Library Assistants, Clerical
In short: today's best AI models could do about 35% 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 65% of the time, stays with people.
Compile records, and sort, shelve, issue, and receive library materials such as books, electronic media, pictures, cards, slides and microfilm. Locate library materials for loan and replace material in shelving area, stacks, or files according to identification number and title. Register patrons to permit them to borrow books, periodicals, and other library materials.
85,520 people do this job in the US. $36,910 a year.
Reviewed on October 1, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on8 h
- A person checks AI's work6 h
- Desk work AI can't do yet0 h
- Stays with people26 h
Library Assistants, Clerical, now: of a 40-hour week, AI could take on 8 hours, 6 hours become checking its work, 0 hours are desk work it can't do yet, and 26 hours stay with people.
The largest pieces: Run a small branch (about 4 h, a person checks); Run library programs (about 1 h, a person checks); Keep circulation records (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.
- 6%
- 29%
- 1%
- 28%
- 35%
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 35% 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, 2%. With Claude Mythos Preview (early), measured in April 2026, 20%. Today's estimate: 35%.
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 384th largest of the 447 jobs that showed up at all; 1 of its 32 tasks showed up. Within each of Anthropic's methods, never across a change: 0.065% to 0.12% (released February and March 2025); 0.06% to 0.067% (August 2025 to February 2026); 0.011% to 0.011% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.065% | 234 of 708 | 4 of 32 |
| Released March 2025 | 0.12% | 132 of 703 | 6 of 32 |
| The week of Aug 4, 2025 (released September 2025) | 0.06% | 217 of 655 | 3 of 32 |
| The week of Nov 13, 2025 (released January 2026) | 0.072% | 194 of 672 | 5 of 32 |
| The week of Feb 5, 2026 (released March 2026) | 0.067% | 211 of 677 | 5 of 32 |
| April 2026 (released June 2026) | 0.011% | 366 of 434 | 1 of 32 |
| May 2026 (released June 2026) | 0.011% | 384 of 447 | 1 of 32 |
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 71% of this job's working time as physical work. For 62% of the working time, a robot has been shown doing most of the task somewhere: 42% in a purpose-built cell, 20% in a structured facility. 0.82 of 3, the 218th highest of the 923 jobs.
- Not physical 29%
- No robot shown 9%
- In a purpose-built cell 42%
- In a structured facility 20%
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
64% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Run a small branch, the in-person work, 22%
- Run library programs, the in-person work, 8%
- Sort and shelve items, the hands-on work, 6%
- Help maintain collections, the hands-on work, 4%
- Schedule and supervise helpers, the in-person work, 3%
- Show patrons how to use the catalog, the in-person work, 2%
What becomes checking the machine
15% 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 20% is desk work that is small if wrong, which can be handed over with lighter checks.
- Run a small branch, costly if wrong, 9%
- Run library programs, costly if wrong, 3%
- Bookkeeping and cash handling, costly if wrong, 3%
What to learn
- The hands-on craft. About 35% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 29% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Running AI inside the business systems. About 29% 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.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- 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.
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 . 32 tasks from .
| Task | ||||
|---|---|---|---|---|
| Operate small branch libraries, under the direction of off-site librarian supervisors.The desk, the shelves and the records, with the librarian reached by phone.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 31% | 6.7 h3.3 h to 1 work day | In person 40%, systems 30%, hands-on 30% | 30%Desk part half by now |
| Plan or participate in library events and programs, such as story time with children.Story time and events with the public, and the planning.Done more than yearly by most who do it. | 11% | 2.5 h50 min to 6.7 h | In person 70%, documents 30% | 29%Desk part half by now |
| Sort books, publications, and other items according to established procedure and return them to shelves, files, or other designated storage areas.Sorting and reshelving.Done hourly or more by most who do it. Robots: in a purpose-built cell, Amazon Robotics 'Stow' system (Vulcan Stow) (deployed). Anthropic's rating. | 6% | 25 min13 min to 50 min | Hands-on 100% | Stays with people |
| Provide assistance to librarians in the maintenance of collections of books, periodicals, magazines, newspapers, and audio-visual and other materials.Shelf work and the records behind it.Done daily by most who do it. Robots: in a purpose-built cell, AuRoSS (Autonomous Robotic Shelf Scanning) (A*STAR Institute for Infocomm Research) (demonstrated). Anthropic's rating. | 6% | 38 min17 min to 1.7 h | Hands-on 70%, systems 30% | 30%Desk part half by now |
| Perform accounting and bookkeeping activities, such as invoicing, maintaining financial records, budgeting, and handling cash.Invoices and records in the system, and the cash counted.Done more than weekly by most who do it.Robots: in a structured facility, EnvisionWare EasyPay Station / OneStop (sold). Anthropic's rating. | 4% | 1.7 h50 min to 3.3 h | Systems 70%, hands-on 30% | 70%Desk part half by now |
| Maintain records of items received, stored, issued, and returned and file catalog cards according to system used.Entries in the system, and cards filed by hand.Done daily by most who do it. Robots: in a purpose-built cell, Lyngsoe Sort Mate 2000 AMH (sold). Anthropic's rating. | 4% | 25 min8 min to 1.3 h | Systems 80%, hands-on 20% | 80%Desk part half by now |
| Schedule, supervise, and train clerical workers, volunteers, student assistants, and other library employees.Leading the helpers on the floor, with the schedule kept.Done more than yearly by most who do it. | 4% | 1.3 h25 min to 3.3 h | In person 70%, systems 30% | 30%Desk part half by now |
| Perform clerical activities, such as answering phones, sorting mail, filing, typing, word processing, and photocopying and mailing out material.Phones, mail, filing and typing.Done hourly or more by most who do it. AI observed on this task.Robots: in a purpose-built cell, Pepper (SoftBank Robotics / RobotLAB) (deployed). Anthropic's rating. | 3% | 13 min4 min to 38 min | Hands-on 40%, documents 30%, conversation 30% | 30%Desk part half by now |
| Manage reserve materials by placing items on reserve for library patrons, checking items in and out of library, and removing out-of-date items.Reserve entries and the items shelved and pulled.Done daily by most who do it. Robots: in a purpose-built cell, D-Tech LendIT library vending kiosk (sold). Anthropic's rating. | 3% | 17 min8 min to 38 min | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Locate library materials for patrons, including books, periodicals, tape cassettes, Braille volumes, and pictures.A catalog lookup, then fetching from the shelves.Done several times a day by most who do it. AI observed on this task.Robots: in a purpose-built cell, lendIT library item vending machine (D-Tech International) (sold). Anthropic's rating. | 3% | 13 min4 min to 25 min | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Review records, such as microfilm and issue cards, to identify titles of overdue materials and delinquent borrowers.Reviewing the circulation records.Done daily by most who do it. | 3% | 38 min13 min to 2.1 h | Systems 100% | 100%Half by now |
| Instruct patrons on how to use reference sources, card catalogs, and automated information systems.Showing patrons at the terminal.Done daily by most who do it. | 2% | 17 min8 min to 33 min | In person 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.
- Library Technicians47% now, 47% by 2028
- File Clerks50% now, 53% by 2028
- Office Clerks, General56% now, 56% by 2028
- Secretaries and Administrative Assistants, Except Legal, Medical, and Executive59% now, 68% by 2028
- Document Management Specialists70% now, 93% by 2028
- Stockers and Order Fillers19% now, 19% 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 Library Assistants, Clerical is within reach of AI now?
- An estimated 35% of the working time is within reach of AI models now and 36% 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 Library Assistants, Clerical 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: Run a small branch (30% of the task's time now); Run library programs (29% of the task's time now); and Keep circulation records (80% of the task's time now).
- Which tasks of Library Assistants, Clerical stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Sort and shelve items; Show patrons how to use the catalog; and Maintain equipment and show its use. Together such tasks are about 18% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Library Assistants, Clerical 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 running AI inside the business systems. By the end of 2028 on the long-run pace at four in five, about 64% of the working time stays with people, 15% becomes checking a model's work and 20% 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 Library Assistants, Clerical?
- 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 384th largest share of the 447 jobs that showed up; 1 of its 32 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 10%. 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 Library Assistants, Clerical?
- Anthropic's robot exposure rating (September 2026) finds 71% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 62% of the working time, 20% in a structured facility. Its index is 0.82 of 3. One example: EnvisionWare EasyPay Station / OneStop for "bookkeeping and cash handling". 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 32 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 1, 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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