Will AI replace Licensed Practical and Licensed Vocational Nurses?
Most of the work in Licensed Practical and Licensed Vocational Nurses still leans on things AI struggles with — research rates its theoretical AI reach at only ~25%, and real-world use lower still.
O*NET-SOC 29-2061
How your 15 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~25%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~25%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 15 tasks, ratedBased on real task-level AI scores — click to collapse
- Record food and fluid intake and output.
- Observe patients, charting and reporting changes in patients' conditions, such as adverse reactions to medication or treatment, and taking any necessary action.
- Measure and record patients' vital signs, such as height, weight, temperature, blood pressure, pulse, or respiration.
- Answer patients' calls and determine how to assist them.
- Evaluate nursing intervention outcomes, conferring with other healthcare team members as necessary.
- Prepare or examine food trays for conformance to prescribed diet.
- Prepare patients for examinations, tests, or treatments and explain procedures.
- Administer prescribed medications or start intravenous fluids, noting times and amounts on patients' charts.
- Provide basic patient care or treatments, such as taking temperatures or blood pressures, dressing wounds, treating bedsores, giving enemas or douches, rubbing with alcohol, massaging, or performing catheterizations.
- Supervise nurses' aides or assistants.
- Work as part of a healthcare team to assess patient needs, plan and modify care, and implement interventions.
- Assemble and use equipment, such as catheters, tracheotomy tubes, or oxygen suppliers.
- Collect samples, such as blood, urine, or sputum from patients, and perform routine laboratory tests on samples.
- Help patients with bathing, dressing, maintaining personal hygiene, moving in bed, or standing and walking.
- Apply compresses, ice bags, or hot water bottles.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.