EXPOSED
Almost nothing a faller does is text or screen work: reading a tree's lean, rot, and crown weight, planning notch and back-cut, clearing escape routes, and running a chainsaw on steep, wet, uneven ground are irreducibly physical and situational. Language AI has essentially no purchase here, and no field robot handles hand-falling on slopes where feller-bunchers can't operate. The real displacement pressure is mechanical, not cognitive — mechanized harvesting has already absorbed most flat-ground production falling, which is why this occupation is small and slowly shrinking despite being AI-proof.
Part 2020 shock, part continued decline in the years since.
Median pay $44,650 → $52,100 -6.7% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
-7.3%
Percentage only. The projection counts a different population from the 3,130 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Shrinking, but not obviously because of AI
The BLS projects -7.3% by 2034, but at 65/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~700 openings a year on average, including replacing people who leave.
AxmanHewerLoggerSawyerArboristLumbermanLumberjackTree CutterTree FallerTree FellerTree TopperHigh ClimberTimber CutterTimber FallerTimber FellerSales ArboristCutter OperatorPulpwood CutterAll-Round LoggerArborist ClimberCross Cut SawyerUtility ArboristClimbing ArboristPaper Wood Cutter
Holding it up: embodiment . Weakest point: trust premium .
Tasks largely resist digitisation Sizing up a Douglas-fir's lean against a side-hill wind, picking the hinge thickness, driving wedges, and bucking a hung-up tree are judgment calls executed through a bar and chain in real time — there is no digitizable intermediate product for a model to generate, which is why this sits at 19 rather than in the mixed band.
Hands-on in uncontrolled environments You work on 50-percent slopes in rain and snow, carrying a 20-pound saw, wedges, and fuel over blowdown and brush, with widowmakers overhead and no fixed workspace — this is the reference case for a 20, not a job with a physical component bolted onto desk work.
Certification preferred, not legally required No state licenses fallers; most crews require only chainsaw safety training, first aid/CPR, and OSHA 1910.266 logging-standard competency, and citations land on the employer's logging operation rather than on you personally — the 6 reflects those real training gates, not a credential that legally reserves the work to you.
Exists to be accountable for ambiguous calls You decide alone whether a rotten, leaning tree gets fallen, roped, or left standing, where the escape path goes, and when wind or a fellow faller's position means stop — a wrong call kills you or the man below you within seconds, with no supervisor to sign off, which puts it at 14; it stops short of higher because the falling itself follows well-established notch-and-backcut technique.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (19/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (6/20) is whether the law requires a licensed human to sign. Trust premium (6/20) is whether buyers specifically pay for a person. Judgment and accountability (14/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 26 of this occupation's 65 points (40%).
Embodiment (20/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to fallers on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 81/100 — SAFE.
Federal/state wildfire and post-fire hazard-tree removal contracts (USFS, CAL FIRE, utility right-of-way clearance after the PG&E and Hawaiian Electric litigation) requiring a named certified faller or faller-in-charge to sign the danger-tree assessment and removal record for each stem, with that signature discoverable in wildfire liability suits.
Adoption of a mandatory, individually-held faller certification with personal accountability on the US side of the border — modeled on BC Forest Safety Council's Certified Faller program (written + field evaluation, revocable ticket). Washington L&I and Oregon OSHA already require 'qualified' or 'certified' faller status in some rule language and for hazard-tree work; converting that from employer attestation to a state-issued, revocable personal credential required on every commercial harvest and federal hazard-tree contract would move this dimension most.
Utility vegetation-management programs formalizing a documented per-tree risk call (retain/remove, fall direction, exclusion zone) attributable to the individual faller rather than to the contracting crew, driven by insurer requirements after utility ignition settlements. Raises the ceiling on an already-high score.
Growth in the niche where the buyer specifically wants hand-falling rather than mechanized harvest: selective/low-impact logging on FSC-certified or conservation-easement parcels, heritage and high-value timber (instrument-grade spruce, veneer logs) where machine damage destroys value, and steep residential lots. This is a small, real premium and unlikely to scale to the whole occupation.
The limit. task_resistance (19) and embodiment (20) are effectively maxed — there is no upward room and no AI route downward either. The displacement pressure on this occupation is mechanized harvesting economics, not AI, and none of the levers above touch that: a stronger certification regime or a hand-falling premium can protect who does the remaining work, not how much work remains on ground a feller-buncher can reach.
| Richmond, VA | 40 | $40,560 -22% |
| Portland-Vancouver-Hillsboro, OR-WA | 30 | $76,940 +48% |
| Portland-Vancouver-Hillsboro, OR-WA | 30 | $76,940 +48% |
| Richmond, VA | 40 | $40,560 -22% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 65. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.