Community Sentiment
Do you agree?
Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Fallers?
Task-level automation exposure for Fallers: 17 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
18%
weighted task index
17 of 17 tasks scored
Median Salary
$52,100 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
3,130 people hold this job — size, not growth
Projected 2025–35
-9.9%
employment change
500 openings a year · BLS line item
Pay by state
BLS OEWS publishes 18 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$26,210 – $84,490
Highest medians
Oregon $84,490
Idaho $78,330
Washington $77,340
South Carolina $76,350
Arkansas $67,060
Lowest medians
Indiana $26,210
New York $36,190
Georgia $39,380
North Carolina $46,080
Tennessee $47,560
State figures come from the 45-4021 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 18%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 63%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 28%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 3/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
17 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 7%
1 tasks
Medium exposure (≥ 35%) 21%
3 tasks
Low exposure (below 35%) 72%
13 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#13486 · Core · 5% automatable
Saw back-cuts, leaving sufficient sound wood to control direction of fall.
physical manipulation — Chainsaw work in unpredictable forest terrain
Cut trees or logs.
#13491 · Core · 5% automatable
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.
physical manipulation — Chainsaw work in unpredictable forest terrain
Cut trees or logs.
#13484 · Core · 7% automatable
Stop saw engines, pull cutting bars from cuts, and run to safety as tree falls.
physical manipulation — Forestry equipment operation is heavy hands-on work
Cut trees or logs. Operate forestry equipment.
#13493 · Core · 42% automatable
Select trees to be cut down, assessing factors such as site, terrain, and weather conditions before beginning work.
decision support — Forestry methods need field and site judgment
Determine forestry techniques or methods.
#13488 · Core · 14% automatable
Measure felled trees and cut them into specified log lengths, using chain saws and axes.
physical manipulation — Hands-on measurement of agricultural products
Measure physical characteristics of forestry or agricultural products. Cut trees or logs.
#13495 · Core · 7% automatable
Insert jacks or drive wedges behind saws to prevent binding of saws and to start trees falling.
physical manipulation — Forestry equipment operation is heavy hands-on work
Cut trees or logs. Operate forestry equipment.
#13489 · Core · 60% automatable
Assess logs after cutting to ensure that the quality and length are correct.
data analysis — Log analysis aided; domain interpretation human
Evaluate log quality.
#13485 · Core · 35% automatable
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.
visual inspection — Crop quality needs physical inspection and sensory judgment
Evaluate quality of plants or crops.
#13487 · Core · 7% automatable
Clear brush from work areas and escape routes, and cut saplings and other trees from direction of falls, using axes, chainsaws, or bulldozers.
physical manipulation — Forestry equipment operation is heavy hands-on work
Trim trees or other vegetation. Operate forestry equipment.
#13496 · Core · 10% automatable
Tag unsafe trees with high-visibility ribbons.
physical manipulation — Marking forestry products is manual field work
Mark agricultural or forestry products for identification.
#13490 · Core · 42% automatable
Determine position, direction, and depth of cuts to be made, and placement of wedges or jacks.
decision support — Forestry methods need field and site judgment
Determine forestry techniques or methods.
#13494 · Core · 18% automatable
Maintain and repair chainsaws and other equipment, cleaning, oiling, and greasing equipment, and sharpening equipment properly.
physical manipulation — Hands-on repair of field equipment in unstructured settings
Maintain forestry, hunting, or agricultural equipment.
#13492 · Core · 5% automatable
Trim off the tops and limbs of trees, using chainsaws, delimbers, or axes.
physical manipulation — Hands-on trimming of vegetation in the field
Trim trees or other vegetation.
#13499 · Supplemental · 10% automatable
Mark logs for identification.
physical manipulation — Marking forestry products is manual field work
Mark agricultural or forestry products for identification.
#13501 · Supplemental · 3% automatable
Place supporting limbs or poles under felled trees to avoid splitting undersides, and to prevent logs from rolling.
physical manipulation — Manual agricultural labor in unstructured fields
Perform manual agricultural, aquacultural, or horticultural tasks.
#13497 · Supplemental · 5% automatable
Secure steel cables or chains to logs for dragging by tractors or for pulling by cable yarding systems.
physical manipulation — Hands-on accessory attachment
Attach equipment extensions or accessories.
#13500 · Supplemental · 8% automatable
Work as a member of a team, rotating between chain saw operation and skidder operation.
physical manipulation — Forestry equipment operation is heavy hands-on work
Operate forestry equipment.
Embed this calculator on your site ↓
Bloggers & authors: Embed this interactive calculator directly in your articles or comparison guides. It loads dynamically via a responsive iframe (auto-resizing to prevent double scrollbars) and provides a direct, high-value tool for your audience.
<div class="giniloh-calculator-embed" data-calculator="fallers"></div>
<script src="https://giniloh.com/embed.js" async></script> Method
How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
See the full method →Related Article
Best AI Proof Jobs in a Changing Market
Worried robots are coming for your paycheck? You’re not alone—but the real story is far more hopeful. This guide cuts through the scare headlines to reveal exactly where smart, sustainable career opportunities exist right now. We’ve curated a list of true ai proof jobs—roles in healthcare, skilled trades, and cybersecurity that not only survive automation but thrive because of human judgment and empathy. You’ll get specific, money-making data on projected growth (the BLS says wind turbine technicians, for instance, will grow 44% by 2032), real salary ranges from active job listings, and a simple self-assessment quiz to identify your own most resilient path. Plus, we name companies hiring today and the niche job boards they use. Stop worrying and start planning—your secure career is closer than you think.
Read Article ->