Computer User Support Specialists
In short: today's best AI models could do about 57% 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 62% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 38% of the time, stays with people.
Provide technical assistance to computer users. Answer questions or resolve computer problems for clients in person, via telephone, or electronically. May provide assistance concerning the use of computer hardware and software, including printing, installation, word processing, electronic mail, and operating systems.
717,190 people do this job in the US. $61,860 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 on7 h
- A person checks AI's work16 h
- Desk work AI can't do yet2 h
- Stays with people15 h
Computer User Support Specialists, now: of a 40-hour week, AI could take on 7 hours, 16 hours become checking its work, 2 hours are desk work it can't do yet, and 15 hours stay with people.
The largest pieces: Investigate and resolve technical problems (about 4 h, a person checks); Evaluate software and hardware (about 3 h, a person checks); Oversee daily system performance (about 3 h, 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.
- 27%
- 34%
- 23%
- 0%
- 15%
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 57% 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, 36%. Today's estimate: 57%.
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, 2.3% went to this job's tasks in May 2026, the 6th largest of the 447 jobs that showed up at all; 6 of its 16 tasks showed up. Within each of Anthropic's methods, never across a change: 0.21% to 0.13% (released February and March 2025); 0.26% to 0.35% (August 2025 to February 2026); 2.5% to 2.3% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.21% | 89 of 708 | 9 of 16 |
| Released March 2025 | 0.13% | 130 of 703 | 8 of 16 |
| The week of Aug 4, 2025 (released September 2025) | 0.26% | 68 of 655 | 9 of 16 |
| The week of Nov 13, 2025 (released January 2026) | 0.3% | 58 of 672 | 9 of 16 |
| The week of Feb 5, 2026 (released March 2026) | 0.35% | 64 of 677 | 8 of 16 |
| April 2026 (released June 2026) | 2.5% | 5 of 434 | 7 of 16 |
| May 2026 (released June 2026) | 2.3% | 6 of 447 | 6 of 16 |
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 18% of this job's working time as physical work. For 18% of the working time, a robot has been shown doing most of the task somewhere: 18% in a purpose-built cell. 0.18 of 3, the 449th highest of the 923 jobs.
- Not physical 82%
- In a purpose-built cell 18%
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
38% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Set up equipment for employees, the hands-on work, 8%
- Establish requirements for new systems, the conversation, 7%
- Install and repair hardware and software, the hands-on work, 5%
- Investigate and resolve technical problems, the conversation, 4%
- Write training materials and train users, the conversation, 4%
- Answer users' questions, the conversation, 4%
What becomes checking the machine
43% 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 18% is desk work that is small if wrong, which can be handed over with lighter checks.
- Investigate and resolve technical problems, costly if wrong, 10%
- Evaluate software and hardware, costly if wrong, 8%
- Oversee daily system performance, costly if wrong, 7%
- Customize commercial programs, costly if wrong, 6%
- Study new office systems for feasibility, costly if wrong, 5%
- Install and repair hardware and software, costly if wrong, 3%
What to learn
- 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 34% 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.
- Briefing a model and checking its drafts. About 27% 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.
- The conversations. About 23% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- The hands-on craft. About 15% of the time is hands-on work, which no model here does; robots are rated separately on this page.
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 . 16 tasks from .
| Task | ||||
|---|---|---|---|---|
| Read technical manuals, confer with users, or conduct computer diagnostics to investigate and resolve problems or to provide technical assistance and support.Diagnostics on the system, manuals read, and talk with the user.Done several times a day by most who do it. AI observed on this task. | 15% | 50 min17 min to 2.5 h | Systems 40%, documents 30%, conversation 30% | 70%Desk part half by now |
| Set up equipment for employee use, performing or ensuring proper installation of cables, operating systems, or appropriate software.Cabling by hand, and software installed.Done more than weekly by most who do it. Robots: in a purpose-built cell, HIL-SERL (Franka arm with wrist cameras, human-in-the-loop RL) (UC Berkeley (Levine lab) / Sergey Levine et al.) (demonstrated). Anthropic's rating. | 13% | 1.3 h25 min to 3.3 h | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Confer with staff, users, and management to establish requirements for new systems or modifications.Meetings with users and management, with notes.Done more than weekly by most who do it. | 9% | 1.3 h25 min to 3.3 h | Conversation 80%, documents 20% | 20%Desk part half by now |
| Prepare evaluations of software or hardware, and recommend improvements or upgrades.Testing and a written evaluation.Done more than monthly by most who do it. AI observed on this task. | 8% | 3.3 h1.7 h to 6.7 h | Documents 80%, systems 20% | 100%Half by now |
| Install and perform minor repairs to hardware, software, or peripheral equipment, following design or installation specifications.Hands on the hardware, and installation on screen.Done more than monthly by most who do it. Robots: in a purpose-built cell, HIL-SERL (human-in-the-loop sample-efficient robotic RL) (UC Berkeley RAIL) (demonstrated). Anthropic's rating. | 8% | 50 min17 min to 1.7 h | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Develop training materials and procedures, or train users in the proper use of hardware or software.Writing the material, and teaching sessions.Done more than monthly by most who do it. AI observed on this task. | 7% | 3.3 h1.3 h to 1 work day | Documents 50%, conversation 50% | 47%Desk part half by now |
| Oversee the daily performance of computer systems.Watching the systems from a console.Done daily by most who do it. | 7% | 25 min13 min to 1.3 h | Systems 100% | 100%Half by now |
| Maintain records of daily data communication transactions, problems and remedial actions taken, or installation activities.Entries in the ticket system.Done daily by most who do it. | 6% | 25 min8 min to 50 min | Systems 100% | 100%Half by now |
| Modify and customize commercial programs for internal needs.Configuration and code, in the program itself.Done more than yearly by most who do it. AI observed on this task. | 6% | 5 h1.7 h to 1.3 work days | Documents 70%, systems 30% | 89%Half by now |
| Answer user inquiries regarding computer software or hardware operation to resolve problems.Live help by phone or chat, with a look at the user's system.Done hourly or more by most who do it. AI observed on this task. | 5% | 13 min4 min to 38 min | Conversation 70%, systems 30% | 30%Desk part half by now |
| Hire, supervise, and direct workers engaged in special project work, problem-solving, monitoring, and installation of data communication equipment and software.Conversation with the team, and review of their work.Done more than yearly by most who do it. | 5% | 6.7 h1.7 h to 4.2 work days | Conversation 70%, documents 30% | 12%Desk part half by Jan 2027 |
| Conduct office automation feasibility studies, including workflow analysis, space design, or cost comparison analysis.Analysis and a written study.Done more than yearly by most who do it. | 5% | 1.3 work days3.3 h to 4.2 work days | Documents 100% | 44%Half by Dec 2026 |
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.
- Computer Network Support Specialists79% now, 82% by 2028
- Network and Computer Systems Administrators54% now, 89% by 2028
- Computer Systems Engineers/Architects45% now, 67% by 2028
- Computer Systems Analysts35% now, 89% by 2028
- Software Developers48% now, 78% by 2028
- Database Administrators68% now, 88% 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 Computer User Support Specialists is within reach of AI now?
- An estimated 57% of the working time is within reach of AI models now and 62% by the end of 2028, on METR's long-run pace at four in five (61% to 62% across the three paces the site shows). 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 User Support Specialists 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: Investigate and resolve technical problems (70% of the task's time now); Evaluate software and hardware (100% of the task's time now); and Oversee daily system performance (100% of the task's time now).
- Which tasks of Computer User Support Specialists stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 38% of the working time.
- What should Computer User Support Specialists learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; running AI inside the business systems; and briefing a model and checking its drafts. By the end of 2028 on the long-run pace at four in five, about 38% of the working time stays with people, 43% becomes checking a model's work and 18% 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 User Support Specialists?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 2.3% went to this job's tasks, the 6th largest share of the 447 jobs that showed up; 6 of its 16 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 47%. 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 User Support Specialists?
- Anthropic's robot exposure rating (September 2026) finds 18% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 18% of the working time. Its index is 0.18 of 3. One example: HIL-SERL (Franka arm with wrist cameras, human-in-the-loop RL) for "set up equipment for employees". 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 16 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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