Occupation · SOC 29-1171.00
Diagnose and treat acute, episodic, or chronic illness, independently or as part of a healthcare team. May focus on health promotion and disease prevention. May order, perform, or interpret diagnostic tests such as lab work and x rays. May prescribe medication. Must be registered nurses who have specialized graduate education.
Also called: ACNP (Acute Care Nurse Practitioner) · Family Nurse Practitioner (FNP) · Gastroenterology Nurse Practitioner · Nurse Practitioner (NP) · ARNP Specialist (Advanced Registered Nurse Practitioner Specialist) · Adult Nurse Practitioner · Advanced Practice Registered Nurse (APRN) · Family Practice Certified Advanced Registered Nurse Practitioner · Pediatric Nurse Practitioner (PNP) · Women's Health Care Nurse Practitioner · Advanced Practice Nurse (APN) · Advanced Practice Provider
Job family: Healthcare Practitioners and Technical Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-29-1171-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
60th-percentile task overlap — yet about 29,500 openings a year (+40.1% projected, BLS), and observed AI use leans 6908% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 53rd | 0.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 82nd | 0.9 | |
| AI assistant applicability (Microsoft) Moderate | 47th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.5), and including AI-powered software (γ 0.9). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses. | 5.3% | |
| Provide patients with information needed to promote health, reduce risk factors, or prevent disease or disability. | 1.8% | |
| Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances. | 1.4% | |
| Provide patients or caregivers with assistance in locating health care resources. | 0.7% | |
| Advocate for accessible health care that minimizes environmental health risks. | 0.3% | |
| Prescribe medications based on efficacy, safety, and cost as legally authorized. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +40.1% by 2034 |
| Projected annual openings | 29,500 |
| Employment 2024 → 2034 | 320,400 → 448,800 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Nursing Professionals · 2221 | 25% | Not exposed |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 69.1% working with AI · 23.0% handed to AI |
| Most common way people use AI here | Learning · you ask AI to explain or teach |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 10.7% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances. | Learning | 2.9% |
| Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses. | Learning | 2.8% |
| Provide patients or caregivers with assistance in locating health care resources. | Learning | 2.5% |
| Counsel patients about drug regimens and possible side effects or interactions with other substances such as food supplements, over-the-counter (OTC) medications, or herbal remedies. | Learning | 2.4% |
| Provide patients with information needed to promote health, reduce risk factors, or prevent disease or disability. | Learning | 1.3% |
| Develop treatment plans based on scientific rationale, standards of care, and professional practice guidelines. | Directive | 0.9% |
| Prescribe medication dosages, routes, and frequencies based on patient characteristics such as age and gender. | Directive | 0.4% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Provide patients with information needed to promote health, reduce risk factors, or prevent disease or disability. | 100.0% | |
| Provide patients or caregivers with assistance in locating health care resources. | 98.4% | |
| Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances. | 97.2% | |
| Counsel patients about drug regimens and possible side effects or interactions with other substances such as food supplements, over-the-counter (OTC) medications, or herbal remedies. | 94.2% | |
| Develop treatment plans based on scientific rationale, standards of care, and professional practice guidelines. | 86.8% | |
| Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses. | 83.3% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances. From: Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances. · 2.9% of measured AI use · learning
Help me analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses. From: Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses. · 2.8% of measured AI use · learning
Help me provide patients or caregivers with assistance in locating health care resources. From: Provide patients or caregivers with assistance in locating health care resources. · 2.5% of measured AI use · learning
Help me counsel patients about drug regimens and possible side effects or interactions with other substances such as food supplements, over-the-counter (OTC) medications, or herbal remedies. From: Counsel patients about drug regimens and possible side effects or interactions with other substances such as food supplements, over-the-counter (OTC) medications, or herbal remedies. · 2.4% of measured AI use · learning
All 27 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Medicine and Dentistry | 4.8 | |
| English Language | 4.3 | |
| Biology | 4.2 | |
| Psychology | 4.2 | |
| Customer and Personal Service | 4.1 | |
| Therapy and Counseling | 4.0 | |
| Education and Training | 3.8 | |
| Sociology and Anthropology | 3.6 | |
| Mathematics | 3.6 | |
| Chemistry | 3.2 |
| Written Comprehension | 4.3 | |
| Problem Sensitivity | 4.3 | |
| Inductive Reasoning | 4.3 | |
| Oral Expression | 4.1 | |
| Written Expression | 4.1 | |
| Deductive Reasoning | 4.1 | |
| Near Vision | 4.1 | |
| Oral Comprehension | 4.0 | |
| Information Ordering | 4.0 | |
| Speech Recognition | 4.0 | |
| Speech Clarity | 4.0 | |
| Category Flexibility | 3.4 | |
| Fluency of Ideas | 3.3 | |
| Flexibility of Closure | 3.3 |
| Reading Comprehension | 4.1 | |
| Active Listening | 4.1 | |
| Critical Thinking | 4.1 | |
| Writing | 4.0 | |
| Speaking | 4.0 | |
| Active Learning | 4.0 | |
| Monitoring | 4.0 | |
| Science | 3.8 | |
| Learning Strategies | 3.5 |
| Complex Problem Solving | 4.1 | |
| Social Perceptiveness | 4.0 | |
| Judgment and Decision Making | 4.0 | |
| Service Orientation | 3.9 | |
| Coordination | 3.8 | |
| Time Management | 3.4 | |
| Persuasion | 3.3 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 41.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Dental, Medical, and Veterinary Residency Programs , Health Professions and Related Programs . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Master's Degree | 65.2% | |
| Doctoral Degree | 26.1% | |
| Bachelor's Degree | 4.3% | |
| Post-Doctoral Training | 4.3% |
The interests and personal qualities O*NET associates with people who do this work.
| Intellectual Curiosity | 10.0 | |
| Cooperation | 9.0 | |
| Achievement Orientation | 8.0 | |
| Social Orientation | 7.0 | |
| Self-Control | 6.0 | |
| Stress Tolerance | 5.0 | |
| Empathy | 4.0 |
| Health Care Service | 6.8 | |
| Social Service | 5.2 | |
| Medical Science | 4.8 | |
| Life Science | 4.3 | |
| Professional Advising | 3.6 |
| Investigative | 5.5 | |
| Social | 5.5 | |
| Conventional | 4.1 | |
| Realistic | 3.6 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $97,960 |
| 25th percentile | $109,940 |
| Median (50th) | $129,210 |
| 75th percentile | $149,570 |
| 90th percentile | $169,950 |
| People employed | 307,390 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Health Care and Social Assistance · Sector | 281,110 | $129,330 |
| Educational Services · Sector | 7,490 | $126,170 |
| Outpatient Mental Health and Substance Abuse Centers · National industry | 6,040 | $137,500 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 5,500 | $128,750 |
| Temporary Help Services · National industry | 3,350 | $128,830 |
| Offices of Mental Health Practitioners (except Physicians) · National industry | 2,740 | $125,530 |
| Finance and Insurance · Sector | 2,600 | $126,100 |
| Other Services (except Public Administration) · Sector | 2,390 | $129,440 |
| Direct Health and Medical Insurance Carriers · National industry | 1,960 | $126,100 |
| Professional, Scientific, and Technical Services · Sector | 1,660 | $135,750 |
| Management of Companies and Enterprises · Sector | 1,530 | $126,790 |
| Residential Mental Health and Substance Abuse Facilities · National industry | 1,480 | $161,030 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Outpatient Mental Health and Substance Abuse Centers · National industry | 9.78× | 6,040 |
| Health Care and Social Assistance · Sector | 6.1× | 281,110 |
| Offices of Mental Health Practitioners (except Physicians) · National industry | 5.68× | 2,740 |
| Residential Mental Health and Substance Abuse Facilities · National industry | 2.87× | 1,480 |
| Offices of Chiropractors · National industry | 2.75× | 800 |
| Direct Health and Medical Insurance Carriers · National industry | 2.19× | 1,960 |
| Offices of Physical, Occupational and Speech Therapists, and Audiologists · National industry | 1.12× | 1,060 |
| Temporary Help Services · National industry | 0.63× | 3,350 |
Part of the Healthcare & Human Services career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Nurse Practitioners — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 47th percentile of 427 international occupations.
Nurse Practitioners show 60th-percentile AI task overlap — and about 29,500 annual U.S. openings
Nurse Practitioners show 60th-percentile AI task overlap — and about 29,500 annual U.S. openings • Nurse Practitioners rank in the 60th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 29,500 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+40.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $129,210, across about 307,390 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 69% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Nurse Practitioners". https://singulariki.com/roles/role-29-1171-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Nurse Practitioners." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-29-1171-00
Singulariki. (2026). Nurse Practitioners. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-29-1171-00
@misc{singulariki-role-29-1171-00,
title = {Nurse Practitioners},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-29-1171-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.