Will AI replace Fallers?
AI reaches little of this job’s work.
14/100
Low exposure
More exposed to AI than 26% of US workers.
- AI already used in
- 0% of the work
- As AI tools mature: up to
- 14% of the work
What the job involves
Use axes or chainsaws to fell trees using knowledge of tree characteristics and cutting techniques to control direction of fall and minimize tree damage.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Within reach(2)
- Determine position, direction, and depth of cuts to be made, and placement of wedges or jacks.
- Select trees to be cut down, assessing factors such as site, terrain, and weather conditions before beginning work.
Out of reach(15)
- Saw back-cuts, leaving sufficient sound wood to control direction of fall.
- Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.
- Clear brush from work areas and escape routes, and cut saplings and other trees from direction of falls, using axes, chainsaws, or bulldozers.
- 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.
- Measure felled trees and cut them into specified log lengths, using chain saws and axes.
and 10 more
Also called
Logger · Timber Cutter · Timber Faller · Timber Feller · Bucker · Axman · Cross Cut Sawyer · Hewer
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics