Forest and Conservation Workers
Forest and Conservation Workers — AI exposure, safer roles, and a pivot plan.
Also known as: Brusher · Chopper · Box Cutter · Box Chipper · Cone Picker · Chemical Sprayer
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
No strongly automatable task in the current data release.
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Confer with other workers to discuss issues, such as safety, cutting heights, or work needs.38
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Sort tree seedlings, discarding substandard seedlings, according to standard charts or verbal instructions.35
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
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Erect signs or fences, using posthole diggers, shovels, or other hand tools.21
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Operate skidders, bulldozers, or other prime movers to pull a variety of scarification or site preparation equipment over areas to be regenerated.24
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Thin or space trees, using power thinning saws.24
Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 29 puts Forest and Conservation Workers in the least-exposed quarter of analyzed occupations. In practice, exposure this level is about the mix: 0 of 17 analyzed tasks lean automatable, 2 augmentable, and 15 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.
One next move: lean into the durable core above and adopt AI on the routine remainder before it becomes a mandate.
Lower-exposure adjacent roles
No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.
Labor-market context
- $43,680median wage
- 6,050employed
- 2,000annual openings
- -4.6%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Your week probably doesn’t match the average
This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.
Personalize my result — $9Related roles
Adjacent by skills or family — no exposure claim implied.
FAQ — Forest and Conservation Workers
- What does a score of 29 mean for a Forest and Conservation Workers?
- It means that, weighted across the 17 tasks we analyzed for this role, the task mix sits at 29 on a 0–100 exposure scale — in the least-exposed quarter of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
- Which tasks in this role are most exposed to AI?
- This role has no strongly automatable task in the current data release.
- Which parts of this job are most durable?
- The most durable responsibilities are: Erect signs or fences, using posthole diggers, shovels, or other hand tools; Operate skidders, bulldozers, or other prime movers to pull a variety of scarification or site preparation equipment over areas to be regenerated; Thin or space trees, using power thinning saws. Durable tasks typically depend on judgment, relationships, physical presence, or accountability.
- Is this score personalized to me?
- No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.
Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 17 of 17 tasks carry source-level provenance · methodology