (e.g., MSHA) mandate qualified personnel for roof control and hazard assessment, and the physical/safety risk creates strong liability and procedural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized mining equipment and robotics capable of operating underground would be vastly more expensive than the loaded wage of a roof bolter, with current technology unable to match human flexibility and safety awareness. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute in production, so any hypothetical automation would require expensive specialized robotics far exceeding current human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs autonomous roof bolting or rock removal in active mining operations. This remains a skilled human task requiring real-time sensory judgment and physical dexterity in harsh conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs loose rock scaling/removal in mines as a mature production capability; this remains an unautomated manual mining task. |
Dust rocks after bolting.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.8/5 · click for rater detail
Dust rocks after bolting.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining, particularly underground operations, is a laggard sector for automation due to harsh physical environments, safety regulations, and the technical difficulty of deploying robots in confined, variable spaces. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically intensive sector with minimal AI/robotic automation deployed for tasks like rock dusting; adoption of autonomous systems in this niche is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for the physical act of dusting rocks in a mine; there is no decision support, monitoring, or productivity-enhancing software component to this manual task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with monitoring dust levels or scheduling via sensors and analytics, but it offers little direct assistance to the physical act of dusting rocks after bolting. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Dusting rocks after bolting is a manual, physically dexterous task performed in confined underground spaces that requires real-time adaptation to variable rock surfaces and environmental conditions. Current AI systems cannot operate physical tools or equipment in unstructured underground mining environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring underground mine equipment operation (rock dusting to suppress combustible coal dust) that current AI systems cannot perform end-to-end; it requires physical manipulation in a hazardous environment., not digital/cognitive work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Mining operations are heavily regulated by MSHA and other safety agencies; human workers are legally required to perform bolting and immediate post-bolting inspection/preparation tasks, creating hard legal barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Mine safety regulations (MSHA) mandate specific safety procedures and human oversight for hazard mitigation tasks like rock dusting, and the hazardous underground environment creates substantial physical and regulatory barriers to non-human automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | There are no viable AI or robotic systems in production for this task, making cost comparison meaningless; any theoretical automation would require expensive custom robotics far exceeding the cost of paying a mine worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any AI solution would require full robotic automation with high capital and integration costs exceeding current human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can autonomously perform physical dust removal in underground mining settings. The task requires embodied robotics in hazardous, variable conditions where no production-ready automation exists. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical rock dusting operations in underground mines; this remains a manual or semi-automated mechanical task requiring human presence and judgment on-site. |
Position safety jacks to support underground mine roofs until bolts can be installed.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail
Position safety jacks to support underground mine roofs until bolts can be installed.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mining remains a capital-intensive, traditional industry with slow adoption of full automation; physical task automation in underground mines is nascent, and roof bolting specifically remains labor-dependent due to regulatory requirements and site-specific hazards. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Underground mining is a low-digitization, physically demanding sector with minimal AI/robotic adoption for hazardous manual tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI-powered monitoring of roof conditions or stress analysis could assist planning, current tools offer minimal assistance with the core physical task of jack positioning, which remains primarily manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with sensor-based roof stability monitoring or planning support placement, but it offers little direct assistance to the physical act of positioning jacks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Positioning safety jacks requires real-time assessment of uneven underground terrain, spatial reasoning about roof stability, and precise mechanical placement in confined spaces—tasks that demand embodied physical manipulation and safety judgment that current AI systems cannot reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy equipment in a hazardous underground environment with real-time structural judgment; no current AI system can perform this physical task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Mining safety is heavily regulated (MSHA regulations in the US, equivalent international standards) and requires certified human workers to inspect conditions and assume liability for roof stability and bolting operations; automation faces hard legal and liability barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Mine safety regulations (e.g., MSHA) mandate certified personnel for roof support activities, and the extreme liability and life-safety risks make human authorization essentially mandatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized mining robot system capable of this task would require significant capital investment, maintenance, and custom engineering for each mine site, far exceeding the wage cost of a trained roof bolter. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at any cost point for this physical safety task, making the human the only current option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task autonomously or reliably in underground mining environments, which are characterized by variable geology, confined spaces, and critical safety constraints that exceed current robotic system capabilities in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product positions safety jacks in underground mines today; this remains manual, specialized labor performed by trained miners. |
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.