SAFE
The core of this job is catching a frightened loose dog in traffic, entering a hoarding house, restraining an injured raccoon, and reading a tense doorstep conversation with an owner — none of which robotics or language models can touch. AI will absorb the paperwork tier: citation drafting, cruelty case narratives, license records, call triage and dispatch routing. The exposure is not automation but municipal budgets, which already keep this occupation small and understaffed.
Roughly flat across the period, with year-to-year wobble.
Median pay $37,590 → $45,660 -2.8% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+3.9% 12,200 → 12,700 on the projections basis
Hard to automate, and growing
The work resists current AI and the BLS projects +3.9% more of these jobs by 2034. Note that safe does not mean well paid — several of the fastest-growing resistant occupations are among the lowest paid on the register.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~1,300 openings a year on average, including replacing people who leave.
Animal CopDog WardenDog CatcherHumane AgentAnimal OfficerAnimal RescuerHumane OfficerControl OfficerAnimal AttendantAdoption CounselorAnimal Safety OfficerWildlife RehabilitatorAnimal Services OfficerCode Enforcement OfficerAnimal Control SpecialistAnimal Instructor OfficerCommunity Service OfficerAnimal Enforcement OfficerCode Enforcement InspectorAnimal Cruelty InvestigatorCode Enforcement SpecialistWildlife Removal SpecialistACO (Animal Control Officer)Animal Treatment Investigator
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Noosing a fear-biting stray from under a porch, sweeping a hoarding house for 40 cats while judging which are salvageable, and setting a leg-hold trap for a coyote that has learned the neighborhood are the actual shift — the digitizable slice is limited to the citation forms, bite-report entry and impound records you finish after the truck is back.
Hands-on in uncontrolled environments You work at 2am on a shoulder of a state highway, in crawlspaces, in urine-soaked living rooms, hauling a 90-pound dog into a truck box, with rabies exposure and dog bites as routine occupational injuries; the only reason this is 19 and not 20 is the dispatch radio and report writing that happen sitting down.
Certification preferred, not legally required Most states require euthanasia certification and chemical-capture/NACA training, and in many jurisdictions you carry limited peace-officer authority to issue citations and swear complaints — but the licence is a training credential, not a professional licence you can lose your livelihood over, and the city attorney and department, not you, defend the seizure in court.
Meaningful discretion You decide on the doorstep whether an animal is a dangerous-dog seizure or a warning, whether a dog is in immediate distress justifying warrantless entry under state cruelty statutes, and whether a suffering animal is euthanized on scene — real discretion with due-process and Fourth Amendment consequences, though state cruelty codes, quarantine schedules and department policy set the frame around those calls.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (16/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (10/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 34 of this occupation's 69 points (49%).
Embodiment (19/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 80/100, still SAFE.
Post-incident litigation and consent-decree pressure on municipal seizure decisions (Fourth Amendment claims over animal seizures, dangerous-dog determination appeals) pushing cities to formally vest the seize/no-seize and dangerous-dog designation call in a named officer with documented reasoning, rather than treating it as a dispatch outcome. Also body-camera adoption spreading from police to ACOs, which makes the officer's on-scene call reviewable and therefore owned.
Task-mix shift: this occupation has a genuine two-tier structure. If dispatch triage, citation drafting, cruelty-narrative writing, license/rabies-tag records and public inquiry response are absorbed by municipal AI systems, the residual day is almost entirely field capture, hoarding-house entry, owner confrontation and court testimony — raising the share of work AI cannot do. Note the risk: the same shift lets a city cut headcount while raising per-worker resistance.
State-level mandatory certification for animal control officers with statutory authority to seize animals and sign sworn cruelty affidavits — e.g. expansion of the NACA/state ACO certification regimes (Illinois, Florida, Ohio already have training mandates) into a licensure model where only the certified officer may execute a warrantless seizure or euthanasia decision, and is personally named in the case file. Rabies-exposure quarantine and euthanasia orders already require a named officer or veterinarian signature in most state health codes; tightening that to bar AI-generated determinations without officer countersignature would harden the shield.
Weak but real route: if humane societies and SPCAs contracting with cities market officer-delivered field response as a welfare guarantee (the shift away from 'dog catcher' toward community-support models, e.g. Human Animal Support Services network), residents and donors may specifically fund human field officers over automated call handling. This is philanthropic and reputational, not a consumer premium, so the ceiling is low.
The limit. Embodiment at 19 and task_resistance at 16 are near maximum; there is almost no headroom on the capability side. The binding constraint on this occupation is municipal appropriations, not AI, and no dimension in this register tracks budget. A high score here can coexist with the job count falling if cities consolidate animal control into police patrol or contract it out.
| Los Angeles-Long Beach-Anaheim, CA | 430 | $62,810 +38% |
| New York-Newark-Jersey City, NY-NJ | 400 | $55,790 +22% |
| Dallas-Fort Worth-Arlington, TX | 350 | $45,690 +0% |
| Chicago-Naperville-Elgin, IL-IN | 210 | $49,370 +8% |
| Atlanta-Sandy Springs-Roswell, GA | 200 | $42,840 -6% |
| St. Louis, MO-IL | 180 | $45,540 +0% |
| Boston-Cambridge-Newton, MA-NH | 170 | $63,140 +38% |
| Riverside-San Bernardino-Ontario, CA | 160 | $59,870 +31% |
| Seattle-Tacoma-Bellevue, WA | 80 | $78,750 +72% |
| San Francisco-Oakland-Fremont, CA | 120 | $77,540 +70% |
| San Jose-Sunnyvale-Santa Clara, CA | 30 | $74,260 +63% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 69. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.