EXPOSED
Foresters split their week between screen work — GIS mapping, growth-and-yield modeling, timber inventory calculations, drafting management plans and permit paperwork — and boots-on-the-ground work: cruising stands, marking harvest boundaries, assessing insect and disease damage, supervising logging crews and prescribed burns. The screen half is squarely in reach of AI plus remote sensing: LiDAR and satellite imagery already replace much manual cruising, and plan drafting is templated text. What holds is the walk-through judgment on a specific stand, the accountability for a burn or harvest prescription, and the landowner relationship where trust in the person recommending a $200k timber sale is the product.
Headcount grew steadily across the period.
Median pay $61,790 → $76,400 -1.1% 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
+1.2%
Percentage only. The projection counts a different population from the 10,430 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +1.2% more of these jobs by 2034, and at 60/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~1,100 openings a year on average, including replacing people who leave.
ForesterArea ForesterTimber MarkerDebris MonitorForest ManagerSilviculturistUrban ForesterForest ExaminerForest EcologistService ForesterUtility ArboristUtility ForesterDistrict ForesterForest SupervisorResource ForesterExtension ForesterForest PathologistForestry ScientistForestry ConsultantForestry SpecialistForestry SupervisorOperations ForesterProcurement ForesterSilviculture Forester
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier LiDAR-derived stand metrics, automated growth-and-yield runs, and templated management plans already cover a real share of a forester's week, which is why this sits at 12 rather than 16 — but stem-quality calls, defect deduction on individual trees, and reading a blowdown or bark-beetle pocket on the ground still need someone in the stand.
Hands-on in uncontrolled environments Cruising plots in steep terrain, running a drip torch on a prescribed burn under a burn window, marking boundaries with paint and flagging, and standing on an active logging site with a running skidder are all uncontrolled-environment work — a 16 rather than 20 because the plan-writing, permit filing, and GIS half of the job happens at a desk.
Certification preferred, not legally required Roughly a dozen states license or register foresters and SAF Certified Forester status matters in consulting work, but in most states anyone can call themselves a forester and write a prescription, so there is no universal personal-liability hook — the 9 reflects that patchwork rather than a real statutory monopoly.
Meaningful discretion A burn prescription that escapes, a harvest that silts a stream and draws a state BMP violation, or a salvage call that misjudges beetle spread are decisions the forester on the ground owns with incomplete information — a 13 rather than higher because much of the work executes against agency silvicultural handbooks, state forest practices rules, and pre-set stocking targets.
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 (12/20 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 (9/20) is whether the law requires a licensed human to sign. Trust premium (10/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) 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 32 of this occupation's 60 points (53%).
Embodiment (16/20) 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 foresters 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 78/100 — SAFE.
Prescribed-fire liability regimes: state burn-boss certification statutes (Florida, Georgia, Oklahoma, and post-2020 California/Oregon fire bills) that grant gross-negligence-only immunity ONLY when a certified burn boss with a written, signed prescription is on site. Insurer requirements for prescribed-burn associations pushing the same way after escaped-burn claims.
Task-mix shift: as LiDAR/imagery pipelines absorb cruising, plan drafting, and yield modeling, the residual role concentrates in defensible calls — go/no-go on a burn window, salvage vs. hold after beetle kill, road placement across a stream buffer — each carrying named personal exposure in litigation or audit findings. Watch for job postings shifting from 'inventory forester' to 'silviculture prescription' and 'fire prescription' titles.
Expansion of state forester licensing/registration acts (currently ~15 states, e.g. Georgia, Alabama, Maine, Mississippi) to require a licensed/registered forester's seal on any management plan submitted for cost-share, tax-classification, or state harvest permits — plus explicit rules that a licensed forester must sign and personally own AI- or remote-sensing-derived prescriptions. A parallel route: Forest Stewardship Council / SFI certification audits requiring a named credentialed forester of record for each stand prescription, and NRCS EQIP/Forest Stewardship program rules naming a qualified plan writer.
Same two-tier shift: routine inventory and permit text automates, leaving field verification of model error (mistyped stands, blowdown, invasive understory that overhead sensing misses) and contested-site work. Rises only if agencies keep requiring ground-truth plots — e.g. FIA protocol or certification auditors mandating physical plot validation of remote-sensed estimates at a fixed sampling rate.
Consulting foresters representing landowners in timber sales: ACF (Association of Consulting Foresters) membership standards and state landowner-assistance programs that market a fiduciary, commission-based human agent against mill buyers. Trust premium rises further if timber-fraud disclosure statutes (like Virginia's and Mississippi's timber theft provisions) require a named independent forester's valuation before sale.
The limit. Trust premium is capped by buyer mix: most acreage decisions are made by industrial timberland owners and TIMOs who buy analytics, not relationships, and will not pay for a human. The trust premium lives almost entirely in the small-private-nonindustrial segment, which is a minority of the work and shrinking as parcels consolidate. Embodiment is already near ceiling and cannot rise much.
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 280 | $74,710 -2% |
| Seattle-Tacoma-Bellevue, WA | 240 | $86,310 +13% |
| Portland-Vancouver-Hillsboro, OR-WA | 180 | $89,780 +18% |
| Chicago-Naperville-Elgin, IL-IN | 130 | $85,610 +12% |
| Los Angeles-Long Beach-Anaheim, CA | 110 | $107,690 +41% |
| Olympia-Lacey-Tumwater, WA | 110 | $76,600 +0% |
| Sacramento-Roseville-Folsom, CA | 110 | $107,750 +41% |
| Salem, OR | 110 | $89,220 +17% |
| San Francisco-Oakland-Fremont, CA | 70 | $112,560 +47% |
| Sacramento-Roseville-Folsom, CA | 110 | $107,750 +41% |
| Los Angeles-Long Beach-Anaheim, CA | 110 | $107,690 +41% |
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 60. 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.