Start engines, move throttles, switches, or levers, or depress pedals to operate machines, such as bulldozers, trench excavators, road graders, or backhoes.[2]
Where your edge is
Time Machine
Operating Engineers and Other Construction Equipment Operators
Scrub through 199 years of this role's history — from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
1850187519001925195019752000now
Drag the dot, click anywhere on the track, or use ← → arrow keys (Shift for 10-year jumps, PgUp/PgDn for 25).
2026
Known today as Operating Engineers and Other Construction Equipment Operators (SOC 47-2073)
US Employment
489K
BLS National Employment Matrix baseline (2024) for SOC 47-2073: 489.3 thousand total workers (477.0 thousand wage-and-salary; 12.3 thousand self-employed). This is the highest recorded headcount for this occupation, driven by the IIJA ($1.2 trillion signed November 2021, peak spending 2023-2026), IRA clean-energy civil construction (solar and wind site preparation, battery plant construction), and hyperscale datacenter campus earthwork in Texas, Virginia, Georgia, and Ohio. The IUOE reported approximately 400,000+ members across 123 local unions as of 2024.
Median Annual Wage
$54,810
Source: BLS-OEWS
Tool of the era · Semi-autonomous construction equipment (Built Robotics Exosystem 2018/2021; Komatsu Smart Construction; Caterpillar MineStar Command)
Built Robotics was founded in 2016 in San Francisco by Noah Ready-Campbell (former Google product manager) and Andrew Liang. Their "Exosystem" — an aftermarket kit using GPS, cameras, and AI — can be installed on existing Caterpillar, Hitachi, and John Deere excavators to enable autonomous operation on constrained tasks: trenching along a predefined GPS path, compacting a known area, performing rough dozer grading to a digital terrain model. Built launched its first AI Guidance System product in 2018 and brought the full Exosystem to market in 2021. In March 2020, the IUOE partnered with Built Robotics to train operators — an unusual labor-technology partnership that reflects the IUOE's strategy: if autonomous equipment is coming, IUOE members should be the ones running it, monitoring it, and transitioning into the technician roles that maintain it. Komatsu's Smart Construction platform (digital twin of the job site + machine guidance) and Caterpillar's Cat Command remote-control and semi-autonomous excavator systems are expanding in parallel. None of these systems are yet capable of fully unattended operation on a typical construction site: the technology monitors and assists rather than replaces the operator on all but the simplest repetitive tasks.
The IUOE-Built Robotics partnership (signed 2020, renewed through 2026 in 2023) signals the labor movement's calculation: semi-autonomous equipment will initially augment operators, not replace them, because the unstructured civil construction environment requires judgment that GPS-and-camera systems cannot yet match. BLS projects +3.6% employment growth 2024-34, consistent with augmentation rather than net substitution over the next decade.
Projection cone · present → 2038
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread you see below is the range of futures credible sources project for this role.
IUOE / IIJA + IRA construction demand scenario
→ 2030
+8%
The IUOE, the Associated General Contractors (AGC), and infrastructure economists project that IIJA spending is not yet at peak deployment in 2024; the pipeline of awarded projects under the $1.2 trillion law is back-loaded into 2025-2028. Combined with IRA-driven industrial construction (semiconductor fabs, battery gigafactories, solar manufacturing, EV assembly plants — all requiring substantial earthmoving) and hyperscale datacenter campus construction, the IUOE projects a shortage of operating engineers in many US markets by 2026-2028. If the shortage materializes and is resolved through wage increases and accelerated apprenticeship, employment could grow 6-10% from the 2024 baseline by 2030. This is the optimistic tail of the uncertainty cone.
BLS National Employment Matrix 2024-34
→ 2034
+3.6%
BLS Employment Projections 2024-34 cycle (most current as of May 2026). Baseline 489.3 thousand (2024); projected 507.1 thousand (2034); absolute change +17.8 thousand; percent change +3.6%. BLS projects continued demand from IIJA infrastructure spending (highway, bridge, water, transit), IRA-driven civil construction (solar and wind site preparation, battery plant and EV charger facility earthwork), and hyperscale datacenter campus construction. BLS explicitly does not model speculative autonomous-equipment substitution scenarios in its 10-year matrix; the projection reflects current technology and policy trajectories only.
Eloundou et al. — "GPTs are GPTs" (2023)
→ 2028
-2%
GPT-4 task-by-task LLM exposure labeling on O*NET tasks. Construction equipment operators score low on LLM exposure because the core tasks — operating a bulldozer, excavating a trench, reading a job-site grade stake, troubleshooting a hydraulic system — are physical tasks an LLM cannot perform. The -2% estimate represents the marginal near-term displacement from AI-assisted job-site planning tools, digital terrain model updates, and GPS machine control optimization rather than from robotic substitution. This is firmly in the augmentation regime for the next 5 years. The longer-term picture is more uncertain: if semi-autonomous systems from Built Robotics, Komatsu, and Caterpillar mature to handle the unstructured site environment, the Eloundou model undersells the medium-term displacement risk.
Autonomous construction equipment substitution scenario (2030-2040 horizon)
→ 2038
-18%
Speculative scenario based on the trajectory of semi-autonomous systems from Built Robotics, Komatsu Smart Construction, and Caterpillar Command. If the technology matures to handle the full range of excavator, dozer, and grader tasks on typical construction sites without continuous operator oversight — a capability that does not exist as of 2025 — the substitution potential is substantial. The mining precedent (Komatsu FrontRunner at Rio Tinto Pilbara: 100+ autonomous haul trucks, first deployed 2008, operational for 15+ years) demonstrates that commercial-scale autonomous heavy equipment is feasible where the operating environment is structured. The key uncertainty is whether construction sites can be made structured enough for the same technology within the 2030-2040 horizon. A -18% scenario by 2038 represents the pessimistic tail if technology adoption accelerates beyond BLS assumptions. This is not a BLS projection.
Frey & Osborne (2013)
→ 2033
-35%
Gaussian-process classifier on O*NET task features. Frey & Osborne assigned construction equipment operators a probability of computerization of approximately 0.96 — placing them among the highest-risk occupations in the 702-occupation dataset. The bottleneck analysis identified high "finger dexterity" and "manual dexterity" scores as the primary barriers to automation, but the 0.96 probability reflected that most other tasks (operating controls, monitoring gauges, following route instructions) were deemed highly automatable. This was written before the distinction between structured mining environments (where autonomous haul trucks have since been commercially deployed) and unstructured civil construction sites (where full autonomy remains a research horizon) was well understood. The F&O prediction has aged poorly for construction-site operating engineers: employment is at its highest recorded level in 2024, 14 years after F&O's baseline year. The -35% figure represents the severe-substitution tail of the uncertainty cone if F&O's probability were partially realized — it is not a BLS projection.
Today, in this role
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting — and what stays yours.
Align machines, cutterheads, or depth gauge makers with reference stakes and guidelines or ground or position equipment, following hand signals of other workers.[2]
Where your edge is
Locate underground services, such as pipes or wires, prior to beginning work.[2]
Where your edge is
Present-day sources
Sources
Every claim on this page traces back to one of the following. Updated 2026-05-30.
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