Gas Compressor and Gas Pumping Station Operators
53-7071.00Median wage $77,320/yr3,510 employed (US)Rank #264 of 923 scored · top 29% by substitution
Operate steam-, gas-, electric motor-, or internal combustion-engine driven compressors. Transmit, compress, or recover gases, such as butane, nitrogen, hydrogen, and natural gas.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution35
Exposure33
Augmentation51
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
13 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
23%
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%33
panel mean rating 2.3/5 → substitution pressure 33/100
Technical feasibility todayw 20%32
panel mean rating 2.3/5 → substitution pressure 32/100
Cost vs. human wagew 15%41
panel mean rating 2.6/5 → substitution pressure 41/100
Adoption barriersw 20%inverted — strong barriers lower the score39
panel mean rating 3.5/5 (barrier strength) → substitution pressure 39/100
Sector adoption velocityw 10%32
panel mean rating 2.3/5 → substitution pressure 32/100
Task breakdown (13 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Monitor meters and pressure gauges to determine consumption rate variations, temperatures, and pressures.
77CI 59–95 · exposure 80 · augmentation 88 · importance 4.4/5 · click for rater detail
Monitor meters and pressure gauges to determine consumption rate variations, temperatures, and pressures.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | The energy and utilities sectors have aggressively adopted automated monitoring systems for decades. SCADA and industrial IoT platforms are now standard practice in gas compression operations at major facilities. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Oil and gas midstream operations have adopted SCADA and remote monitoring broadly, but full autonomous operation is slower due to safety regulation and legacy infrastructure in this physically-oriented sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems assist human operators by continuously monitoring all parameters, flagging anomalies, and summarizing trends, allowing operators to focus on analysis and response. Human expertise in interpreting complex interactions remains valuable despite strong automation potential. |
| Augmentation potential | claude-sonnet-5 | 5/5 | Automated gauges, alarms, and dashboards significantly enhance an operator's ability to track multiple variables simultaneously and respond faster than manual observation alone. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Monitoring meters and pressure gauges is a routine, structured task that current AI systems can perform end-to-end with automated sensor reading, anomaly detection, and threshold alerting. Modern SCADA systems and AI-powered monitoring already achieve >50% time savings by eliminating manual gauge checks and automating alert generation. |
| Task automatability | claude-sonnet-5 | 3/5 | Sensor monitoring and threshold-based alerting can be automated with SCADA/IoT systems, but full task includes contextual judgment and physical verification that still requires human oversight in many facilities today.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some facilities may have regulatory oversight and safety sign-off requirements tied to human operators, the monitoring function itself faces minimal legal barriers. Operators must still be present for emergency response, but routine monitoring is already widely automated. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety-critical infrastructure often requires certified operators to be present or on-call, and regulatory oversight (e.g., PHMSA) mandates human accountability even where sensors do the primary monitoring. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Sensor automation and AI monitoring cost orders of magnitude less than continuous human operator labor. A single AI system monitors multiple stations continuously at near-zero marginal cost per additional measurement. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated sensor networks and remote monitoring systems are far cheaper per data-point-hour than continuous human observation, though initial infrastructure investment and maintenance add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature products exist in industrial monitoring (SCADA, IoT platforms, automated sensor analytics) deployed at scale in gas compression facilities today. AI-driven pressure and temperature monitoring systems are in production use across the energy sector. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | SCADA and automated telemetry systems are mature, deployed products widely used in gas pipeline and compressor operations for real-time monitoring of pressure, temperature, and flow data. |
Record instrument readings and operational changes in operating logs.
71CI 62–79 · exposure 70 · augmentation 63 · importance 4.3/5 · click for rater detail
Record instrument readings and operational changes in operating logs.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | The energy and utilities sectors have been early and sustained adopters of automated monitoring and logging systems; SCADA and digital logging are now industry standard. Major operators have already deployed versions of this automation, indicating fast and deep adoption in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Oil and gas is a moderately digitized industrial sector with growing SCADA/IoT adoption, but it lags behind information/finance sectors in full automation of field operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI logging systems augment operator productivity by eliminating manual transcription and reducing data-entry errors, freeing time for other monitoring tasks. However, the assistance is primarily on routine recording; higher-level interpretation of anomalies still requires human expertise. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted monitoring dashboards and anomaly detection can help operators track trends and flag issues while they remain responsible for verification and operational decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording instrument readings and operational changes is highly structured data entry involving clear numeric values and standardized log formats. Current AI systems with sensor integration and automated logging can capture and transcribe readings with minimal human intervention, achieving substantial time savings. Some judgment around what constitutes a noteworthy operational change may require human oversight, but the core activity is automatable. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording instrument readings is largely a data capture/transcription task that can be automated via sensors, SCADA integration, and automated logging systems with minimal human input, meeting the time-saving threshold where digital instrumentation exists. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory compliance requirements (e.g., EPA, OSHA record-keeping standards) and potential operator licensing may require human verification or sign-off on critical readings, creating moderate friction. However, automation of the recording itself faces fewer hard legal barriers than operations decisions, leaving room for human-in-the-loop systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There's some regulatory expectation of accurate recordkeeping and potential liability for safety-critical data, but no requirement that a licensed human personally record readings when automated systems are validated. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated logging systems have minimal per-reading inference costs and operate continuously across thousands of data points, making the cost per unit reading orders of magnitude cheaper than paying an operator to manually record the same data. Integration costs are typically recovered quickly in industrial settings. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once sensors and a data historian are installed, automated logging costs almost nothing per reading compared to a human operator's time, though upfront instrumentation and integration costs exist. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Industrial facilities already deploy SCADA systems, automated data logging software, and IoT sensor networks that reliably record instrument readings in production environments. While some facilities still rely on manual logs, mature products exist that demonstrate reliable automated reading capture and storage at scale in the energy and utilities sector. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated data historians and SCADA logging are mature and widely deployed in gas facilities, but many operations still rely on manual gauge readings and paper/manual log entries in older or smaller facilities, limiting universal deployment. |
Submit daily reports on facility operations.
71CI 65–76 · exposure 70 · augmentation 75 · importance 4.0/5 · click for rater detail
Submit daily reports on facility operations.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Utilities and energy infrastructure are moderately digitized and adopt automation selectively; SCADA and reporting systems are common, but AI-driven automated daily reporting is still in early-to-mid adoption, with pilots outpacing widespread production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas/utility operations are a moderately low-digitization, safety-conservative sector where AI adoption for routine reporting is still emerging rather than widespread. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can draft complete daily reports from sensor feeds in seconds, allowing operators to review, verify, and add contextual notes rather than transcribe data manually. This substantially raises operator productivity and shifts focus to exception handling and analysis. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can efficiently draft, summarize, and format daily operations data pulled from monitoring systems, significantly speeding up the reporting task while the operator reviews and finalizes it. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Daily operations reports involve collecting structured data (pressure readings, flow rates, incidents, maintenance notes) and formatting them into standard templates. Current AI systems can extract sensor data, fill forms, and generate narrative summaries with minimal human intervention, achieving >50% time savings for routine reports. Non-routine anomalies may require human review but represent a small fraction of typical daily work. |
| Task automatability | claude-sonnet-5 | 4/5 | Report generation from sensor/SCADA data logs is largely templated text and numbers that current AI can compile and draft with minimal human review, meeting the 50% time-saving bar for most of the writing/compilation portion. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Reports are typically internal documentation with no regulatory mandate that a human must author them, though audit trails and accountability expectations create light compliance friction. Most facilities can adopt automated reporting without licensing barriers, though some may prefer operator sign-off for liability reasons. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for report writing itself, though operators may need to verify accuracy and sign off for regulatory/safety compliance, creating mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated data extraction and report generation costs pennies per report (inference + API calls). A human operator spending 30–60 minutes daily on reporting at typical utility wages ($50–70k/year loaded) far exceeds the AI cost per task instance, representing >10x cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated report generation via existing SCADA integration and templated NLG is very cheap per report compared to an operator's time spent compiling and writing it manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production systems (SCADA integration with AI report generation, log aggregation tools, and LLM-based summarization) are deployed in energy and utilities sectors. Errors remain possible on novel situations, but systems reliably handle standard daily reporting at scale in real operations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Industrial reporting software and AI-assisted document generation exist and are used in some facilities, but full automated ingestion from SCADA/field data into narrative reports is not yet universally deployed across the sector. |
Read gas meters, and maintain records of the amounts of gas received and dispensed from holders.
61CI 45–76 · exposure 55 · augmentation 75 · importance 4.0/5 · click for rater detail
Read gas meters, and maintain records of the amounts of gas received and dispensed from holders.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Utility and energy sectors show moderate adoption of automated meter reading and SCADA systems, but legacy infrastructure and regulatory conservatism mean widespread replacement remains incomplete; pilots and hybrid approaches are common. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Utilities and energy sectors have adopted SCADA/telemetry substantially over recent decades, but full modernization across all gas compressor stations is uneven, especially at smaller or older facilities. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered dashboards, anomaly detection, and automated alerts substantially assist operators in monitoring multiple meters and spotting discrepancies, raising their ability to manage larger volumes while maintaining oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation lags, digital dashboards and automated alerts significantly assist operators in monitoring and recording gas volumes accurately and efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reading digital gas meters can be partially automated with computer vision or sensor integration, but maintaining accurate records requires integration with multiple systems and exception handling for anomalies, making full end-to-end automation without significant setup unrealistic for 50% time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | Reading meter values and logging amounts is largely a data capture/record-keeping task that can be automated via IoT sensors, SCADA telemetry, and automated logging systems, though physical verification checks may remain in some legacy sites. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory standards for gas measurement and record-keeping require documented accuracy and audit trails, and some jurisdictions mandate human verification of critical meter readings, creating moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically mandates human meter reading, though regulatory record-keeping accuracy and safety compliance create moderate procedural friction and require validated, auditable systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Meter reading and basic record-keeping via sensors and data pipelines cost significantly less than a human operator's loaded wage, especially at scale across multiple stations. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated sensor-based metering and digital record systems cost far less per reading-cycle than a human operator's time once installed, offering order-of-magnitude savings at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Automated meter reading (AMR) systems exist in production for utility companies, but they typically cover only the meter-reading portion; full record-keeping including reconciliation and error correction remains semi-manual in most deployed systems. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | SCADA and automated metering/telemetry systems are widely deployed in gas infrastructure today and reliably capture and log meter readings in production environments, though not all facilities have upgraded from manual gauges. |
Turn knobs or switches to regulate pressures.
34CI 25–42 · exposure 30 · augmentation 50 · importance 3.8/5 · click for rater detail
Turn knobs or switches to regulate pressures.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | The oil, gas, and chemical industries have already widely deployed automated pressure-control systems (PLC/SCADA) over decades, with manual knob-turning largely displaced in new facilities; adoption in existing legacy systems is slower but steady. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas midstream operations are physical, safety-critical, and have historically been slower to adopt full AI-driven autonomous control compared to information-sector industries, though SCADA automation has existed for decades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by providing real-time pressure trend analysis, predictive alerts for equipment stress, and decision-support recommendations to the human operator, moderately enhancing situational awareness and response time. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based predictive analytics and automated control recommendations can help operators optimize pressure settings and detect anomalies, improving decision quality even if the physical knob-turning remains human-performed or automated via non-AI industrial controls. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While turning knobs or switches is mechanically simple, modern SCADA and PLC systems already automate pressure regulation; however, the task requires real-time sensory feedback and occasional manual override in unpredictable conditions, preventing full end-to-end automation at current AI capability levels. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manual control action requiring on-site presence and real-time sensory feedback; while control logic can be automated via SCADA/PLC systems, the literal manual manipulation of knobs/switches by a human operator is not replaceable by generative AI or typical off-the-shelf systems today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and safety barriers exist: process safety management (PSM) regulations often require trained and certified operators to monitor and manually override automated systems, and liability for pressure-system failures creates legal requirements for human accountability and sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas pipeline operations are subject to strict safety regulations and often require certified operators to monitor and intervene in pressure regulation due to high stakes of explosion/leak risk, creating substantial regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated control systems (PLC/SCADA) are substantially cheaper to operate than a full-time human operator once installed, though initial infrastructure costs are significant; ongoing sensor and system maintenance remain necessary. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting stations with automated control systems and sensors involves significant capital and integration costs that may exceed the marginal cost of a human operator especially at smaller facilities, though large-scale automated pipeline systems can be cheaper long-term. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Industrial automation systems reliably handle routine pressure regulation in production environments, but AI agents currently cannot independently monitor complex pressure systems and make nuanced control adjustments without human oversight or integration with existing industrial control hardware. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Industrial control automation (SCADA, DCS, automated valves) exists and is deployed, but many older or smaller compressor stations still rely on manual operator adjustment, and full autonomous regulation without human oversight is not universally deployed. |
Adjust valves and equipment to obtain specified performance.
25CI 25–25 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Adjust valves and equipment to obtain specified performance.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gas compression and pumping operations remain capital-intensive, safety-critical infrastructure with slow digital transformation. While monitoring has improved, the pace of autonomous physical adjustment adoption remains low; most sites rely on human operator expertise and judgment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas midstream operations are a moderately digitized but physically-oriented sector with slower AI/automation adoption compared to information-based industries, though SCADA/DCS systems have existed for decades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered monitoring dashboards and predictive analytics can help operators make faster adjustment decisions by highlighting anomalies and recommending parameter changes, moderately improving their effectiveness. However, the augmentation is advisory rather than transformative, since operators must retain final control and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based predictive analytics and anomaly detection can help operators identify when adjustments are needed and optimize settings, improving decision quality even though the physical act remains human-performed or through non-AI automation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could monitor sensor data and recommend valve adjustments, the task requires real-time physical manipulation and contextual judgment about equipment state that current AI cannot reliably execute autonomously. Some data analysis could be automated, but the core adjustment work remains manual. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of valves and equipment in an industrial gas facility, which current AI systems cannot perform end-to-end without robotic embodiment; only monitoring/recommendation portions are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gas compression and pumping operations are heavily regulated (OSHA, EPA, industry safety codes) and require licensed operators to maintain legal responsibility for system safety and performance. Liability for equipment failure or safety incidents creates hard barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical industrial gas operations are heavily regulated with required certifications, and equipment failure or misadjustment carries severe liability and safety risk, creating strong barriers to full automation without human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI monitoring and advisory systems are expensive to integrate into industrial equipment, while the human operator cost for this specialized role is relatively modest. The infrastructure and safety-critical oversight requirements make automation economically disadvantaged compared to employing trained operators. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated control systems require significant capital investment in sensors, actuators, and control software, making all-in cost often comparable to or higher than a human operator especially at smaller stations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI systems reliably perform physical valve adjustments autonomously in production gas stations. Remote monitoring and advisory systems exist, but autonomous or near-autonomous adjustment to specification remains research-stage; human operators remain essential. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some SCADA and control-system automation exists for valve adjustment in well-instrumented facilities, but full autonomous adjustment across diverse equipment configurations is not a mature deployed product for this occupation broadly. |
Take samples of gases and conduct chemical tests to determine gas quality and sulfur or moisture content, or send samples to laboratories for analysis.
25CI 25–25 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Take samples of gases and conduct chemical tests to determine gas quality and sulfur or moisture content, or send samples to laboratories for analysis.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gas compression operations are capital-intensive, long-lived infrastructure in mature industries; while some larger facilities deploy semi-automated monitors, adoption of fully autonomous sampling and testing remains slow due to safety, regulatory, and capital constraints. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas field operations are a lower-digitization, physically intensive sector where AI/automation adoption for hands-on tasks like this remains slow compared to information-sector benchmarks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Real-time monitoring systems and automated alerting on anomalies (high sulfur, moisture spikes) meaningfully assist operators in interpreting data and scheduling corrective action, though the human operator remains responsible for sampling decisions and test oversight. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled sensors and data analytics can help interpret test results, flag anomalies, and streamline lab communication, meaningfully aiding operators even though the physical sampling remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While sample collection and basic chemical tests could be partially automated with robotic systems and analytical instruments, the task requires physical sampling in operational environments, judgment about which tests to run, and interpretation of results in context—most practical workflows still require significant human oversight and decision-making. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical sampling and instrument-based testing require on-site manipulation of equipment and materials; AI can assist with data logging/analysis but cannot perform the physical sampling or lab handling end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory requirements (API, ASTM, pipeline safety standards) often mandate human-certified or human-verified analysis; liability for incorrect gas quality assessment in pipeline or compression operations is high, creating legal and safety barriers to full automation without licensed oversight. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas quality testing often ties into safety and regulatory compliance (pipeline safety standards, environmental reporting), requiring certified processes and sometimes accountable personnel, creating meaningful barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated analytical equipment is capital-intensive and requires integration, calibration, and maintenance; for routine sampling and testing at a single station, the total cost (equipment + oversight + integration) typically exceeds the wage of a trained operator doing the work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized sensor/analyzer hardware plus maintenance and calibration costs are substantial and don't scale down to be dramatically cheaper than a trained operator performing periodic sampling and testing. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Analytical instruments (GC-MS, moisture analyzers) exist and are deployed, but end-to-end automation of sampling, testing, and interpretation in live industrial settings remains limited; most facilities use semi-automated lab equipment with human technicians running and interpreting tests. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated gas chromatographs and sensor-based analyzers exist and are deployed, but full task including manual sampling and coordinating with labs still relies on human operators; no integrated product autonomously performs the whole task. |
Operate power-driven pumps that transfer liquids, semi-liquids, gases, or powdered materials.
22CI 7–36 · exposure 20 · augmentation 63 · importance 4.1/5 · click for rater detail
Operate power-driven pumps that transfer liquids, semi-liquids, gases, or powdered materials.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Industrial facilities have been investing in automated control systems for decades, but adoption remains incremental and mixed; many plants still rely on human operators due to safety, regulatory, and organizational inertia. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas midstream operations are a moderately digitized but physically-dominated sector, with automation focused on monitoring/control systems rather than replacing on-site operators. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven monitoring, predictive maintenance alerts, anomaly detection, and real-time optimization recommendations can significantly enhance operator productivity and safety decision-making while keeping the human responsible. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based monitoring, predictive maintenance, and SCADA analytics can help operators anticipate equipment issues and optimize pump performance, improving efficiency without replacing the physical operation task. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Routine pump operation (monitoring flows, adjusting settings, responding to standard conditions) can be partially automated via SCADA and control systems, but complex troubleshooting, safety decisions under novel conditions, and physical interventions still require human oversight, falling short of 50% time saving at equal quality end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical equipment-operation task requiring on-site manipulation of pumps and monitoring of pressures/flows, which current AI systems cannot perform end-to-end without robotic embodiment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, environmental compliance, and liability requirements for pumping operations (especially hazardous materials and utilities) typically mandate human operators or certified technicians to monitor, sign off, and respond to emergencies. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pipeline and gas facility operations are subject to safety regulations, certification requirements, and liability concerns that require human oversight and sign-off on operations involving hazardous materials. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial automation and sensor/control systems are capital-intensive; their all-in cost (hardware, integration, maintenance, cybersecurity) often exceeds the wage of a single operator, though cost may improve with scale. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Full automation would require expensive specialized industrial control/robotic hardware and safety systems, making AI-driven substitution costlier than retaining trained operators for this narrow physical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While industrial control systems automate many pump functions in production, fully autonomous end-to-end operation without human monitoring is not a deployed standard; most systems require active human supervision and intervention for safety and reliability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically operates gas pumps or compressor stations autonomously today; SCADA and remote monitoring exist but human operators still directly control and intervene on equipment. |
Move controls and turn valves to start compressor engines, pumps, and auxiliary equipment.
21CI 14–29 · exposure 20 · augmentation 38 · importance 4.2/5 · click for rater detail
Move controls and turn valves to start compressor engines, pumps, and auxiliary equipment.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Gas compression and pumping stations are capital-intensive, safety-critical facilities typically operated by large utilities and legacy industrial firms. Adoption of automation in these sectors is slow due to regulatory conservatism, high failure costs, and the centrality of human expertise to operational safety. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas midstream operations are a moderately digitized but physically-oriented, safety-conservative sector where automation is adopted incrementally through capital projects rather than rapid AI-driven transformation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide monitoring alerts or checklists to assist an operator, but the core task—moving physical controls and turning valves—offers limited opportunity for meaningful AI augmentation that stays within human oversight while materially raising productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based monitoring, predictive maintenance, and control-optimization systems can assist operators in deciding when and how to start equipment and flag anomalies, improving efficiency and safety while the operator remains responsible for physical actions and final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Starting physical machinery via controls and valves requires precise spatial coordination and real-time perception of equipment state. While AI could theoretically plan sequences, executing this reliably end-to-end without human oversight demands either advanced robotics (not yet deployed at scale in industrial settings) or remote operation by humans—neither achieves the 50% time-saving bar with current off-the-shelf systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Physically moving controls and turning valves requires either on-site robotics or pre-existing SCADA/automation retrofits; a generic AI cannot perform the physical actuation itself without embedded control hardware already in place. Where such hardware exists, control logic can be automated, but the task as stated (physical manipulation) is not fully AI-substitutable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Industrial equipment operation is subject to strict safety regulations, environmental permits, and liability requirements. Operators must be trained and certified, and many jurisdictions legally require a licensed human to oversee or sign off on compressor and pump start-up to ensure safe operation and regulatory compliance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas pipeline and compressor operations are heavily regulated (e.g., PHMSA, OSHA) with safety-critical requirements often mandating qualified personnel to monitor and be able to intervene, and liability for pipeline incidents is severe, creating strong barriers to full unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating this task would require expensive robotic systems, sensor integration, and safety-critical oversight infrastructure—far costlier than the loaded wage of an operator who is already on-site and trained for the role. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Once automated control infrastructure is installed, the marginal cost of automated startup sequencing is low, but the capital cost of retrofitting valves and controls with actuators/sensors is substantial, making the all-in cost roughly comparable to or only moderately cheaper than human operators for many facilities. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs this task in production environments. Physical operation of industrial equipment requires embodied agents with real-time sensor feedback and safety certification, which remains largely in research or narrow pilot phases, not mature production systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Modern compressor stations often have SCADA and PLC-based automated startup sequences, but these are engineering/control-system products, not general AI systems, and many stations still rely on manual or semi-manual operator intervention especially for auxiliary equipment and abnormal conditions. |
Respond to problems by adjusting control room equipment or instructing other personnel to adjust equipment at problem locations or in other control areas.
16CI 6–25 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Respond to problems by adjusting control room equipment or instructing other personnel to adjust equipment at problem locations or in other control areas.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gas compression operations remain highly conservative in automation adoption due to safety and regulatory constraints; while monitoring tools are used, autonomous AI control of critical infrastructure is not yet in production deployment in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas midstream operations are a moderately digitized but physically regulated sector with slow adoption of autonomous control decision-making, though monitoring/analytics tools are increasingly used to support operators. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist operators by detecting anomalies, recommending adjustments, and suggesting which personnel to dispatch, but the operator must remain in full control for safety-critical decisions and problem confirmation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based anomaly detection and predictive analytics can flag abnormal conditions and suggest likely causes, helping operators respond faster, but the decision and action to adjust equipment remains human-directed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in diagnosing problems and recommending adjustments, the task requires real-time decision-making in safety-critical infrastructure with significant physical coordination across multiple locations and personnel—something current systems cannot reliably do end-to-end without substantial human oversight and final approval. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time diagnosis of physical equipment anomalies and dispatch of corrective actions across control and field areas, which current AI cannot reliably execute end-to-end without human judgment and physical/operational accountability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gas compression operations are regulated by environmental, safety, and pipeline regulations; liability for incorrect adjustments is severe, and operators must be licensed/certified—creating strong legal and organizational barriers to autonomous automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Gas pipeline operations are subject to strict regulatory oversight (e.g., PHMSA) requiring qualified, trained personnel for control room decisions, with severe liability for incorrect responses to abnormal operating conditions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration and continuous oversight costs for AI monitoring a critical infrastructure operation are substantial, and the need for immediate human intervention in most scenarios limits cost savings to well below parity with a human operator's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While sensor-driven alerts are cheap, the cost of a control system that must safely diagnose and direct corrective action in a hazardous industrial setting includes significant integration, safety certification, and oversight costs that don't yet undercut human operator costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production AI system today reliably handles the dynamic, context-dependent problem diagnosis and cross-site coordination required in gas compression operations; research systems exist for diagnostics but deployed products cannot autonomously adjust equipment or direct personnel in real operational environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously diagnoses and resolves gas compressor/pumping station problems by adjusting equipment or directing personnel; SCADA/AI is used only for monitoring and alerting, not autonomous control response. |
Maintain each station by performing general housekeeping duties such as painting, washing, and cleaning.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.6/5 · click for rater detail
Maintain each station by performing general housekeeping duties such as painting, washing, and cleaning.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automation for routine station maintenance is lagging; most gas compression facilities rely on human workers and contracted maintenance services rather than robotic systems for general housekeeping. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical housekeeping tasks in industrial/energy sectors show minimal AI or robotic adoption; this is a low-digitization, low-priority automation target. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI/robotics offers minimal assistance to a human performing standard painting, washing, and cleaning tasks at a gas station, since these are straightforward manual activities where human judgment and adaptability are already sufficient. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for manual painting, washing, or cleaning tasks; these remain purely physical labor activities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | General housekeeping duties (painting, washing, cleaning) require physical manipulation in unstructured environments with highly variable conditions. Current AI systems lack the embodied dexterity and adaptability to perform these tasks end-to-end at quality parity. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical cleaning, painting, and washing of industrial equipment requires manual dexterity and mobility that current AI systems (software or robotic) cannot perform in unstructured industrial settings.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no formal licensing or regulatory requirements mandating human performance of these housekeeping tasks, but practical barriers exist: on-site presence requirements, safety oversight, and the need for contextual judgment about station-specific conditions reduce substitutability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier requires a human specifically, but the physical nature of the task and need for mobility in constrained industrial environments create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of outdoor maintenance work (if they existed at scale) would be significantly more expensive to acquire, maintain, and deploy than hiring local maintenance workers for periodic housekeeping. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system for this physical task, so any hypothetical robotic solution would be far more costly than a human worker performing routine cleaning and painting. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial systems reliably perform general maintenance painting, washing, and cleaning at compressor stations. While robotic platforms exist for narrow tasks, production-grade systems handling the full scope of these duties do not exist. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs general industrial housekeeping like painting and washing at gas stations; this remains firmly in the domain of human labor or basic non-AI machinery like pressure washers. |
Clean, lubricate, and adjust equipment, and replace filters and gaskets, using hand tools.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Clean, lubricate, and adjust equipment, and replace filters and gaskets, using hand tools.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical maintenance in industrial gas operations remains slow to adopt automation due to the need for human judgment, site-specific conditions, regulatory compliance, and safety-critical decision-making. Pilots are rare and deployment is negligible in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field maintenance is a physically intensive, low-digitization sector with minimal AI/robotic adoption for hands-on equipment upkeep. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with maintenance scheduling or diagnostic support (e.g., predictive analytics flagging wear patterns), but the core physical work—cleaning, lubricating, adjusting, and replacing components—offers limited augmentation without robotic hardware that does not yet exist at scale for this task domain. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with predictive maintenance scheduling or diagnostics to inform when parts need replacement, but offers little help with the physical execution of cleaning and repair. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in industrial settings with high-stakes safety implications, dexterity, and real-time sensory feedback (feel, sound, visual inspection of wear). Current AI systems cannot physically perform maintenance work or reliably diagnose equipment condition without human oversight in production environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Requires physical manipulation of industrial equipment (cleaning, lubricating, replacing filters/gaskets) in the field—current AI cannot perform physical manual labor tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Gas compression and pumping operations are heavily regulated by OSHA, EPA, and state safety codes that typically require licensed or certified human operators/technicians to perform and sign off on maintenance. Liability for equipment failure and safety incidents creates strong legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas compressor stations are regulated industrial sites often requiring certified operators/technicians for safety and compliance, and physical access/liability concerns create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of physical maintenance tasks with required precision and safety would cost far more than the loaded wage of a skilled technician, including acquisition, maintenance, and site-specific configuration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-robotic solution would be far costlier and less capable than a human technician today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously clean, lubricate, adjust, or replace physical components on industrial equipment. While robotic arms exist in controlled manufacturing, they are not general-purpose substitutes for the fine-grained troubleshooting and adaptation required in gas station maintenance. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical maintenance tasks like lubricating equipment or replacing gaskets; robotics for this specific industrial maintenance context remains research-stage at best. |
Connect pipelines between pumps and containers that are being filled or emptied.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail
Connect pipelines between pumps and containers that are being filled or emptied.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The gas and utilities sector has shown limited automation of field operations involving physical manipulation of hazardous materials; automation adoption remains concentrated in remote monitoring and control, not hands-on pipeline work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gas and oil field operations are a physically intensive, low-digitization sector with minimal AI/robotic adoption for hands-on mechanical tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through real-time monitoring alerts or procedural checklists, but the core physical connection task itself offers minimal opportunity for meaningful human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with monitoring, scheduling, or diagnostics around this task, but offers little direct help with the physical act of connecting pipelines. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Connecting physical pipelines between pumps and containers requires dexterous manipulation in real-world, often hazardous industrial environments. Current AI systems lack the embodied robotics, spatial reasoning, and safety-critical decision-making needed to perform this task reliably end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands-on connection of pipelines, valves, and fittings; no current AI or robotic system can perform this end-to-end in typical field settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task involves mandatory human presence for safety compliance, operator licensing requirements, and regulatory oversight of gas-handling operations that necessitate a qualified, accountable human performer. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Handling pressurized gas equipment involves safety regulations, certification requirements, and high liability for leaks or accidents, creating strong barriers to automation without specialized robotics and regulatory approval. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying and maintaining specialized industrial robotics to connect pipelines would far exceed the loaded wage of a trained gas station operator, making human labor significantly more cost-effective today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for physical connection work, so AI cost is not comparable—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform physical pipeline connection tasks in production gas-handling environments. This remains a labor-intensive manual task requiring human presence and judgment on-site. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical pipeline connection at gas compressor stations; this remains a manual, on-site operator task. |
Related occupations — Transportation & Material Moving
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.