EXPOSED
The core of this job is boots-on-the-ground: cruising timber, measuring DBH and stocking, marking cut boundaries, maintaining trails and fencing, holding line on prescribed burns, checking recreation sites and invasive infestations. AI touches the paperwork tier — compiling plot data into reports, drafting management-plan sections, classifying imagery — and drones plus LiDAR are genuinely eating some of the survey and inventory measurement work. What holds is the physical, unpredictable field work and the fact that no software can carry a drip torch or verify a contractor actually thinned the stand.
Headcount grew steadily across the period.
Median pay $45,500 → $54,560 -4.1% 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
-3.2% 33,800 → 32,700 on the projections basis
Shrinking, but not obviously because of AI
The BLS projects -3.2% by 2034, but at 52/100 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.
~3,900 openings a year on average, including replacing people who leave.
Grazing AideForester AideForestry AideGrazing ExaminerRange TechnicianTimber AppraiserForest TechnicianConservation AgentResource TechnicianConservation OfficerBiological Science AideConservation TechnicianForestry Aid TechnicianForestry Aide TechnicianNatural Resource TechnicianPark Technician (Park Tech)Timber Management TechnicianField Technician (Field Tech)Forestry Technician (Forestry Tech)Timber Sale Preparation Forestry Technician (Timber Sale Preparation Forestry Tech)
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier LiDAR-derived canopy height models and drone photogrammetry now produce stand-level volume and stocking estimates that used to require a week of variable-radius plots, and plot data entry, growth projections and permit paperwork are already software tasks — but somebody still has to hang flagging on a unit boundary, bore a tree to age it, run a fireline with a Pulaski, and confirm the marked leave trees are actually the ones standing, which keeps this at 13 rather than down in the mixed-office range.
Hands-on in uncontrolled environments A 17 reflects days spent off-trail on steep slopes with a DBH tape and increment borer, hauling hose lays and holding line during prescribed burns in wind and smoke, clearing blowdown from trails with a chainsaw, and inspecting fencing, culverts and campgrounds — conditions no fixed sensor covers and where footing, weather and fuel behaviour change hour to hour; it is not 20 only because a real share of the week is spent at a plot-data terminal or in a truck.
Certification preferred, not legally required There is no licence to practice as a forest technician — most states restrict only the title 'forester' (registered/licensed forester acts in AL, GA, MS, ME, etc.), so the professional forester or burn boss signs the prescription and the timber sale, while your credentials are typically an associate degree, NWCG red card task books and pesticide applicator certification: real hiring gates but not personal legal exposure, hence 5 rather than 0.
Meaningful discretion A 10 fits calls made inside someone else's prescription: deciding which trees meet the marking guide, judging whether fuel moisture and wind hold within the burn plan window, calling a unit out of spec on an inspection, deciding whether a trail closes — consequential and unsupervised in the field, but the silvicultural prescription, harvest schedule and go/no-go burn authority belong to the forester or burn boss above you.
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 (13/20 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 (5/20) is whether the law requires a licensed human to sign. Trust premium (7/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) 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 22 of this occupation's 52 points (42%).
Embodiment (17/20) 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.
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 71/100 — SAFE.
Task-mix shift: as drone/LiDAR inventory absorbs routine cruising and plot measurement, what remains is the call under ambiguity — go/no-go on a burn window, whether a contractor's thinning meets prescription, whether an infestation warrants treatment. If agency staffing patterns follow this (fewer cruise days, more compliance inspection and burn-boss support per technician), accountability weight rises
NWCG red-card qualification standards (FFT1/ICT5/RXB2) already gate prescribed-fire and suppression line assignments to individually qualified persons; if agencies extend the same personal-qualification requirement to burn-plan compliance monitoring and holding assignments now sometimes covered by contractors or drone observation, the personally-accountable-signer share of the job rises
State forest practices acts increasingly require a credentialed person to certify field conditions: if state boards (e.g., Oregon/Washington FPA rules, California RPF supervision requirements, or Society of American Foresters Certified Forester credentialing) formalize a requirement that a certified technician physically verify and sign harvest-boundary marking, stream-buffer layout, or post-harvest stocking surveys before remote-sensing-derived data can be accepted by the agency, the signature becomes a legally attached human step rather than a convention
If insurers and utilities writing wildfire-liability policies (as PG&E-style vegetation-management litigation and California FAIR Plan underwriting push toward documented inspection) require a named human inspector attesting to defensible-space or right-of-way clearance rather than accepting imagery-only verification, the role owns a consequential, litigable call
Post-fire and post-storm work is intrinsically off-baseline: if federal BAER (Burned Area Emergency Response) and invasive-response workloads grow as a share of the job, the remaining tasks are precisely the ones with no clean prior imagery to classify against
The limit. Trust premium has no realistic route: the buyer is overwhelmingly a public agency or timber company purchasing compliance and data, not a human relationship, and none of them pay extra for a person. Embodiment is already near ceiling at 17 and can only fall as ground robotics and drone payloads improve. Even with all liability levers, this stays a wage-suppressed, seasonal, agency-budget-dependent occupation — headcount risk here comes from appropriations as much as from automation.
| Los Angeles-Long Beach-Anaheim, CA | 590 | $61,110 +12% |
| Riverside-San Bernardino-Ontario, CA | 590 | $63,150 +16% |
| Sacramento-Roseville-Folsom, CA | 590 | — |
| Boise City, ID | 440 | $57,660 +6% |
| Bend, OR | 430 | $58,990 +8% |
| Chicago-Naperville-Elgin, IL-IN | 380 | $49,630 -9% |
| San Diego-Chula Vista-Carlsbad, CA | 370 | $61,880 +13% |
| Fresno, CA | 350 | — |
| Baton Rouge, LA | 50 | $87,500 +60% |
| Santa Rosa-Petaluma, CA | 90 | $74,410 +36% |
| Carson City, NV | 160 | $67,040 +23% |
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 52. 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.