Librarians and Media Collections Specialists
In short: today's best AI models could do about 54% 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 60% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 40% of the time, stays with people.
Administer and maintain libraries or collections of information, for public or private access through reference or borrowing. Work in a variety of settings, such as educational institutions, museums, and corporations, and with various types of informational materials, such as books, periodicals, recordings, films, and databases. Tasks may include acquiring, cataloging, and circulating library materials, and user services such as locating and organizing information, providing instruction on how to access information, and setting up and operating a library's media equipment.
133,790 people do this job in the US. $68,270 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 on17 h
- A person checks AI's work5 h
- Desk work AI can't do yet2 h
- Stays with people16 h
Librarians and Media Collections Specialists, now: of a 40-hour week, AI could take on 17 hours, 5 hours become checking its work, 2 hours are desk work it can't do yet, and 16 hours stay with people.
The largest pieces: Keep circulation records and inventory (about 3 h); Supervise library operations (about 3 h, a person checks); Build information access aids (about 3 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.
- 36%
- 23%
- 6%
- 27%
- 8%
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 54% 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, 27%. Today's estimate: 55%.
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, 4.5% went to this job's tasks in May 2026, the 2nd largest of the 447 jobs that showed up at all; 3 of its 30 tasks showed up. Within each of Anthropic's methods, never across a change: 0.13% to 0.54% (released February and March 2025); 0.34% to 0.25% (August 2025 to February 2026); 4.5% to 4.5% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.13% | 131 of 708 | 10 of 30 |
| Released March 2025 | 0.54% | 31 of 703 | 10 of 30 |
| The week of Aug 4, 2025 (released September 2025) | 0.34% | 52 of 655 | 9 of 30 |
| The week of Nov 13, 2025 (released January 2026) | 0.22% | 79 of 672 | 9 of 30 |
| The week of Feb 5, 2026 (released March 2026) | 0.25% | 79 of 677 | 11 of 30 |
| April 2026 (released June 2026) | 4.5% | 2 of 434 | 3 of 30 |
| May 2026 (released June 2026) | 4.5% | 2 of 447 | 3 of 30 |
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 9% of this job's working time as physical work. For 5% of the working time, a robot has been shown doing most of the task somewhere: 5% in a purpose-built cell. 0.05 of 3, the 577th highest of the 923 jobs.
- Not physical 91%
- No robot shown 4%
- In a purpose-built cell 5%
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
40% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Supervise library operations, the in-person work, 7%
- Direct and train library staff, the in-person work, 4%
- Teach patrons computer skills, the in-person work, 3%
- Train faculty on software and equipment, the in-person work, 3%
- Keep circulation records and inventory, the hands-on work, 2%
- Confer on programs and services, the conversation, 2%
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 48% is desk work that is small if wrong, which can be handed over with lighter checks.
- Supervise library operations, costly if wrong, 7%
- Evaluate vendors and place orders, costly if wrong, 5%
What to learn
- Briefing a model and checking its drafts. About 36% 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 33% 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 23% 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 . 30 tasks from .
| Task | ||||
|---|---|---|---|---|
| Supervise daily library operations, budgeting, planning, and personnel activities, such as hiring, training, scheduling, and performance evaluations.Running the library day to day, with the budget, planning and reviews on paper.Done daily by most who do it. | 15% | 3.3 h1.7 h to 6.7 h | Documents 50%, in person 50% | 50%Desk part half by now |
| Keep up-to-date records of circulation and materials, maintain inventory, and correct cataloging errors.Records kept in the system, with shelf checks.Done daily by most who do it. | 10% | 1.3 h25 min to 3.3 h | Systems 80%, hands-on 20% | 80%Desk part half by now |
| Develop, maintain, and troubleshoot information access aids, such as databases, annotated bibliographies, Web pages, electronic pathfinders, software programs, and online tutorials.Databases, web pages and tutorials developed.Done more than monthly by most who do it. AI observed on this task. | 10% | 3.3 h50 min to 1.7 work days | Documents 70%, systems 30% | 71%Half by now |
| Evaluate vendor products and performance, negotiate contracts, and place orders.Vendors compared on paper, terms negotiated, orders placed.Done more than monthly by most who do it. | 7% | 3.3 h50 min to 1.3 work days | Documents 50%, conversation 30%, systems 20% | 56%Desk part half by now |
| Review and evaluate materials, using book reviews, catalogs, faculty recommendations, and current holdings to select and order print, audio-visual, and electronic resources.Reviews and catalogs read, and orders placed.Done daily by most who do it. | 7% | 1.7 h25 min to 5 h | Documents 70%, systems 30% | 94%Half by now |
| Search standard reference materials, including online sources and the Internet, to answer patrons' reference questions.Searching sources for the patron's question, at the desk.Done several times a day by most who do it. AI observed on this task. | 5% | 21 min8 min to 50 min | Documents 70%, in person 30% | 70%Desk part half by now |
| Direct and train library staff in duties, such as receiving, shelving, researching, cataloging, and equipment use.Leading staff on the floor, with the planning.Done daily by most who do it. | 5% | 1.7 h25 min to 3.3 h | In person 70%, documents 30% | 30%Desk part half by now |
| Plan and deliver client-centered programs and services, such as special services for corporate clients, storytelling for children, newsletters, or programs for special groups.Programs planned on paper and delivered in person.Done more than monthly by most who do it. | 4% | 3.3 h1.7 h to 1 work day | Documents 50%, in person 50% | 49%Desk part half by now |
| Arrange for interlibrary loans of materials not available in a particular library.Loan requests placed with other libraries.Done daily by most who do it. | 4% | 25 min8 min to 1.1 h | Systems 100% | 100%Half by now |
| Teach library patrons basic computer skills, such as searching computerized databases.Showing patrons how to search databases.Done more than weekly by most who do it. | 3% | 50 min17 min to 1.7 h | In person 100% | Stays with people |
| Code, classify, and catalog books, publications, films, audio-visual aids, and other library materials, based on subject matter or standard library classification systems.Classification and cataloging entered in the catalog.Done several times a day by most who do it. | 3% | 25 min8 min to 1.3 h | Systems 100% | 100%Half by now |
| Maintain hardware and software, including computers, media equipment, scanners, color copiers, and color laser printers.Hands on the equipment, with the software kept up.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 3% | 50 min17 min to 2.5 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.
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 Librarians and Media Collections Specialists is within reach of AI now?
- An estimated 55% of the working time is within reach of AI models now and 60% by the end of 2028, on METR's long-run pace at four in five (59% to 60% 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 Librarians and Media Collections 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: Keep circulation records and inventory (80% of the task's time now); Supervise library operations (50% of the task's time now); and Build information access aids (71% of the task's time now, 100% by the end of 2028).
- Which tasks of Librarians and Media Collections Specialists stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Teach patrons computer skills; Train faculty on software and equipment; and Confer on programs and services. Together such tasks are about 15% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Librarians and Media Collections 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 briefing a model and checking its drafts; the conversations; and running AI inside the business systems. By the end of 2028 on the long-run pace at four in five, about 40% of the working time stays with people, 12% becomes checking a model's work and 48% 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 Librarians and Media Collections Specialists?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 4.5% went to this job's tasks, the 2nd largest share of the 447 jobs that showed up; 3 of its 30 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 20%. 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 Librarians and Media Collections Specialists?
- Anthropic's robot exposure rating (September 2026) finds 9% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 5% of the working time. Its index is 0.05 of 3. One example: Tally (Simbe Robotics) for "inventory audiovisual 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 30 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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