Will AI replace curators?

Some tasks are already being done by AI or machines; the rest still needs a person. About 3h 42m of a typical shift is made up of tasks that AI or robots have done on their own in real use somewhere. That doesn't mean those hours are automated in a typical workplace. The breakdown: 47m done by people with AI help, 3h 25m that AI mostly does alone, 17m that robots have done in real workplaces. The other 3h 31m (44%) stays with people. Separately, BLS expects about 4.9% more of these jobs by 2035 (all jobs: +3.5%).

AI shows up regularly in 5 of 15 tasks, about 4h 12m of a typical shift. The biggest ones:

• Plan and organize the acquisition, storage, and exhibition of collections and related materials, including the selection of exhibition themes and designs, and develop or install exhibit materials. (1h 28m a shift, AI mostly does it alone)
• Plan and conduct special research projects in area of interest or expertise. (47m a shift, AI mostly does it alone)
• Provide information from the institution's holdings to other curators and to the public. (47m a shift, AI mostly does it alone)
• Study, examine, and test acquisitions to authenticate their origin, composition, history, and to assess their current value. (23m a shift, AI mostly does it alone)
• Write and review grant proposals, journal articles, institutional reports, and publicity materials. (47m a shift, people use AI to help)

For some tasks, yes. Robots have done 1 of this job's tasks in workplaces like this one, about 17m of a typical shift. For 1 physical task, no robot has shown it can do the work yet.

• Inspect premises to assess the need for repairs and to ensure that climate and pest control issues are addressed. (18m a shift; e.g. Spot (with sensor payloads, Orbit fleet software), in use)

3h 31m of a typical shift, or 44%, goes to tasks with no AI or robot evidence yet. No AI usage or robot deployment has shown up for these so far. The biggest:

• Develop and maintain an institution's registration, cataloging, and basic record-keeping systems, using computer databases. (53m a shift)
• Design, organize, or conduct tours, workshops, and instructional or educational sessions to acquaint individuals with an institution's facilities and materials. (35m a shift)
• Train and supervise curatorial, fiscal, technical, research, and clerical staff, as well as volunteers or interns. (35m a shift)
• Negotiate and authorize purchase, sale, exchange, or loan of collections. (23m a shift)
• Attend meetings, conventions, and civic events to promote use of institution's services, to seek financing, and to maintain community alliances. (23m a shift)

Much of it comes down to where and how the work happens. In-person, hands-on and high-stakes work is where AI and robots have made the least headway so far. In the official O*NET survey of people in this job:

• 81% talk face to face with others every day
• 74% say dealing with the public is very or extremely important
• 50% are in constant contact with other people

For the 2 tasks in this job where AI shows up and timing data exists, a typical request takes about 6h 32m for a person alone and about 44m with AI. These are Claude's own estimates from real conversations in April and May 2026 (Anthropic Economic Index). They describe single requests, not whole workdays, and time saved on a task doesn't mean jobs are cut. The biggest gaps:

• Write and review grant proposals, journal articles, institutional reports, and publicity materials (about 8h 50m alone, 1h 4m with AI)
• Plan and conduct special research projects in area of interest or expertise (about 4h 13m alone, 24m with AI)

On Indeed, job postings in the Education & Instruction sector are at 107% of their February 2020 level (all U.S. postings: 104%), down 9.5% from a year earlier, as of Oct 9, 2026. BLS separately expects about 1,600 openings a year in this job through 2035. Across all U.S. postings, 7.3% now mention AI skills. Sector postings cover more than this one job and move for many reasons besides AI.

The median pay is about $63,420 a year ($30.49 an hour), 24% above the median for all U.S. jobs ($50,980). Most people in this job earn between $40,470 and $107,140 a year; that range covers the middle 80%. Pay figures are from BLS, May 2025, and vary a lot by state and employer. Pick a state to compare:

BLS projects growth of 4.9% from 2025 to 2035, compared with 3.5% growth for all jobs. That's about 15,000 jobs in 2035. It expects about 1,600 openings a year, mostly from people retiring or changing careers. These projections don't single out AI as a cause.

About 12,000 people in the U.S., roughly 0.01% of all workers (BLS, May 2025). That makes it the 640th largest of the 796 jobs on this site. BLS expects about 15,000 in 2035.

The most common requirement is a master's degree: about 44% of people in the job said so in the official O*NET survey. O*NET rates the job at level 5 of 5 for preparation, which usually means: graduate degree. Requirements vary by state and employer, especially for licensed work.

These come from the job's official skill ratings and from the tasks that AI and robots touch least today. Building them keeps you strong in the parts of the work that still need a person:

• Skills that matter more here than in most jobs: Management of Material Resources, Systems Analysis, Writing, Systems Evaluation
• Get strong at the parts of the job that stay with people (listed above), which take 3h 31m of a typical shift
• Tools employers list: Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, Microsoft Office software, Microsoft Excel
• Learn to use AI well on the tasks where it already shows up in this job (listed above)

These use similar skills, with less of a typical shift touched by AI or robots. Matches come from 109 official ratings of skills, knowledge and work activities, and each needs the same or less preparation unless the official list relates the jobs directly:

• Archivists (1h 2m touched, $65k median pay)
• Historians (12m touched, $77k median pay)
• Set and Exhibit Designers (0m touched, $75k median pay)

Each task carries its own evidence label. For this job: 5 observed in real AI use, 1 with robots in real deployments, 9 with no AI or robot evidence found. The minutes per task are estimates, not measured hours, and "no evidence" doesn't prove a task is safe.