EXPOSED
Almost nothing in this job is text or screen work — trimming, edging, hauling debris, spreading mulch, clearing storm damage, and repairing irrigation heads happen outdoors on uneven ground where today's robotics still struggles. What AI does touch is the paperwork around the work: route scheduling, treatment logs, estimates, and irrigation-timer optimization. The real displacement pressure here is cheap autonomous mowers on large open turf and general wage competition, not language models.
Roughly flat across the period, with year-to-year wobble.
Median pay $32,090 → $46,860 +16.8% 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
+2.4% 14,100 → 14,500 on the projections basis
Growing, and only partly exposed
The BLS expects +2.4% more of these jobs by 2034, and at 51/100 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.
~1,900 openings a year on average, including replacing people who leave.
Garden WorkerCemetery WorkerChimney SweeperTree Trimmer HelperCampground AttendantWeed Control InspectorInsect Control InspectorTrail Maintenance WorkerDisease Control InspectorTrail Construction WorkerTrailhead Maintenance WorkerTrailhead Construction WorkerSpecial Effects and Instruction Models Gardener
The BLS uses Grounds Maintenance Workers, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Setting a string trimmer against a chain-link fence line, digging out a broken sprinkler lateral, raking leaves out of shrub beds, and dragging limbs to a chipper are physical sequences no software executes — the 16 rather than 19 reflects that wide, flat turf mowing and irrigation-timer setpoints are already being handed to autonomous mowers and smart controllers.
Hands-on in uncontrolled environments You work in whatever weather the day brings, on slopes, in mulch beds, around traffic and pedestrians, carrying blowers and handling fuel, with buried lines and stumps you cannot see until you hit them — that is an uncontrolled environment by any definition, short of 20 only because much of the site is at least mapped and repeat-visited.
No licence, no signature requirement No licence gates entry to this work; the pesticide applicator card that some crews carry sits with one certified person and the 4 acknowledges that most crew members can be hired and put on a mower the same week.
Meaningful discretion You decide when the ground is too saturated to drive on, whether a limb is safe to cut or needs a tree service, and how hard to cut back a shrub this late in the season, but the scope, schedule, and specs come down from a supervisor or contract scope of work.
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 (16/20 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 (4/20) 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 (7/20) 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 17 of this occupation's 51 points (33%).
Embodiment (18/20) 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 65/100, still EXPOSED.
State pesticide/herbicide applicator licensing already requires a certified applicator (EPA 40 CFR 171, state DoA programs) to sign application records; if grounds crews shift toward roles where the licensed applicator personally signs off on every treatment — including any robot- or drone-applied product — the license attaches to the person, not the machine. Drone-applied pesticide work already requires both Part 137 and applicator certification.
Tree and storm-damage work: if ISA Certified Arborist or TRAQ (Tree Risk Assessment Qualification) credentials become an insurer or municipal contract requirement for hazard-tree calls — some utility and city contracts already specify TRAQ — the role owns a consequential, documented risk call under ambiguity.
Task-mix shift: autonomous mowers take the open-turf tier, leaving edging around obstacles, storm cleanup, irrigation repair, planting-bed work, and hardscape repair — the genuinely unstructured tier. This raises the share of the day AI/robotics cannot do without any new law.
Already near ceiling; only rises in the sense that the residual work concentrates in the least structured environments (slopes, root zones, debris fields) where wheeled autonomy is excluded.
Backflow-prevention and irrigation-system certification (e.g., state cross-connection control rules, ASSE/ABPA tester certification) requires a named certified tester to submit annual test reports to the water utility; if municipalities extend mandatory annual testing and reporting to more commercial/HOA sites, more of this workforce carries personal sign-off.
The limit. Trust premium has no realistic route — buyers of grounds maintenance are facilities managers and HOAs procuring on price per square foot, and there is no consumer market that pays extra for a human to spread mulch. The credential levers (applicator, backflow, TRAQ) also only attach to a minority of these 13,630 workers; the median crew member without a license gains nothing from them, and wage competition, not AI, remains the binding pressure.
| Los Angeles-Long Beach-Anaheim, CA | 1,030 | $50,480 +8% |
| Portland-Vancouver-Hillsboro, OR-WA | 390 | $48,880 +4% |
| San Diego-Chula Vista-Carlsbad, CA | 390 | $48,970 +5% |
| San Francisco-Oakland-Fremont, CA | 370 | $48,830 +4% |
| Riverside-San Bernardino-Ontario, CA | 350 | $47,030 +0% |
| Sacramento-Roseville-Folsom, CA | 310 | $48,900 +4% |
| Atlanta-Sandy Springs-Roswell, GA | 270 | $39,870 -15% |
| San Jose-Sunnyvale-Santa Clara, CA | 200 | $56,840 +21% |
| Cincinnati, OH-KY-IN | 30 | $80,080 +71% |
| Cleveland, OH | 40 | $71,800 +53% |
| Minneapolis-St. Paul-Bloomington, MN-WI | 60 | $71,050 +52% |
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 51. 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.