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Will AI replace 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. The percentage shown is a task-exposure model, not a prediction of certain job loss.

O*NET 30.3 · 45-4021.00 · modeled exposure, not guaranteed job loss
LOW TASK EXPOSURE
24%
Estimated share of representative tasks exposed to current AI capabilities.
Last updatedAugust 20, 2026
Evidence confidenceMedium
Browse this risk tierLower exposure jobs →
Task automation shareⓘ
24%Automatable
Automatable24%
Augmentable42%
Hard to automate34%
Based on analysis of representative responsibilities and current AI capability maturity.
Category#
Trades & Physical
Field & Physical
Modeled pay midpoint$
$90,500/yr
Directional estimate — verify locally
Time until impact⌁
6–10+ years
Earliest 2032 · widespread 2036+
Task-level replaceability ⓘ
Representative tasks ranked from highest to lowest AI exposure.
◇Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.34%
✦Physical dexterity in unpredictable settings26%
⚙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.23%
✦Safety responsibility and on-site judgment22%
▥Saw back-cuts, leaving sufficient sound wood to control direction of fall.12%
Impact timeline ⓘ
How AI assistance may move from early support to wider adoption.
2032Next2036+
Light supportAI provides limited help with stop saw engines, pull cutting bars from cuts, and run to safety as tree falls..
AugmentationTools improve productivity without owning outcomes.
Human-ledCore work stays anchored in physical dexterity in unpredictable settings.
6–10+ yearsdirectional confidence window
HOW AI CHANGES THIS JOBtyping the logic in plain English
01>Start with the real work done by Fallers.
02>AI can help with: Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.
03>If a task is repetitive and easy to check, AI pressure goes up.
04>But a person is still needed for: Physical dexterity in unpredictable settings.
05>Likely result: AI mostly acts as a helper while the core job stays human-led.
06>Important: 24% is estimated task exposure—not a 24% chance of losing the job.
What AI can do
Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.LOW MATCH
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.LOW MATCH
Saw back-cuts, leaving sufficient sound wood to control direction of fall.LOW MATCH
How estimates work →
Your human moat
◆

Physical dexterity in unpredictable settings

HIGH MOAT

Combined with safety responsibility and on-site judgment, this keeps a human in the loop.

Strength8.8
Similar-role comparison
RoleRisk
Fallers24%
Agricultural Inspectors24%
First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers24%
Automotive Mechanic25%
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Why this score?

✓The model evaluates 3 representative core tasks.
✓The core work depends on presence, dexterity, or accountability.
✓Occupation and task language comes from O*NET 30.3.
✓The strongest human advantage is physical dexterity in unpredictable settings.
Research basis

O*NET 30.3 occupation tasks plus the ILO 2025 exposure framework. Scores are directional estimates.

Read ILO research →
Data: O*NET 30.3 · Model framework: ILO 2025O*NET 45-4021.00Share report
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Sources for this report

Official occupation tasks; timeline remains uncertain Scores are directional estimates and sources do not endorse this site’s exact percentage.

O*NET 45-4021.00 occupation profile ↗ILO Generative AI and Jobs 2025 ↗Scoring methodology →

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