AI exposure: Excavating and Loading Machine and Dragline Operators, Surface Mining
Operate or tend machinery at surface mining site, equipped with scoops, shovels, or buckets to excavate and load loose materials.
Reading this score
computedAt 11.0% of weighted task load, Excavating and Loading Machine and Dragline Operators, Surface Mining sits at the 16th percentile, below the point where a job's centre of gravity has moved. 83.6% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.
What holds the line here is embodiment. Across this occupation's 16 tasks it averages 2.41 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Receive written or oral instructions regarding material movement or excavation, at 60.0%. The least is Perform manual labor to prepare or finish sites, at 0.0%. A gap of 60.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within construction and extraction occupations, this one is more exposed than most. The median across the 61 roles in the group is 5.0%, and only 11 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 16 tasks, 4 are currently banded exposed, 0 assisted and 12 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 0.69 | 0-4 |
| Embodiment | 2.41 | 0-3 |
| Presence | 0.94 | 0-3 |
| Accountability | 0.66 | 0-3 |
| Context | 1.00 | 0-3 |
| Verification cost | 0.91 | 0-3 |
Task by task
16 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Receive written or oral instructions regarding material movement or excavation. | 60.0% | 15.0% | 25.0% | 4.04 | exposed |
| Measure and verify levels of rock or gravel, bases, or other excavated material. | 38.3% | 11.7% | 50.0% | 3.98 | exposed |
| Direct workers engaged in placing blocks or outriggers to prevent capsizing of machines when lifting heavy loads. | 30.0% | 20.0% | 50.0% | 4.00 | exposed |
| Direct ground workers engaged in activities such as moving stakes or markers, or changing positions of towers. | 30.0% | 20.0% | 50.0% | 3.76 | exposed |
| Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application. | 13.3% | 11.7% | 75.0% | 4.43 | untouched |
| Set up or inspect equipment prior to operation. | 11.7% | 13.3% | 75.0% | 4.56 | untouched |
| Move levers, depress foot pedals, and turn dials to operate power machinery, such as power shovels, stripping shovels, scraper loaders, or backhoes. | 0.0% | 0.0% | 100.0% | 4.56 | untouched |
| Observe hand signals, grade stakes, or other markings when operating machines so that work can be performed to specifications. | 0.0% | 0.0% | 100.0% | 4.39 | untouched |
| Operate machinery to perform activities such as backfilling excavations, vibrating or breaking rock or concrete, or making winter roads. | 0.0% | 0.0% | 100.0% | 4.22 | untouched |
| Move materials over short distances, such as around a construction site, factory, or warehouse. | 0.0% | 0.0% | 100.0% | 3.99 | untouched |
| Create or maintain inclines or ramps. | 0.0% | 0.0% | 100.0% | 3.90 | untouched |
| Lubricate, adjust, or repair machinery and replace parts, such as gears, bearings, or bucket teeth. | 0.0% | 0.0% | 100.0% | 3.85 | untouched |
| Handle slides, mud, or pit cleanings or maintenance. | 0.0% | 0.0% | 100.0% | 3.69 | untouched |
| Adjust dig face angles for varying overburden depths and set lengths. | 0.0% | 0.0% | 100.0% | 3.74 | untouched |
| Drive machines to work sites. | 0.0% | 0.0% | 100.0% | 3.68 | untouched |
| Perform manual labor to prepare or finish sites, such as shoveling materials by hand. | 0.0% | 0.0% | 100.0% | 3.52 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.