First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers
In short: today's best AI models could do about 29% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 30% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 70% of the time, stays with people.
Directly supervise and coordinate activities of workers engaged in landscaping or groundskeeping activities. Work may involve reviewing contracts to ascertain service, machine, and workforce requirements; answering inquiries from potential customers regarding methods, material, and price ranges; and preparing estimates according to labor, material, and machine costs.
130,760 people do this job in the US. $58,430 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on5 h
- A person checks AI's work7 h
- Desk work AI can't do yet0 h
- Stays with people28 h
First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers, now: of a 40-hour week, AI could take on 5 hours, 7 hours become checking its work, 0 hours are desk work it can't do yet, and 28 hours stay with people.
The largest pieces: Set procedures and safety standards (about 2 h, a person checks); Review contracts for requirements (about 2 h, a person checks); Schedule crews (about 1 h).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 23%
- 7%
- 8%
- 23%
- 39%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 29% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 21%. Today's estimate: 29%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 28 tasks showed up. Within each of Anthropic's methods, never across a change: 0.022% to 0.071% (released February and March 2025); 0.043% to 0.076% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.022% | 434 of 708 | 2 of 28 |
| Released March 2025 | 0.071% | 213 of 703 | 1 of 28 |
| The week of Aug 4, 2025 (released September 2025) | 0.043% | 259 of 655 | 1 of 28 |
| The week of Nov 13, 2025 (released January 2026) | 0.046% | 248 of 672 | 3 of 28 |
| The week of Feb 5, 2026 (released March 2026) | 0.076% | 198 of 677 | 2 of 28 |
| April 2026 (released June 2026) | none | n/a | 0 of 28 |
| May 2026 (released June 2026) | none | n/a | 0 of 28 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 41% of this job's working time as physical work. For 21% of the working time, a robot has been shown doing most of the task somewhere: 9% in a purpose-built cell, 12% in a structured facility. 0.33 of 3, the 371st highest of the 923 jobs.
- Not physical 59%
- No robot shown 20%
- In a purpose-built cell 9%
- In a structured facility 12%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
70% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Plant and maintain vegetation, the hands-on work, 10%
- Install and maintain landscaped areas, the hands-on work, 9%
- Direct or do equipment repairs, the in-person work, 6%
- Tour grounds to inspect plants and soil, the hands-on work, 5%
- Set procedures and safety standards, the in-person work, 4%
- Help the crew meet deadlines, the hands-on work, 4%
What becomes checking the machine
18% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 11% is desk work that is small if wrong, which can be handed over with lighter checks.
- Set procedures and safety standards, severe if wrong, 4%
- Review contracts for requirements, costly if wrong, 4%
- Design and oversee sprinkler installation, costly if wrong, 3%
- Prepare estimates and budgets, costly if wrong, 2%
- Plan with managers and landscape architects, costly if wrong, 2%
- Monitor project progress, costly if wrong, 1%
What to learn
- The hands-on craft. About 39% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 31% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check or sign: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 23% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 28 tasks from .
| Task | ||||
|---|---|---|---|---|
| Plant or maintain vegetation through activities such as mulching, fertilizing, watering, mowing, or pruning.Mulching, mowing, watering and pruning.Done daily by most who do it. Robots: in a structured facility, Husqvarna Automower wire-free range (EPOS / Automower iQ) (sold). Anthropic's rating. | 10% | 1.7 h38 min to 4.2 h | Hands-on 100% | Stays with people |
| Install or maintain landscaped areas, performing tasks such as removing snow, pouring cement curbs, or repairing sidewalks.Snow removal, curbs and sidewalks by hand.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 9% | 3.3 h1.7 h to 1 work day | Hands-on 100% | Stays with people |
| Establish and enforce operating procedures and work standards that will ensure adequate performance and personnel safety.Standards written and enforced with the crew.Done daily by most who do it. | 9% | 2.5 h50 min to 3.3 h | Documents 50%, in person 50% | 50%Desk part half by now |
| Direct or assist workers engaged in the maintenance or repair of equipment, such as power tools or motorized equipment.Working on the equipment with the crew.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 6% | 1.3 h25 min to 2.5 h | In person 50%, hands-on 50% | Stays with people |
| Design or supervise the installation of sprinkler systems, calculating water pressure, or valve and pipe coverage needs.The system calculated on paper and the installation overseen on site.Done more than yearly by most who do it. | 5% | 5 h1.7 h to 1.7 work days | Documents 50%, in person 50% | 40%Desk part half by now |
| Tour grounds, such as parks, botanical gardens, cemeteries, or golf courses, to inspect conditions of plants and soil.Walking the grounds.Done daily by most who do it. Robots: in a purpose-built cell, Corvus Drones greenhouse scouting drone (sold). Anthropic's rating. | 5% | 1.3 h25 min to 2.5 h | Hands-on 100% | Stays with people |
| Confer with managers or landscape architects to develop plans or schedules for landscaping maintenance or improvement.Conversations about plans and schedules, with the plans on paper.Done more than monthly by most who do it. | 5% | 1.3 h25 min to 2.5 h | Conversation 60%, documents 40% | 40%Desk part half by now |
| Schedule work for crews, depending on work priorities, crew or equipment availability, or weather conditions.Building the schedule in a system, with some planning and telling people.Done daily by most who do it. | 4% | 50 min17 min to 1.7 h | Systems 50%, documents 30%, conversation 20% | 80%Desk part half by now |
| Monitor project activities to ensure that instructions are followed, deadlines are met, and schedules are maintained.Watching the work on site against the schedule.Done daily by most who do it. AI observed on this task. | 4% | 50 min17 min to 2.5 h | In person 70%, documents 30% | 30%Desk part half by now |
| Review contracts or work assignments to determine service, machine, or workforce requirements for jobs.Reading contracts and assignments to size the job.Done daily by most who do it. | 4% | 1.1 h25 min to 2.5 h | Documents 100% | 100%Half by now |
| Provide workers with assistance in performing duties as necessary to meet deadlines.Working alongside the crew.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 50 min13 min to 2.5 h | Hands-on 100% | Stays with people |
| Inspect completed work to ensure conformance to specifications, standards, and contract requirements.Walking the finished work against the contract.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 50 min17 min to 1.7 h | Hands-on 100% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- First-Line Supervisors of Construction Trades and Extraction Workers32% now, 32% by 2028
- First-Line Supervisors of Farming, Fishing, and Forestry Workers23% now, 24% by 2028
- First-Line Supervisors of Mechanics, Installers, and Repairers47% now, 57% by 2028
- First-Line Supervisors of Housekeeping and Janitorial Workers43% now, 44% by 2028
- First-Line Supervisors of Material-Moving Machine and Vehicle Operators45% now, 45% by 2028
- First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services27% now, 28% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers is within reach of AI now?
- An estimated 29% of the working time is within reach of AI models now and 30% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Set procedures and safety standards (50% of the task's time now); Review contracts for requirements (100% of the task's time now); and Schedule crews (80% of the task's time now).
- Which tasks of First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Plant and maintain vegetation; Install and maintain landscaped areas; and Direct or do equipment repairs. Together such tasks are about 51% of the working time, and the people and body parts of the other tasks stay with people too.
- What should First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; the conversations; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 70% of the working time stays with people, 18% becomes checking a model's work and 11% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 11%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers?
- Anthropic's robot exposure rating (September 2026) finds 41% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 21% of the working time, 12% in a structured facility. Its index is 0.33 of 3. One example: Husqvarna Automower wire-free range (EPOS / Automower iQ) for "plant and maintain vegetation". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 28 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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