AI exposure: Fallers
Use axes or chainsaws to fell trees using knowledge of tree characteristics and cutting techniques to control direction of fall and minimize tree damage.
Reading this score
computedFallers is among the least exposed occupations measured, at 6.9% of weighted task load, rank 848 of 923. 85.3% 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 17 tasks it averages 2.76 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 Select trees to be cut down, assessing factors such as site, terrain, and weather conditions..., at 35.0%. The least is Work as a member of a team, rotating between chain saw operation and skidder operation, at 0.0%. A gap of 35.0% 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 farming, fishing and forestry occupations, this one is less exposed than the median of 10.8% across the group's 12 roles, with 8 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 17 tasks, 0 are currently banded exposed, 2 assisted and 15 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 | 0.47 | 0-4 |
| Embodiment | 2.76 | 0-3 |
| Presence | 0.38 | 0-3 |
| Accountability | 0.85 | 0-3 |
| Context | 1.35 | 0-3 |
| Verification cost | 1.32 | 0-3 |
Task by task
17 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Select trees to be cut down, assessing factors such as site, terrain, and weather conditions before beginning work. | 35.0% | 40.0% | 25.0% | 4.51 | assisted |
| Determine position, direction, and depth of cuts to be made, and placement of wedges or jacks. | 35.0% | 40.0% | 25.0% | 4.27 | assisted |
| Assess logs after cutting to ensure that the quality and length are correct. | 11.7% | 13.3% | 75.0% | 4.43 | untouched |
| Appraise trees for certain characteristics, such as twist, rot, and heavy limb growth, and gauge amount and direction of lean, to determine how to control the direction of a tree's fall with the least damage. | 11.7% | 13.3% | 75.0% | 4.40 | untouched |
| Saw back-cuts, leaving sufficient sound wood to control direction of fall. | 0.0% | 0.0% | 100.0% | 4.64 | untouched |
| Control the direction of a tree's fall by scoring cutting lines with axes, sawing undercuts along scored lines with chainsaws, knocking slabs from cuts with single-bit axes, and driving wedges. | 0.0% | 0.0% | 100.0% | 4.62 | untouched |
| Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls. | 0.0% | 0.0% | 100.0% | 4.55 | untouched |
| Measure felled trees and cut them into specified log lengths, using chain saws and axes. | 0.0% | 0.0% | 100.0% | 4.50 | untouched |
| Insert jacks or drive wedges behind saws to prevent binding of saws and to start trees falling. | 0.0% | 0.0% | 100.0% | 4.49 | untouched |
| Clear brush from work areas and escape routes, and cut saplings and other trees from direction of falls, using axes, chainsaws, or bulldozers. | 0.0% | 0.0% | 100.0% | 4.38 | untouched |
| Tag unsafe trees with high-visibility ribbons. | 0.0% | 0.0% | 100.0% | 4.30 | untouched |
| Maintain and repair chainsaws and other equipment, cleaning, oiling, and greasing equipment, and sharpening equipment properly. | 0.0% | 0.0% | 100.0% | 3.95 | untouched |
| Trim off the tops and limbs of trees, using chainsaws, delimbers, or axes. | 0.0% | 0.0% | 100.0% | 3.50 | untouched |
| Mark logs for identification. | 0.0% | 0.0% | 100.0% | 4.00 | untouched |
| Place supporting limbs or poles under felled trees to avoid splitting undersides, and to prevent logs from rolling. | 0.0% | 0.0% | 100.0% | 3.90 | untouched |
| Secure steel cables or chains to logs for dragging by tractors or for pulling by cable yarding systems. | 0.0% | 0.0% | 100.0% | 3.24 | untouched |
| Work as a member of a team, rotating between chain saw operation and skidder operation. | 0.0% | 0.0% | 100.0% | 2.80 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean. 1 task(s) lacked a usable O*NET weight and are shown but excluded from the weighting.
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.