Share and intensity of work current AI systems can materially affect.
SOC 33-3031
Fish and Game Wardens AI displacement risk
Patrolling lakes, forests, and backcountry to enforce wildlife law is fieldwork no drone completes: checking licenses, investigating poaching, and operating alone in remote areas. Camera traps and tracking data inform patrols the warden still conducts.
Likely potential for exposed tasks to move to software after workflow integration.
Conservation technology extends surveillance coverage, but enforcement requires physical presence, legal authority, and evidence handling. Remote terrain and small agency staffing make this one of the least automatable public safety roles.
Distribution
Where Fish and Game Wardens sits across 620 tracked roles
Displacement pressure 12 — higher than 2% 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.
23 O*NET task statements matched to SOC 33-3031. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $74,060 (May 2025, US national). The latest BLS row matched SOC 33-3031.
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 Fish and Game Wardens
SOC 33-3031 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 12/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
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 Fish and Game Wardens
The current evidence import matched 23 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 tasks23
SOC33-3031
- Core task / ID 20586
Participate in search-and-rescue operations.
- Core task / ID 20582
Compile and present evidence for court actions.
- Core task / ID 7918
Protect and preserve native wildlife, plants, or ecosystems.
- Core task / ID 7915
Patrol assigned areas by car, boat, airplane, horse, or on foot to enforce game, fish, or boating laws or to manage wildlife programs, lakes, or land.
- Core task / ID 20583
Investigate hunting accidents or reports of fish or game law violations.
- Core task / ID 7921
Provide assistance to other local law enforcement agencies as required.
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
Patrol assigned areas and enforce laws
Exposure 16, automation 5%, augmentation 28%.
O*NET evidence: Patrol assigned areas by car, boat, airplane, horse, or on foot to enforce game, fish, ... (ID 7915)
Investigate violations and accidents
Exposure 26, automation 9%, augmentation 48%.
O*NET evidence: Investigate hunting accidents or reports of fish or game law violations. (ID 20583)
Compile evidence for court actions
Exposure 42, automation 19%, augmentation 58%.
O*NET evidence: Compile and present evidence for court actions. (ID 20582)
Protect wildlife and ecosystems
Exposure 18, automation 6%, augmentation 36%.
O*NET evidence: Protect and preserve native wildlife, plants, or ecosystems. (ID 7918)
TaskExposureAutomationAugmentation
Patrol assigned areas and enforce laws165%28%
Investigate violations and accidents269%48%
Compile evidence for court actions4219%58%
Protect wildlife and ecosystems186%36%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowGame Warden Captain
Training horizon: 3-6 months. Skill overlap 76. Wage preservation signal 118.
- Lead district patrols
- Manage investigations
- Coordinate with federal agencies
credentialed transition
LowWildlife Biologist
Training horizon: 12-24 months. Skill overlap 52. Wage preservation signal 104.
- Complete biology coursework
- Build field research experience
- Learn population survey methods
Comparison guides
Compare the next move before you commit
3-6 months
Fish and Game Wardens to Game Warden Captain
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Fish and Game Wardens into Game Warden Captain.
12-24 months
Fish and Game Wardens to Wildlife Biologist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Fish and Game Wardens into Wildlife Biologist.
What the AI risk score means for Fish and Game Wardens
The displacement pressure score for Fish and Game Wardens is 12. 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. Compile evidence for court actions carries 19% automation pressure, while Compile evidence for court actions 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: $74,060 (May 2025, US national). Employment context: Conservation law enforcement in remote terrain. Typical education: Bachelor's degree common; academy training required.
Wage vulnerability is 44, while transition feasibility is 60. 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
- Remote fieldwork resists automation
- Conservation demand is stable
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Fish and Game Wardens, 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.
Field enforcement
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.
Investigation
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.
Evidence documentation
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.
Wildlife law enforcement
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 Game Warden Captain, such as lead district patrols.
- By 90 days, compare internal openings and external postings for Game Warden Captain or Wildlife Biologist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Fish and Game Wardens
Will AI replace Fish and Game Wardens?
Patrolling lakes, forests, and backcountry to enforce wildlife law is fieldwork no drone completes: checking licenses, investigating poaching, and operating alone in remote areas. Camera traps and tracking data inform patrols the warden still conducts. 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 Fish and Game Wardens work are most exposed to AI?
Compile evidence for court actions and Investigate violations and accidents show the strongest automation pressure in this model. Compile evidence for court actions and Investigate violations and accidents are better treated as AI-augmented work.
What should Fish and Game Wardens learn next?
Start with Field enforcement, Investigation, Evidence documentation. The most practical adjacent paths in this model are Game Warden Captain and Wildlife Biologist.
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
OECD / 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