AI exposure: 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.
Reading this score
computedPile Driver Operators is among the least exposed occupations measured, at 2.0% of weighted task load, rank 904 of 923. 94.9% 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 5 tasks it averages 3.00 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 Conduct pre-operational checks on equipment to ensure proper functioning, at 10.0%. The least is Clean, lubricate, and refill equipment, at 0.0%. A gap of 10.0% 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 less exposed than the median of 5.1% across the group's 61 roles, with 47 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.
What would move this score. Of 5 tasks, 0 are currently banded exposed, 0 assisted and 5 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 | 0.20 | 0-4 |
| Embodiment | 3.00 | 0-3 |
| Presence | 0.70 | 0-3 |
| Accountability | 1.00 | 0-3 |
| Context | 0.90 | 0-3 |
| Verification cost | 1.30 | 0-3 |
Task by task
5 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Conduct pre-operational checks on equipment to ensure proper functioning. | 10.0% | 15.0% | 75.0% | 4.47 | untouched |
| Move hand and foot levers of hoisting equipment to position piling leads, hoist piling into leads, and position hammers over pilings. | 0.0% | 0.0% | 100.0% | 4.69 | untouched |
| Move levers and turn valves to activate power hammers, or to raise and lower drophammers that drive piles to required depths. | 0.0% | 0.0% | 100.0% | 4.58 | untouched |
| Drive pilings to provide support for buildings or other structures, using heavy equipment with a pile driver head. | 0.0% | 0.0% | 100.0% | 4.55 | untouched |
| Clean, lubricate, and refill equipment. | 0.0% | 0.0% | 100.0% | 4.29 | 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.