EXPOSED
The written half of this job — grant proposals, appeal letters, donor research briefs, LYBUNT segmentation, acknowledgment letters, impact reports — is exactly what current AI drafts at usable quality, and prospect-screening tools already rank wealth capacity better than a human scanning records. What survives is the part that requires a face across a table: cultivating a major donor over two years, judging when and how much to ask, and being the person a family trusts with a legacy gift. Modal worker here is a mid-level development officer whose week splits between screen work and donor meetings, so the job compresses toward relationship management rather than disappearing.
Headcount grew steadily across the period.
Median pay $57,970 → $72,550 +0.1% 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
+4.3%
Percentage only. The projection counts a different population from the 111,040 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +4.3% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~10,200 openings a year on average, including replacing people who leave.
FundraiserGrant WriterDonor OfficerDonor SpecialistGrant CoordinatorGrants SpecialistCampaign FundraiserDevelopment OfficerDonation SpecialistFundraising OfficerMajor Gifts OfficerCommunity FundraiserDonation CoordinatorNonprofit FundraiserPhilanthropy OfficerAnnual Giving OfficerContract Grant WriterDevelopment AssociateFundraising AssociateFundraising ConsultantFundraising SpecialistPlanned Giving OfficerDevelopment CoordinatorDonor Relations Officer
Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier At 7 the balance tips just past the automatable line: the grant calendar, appeal copy, wealth-screening scores, moves-management data entry and gift acknowledgments are all machine-draftable, and what holds the number above 6 is the irreducible cultivation visit, the board-member-plus-prospect lunch, and the live ask where the number changes mid-conversation.
Some physical or field component A 7 reflects that donor meetings, site tours, galas and regional travel put you off the desk several days a month, but the environments are boardrooms, homes and hotel ballrooms rather than anything uncontrolled — nobody's manual dexterity is load-bearing beyond a handshake and a check handoff.
No licence, no signature requirement CFRE is voluntary and no state requires a licence to solicit — the charity itself files the state charitable-solicitation registration and the Form 990 Schedule G, and it is the CFO and board who sign, so a 2 rather than 0 only because paid-solicitor registration statutes in some states name individuals.
Meaningful discretion An 11 covers real discretion with a supervisor above it — you decide the ask amount, the timing after a spouse's death, whether to accept a restricted or non-cash gift, and when to walk away from a donor whose conditions compromise the mission — but gift-acceptance policies, the campaign plan and the ED's sign-off bound the largest 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 (7/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (15/20) is whether buyers specifically pay for a person. Judgment and accountability (11/20) 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 42 points (67%).
Embodiment (7/20) 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.
Personal Financial Advisors EXPOSED
Real Estate Brokers EXPOSED
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 54/100, still EXPOSED.
Task-mix shift is genuine here: if grant writing, acknowledgments, LYBUNT segmentation and research briefs are fully absorbed by AI, the residual job is major-gift solicitation, planned-giving conversations, and board/volunteer management — work with no usable AI substitute. The role's score rises because the automatable tier is gone, not because AI got worse.
Major and planned giving already turns on a named human relationship; donor-facing disclosure norms would sharpen this. Watch for state charitable-solicitation regulators (NY AG Charities Bureau, California AB 488-style registration rules for platforms) or AFP's Code of Ethical Standards adding a requirement that AI-generated donor communications be disclosed as such — which makes unmarked human contact the premium product.
Gift-acceptance decisions under reputational ambiguity — declining or returning tainted donations (Sackler, Epstein-type cases) — are increasingly formalized in written gift acceptance policies where a named development officer recommends to the board. If institutions push that named-recommender role into policy, the role owns a consequential, defensible call.
Narrow route only: professional certification (CFRE) plus state registration of paid solicitors already exists, and some states require a registered individual solicitor to be named and bonded. If registration rules were extended so that a named registered fundraiser must attest to the accuracy of solicitation materials — including AI-drafted appeals — a thin personal-liability hook appears. Nothing like this is currently in motion for in-house development staff.
The limit. Even with every lever, this stays a mid-scoring occupation: there is no licensure gate, no physical work, and the headcount justification has always been ratio-of-dollars-raised, which AI-assisted teams can hit with fewer people. The likely outcome is a smaller, more senior occupation with higher per-person scores rather than a more resistant one at current size.
| New York-Newark-Jersey City, NY-NJ | 9,520 | $82,180 +13% |
| Boston-Cambridge-Newton, MA-NH | 4,620 | $80,340 +11% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4,450 | $80,610 +11% |
| Los Angeles-Long Beach-Anaheim, CA | 4,230 | $77,550 +7% |
| Chicago-Naperville-Elgin, IL-IN | 3,540 | $72,550 +0% |
| San Francisco-Oakland-Fremont, CA | 3,020 | $82,930 +14% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,960 | $74,370 +3% |
| Seattle-Tacoma-Bellevue, WA | 2,630 | $80,920 +12% |
| San Jose-Sunnyvale-Santa Clara, CA | 1,060 | $106,760 +47% |
| Merced, CA | 40 | $84,570 +17% |
| Logan, UT-ID | 110 | $84,090 +16% |
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 42. 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.