EXPOSED
Cooperative extension agents spend much of their time producing fact sheets, newsletters, grant reports, and workshop curricula — text work that generative AI drafts at usable quality, and farmers can now query an AI directly for the agronomic lookup questions that once required a call to the county office. What survives is the part that requires boots in a field: walking a soybean stand to diagnose a disease, running a pesticide-safety or food-preservation demonstration, and holding decades of trust with specific farm families who take advice from a known person, not a chatbot. Scored for the modal county-level agent; research-station specialists sit somewhat higher.
This fall is concentrated in 2020 and has not recovered since.
Median pay $49,710 → $60,220 -3.1% 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
-2.5%
Percentage only. The projection counts a different population from the 8,220 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Shrinking, but not obviously because of AI
The BLS projects -2.5% by 2034, but at 51/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
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.
~1,100 openings a year on average, including replacing people who leave.
Teacher4-H AgentFarm AgentHome AgentCounty AgentFarm AdvisorFeed AdvisorHome Advisor4-H Club AgentHome EconomistExtension AgentFarm ConsultantExtension WorkerExtension OfficerFarm DemonstratorSmart Home Expert4-H Youth EducatorAdjunct InstructorAgricultural AgentCommunity EducatorCooking InstructorExtension EducatorCounty DemonstratorExtension Specialist
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half the job — soil-test interpretation, budget worksheets, 4-H curriculum writing, monthly newsletters, needs assessments, and USDA/NIFA reporting — is text and spreadsheet work AI now drafts, while on-farm scouting visits, hands-on canning and pesticide-applicator trainings, and county fair judging still need a person present, which lands it at 11 rather than in either extreme.
Some physical or field component A county agent drives a truck to farms in mud and heat, pulls soil cores, inspects livestock and pest damage, and sets up demonstration plots, but the majority of hours are still spent in an office at a desk or in a climate-controlled meeting room, so it sits at 12 rather than up with field crop workers.
No licence, no signature requirement No state licence gates this work — a bachelor's or master's in agriculture plus a state pesticide-applicator or ServSafe credential is the norm, and extension advice is delivered as land-grant recommendation, not certified professional opinion, so nobody's signature carries personal liability if a nitrogen rate underperforms.
Meaningful discretion Advice is anchored to published land-grant recommendations, label rates, and USDA program rules, but the agent independently decides what a failing field actually needs, whether a family's finances warrant a program referral, and when a food-safety practice is unsafe enough to shut down — real discretion within someone else's guidelines, hence 10.
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 (11/20 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 (4/20) is whether the law requires a licensed human to sign. Trust premium (14/20) is whether buyers specifically pay for a person. Judgment and accountability (10/20) 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 28 of this occupation's 51 points (55%).
Embodiment (12/20) 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.
Chefs and Head Cooks SAFE
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 69/100 — SAFE.
Task-mix shift: once fact sheets, newsletters and grant reports are drafted by AI, the residual role is field diagnosis, on-farm trial design, and pesticide/food-preservation demonstration — genuinely a second tier here. Raises task_resistance only if headcount is held (state and federal Smith-Lever/county appropriations sustained) rather than the county office being consolidated
State pesticide-applicator and food-safety training rules that require a certified, named instructor of record to sign off on hands-on competency (e.g. state lead agencies' EPA-approved certification plans under the revised 40 CFR 171, and FSMA Produce Safety Rule / Preventive Controls 'qualified individual' training that must be delivered by an AFDO/PSA-listed trainer) tightened so AI-generated or self-paced modules cannot substitute for the practical exam — making the extension agent the personally accountable signer on the certificate
Formal role in disaster and disease response — county agents designated in state emergency plans as the on-ground verifier for USDA disaster program loss documentation, or for regulated pest/disease quarantine scouting (HPAI, citrus greening, spotted lanternfly), where the call triggers destruction or payment
Extension agents added as required credentialed writers/verifiers of nutrient management or manure management plans in state CAFO permitting (as some states already restrict plan writing to certified specialists), so the agronomic recommendation carries a named liable author
Fee-for-service or contract farm-visit programs (already used by some land-grant systems and by UK/AHDB-style paid advisory models) where growers or ag lenders pay specifically for an independent, non-vendor human agronomic opinion to counter input-dealer bias
The limit. Funding, not capability, is the binding constraint: this is a small appropriated workforce, and county-office consolidation can cut headcount even as the surviving job gets more judgment-heavy. Liability levers apply mainly to the certification-and-permitting slice of the work, not to the general education mission.
| Raleigh-Cary, NC | 720 | $64,280 +7% |
| Madison, WI | 660 | $60,920 +1% |
| San Juan-Bayamon-Caguas, PR | 320 | $53,620 -11% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 230 | $79,780 +32% |
| Blacksburg-Christiansburg-Radford, VA | 120 | $51,670 -14% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 110 | $49,990 -17% |
| Ames, IA | 100 | $60,840 +1% |
| Des Moines-West Des Moines, IA | 90 | $43,720 -27% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 230 | $79,780 +32% |
| Richmond, VA | 40 | $78,900 +31% |
| Virginia Beach-Chesapeake-Norfolk, VA-NC | 40 | $77,870 +29% |
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 51. 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.