Dredge Operators
53-7031.00Median wage $49,640/yr1,040 employed (US)Rank #897 of 923 scored · top 97% by substitution
Operate dredge to remove sand, gravel, or other materials in order to excavate and maintain navigable channels in waterways.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution8
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
6 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%6
panel mean rating 1.3/5 → substitution pressure 6/100
Technical feasibility todayw 20%0
panel mean rating 1.0/5 → substitution pressure 0/100
Cost vs. human wagew 15%0
panel mean rating 1.0/5 → substitution pressure 0/100
Adoption barriersw 20%inverted — strong barriers lower the score31
panel mean rating 3.8/5 (barrier strength) → substitution pressure 31/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (6 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.
Start and stop engines to operate equipment.
16CI 14–19 · exposure 16 · augmentation 25 · importance 4.4/5 · click for rater detail
Start and stop engines to operate equipment.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a physical, low-digitization sector with strong union representation and regulatory oversight. Adoption of autonomous engine control remains minimal, with industry moving slowly toward remote operation rather than full automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dredging is a low-digitization, physical, heavy-industrial sector with minimal AI/automation adoption for equipment control tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide engine diagnostics, predictive alerts, or operational recommendations to guide the operator's decisions, but current systems offer limited assistance for the core physical actuation task itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Basic engine monitoring/alert systems can assist operators in timing starts/stops, but this offers marginal productivity benefit for such a simple task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Starting and stopping engines involves physical actuation and real-time monitoring of equipment state, which requires on-site robotic or mechanical automation. Current AI systems can control software-based systems but lack the embodied capability to physically engage ignition systems and monitor live engine responses on dredging equipment. |
| Task automatability | claude-sonnet-5 | 2/5 | Starting/stopping engines is a simple physical control action but occurs within a heavy-equipment operating context requiring on-site presence and situational judgment, limiting full automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime regulations, equipment safety certifications, and liability requirements typically mandate that licensed operators personally control critical engine functions. Insurance and legal frameworks create strong barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for engine start/stop, but safety protocols, equipment liability, and marine/heavy equipment operating rules create organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotics or automated systems capable of physically starting/stopping engines on industrial dredging equipment far exceeds the loaded wage of a dredge operator, and integration costs are substantial. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Retrofitting automation/remote control systems onto dredge engines would require significant capital investment exceeding the marginal cost of a human operator performing this simple action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task autonomously in production dredging operations. While remote monitoring and diagnostics exist, actual engine start/stop requires physical presence and real-time troubleshooting that current systems cannot do at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously starts/stops dredge engines as part of routine operation; remote/automated dredging is still largely research or niche pilot stage. |
Pump water to clear machinery pipelines.
9CI 5–14 · exposure 8 · augmentation 25 · importance 4.3/5 · click for rater detail
Pump water to clear machinery pipelines.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a capital-intensive, low-digitization sector with aging equipment, small operator populations, and entrenched manual practices; adoption of automation remains minimal and pilots are rare in this traditionally conservative industry. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dredging and heavy equipment operation is a low-digitization, physical-labor sector with minimal AI adoption for hands-on machinery tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with real-time pipeline diagnostics or automated alerts for pressure thresholds, but the core manual operation of pumps and immediate response to equipment failure offers limited scope for meaningful augmentation without full human presence. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors and monitoring systems could alert operators to pipeline blockages or pressure issues, offering minor assistance, but the core physical task is unaffected. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Pumping water through pipes involves physical operation of equipment in variable field conditions; while pump activation is mechanical, real-time monitoring for clogs, pressure anomalies, and environmental hazards requires continuous human supervision that current AI cannot reliably replace end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical operational task requiring hands-on control of dredging equipment and pipeline hardware; current AI cannot physically pump water or clear pipelines. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime and dredging operations are heavily regulated (USCG, EPA, state licensing); operators often require specific certifications, and liability for environmental damage, equipment damage, or worker safety creates legal barriers that prevent full automation without licensed human sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy machinery operation involves safety regulations, physical presence requirements, and liability concerns that necessitate a trained human operator on-site. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Dredge operators require expensive on-site presence, specialized equipment operation, and safety oversight; AI-based remote pumping systems would demand significant infrastructure investment and continuous human oversight, making all-in costs comparable to or exceeding human wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical action, so AI cost is not comparable—human labor with equipment remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product autonomously operates dredging or pipeline-clearing equipment in production environments; the task requires integration with legacy machinery, real-time hydraulic/pressure feedback, and physical intervention that exceeds current automation scope. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical pipeline clearing operations; this remains a manual/mechanical operator task in the field. |
Start power winches that draw in or let out cables to change positions of dredges, or pull in and let out cables manually.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Start power winches that draw in or let out cables to change positions of dredges, or pull in and let out cables manually.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a traditional, capital-intensive maritime sector with minimal AI adoption patterns. Most dredging firms are small to mid-sized, with low digitization and physical infrastructure that resists rapid automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dredging and marine construction are low-digitization, physically intensive sectors with minimal AI/robotics adoption for direct equipment operation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Modern dredges may have computerized positioning systems (GPS, sensors), but AI augmentation of cable control remains limited; the human operator remains central to dynamic decision-making and safety in this inherently physical task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring or automated tension/position feedback systems could assist operators, but current AI assistance for this specific manual winch task is minimal. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of mechanical equipment (power winches, cables) in a dynamic marine environment, requiring real-time spatial awareness and manual control. Current AI systems cannot operate heavy machinery directly without substantial custom robotics, which does not exist at scale for dredging operations. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual/machine control task requiring real-time perception of cable tension, dredge positioning, and environmental conditions; no off-the-shelf AI system can execute this physical operation end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime operations are regulated by federal maritime law and classification societies (ABS, DNV); operators typically require licensing (USCG credentials). The task involves safety-critical positioning of heavy equipment where human judgment and responsibility are legally required. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like some trades, marine equipment operation involves safety liability, physical risk, and often requires trained/certified personnel per maritime regulations, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotics for dredge operation would be extremely expensive to develop, integrate, and maintain, far exceeding the cost of a skilled dredge operator's wage. No viable off-the-shelf solution exists. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying robotic/automated winch control would require expensive specialized hardware and sensors, making it far costlier than a human operator for this niche task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products operate dredge winches or perform cable positioning tasks in production. This requires embodied robotics integration specific to maritime equipment, which remains at research/prototype stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product operates dredge winches or pulls cables; automated marine equipment control remains largely research/prototype stage for such specific physical tasks. |
Lower anchor poles to verify depths of excavations, using winches, or scan depth gauges to determine depths of excavations.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Lower anchor poles to verify depths of excavations, using winches, or scan depth gauges to determine depths of excavations.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a capital-intensive, geographically dispersed, and physically-bound sector with slow digitization; adoption of autonomous equipment is minimal and remains in pilot phases at best. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dredging and heavy equipment operation is a low-digitization, physical-labor sector with minimal AI agent adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital depth gauges and sensor integration can assist operators in reading measurements, but AI does not meaningfully augment the core task of physically operating winches and poles or making judgment calls on site safety and dredging strategy. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital depth gauges and sensor readouts can assist operators with real-time data, but this is existing instrumentation rather than AI-driven augmentation, offering limited additional productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires on-site physical operation of equipment (winches, anchor poles) and real-time spatial judgment in dynamic water conditions. Current AI systems cannot operate heavy machinery or navigate unstructured physical environments autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of winches and anchor poles or reading physical depth gauges on heavy equipment in a marine/excavation environment, which is beyond current AI capabilities without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime safety regulations, equipment licensing, and liability for excavation verification require skilled human operators; regulatory bodies and insurers mandate certified personnel oversight of dredging operations. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in most jurisdictions, dredge operation involves safety-critical judgment, equipment liability, and often union/certification requirements that create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Remote sensing or autonomous depth verification systems remain expensive relative to human operators on-site; the hardware, integration, and maintenance costs exceed typical dredge operator wages for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for this physical task, so any automation would require expensive robotic/sensor retrofits far exceeding the cost of a human operator. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently operate dredge equipment or physically lower anchor poles. Autonomous dredging systems in research/early prototype stages do not yet perform this task reliably in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical dredging depth-verification task; sensor-based depth monitoring exists but not as an autonomous replacement for the operator's physical control and verification role. |
Move levers to position dredges for excavation, to engage hydraulic pumps, to raise and lower suction booms, and to control rotation of cutterheads.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Move levers to position dredges for excavation, to engage hydraulic pumps, to raise and lower suction booms, and to control rotation of cutterheads.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a low-digitization, physical, geographically dispersed sector with significant regulatory and safety inertia. Adoption of automation is minimal; most dredge operations remain highly manual. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Marine construction and dredging is a low-digitization, physical-labor-heavy sector with minimal AI/robotic adoption in day-to-day equipment operation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While remote monitoring or sensor overlays might assist situational awareness, the core task of lever control is inherently hands-on and difficult to augment with AI without substantial tele-operation infrastructure that is not widely deployed in the dredging industry. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-assisted or semi-automated control systems can provide operators with positioning data or automated depth control, but this offers only limited assistance to the core manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time, physically situated control of heavy machinery in a dynamic environment—adjusting boom position, pump engagement, and cutterhead rotation based on visual feedback and water/material conditions. Current AI cannot reliably operate physical levers or coordinate multi-axis hydraulic systems in unstructured waterway settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires direct physical manipulation of heavy machinery controls in a dynamic underwater/marine excavation environment, which current AI systems cannot perform end-to-end without robotic embodiment far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Dredging is heavily regulated by marine/environmental authorities; operators often require licensure and must comply with strict protocols. Liability for environmental damage or equipment failure creates strong legal barriers to removing human oversight from control decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy equipment operation involves safety regulation, environmental liability, and typically requires certified operators, and errors (e.g., pipeline damage, environmental spills) carry high liability, creating strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An autonomous dredge system would require custom hardware (actuators, sensors, hydraulic integration) and substantial development cost that far exceeds the loaded wage of a dredge operator, with no mature cost-competitive solution available. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based automation would require costly custom robotics/sensor integration far exceeding a human operator's wage today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously operate dredge controls today. The task demands continuous haptic feedback, spatial reasoning in murky water, and adaptive responses to changing sediment and currents that exceed current robotic or teleoperation capabilities in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously operate dredge levers, pumps, booms, and cutterheads in production; this remains outside current commercial automation entirely, robotic dredging automation is at best experimental. |
Direct or assist workers placing shore anchors and cables, laying additional pipes from dredges to shore, and pumping water from pontoons.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Direct or assist workers placing shore anchors and cables, laying additional pipes from dredges to shore, and pumping water from pontoons.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dredging is a traditional, physical-environment sector with low digitization, small operator counts, and strong regulatory conservatism. Adoption of autonomous systems in this domain has been negligible; pilots are rare and production deployment is not visible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dredging and marine construction is a low-digitization, heavy-equipment physical sector with minimal AI agent adoption for on-site directing of workers and physical rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI could assist with monitoring water levels in pontoons (sensor data logging) or providing visual overlays for anchor positioning, but the core task of directing crew placement and managing dynamic physical systems offers limited scope for meaningful AI assistance without full autonomy. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with logistics planning, scheduling, or monitoring sensor data related to dredge operations, but offers little direct help with the hands-on directing and physical execution described. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical coordination of workers, positioning of heavy equipment in real-world environments (water, shore), and real-time decision-making about anchor placement and cable management. Current AI systems cannot autonomously perform the physical manipulation, spatial coordination, or safety-critical oversight required. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical field work involving directing workers, manipulating heavy equipment like anchors and cables, and manual pipe laying on water-adjacent terrain, none of which current AI systems can perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: maritime and construction safety regulations require human oversight and decision-making; liability for anchor failure or pipe damage is high; and the physical environment (water conditions, site variability) creates legal and insurance requirements for licensed, accountable operators on-site. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical marine operations involving heavy equipment, anchor placement, and worker coordination require on-site human judgment, physical presence, and often certification, creating strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Dredge operators are relatively low-cost labor, and the specialized equipment and site-specific setup needed to automate these tasks (robotics, positioning systems, integration with dredges) would exceed the loaded cost of a skilled operator for years. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical coordination and labor task, so AI cost comparison is not applicable and effectively AI is far more expensive since it cannot perform the task at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform autonomous dredge operation crew direction or the physical work of placing anchors, laying pipes, and managing pontoon water systems. This requires embodied robotics and field coordination at a level not yet deployed at scale in dredging operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product directs marine dredging crews or physically manages anchor/cable placement and pipe-laying operations; this remains firmly in human physical labor and supervisory domain. |
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.