Maintenance and Repair Workers, General
In short: today's best AI models could do about 11% 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 13% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 87% of the time, stays with people.
Perform work involving the skills of two or more maintenance or craft occupations to keep machines, mechanical equipment, or the structure of a building in repair. Duties may involve pipe fitting; HVAC maintenance; insulating; welding; machining; carpentry; repairing electrical or mechanical equipment; installing, aligning, and balancing new equipment; and repairing buildings, floors, or stairs.
1,529,700 people do this job in the US. $49,590 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 on0 h
- A person checks AI's work4 h
- Desk work AI can't do yet1 h
- Stays with people35 h
Maintenance and Repair Workers, General, now: of a 40-hour week, AI could take on 0 hours, 4 hours become checking its work, 1 hours are desk work it can't do yet, and 35 hours stay with people.
The largest pieces: Design equipment for repair work (about 2 h, a person checks); Diagnose problems and decide the fix (under an hour, a person checks); Estimate repair costs (under an hour, a person checks).
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.
- 12%
- 1%
- 0%
- 3%
- 84%
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 11% 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, 5%. Today's estimate: 11%.
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, 0.011% went to this job's tasks in May 2026, the 385th largest of the 447 jobs that showed up at all; 1 of its 27 tasks showed up. Within each of Anthropic's methods, never across a change: 0.031% to 0.011% (released February and March 2025); 0.0058% to 0.0068% (August 2025 to February 2026); 0.011% to 0.011% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.031% | 357 of 708 | 3 of 27 |
| Released March 2025 | 0.011% | 491 of 703 | 1 of 27 |
| The week of Aug 4, 2025 (released September 2025) | 0.0058% | 519 of 655 | 2 of 27 |
| The week of Nov 13, 2025 (released January 2026) | 0.006% | 554 of 672 | 1 of 27 |
| The week of Feb 5, 2026 (released March 2026) | 0.0068% | 541 of 677 | 1 of 27 |
| April 2026 (released June 2026) | 0.011% | 368 of 434 | 1 of 27 |
| May 2026 (released June 2026) | 0.011% | 385 of 447 | 1 of 27 |
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 86% of this job's working time as physical work. For 33% of the working time, a robot has been shown doing most of the task somewhere: 30% in a purpose-built cell, 2% in a structured facility. 0.35 of 3, the 370th highest of the 923 jobs.
- Not physical 14%
- No robot shown 53%
- In a purpose-built cell 30%
- In a structured facility 2%
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
87% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Repair machines and structures, the hands-on work, 14%
- Install and repair wiring and plumbing, the hands-on work, 9%
- Paint and repair building parts, the hands-on work, 7%
- Run machine tools to make parts, the hands-on work, 6%
- Dismantle machines to remove parts, the hands-on work, 5%
- Test machinery to diagnose faults, the hands-on work, 4%
What becomes checking the machine
12% 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 1% is desk work that is small if wrong, which can be handed over with lighter checks.
- Design equipment for repair work, costly if wrong, 7%
- Diagnose problems and decide the fix, costly if wrong, 1%
- Estimate repair costs, costly if wrong, 1%
- Plan and lay out repair work, costly if wrong, 1.0%
- Train and manage maintenance staff, costly if wrong, 0.8%
What to learn
- The hands-on craft. About 84% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- 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 12% 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.
- Signing off under your license. Some tasks need a licensed, certified or sworn person to do or sign them. The license, and the judgment it stands for, stays with the person.
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 . 27 tasks from .
| Task | ||||
|---|---|---|---|---|
| Repair machines, equipment, or structures, using tools such as hammers, hoists, saws, drills, wrenches, or equipment such as precision measuring instruments or electrical or electronic testing devices.Hands-on repair with tools.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 14% | 2.5 h50 min to 5 h | Hands-on 100% | Stays with people |
| Design new equipment to aid in the repair or maintenance of machines, mechanical equipment, or building structures.Design on paper, with a look at the machines it serves.Done more than yearly by most who do it. | 10% | 6.7 h3.3 h to 2.5 work days | Documents 70%, hands-on 30% | 56%Desk part half by now |
| Assemble, install, or repair wiring, electrical or electronic components, pipe systems, plumbing, machinery, or equipment.Hands-on trade work.Done more than monthly by most who do it. Robots: in a purpose-built cell, Kablator MPC325 automatic robotic wiring machine (sold). Anthropic's rating. | 9% | 3.3 h50 min to 6.7 h | Hands-on 100% | Stays with people |
| Paint or repair roofs, windows, doors, floors, woodwork, plaster, drywall, or other parts of building structures.Hands-on building repair.Done more than monthly by most who do it. Robots: no robot shown. Anthropic's rating. | 7% | 3.3 h1.3 h to 6.7 h | Hands-on 100% | Stays with people |
| Set up and operate machine tools to repair or fabricate machine parts, jigs, fixtures, or tools.Setting up and running machine tools.Done daily by most who do it. Robots: in a purpose-built cell, Robotic mill-turn cell with integrated Mitutoyo CMM at Acutec Precision Aerospace (deployed). Anthropic's rating. | 6% | 3.3 h1.7 h to 6.7 h | Hands-on 100% | Stays with people |
| Dismantle machines, equipment, or devices to access and remove defective parts, using hoists, cranes, hand tools, or power tools.Hands-on work with hoists and tools.Done more than monthly by most who do it. Robots: no robot shown. Anthropic's rating. | 5% | 2.5 h50 min to 5 h | Hands-on 100% | Stays with people |
| Inspect, operate, or test machinery or equipment to diagnose machine malfunctions.Running and testing the machine in person.Done more than weekly by most who do it. Robots: in a purpose-built cell, Boston Dynamics Spot Arm (deployed). Anthropic's rating. | 4% | 1.3 h25 min to 2.1 h | Hands-on 100% | Stays with people |
| Assemble boilers at installation sites, using tools such as levels, plumb bobs, hammers, torches, or other hand tools.Hands-on assembly with tools.Done yearly or less by most who do it.Robots: in a purpose-built cell, ism-technic / KUKA automated boiler welding line at Windhager (KR CYBERTECH ARC robots) (deployed). Anthropic's rating. | 4% | 1 work day3.3 h to 2.6 work days | Hands-on 100% | Stays with people |
| Train or manage maintenance personnel or subcontractors.Leading a crew and contractors on site.Done more than yearly by most who do it. | 4% | 1.7 h25 min to 3.3 h | In person 80%, documents 20% | 20%Desk part half by now |
| Perform general cleaning of buildings or properties.Cleaning by hand.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 1.7 h25 min to 3.3 h | Hands-on 100% | Stays with people |
| Maintain or repair specialized equipment or machinery located in cafeterias, laundries, hospitals, stores, offices, or factories.Hands-on repair.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 2.1 h50 min to 5 h | Hands-on 100% | Stays with people |
| Fabricate or repair counters, benches, partitions, or other wooden structures, such as sheds or outbuildings.Carpentry with hand and power tools.Done more than yearly by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 3.3 h1.7 h to 6.7 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.
- Millwrights0% now, 0% by 2028
- Aircraft Structure, Surfaces, Rigging, and Systems Assemblers4% now, 4% by 2028
- Plumbers, Pipefitters, and Steamfitters23% now, 23% by 2028
- Helpers--Installation, Maintenance, and Repair Workers2% now, 2% by 2028
- Industrial Machinery Mechanics13% now, 13% by 2028
- Mobile Heavy Equipment Mechanics, Except Engines6% now, 6% 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 Maintenance and Repair Workers, General is within reach of AI now?
- An estimated 11% of the working time is within reach of AI models now and 13% 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 Maintenance and Repair Workers, General 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: Design equipment for repair work (56% of the task's time now, 70% by the end of 2028); Diagnose problems and decide the fix (40% of the task's time now); and Estimate repair costs (70% of the task's time now).
- Which tasks of Maintenance and Repair Workers, General stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Repair machines and structures; Install and repair wiring and plumbing; and Paint and repair building parts. Together such tasks are about 78% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Maintenance and Repair Workers, General 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; judgment where errors are severe; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 87% of the working time stays with people, 12% becomes checking a model's work and 1% 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 Maintenance and Repair Workers, General?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.011% went to this job's tasks, the 385th largest share of the 447 jobs that showed up; 1 of its 27 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. 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 Maintenance and Repair Workers, General?
- Anthropic's robot exposure rating (September 2026) finds 86% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 33% of the working time, 2% in a structured facility. Its index is 0.35 of 3. One example: Scythe Robotics M.52 for "do groundskeeping". 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 27 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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