Computer, Automated Teller, and Office Machine Repairers
In short: today's best AI models could do about 18% 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 82% of the time, stays with people.
Repair, maintain, or install computers, word processing systems, automated teller machines, and electronic office machines, such as duplicating and fax machines.
65,600 people do this job in the US. $47,810 a year.
Reviewed on October 2, 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 work5 h
- Desk work AI can't do yet0 h
- Stays with people33 h
Computer, Automated Teller, and Office Machine Repairers, now: of a 40-hour week, AI could take on 2 hours, 5 hours become checking its work, 0 hours are desk work it can't do yet, and 33 hours stay with people.
The largest pieces: Install and configure new equipment (about 2 h, a person checks); Analyze equipment performance records (about 1 h, a person checks); Record maintenance and repairs (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.
- 4%
- 14%
- 2%
- 5%
- 76%
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 18% 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, 1%. With Claude Mythos Preview (early), measured in April 2026, 16%. Today's estimate: 18%.
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 25 tasks showed up. Within each of Anthropic's methods, never across a change: 0.2% to 0.15% (released February and March 2025); 0.35% to 0.37% (August 2025 to February 2026); 0.011% to none (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.2% | 92 of 708 | 7 of 25 |
| Released March 2025 | 0.15% | 116 of 703 | 7 of 25 |
| The week of Aug 4, 2025 (released September 2025) | 0.35% | 50 of 655 | 7 of 25 |
| The week of Nov 13, 2025 (released January 2026) | 0.32% | 54 of 672 | 6 of 25 |
| The week of Feb 5, 2026 (released March 2026) | 0.37% | 59 of 677 | 7 of 25 |
| April 2026 (released June 2026) | 0.011% | 346 of 434 | 1 of 25 |
| May 2026 (released June 2026) | none | n/a | 0 of 25 |
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 75% of this job's working time as physical work. For 43% of the working time, a robot has been shown doing most of the task somewhere: 43% in a purpose-built cell. 0.43 of 3, the 335th highest of the 923 jobs.
- Not physical 25%
- No robot shown 32%
- In a purpose-built cell 43%
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
81% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Assemble machines to specification, the hands-on work, 13%
- Disassemble machines to inspect parts, the hands-on work, 11%
- Repair or replace components, the hands-on work, 10%
- Travel to customer sites, the hands-on work, 9%
- Reassemble repaired machines, the hands-on work, 8%
- Install and configure new equipment, the hands-on work, 6%
What becomes checking the machine
13% 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 5% is desk work that is small if wrong, which can be handed over with lighter checks.
- Install and configure new equipment, costly if wrong, 4%
- Analyze equipment performance records, costly if wrong, 3%
- Reinstall software and adjust settings, costly if wrong, 2%
- Copy programs and store schematics, costly if wrong, 1%
- Test circuits to find defects, costly if wrong, 1.0%
- Complete bills and shop paperwork, costly if wrong, 0.9%
What to learn
- The hands-on craft. About 76% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- 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: spotting what a draft gets wrong is a skill of its own.
- Running AI inside the business systems. About 14% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
- 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 . 25 tasks from .
| Task | ||||
|---|---|---|---|---|
| Assemble machines according to specifications, using hand or power tools and measuring devices.Building the machine up from its parts to the drawings.Done more than monthly by most who do it. Robots: in a purpose-built cell, Inspur Intelligent Factory robotic server/rack assembly line (deployed). Anthropic's rating. | 13% | 1.3 h25 min to 3.3 h | Hands-on 100% | Stays with people |
| Disassemble machines to examine parts, such as wires, gears, or bearings for wear or defects, using hand or power tools and measuring devices.Taking the machine apart to look the wires, gears and bearings over.Done several times a day by most who do it. Robots: no robot shown. Anthropic's rating. | 11% | 50 min25 min to 1.7 h | Hands-on 100% | Stays with people |
| Install and configure new equipment, including operating software or peripheral equipment.Setting the machine in place and loading and configuring its software.Done more than monthly by most who do it. AI observed on this task.Robots: in a purpose-built cell, Learning-Augmented Robotic Automation (Neuromeka) (deployed). Anthropic's rating. | 11% | 1.7 h38 min to 4.2 h | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Repair, adjust, or replace electrical or mechanical components or parts, using hand tools, power tools, or soldering or welding equipment.Replacing and soldering parts with hand and power tools.Done several times a day by most who do it. Robots: in a purpose-built cell, Daisy (Apple recycling robot) (deployed). Anthropic's rating. | 10% | 1.1 h17 min to 2.5 h | Hands-on 100% | Stays with people |
| Travel to customers' stores or offices to service machines or to provide emergency repair service.Driving to the customer's office or store with the parts and tools.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 9% | 50 min25 min to 1.7 h | Hands-on 100% | Stays with people |
| Reassemble machines after making repairs or replacing parts.Putting the machine back together after the repair.Done daily by most who do it. Robots: in a purpose-built cell, HIL-SERL (human-in-the-loop sample-efficient RL) on Franka arm (UC Berkeley) (demonstrated). Anthropic's rating. | 8% | 38 min17 min to 1.3 h | Hands-on 100% | Stays with people |
| Test components or circuits of faulty equipment to locate defects, using oscilloscopes, signal generators, ammeters, voltmeters, or special diagnostic software programs.Probing the circuits with meters and diagnostic programs to find the fault.Done more than monthly by most who do it. Robots: no robot shown. Anthropic's rating. | 5% | 1.1 h25 min to 2.1 h | Hands-on 80%, systems 20% | 20%Desk part half by now |
| Maintain parts inventories and order any additional parts needed for repairs.Counting the parts on hand and placing the orders.Done daily by most who do it. Robots: in a purpose-built cell, Corvus One Autonomous Inventory Management System (deployed). Anthropic's rating. | 5% | 25 min13 min to 50 min | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Operate machines to test functioning of parts or mechanisms.Running the machine to see the parts work.Done daily by most who do it. Robots: in a purpose-built cell, ABB YuMi (IRB 14000) with Abrantix Test Environment (deployed). Anthropic's rating. | 3% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
| Advise customers concerning equipment operation, maintenance, or programming.Showing the customer at the machine, or telling them by phone, how to run and care for it.Done daily by most who do it. | 3% | 17 min8 min to 38 min | In person 70%, conversation 30% | Stays with people |
| Update existing equipment, performing tasks such as installing updated circuit boards or additional memory.Fitting new boards or memory into the machine.Done more than yearly by most who do it. Robots: in a purpose-built cell, OnRobot (OptoForce) force/torque sensor with industrial robot arm, DDR4 memory-module assembly application (sold). Anthropic's rating. | 3% | 50 min25 min to 1.7 h | Hands-on 100% | Stays with people |
| Analyze equipment performance records to assess equipment functioning.Reading the service and error records to see how the machine is behaving.Done more than weekly by most who do it. AI observed on this task. | 3% | 33 min17 min to 1.1 h | Documents 100% | 100%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.
- Electrical and Electronics Repairers, Commercial and Industrial Equipment26% now, 32% by 2028
- Calibration Technologists and Technicians37% now, 55% by 2028
- Telecommunications Equipment Installers and Repairers, Except Line Installers13% now, 13% by 2028
- Electrical and Electronic Engineering Technologists and Technicians39% now, 48% by 2028
- Coin, Vending, and Amusement Machine Servicers and Repairers15% now, 15% by 2028
- Electromechanical Equipment Assemblers2% now, 2% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
- Professional and Commercial Equipment and Supplies Merchant Wholesalers19,070 people
- Electronics and Appliance Retailers17,720 people
- Electronic and Precision Equipment Repair and Maintenance11,940 people
- Computer Systems Design and Related Services3,180 people
- Miscellaneous Store Retailers (4594 and 4595 only)2,190 people
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 Computer, Automated Teller, and Office Machine Repairers is within reach of AI now?
- An estimated 18% of the working time is within reach of AI models now and 18% 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 Computer, Automated Teller, and Office Machine Repairers 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: Install and configure new equipment (40% of the task's time now); Analyze equipment performance records (100% of the task's time now); and Record maintenance and repairs (100% of the task's time now).
- Which tasks of Computer, Automated Teller, and Office Machine Repairers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Assemble machines to specification; Disassemble machines to inspect parts; and Repair or replace components. Together such tasks are about 67% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Computer, Automated Teller, and Office Machine Repairers 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; reviewing work you did not do yourself; and running AI inside the business systems. By the end of 2028 on the long-run pace at four in five, about 81% of the working time stays with people, 13% becomes checking a model's work and 5% 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 Computer, Automated Teller, and Office Machine Repairers?
- 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 Computer, Automated Teller, and Office Machine Repairers?
- Anthropic's robot exposure rating (September 2026) finds 75% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 43% of the working time. Its index is 0.43 of 3. One example: Inspur Intelligent Factory robotic server/rack assembly line for "assemble machines to specification". 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 25 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 2, 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.
This job in your state or metro area
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The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.