Share and intensity of work current AI systems can materially affect.
SOC 29-1151
Nurse Anesthetists AI displacement risk
Anesthesia information systems and smart pumps automate monitoring records, but inducing, maintaining, and emerging patients from anesthesia is minute-to-minute clinical judgment with life-safety accountability. One of the most automation-resistant clinical roles.
Likely potential for exposed tasks to move to software after workflow integration.
Monitoring technology has improved for decades while CRNA autonomy expanded. The liability and real-time physiology judgment of anesthesia keep a licensed clinician at the head of the bed, regardless of tooling.
Distribution
Where Nurse Anesthetists sits across 620 tracked roles
Displacement pressure 14 — higher than 8% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
24 O*NET task statements matched to SOC 29-1151. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $236,590 (May 2025, US national). The latest BLS row matched SOC 29-1151.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Nurse Anesthetists
SOC 29-1151 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 14/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Nurse Anesthetists
The current evidence import matched 24 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks24
SOC29-1151
- Core task / ID 18361
Manage patients' airway or pulmonary status, using techniques such as endotracheal intubation, mechanical ventilation, pharmacological support, respiratory therapy, and extubation.
- Core task / ID 18365
Respond to emergency situations by providing airway management, administering emergency fluids or drugs, or using basic or advanced cardiac life support techniques.
- Core task / ID 18362
Monitor patients' responses, including skin color, pupil dilation, pulse, heart rate, blood pressure, respiration, ventilation, or urine output, using invasive and noninvasive techniques.
- Core task / ID 18366
Select, order, or administer anesthetics, adjuvant drugs, accessory drugs, fluids or blood products as necessary.
- Core task / ID 18370
Select, prepare, or use equipment, monitors, supplies, or drugs for the administration of anesthetics.
- Core task / ID 18367
Assess patients' medical histories to predict anesthesia response.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Administer anesthetics and manage airways
Exposure 16, automation 5%, augmentation 32%.
O*NET evidence: Manage patients' airway or pulmonary status, using techniques such as endotracheal intu... (ID 18361)
Monitor patient responses continuously
Exposure 26, automation 11%, augmentation 52%.
O*NET evidence: Monitor patients' responses, including skin color, pupil dilation, pulse, heart rate, b... (ID 18362)
Assess histories to predict response
Exposure 34, automation 13%, augmentation 58%.
O*NET evidence: Assess patients' medical histories to predict anesthesia response. (ID 18367)
Respond to intraoperative emergencies
Exposure 12, automation 3%, augmentation 26%.
O*NET evidence: Respond to emergency situations by providing airway management, administering emergency... (ID 18365)
TaskExposureAutomationAugmentation
Administer anesthetics and manage airways165%32%
Monitor patient responses continuously2611%52%
Assess histories to predict response3413%58%
Respond to intraoperative emergencies123%26%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowChief CRNA
Training horizon: 3-8 months. Skill overlap 76. Wage preservation signal 110.
- Lead anesthesia teams
- Own quality and outcome metrics
- Manage OR scheduling coordination
credentialed transition
LowPain Management Specialist
Training horizon: 6-12 months. Skill overlap 64. Wage preservation signal 104.
- Complete pain fellowship training
- Build regional anesthesia expertise
- Develop clinic protocols
Comparison guides
Compare the next move before you commit
3-8 months
Nurse Anesthetists to Chief CRNA
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Nurse Anesthetists into Chief CRNA.
6-12 months
Nurse Anesthetists to Pain Management Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Nurse Anesthetists into Pain Management Specialist.
What the AI risk score means for Nurse Anesthetists
The displacement pressure score for Nurse Anesthetists is 14. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Assess histories to predict response carries 13% automation pressure, while Assess histories to predict response carries 58% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $236,590 (May 2025, US national). Employment context: Advanced practice anesthesia role among the highest-paid nursing specialties. Typical education: Doctoral degree plus CRNA certification.
Wage vulnerability is 22, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Very low displacement pressure
- Among the highest-paid nursing roles
- Safety accountability is immovable
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Nurse Anesthetists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Anesthesia management
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Airway procedures
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Physiological monitoring
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Emergency response
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Chief CRNA, such as lead anesthesia teams.
- By 90 days, compare internal openings and external postings for Chief CRNA or Pain Management Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Nurse Anesthetists
Will AI replace Nurse Anesthetists?
Anesthesia information systems and smart pumps automate monitoring records, but inducing, maintaining, and emerging patients from anesthesia is minute-to-minute clinical judgment with life-safety accountability. One of the most automation-resistant clinical roles. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Nurse Anesthetists work are most exposed to AI?
Assess histories to predict response and Monitor patient responses continuously show the strongest automation pressure in this model. Assess histories to predict response and Monitor patient responses continuously are better treated as AI-augmented work.
What should Nurse Anesthetists learn next?
Start with Anesthesia management, Airway procedures, Physiological monitoring. The most practical adjacent paths in this model are Chief CRNA and Pain Management Specialist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics