Will AI replace Excavating and Loading Machine and Dragline Operators, Surface Mining?
Most of the work in Excavating and Loading Machine and Dragline Operators, Surface Mining still leans on things AI struggles with — research rates its theoretical AI reach at only ~0%, and real-world use lower still.
O*NET-SOC 47-5022
How your 10 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~0%). By late 2025, real-world AI use had caught up to about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~0%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 10 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- No tasks in this middle tier.
- Move levers, depress foot pedals, and turn dials to operate power machinery, such as power shovels, stripping shovels, scraper loaders, or backhoes.
- Set up or inspect equipment prior to operation.
- Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application.
- Observe hand signals, grade stakes, or other markings when operating machines so that work can be performed to specifications.
- Operate machinery to perform activities such as backfilling excavations, vibrating or breaking rock or concrete, or making winter roads.
- Receive written or oral instructions regarding material movement or excavation.
- Move materials over short distances, such as around a construction site, factory, or warehouse.
- Create or maintain inclines or ramps.
- Lubricate, adjust, or repair machinery and replace parts, such as gears, bearings, or bucket teeth.
- Handle slides, mud, or pit cleanings or maintenance.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.