AI exposure: Helpers--Extraction Workers
Help extraction craft workers, such as earth drillers, blasters and explosives workers, derrick operators, and mining machine operators, by performing duties requiring less skill. Duties include supplying equipment or cleaning work area.
Reading this score
computedHelpers--Extraction Workers is among the least exposed occupations measured, at 6.0% of weighted task load, rank 865 of 923. 86.2% of this job is work current AI systems cannot produce at all. That is not a statement about skill or value. It is a statement about what these systems can and cannot do.
What holds the line here is embodiment. Across this occupation's 14 tasks it averages 2.86 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 Organize materials to prepare for use, at 40.0%. The least is Dig trenches, at 0.0%. A gap of 40.0% 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 27 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 14 tasks, 1 are currently banded exposed, 0 assisted and 13 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 | 0.43 | 0-4 |
| Embodiment | 2.86 | 0-3 |
| Presence | 0.86 | 0-3 |
| Accountability | 0.82 | 0-3 |
| Context | 1.18 | 0-3 |
| Verification cost | 1.46 | 0-3 |
Task by task
14 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Organize materials to prepare for use. | 40.0% | 35.0% | 25.0% | 3.89 | exposed |
| Set up and adjust equipment used to excavate geological materials. | 11.7% | 13.3% | 75.0% | 3.93 | untouched |
| Drive moving equipment to transport materials and parts to excavation sites. | 8.3% | 16.7% | 75.0% | 4.07 | untouched |
| Observe and monitor equipment operation during the extraction process to detect any problems. | 5.0% | 20.0% | 75.0% | 4.28 | untouched |
| Unload materials, devices, and machine parts, using hand tools. | 0.0% | 0.0% | 100.0% | 4.00 | untouched |
| Repair and maintain automotive and drilling equipment, using hand tools. | 0.0% | 0.0% | 100.0% | 3.86 | untouched |
| Clean up work areas and remove debris after extraction activities are complete. | 0.0% | 0.0% | 100.0% | 3.76 | untouched |
| Clean and prepare sites for excavation or boring. | 0.0% | 0.0% | 100.0% | 3.58 | untouched |
| Load materials into well holes or into equipment, using hand tools. | 0.0% | 0.0% | 100.0% | 3.54 | untouched |
| Provide assistance to extraction craft workers, such as earth drillers and derrick operators. | 0.0% | 0.0% | 100.0% | 4.30 | untouched |
| Collect and examine geological matter, using hand tools and testing devices. | 0.0% | 0.0% | 100.0% | 3.84 | untouched |
| Signal workers to start geological material extraction or boring. | 0.0% | 0.0% | 100.0% | 3.78 | untouched |
| Dismantle extracting and boring equipment used for excavation, using hand tools. | 0.0% | 0.0% | 100.0% | 3.70 | untouched |
| Dig trenches. | 0.0% | 0.0% | 100.0% | 3.38 | 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.