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Excavating Machine Operators

Construction, Maintenance & Repair
Projected change 2024–34: +3.6%
Median annual wage (2024): $58,710
Employment (2024): 489K

United States · BLS Employment Projections 2024–34 (figures for SOC 47-2073 occupational group)

AI exposure in published research

Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.

Data provider: OpenAI · "GPTs are GPTs"

Time basis: 2023 baselineex-ante estimate

Scored against GPT-4-generation capability.

Human rater basis
8.7%
GPT-4 rater basis
5.4%

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

28 rated tasks · 2 tasks with β ≥ 0.5 (7.1%)

Version:
gh-main-0471612
License:
MIT License · Copyright (c) 2024 OpenAI

Data provider: Anthropic Economic Index

Time basis: Published 2026-03-05composite index

Observed exposure
0.0%

Theoretical exposure index weighted by measured Claude usage, per the original report's definition.

27 tasks · usage observed in 0 · mean 0.0%

Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex

AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.

Version:
hf-lmi-2026-03
License:
CC BY 4.0
Observation period:
(not applicable to this release)
Model:
(not stated by the source)

2023 prediction vs observation-based index published 2026-03-05

One card (GPTs are GPTs) is a 2023 estimate of what AI could theoretically do; the other (Anthropic Economic Index) is built from observed usage and was published on 2026-03-05 — that is its publication date, not the period it observed. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.

Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.

The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.

The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.

These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.

Task Breakdown

  • Operate hydraulic excavators and backhoes
  • Review site plans and grade stakes for excavation depth
  • Perform daily equipment safety inspections and maintenance

About This Occupation

If you work as an Excavating Machine Operator, AI is beginning to augment your planning tasks. With an automation risk of 15/100 and overall exposure at 26%, this role faces low transformation. GPS-guided grading and site plan review see the most AI integration at 42%. BLS projects +4% growth through 2034.

ISCO-08 classification

Unit group 8342ILO official

Earthmoving and Related Plant Operators

Definition

ILO original text (English)

Earthmoving and related plant operators operate machines to excavate, grade, level, smooth and compact earth or similar materials.

Definition & vocabulary source

Source: International Labour Organization (ILO) — ISCO-08 Structure

License: ILO CC BY 4.0

View original

Official occupational information

  • ONET47-2071.00

    Paving, Surfacing, and Tamping Equipment Operators

    Operate equipment used for applying concrete, asphalt, or other materials to road beds, parking lots, or airport runways and taxiways or for tamping gravel, dirt, or other materials. Includes concrete and asphalt paving machine operators, form tampers, tamping machine operators, and stone spreader operators.

    View original
  • ONET47-2072.00

    Pile Driver Operators

    Operate pile drivers mounted on skids, barges, crawler treads, or locomotive cranes to drive pilings for retaining walls, bulkheads, and foundations of structures such as buildings, bridges, and piers.

    View original
  • ONET47-2073.00

    Operating Engineers and Other Construction Equipment Operators

    Operate one or several types of power construction equipment, such as motor graders, bulldozers, scrapers, compressors, pumps, derricks, shovels, tractors, or front-end loaders to excavate, move, and grade earth, erect structures, or pour concrete or other hard surface pavement. May repair and maintain equipment in addition to other duties.

    View original
  • ONET53-7031.00

    Dredge Operators

    Operate dredge to remove sand, gravel, or other materials in order to excavate and maintain navigable channels in waterways.

    View original

Source: O*NET 30.2, U.S. DOL/ETA

License: CC BY 4.0

View original

Frequently Asked Questions

The Anthropic Economic Index puts observed exposure at 0.0%. The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 8.7% under human raters. Both figures are reproduced from published research without modification.

They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.

No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.

Recent Changes Affecting This Occupation

Mar 2026: Published evergreen blog analyzing AI impact on heavy equipment operators

[Source: Blog Wave 19]