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Grant writer / grants officer
Finds funding, writes applications to funders, keeps them within each funder's rules and reports on what the money achieved. Generative AI is now common in the writing: a study of US federal research proposals found language-model use rising sharply from 2023. Funders have answered with rules: the US National Institutes of Health will not treat applications substantially developed by AI as the applicants' own ideas and capped each investigator at six applications a year after seeing some submit more than 40 in one round, and UK Research and Innovation forbids generating whole applications or sections without human involvement. Most of this evidence comes from research funding rather than charities. The US projects fundraiser jobs, where grant writers are counted, to grow 6 percent to 2035, without mentioning AI.
List the funders you apply to and check what each says about AI in applications.
This is not a probability of losing your job. It combines how much of the role's task load is exposed to automation with how far adoption has actually gone — useful for comparing occupations on one consistent basis, and for nothing else.
Written for grant writers, grants officers and research development staff in charities, universities and research teams. Authors, copywriters and technical writers have their own pages. The evidence is a US labour projection for fundraisers, two research funders' rules on AI in applications, a study of language-model use in US federal research proposals and a medical school's tool for checking proposal wording; it comes mostly from research funding, and it establishes how funders constrain AI-written applications and how widely AI is used in proposals, not how grant writers' numbers or pay have changed.
What is actually changing#
The unit of analysis is the task, not the job title. A role is not replaced — its task mix shifts.
Each tile is one task. Its size is how much of the job it is; its colour is where the task is heading. Click a tile to see what the judgement does not establish.
The absence of a record and one funder's cap; it does not measure whether search tools change how opportunities are found.
Research funders' rules and a study of researchers' proposals; they do not measure grant writers in charities or how much of their drafting AI does.
One institution's tool, built and evaluated by its own authors; it does not show how widely such tools are used.
The absence of a record; it does not measure whether AI tools help draft reports.
Is this your job? Say so and this page narrows to your share of it.
A job title is a bundle of tasks bought together, and no two people hold the same bundle. Nothing is sent anywhere — it stays in this browser.
Read all 4 tasks in full — direction, reasoning and limits →
Recent changes#
United States. The statistics bureau projects fundraiser employment to grow 6 percent from 2025 to 2035, from 140,900 to 148,700, with about 10,000 openings a year. O*NET lists grant writer among this occupation's titles. The page does not mention artificial intelligence or automation.
A named person with standing publicly predicted something, on a date, in an attributable statement. It is recorded so that who said what, and when, stays checkable — and it never moves a task's assessment, because a prediction is not an observation. Its value arrives later: the record sits on the same page as the evidence about that occupation, so anyone reading the forecast reads the record of what happened next beside it. That is the reckoning; this site publishes no verdict on whether a forecast came true.
United States. Combining confidential NSF and NIH proposal submissions from two large research universities with all public NSF and NIH awards, the study finds language-model use rising sharply from 2023 with a split between minimal and substantive use; higher use is associated with less distinctive projects, and with proposal success at NIH but not at NSF. It measures the researchers who apply, not specialist grant writers.
Measured, large-scale use of a tool for real work, where the decision to use it was the worker's rather than an employer's. It is more than a capability record — the work is real, not a demo — and less than a deployment record, because no employer put it into production, required it, or built a process around it. Weighted `cautious`: `automating` means the machine can do the task AND there are adoption signs, and this is an adoption sign — but usage can be experimental, and much of the measurement comes from a party with a stake, so one record is never enough and two independent ones are. Note who is counting. Vendor telemetry sees this directly and sells the tool, so such a record names that stake in its scope; a statistics agency asking firms whether their workers use AI in tasks sees the same channel with no stake at all, and that is the better source where it exists.
United States. A medical school built a secure tool combining rules and language models to detect grant language restricted by evolving state and federal policies and suggest alternatives; it reached precision 1.00, recall 0.73 and F1 0.84, ahead of general-purpose models, and 25 faculty and staff rated its usability highly. Built and evaluated by its own developers at one institution.
Small-scale trial in a real setting. Tells us the deployment conditions are being tested, not that they hold — so one pilot is never enough on its own; two independent ones are.
United States. The research funder says it will not consider applications that are substantially developed by AI, or contain sections substantially developed by AI, to be original ideas of applicants, and may refer AI detected after award for possible research misconduct. Having seen some investigators submit more than 40 distinct applications in a single round, it limits each principal investigator to six new, renewal, resubmission or revision applications per calendar year, from the September 25, 2025 receipt date. A research funder's rule; it binds applicants, not grant staff by name.
Regulation, subsidy or public procurement is requiring or funding adoption — the mirror of a constraint. It shows adoption is being required, not that it has happened, so one mandate is never enough on its own; two independent ones are.
United Kingdom. The funder's policy lets applicants use generative AI with caution but says they must not use it to generate an entire application, or sections of an application, without human involvement, that applications are expected to be transparent where it was used, and that assessors must not use it in assessment except to refine their language. A research funder's rule; it binds applicants and assessors.
Regulation, subsidy or public procurement is requiring or funding adoption — the mirror of a constraint. It shows adoption is being required, not that it has happened, so one mandate is never enough on its own; two independent ones are.
What this means for you#
If you are starting out, expect AI to draft first versions and funders to ask whether you used it. Build what funders reward and AI cannot claim: knowing your organisation's work, fitting it to each funder's priorities, and keeping applications within the rules.
Expect faster first drafts and more rules on declaring AI use. Strategy, funder relationships and compliance stay with you.
Your options#
Four directions, each with its real constraints and one thing you can test this week. Continuing as you are is a legitimate choice — it just has to be a chosen one.
Become the person who knows each funder's AI rules
Funders now set different rules on AI use and disclosure, and an application that breaks them can be rejected.
The rules differ by funder and change often, so this needs steady reading.
List the funders you apply to and check what each says about AI in applications.
Stay in strategy and funder relationships
Caps on applications and rules on originality make choosing and shaping fewer, stronger applications worth more.
Relationship work depends on the organisation trusting you with its strategy.
List which of your applications last year were funded and what the successful ones had in common.
Move into research development or grants management
Universities and research institutes need staff who manage compliance, budgets and reporting across many grants.
It needs knowledge of research funding systems and often institutional experience.
Look up research development or grants officer roles at a university near you.
Common questions#
Not on present evidence. AI is widely used in drafting, but funders now limit it: NIH will not treat substantially AI-developed applications as original, and UKRI forbids whole applications generated without human involvement. The US projects fundraiser jobs, which include grant writers, to grow 6 percent from 2025 to 2035.
We do not answer that with a number of years. Watch what the funders you apply to say about AI-written applications, and whether caps like NIH's change how many applications organisations send. Those tell you more than any date.
It depends on the funder. UKRI lets applicants use it but not to generate whole applications or sections without human involvement; NIH will not treat applications substantially developed by AI as the applicants' original ideas. Check each funder's own policy.
NIH said it had seen some investigators use AI to submit more than 40 distinct applications in a single round, and capped each investigator at six applications a year from September 2025.
What these judgements rest on#
2 of 4 task judgements on this page are backed by a verified event and 2 are platform inference, each labelled where it appears. Behind them sit 1 technology dimensions, a reconstructed trajectory since language models reached the public, and 5 verified events.
See which technologies, how it got here, and the method →
Where it sits in the official classification: skills, knowledge, related jobs →
Other roles in the same function#
A company divides its work into functions before it divides it into jobs. These sit in Technology & data alongside this one — a fact about org charts, not a judgement that they are similar or that they are changing in the same direction.
Junior software developer · Experienced software engineer · Frontend developer · Backend developer · Data engineer · Machine learning engineer · AI researcher · Software tester / QA engineer · Data analyst · Data scientist · Statistician · Business analyst · Business systems owner · IT support specialist / helpdesk · Security analyst (SOC) · DevOps engineer / platform engineer / SRE · Network engineer · Technical writer / documentation engineer