Maintenance Workers, Machinery
In short: today's best AI models could do about 4% 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 the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 96% of the time, stays with people.
Lubricate machinery, change parts, or perform other routine machinery maintenance.
60,020 people do this job in the US. $60,850 a year.
Reviewed on October 3, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on2 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people38 h
Maintenance Workers, Machinery, now: of a 40-hour week, AI could take on 2 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 38 hours stay with people.
The largest pieces: Record maintenance information (under an hour); Inventory and order parts (under an hour); Read work orders (under an hour).
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.
- 1%
- 3%
- 0%
- 5%
- 91%
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 4% 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, 4%. Today's estimate: 4%.
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
None of this job's 18 tasks showed up in the Claude.ai conversations Anthropic sampled in any of its 7 releases, from February 2025 to May 2026.
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 90% of this job's working time as physical work. For 60% of the working time, a robot has been shown doing most of the task somewhere: 50% in a purpose-built cell, 10% in a structured facility. 0.71 of 3, the 258th highest of the 923 jobs.
- Not physical 10%
- No robot shown 29%
- In a purpose-built cell 50%
- In a structured facility 10%
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
96% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Dismantle machines, the hands-on work, 17%
- Reassemble machines, the hands-on work, 15%
- Repair machines with coworkers, the hands-on work, 11%
- Install and change parts, the hands-on work, 8%
- Clean machines and parts, the hands-on work, 6%
- Set up and operate machines, the hands-on work, 5%
What becomes checking the machine
No desk work within reach by then in tasks where a mistake costs something. Another 4% is desk work that is small if wrong, which can be handed over with lighter checks.
What to learn
- The hands-on craft. About 91% 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.
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 . 18 tasks from .
| Task | ||||
|---|---|---|---|---|
| Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.Machines taken apart with hand tools, chain falls, jacks, cranes and hoists.Done several times a day by most who do it. Robots: no robot shown. Anthropic's rating. | 17% | 1.7 h25 min to 5 h | Hands-on 100% | Stays with people |
| Reassemble machines after the completion of repair or maintenance work.Machines put back together after the repair.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 15% | 2.1 h38 min to 5 h | Hands-on 100% | Stays with people |
| Collaborate with other workers to repair or move machines, machine parts, or equipment.Working alongside others to repair or move machines.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 11% | 1.3 h25 min to 3.3 h | Hands-on 70%, in person 30% | Stays with people |
| Install, replace, or change machine parts and attachments, according to production specifications.Parts and attachments installed or changed to the specification.Done daily by most who do it. Robots: in a purpose-built cell, AMADA HG-ATC press brake with Automatic Tool Changer (sold). Anthropic's rating. | 8% | 50 min17 min to 2.5 h | Hands-on 100% | Stays with people |
| Clean machines and machine parts, using cleaning solvents, cloths, air guns, hoses, vacuums, or other equipment.Cleaning with solvents, cloths, air guns, hoses and vacuums.Done daily by most who do it. Robots: in a purpose-built cell, Kawasaki machine-tending robot cells with air blow-off (sold). Anthropic's rating. | 6% | 50 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Set up and operate machines, and adjust controls to regulate operations.Machines set up and run, controls adjusted.Done daily by most who do it. Robots: in a purpose-built cell, Flexxbotics FlexxCORE robot-driven workcell at Precision Metal Industries (deployed). Anthropic's rating. | 5% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment.Buildup broken off with abrasives, power tools, jackhammers and sledgehammers.Done more than monthly by most who do it. Robots: in a purpose-built cell, Matrix robotic finishing / deburring cells (force-controlled grinding) (sold). Anthropic's rating. | 5% | 1.3 h25 min to 2.5 h | Hands-on 100% | Stays with people |
| Inspect or test damaged machine parts, and mark defective areas or advise supervisors of repair needs.Parts inspected or tested, defects marked and the supervisor told.Done daily by most who do it. Robots: in a purpose-built cell, Robotic measurement cell (Mitutoyo MiSTAR + Engineering Data robot loader) at Méca-Précis (deployed). Anthropic's rating. | 5% | 38 min13 min to 1.3 h | Hands-on 80%, in person 20% | Stays with people |
| Replace or repair metal, wood, leather, glass, or other lining in machines, or in equipment compartments or containers.Metal, wood, leather or glass linings repaired or replaced.Done more than monthly by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 1.7 h38 min to 3.3 h | Hands-on 100% | Stays with people |
| Start machines and observe mechanical operation to determine efficiency and to detect problems.Machines started and observed for efficiency and problems.Done daily by most who do it. Robots: in a purpose-built cell, Flexxbotics FlexxCORE / Intelligent Recovery with FANUC RoboDrill machine-tending cells (Ruland Manufacturing) (deployed). Anthropic's rating. | 4% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
| Transport machine parts, tools, equipment, and other material between work areas and storage, using cranes, hoists, or dollies.Parts, tools and equipment moved with cranes, hoists or dollies.Done daily by most who do it. Robots: in a structured facility, MiR250 / MiR500 AMRs with ROEQ carts (Mobile Industrial Robots) (deployed). Anthropic's rating. | 4% | 33 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished.Counting the stock and entering the requisitions.Done daily by most who do it. Robots: in a structured facility, Tally (Simbe Vision) (deployed). Anthropic's rating. | 4% | 38 min13 min to 1.7 h | Hands-on 60%, systems 40% | 40%Desk part half by now |
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.
- Industrial Machinery Mechanics13% now, 13% by 2028
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic10% now, 10% by 2028
- Electric Motor, Power Tool, and Related Repairers3% now, 3% by 2028
- Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic10% now, 10% by 2028
- Mobile Heavy Equipment Mechanics, Except Engines6% now, 6% by 2028
- Tool Grinders, Filers, and Sharpeners6% 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 Workers, Machinery is within reach of AI now?
- An estimated 4% of the working time is within reach of AI models now and 4% 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 Workers, Machinery 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: Record maintenance information (100% of the task's time now); Inventory and order parts (40% of the task's time now); and Read work orders (50% of the task's time now).
- Which tasks of Maintenance Workers, Machinery stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Dismantle machines; Reassemble machines; and Repair machines with coworkers. Together such tasks are about 93% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Maintenance Workers, Machinery 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; and judgment where errors are severe. By the end of 2028 on the long-run pace at four in five, about 96% of the working time stays with people, 0% becomes checking a model's work and 4% 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 Workers, Machinery?
- 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 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 Workers, Machinery?
- Anthropic's robot exposure rating (September 2026) finds 90% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 60% of the working time, 10% in a structured facility. Its index is 0.71 of 3. One example: MiR250 / MiR500 AMRs with ROEQ carts for "move parts and equipment". 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 18 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 October 3, 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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