Can AI Actually Write a Grant Proposal? What the Hallucination Research Says

Ask any large language model to help write a grant proposal and it will produce a problem statement, a set of objectives, and a polished executive summary in under a minute. The output reads like a finished proposal. Whether it actually is one is a different question — and the honest answer depends on which sentences you're looking at.

This isn't an argument for or against using AI in grant writing. It's an attempt to separate what current research on AI-generated fact fabrication actually shows from what gets repeated about it secondhand, and to lay out plainly where a freelance grant writer can lean on AI and where every word needs a human check before it reaches a funder.

What the hallucination research actually measured

The most concrete number in this conversation traces back to a paper called GhostCite, posted to arXiv in February 2026 by a research team led by Zuyao Xu. The study benchmarked 13 large language models on the task of generating citations across 40 research domains — 375,440 generated citations in total. Its finding, stated exactly: all 13 models fabricated citations, at rates ranging from 14.23% to 94.93% depending on the model and the domain (Xu et al., "GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models"). That wide spread — one model getting a citation right roughly 19 times out of 20, another getting it wrong almost as often — is itself part of the finding: hallucination rates vary enormously by model and by task, which makes any single figure a poor stand-in for "the" AI hallucination rate.

The Science Talk, a consultancy that advises European researchers on grant applications, cites this same range — 14% to 95% — in its own guidance on AI in grant writing, describing it as "research published in 2025" (The Science Talk, "AI in Grant Writing: What Works, What's Risky, and What European Funders Now Require"). Checking the paper directly turned up one small correction worth flagging: GhostCite was posted to arXiv in February 2026, not 2025. A minor point, but a useful reminder to check a primary source before repeating a number secondhand — exactly the habit this article is arguing for.

The more important correction is about scope, not date. GhostCite measured citation fabrication in academic and technical writing — computer science and security research papers, not grant proposals. No published study we could find has measured hallucination rates specifically inside AI-drafted grant proposals, foundation letters of inquiry, or budget narratives. So the honest way to use this number is not "AI gets facts wrong in grant proposals X% of the time." It's this: the same underlying failure — a model stating a specific, checkable fact with full confidence, and being wrong — has been measured at meaningful scale in an adjacent high-stakes writing task, and there's no reason to assume grant narratives are immune to it. If anything, a grant proposal regularly asks for exactly the kind of specific, checkable claim — a funder's past giving history, a named collaborating organization, a statistic about the problem being solved — where this failure mode shows up.

What this looks like from the nonprofit side

FreeWill, a company that builds fundraising tools for nonprofits, publishes its own guidance on AI grant writing aimed at the U.S. nonprofit sector. It doesn't cite a specific hallucination percentage, but it names the same failure mode in plain terms: "AI models frequently hallucinate—meaning they generate completely made-up statistics, stories, or outcomes with high confidence. Submitting unverified details can permanently damage your credibility with funders" (FreeWill, "Can you use AI for grant writing? What you need to know", by Patrick Schmitt). FreeWill's guidance also names two risks worth taking as seriously as fabricated facts: publicly available AI tools may use what you type into them to train their models, so sensitive financial records, staff data, or donor details shouldn't go into a public chat window; and a model trained on historical data can reproduce non-inclusive language or stereotypes if a draft goes out unedited.

FreeWill's core recommendation lands on the same principle that shows up in nearly every credible source on this topic, phrased as a workflow rule: "Never copy and paste AI outputs directly into a grant proposal. Establish a rigid mandatory review process where your team fact-checks every statistic and refines the narrative tone" (FreeWill, same source). That's not a claim about any specific tool — it's the human-in-the-loop principle that this article, The Science Talk's guidance, and FreeWill's guidance all converge on independently, whether the intended reader is a European research scientist or U.S. nonprofit development staff.

Funders are already writing rules about this

This isn't a hypothetical risk for some future proposal cycle. Several funders already have formal AI policies in place, and they differ enough from each other that "check the specific funder's current guidelines" is the only safe default, not a throwaway line.

On the federal research side, NIH's policy — effective for the September 25, 2025 receipt date and afterward — states plainly: "Applications that are either substantially developed by AI or containing sections substantially developed by AI are not considered the original ideas of applicants and will not be considered by NIH" (National Institutes of Health, "Apply Responsibly: Policy on AI Use in NIH Research Applications"). NIH's stated reason for the policy was a spike in individual investigators submitting an unusually high number of applications, some apparently AI-generated — the fix targets volume and originality, not AI-assisted editing specifically, but the line between the two isn't always obvious to a reviewer reading a submitted application.

On the foundation side — closer to the funders most freelance grant writers actually work with — the Spencer Foundation, a U.S. education-research funder, now requires every applicant to acknowledge a generative-AI policy before submitting: "While submitting your application, you will be asked to acknowledge that you have read, understood, and agree to comply with this policy" (The Spencer Foundation, "Applicant Information and Policies"). And in Europe, Horizon Europe now requires explicit disclosure of AI tool use on its Standard Application Form, with the ERC issuing separate guidance covering both applicants and the reviewers evaluating their proposals (The Science Talk, "AI in Grant Writing"). Horizon Europe and the ERC don't fund U.S. nonprofits, so that example won't apply directly to most readers here — but it's a concrete illustration of where funder policy is heading, not just a hypothetical.

Three funders, three different rules, no common standard between them. The only reliable move is the same one this site has recommended for other funder-specific questions: read that particular funder's current guidelines before drafting anything with AI, rather than relying on a general article — including this one.

Where AI genuinely earns its place in the process

None of this argues for avoiding AI in grant writing, and neither of the two sources cited above makes that argument either. The Science Talk's guidance, aimed at researchers applying for competitive European grants, draws a specific line: AI is well-suited to literature synthesis and background research, drafting non-technical sections such as dissemination plans and team-composition narratives, translating technical content into plain-language summaries, and structural editing of a draft you've already written. It should not replace your own thinking on "the scientific vision, the originality of the research idea, and the specific claims about your methodology and expected results," because experienced reviewers notice when that part of a proposal reads as generic (The Science Talk, "AI in Grant Writing"). FreeWill's guidance draws a similar line for the nonprofit sector: AI can reasonably draft background language, summaries, and outlines, but a "winning grant proposal" still has to be tailored to the specific project, funder, and organization in a way that only the person who actually knows them can verify (FreeWill, "Can you use AI for grant writing?").

That's the same design principle this site's toolkit is built on: each prompt is meant to turn details you already have — your program notes, a funder's own guidelines text, a previous draft — into a faster first draft of a specific section, not to invent the underlying facts on its own.

A verification habit that holds up under deadline pressure

The advice above is easy to agree with and easy to skip when a proposal is due in six hours. A few concrete habits, drawn from both sources above:

• Treat every specific, checkable fact in an AI draft — a funder's past grant amount, a cited study, a partner organization's name, a statistic about the problem — as unverified until you've confirmed it against a primary source, not an AI-generated summary of one.
• Before drafting anything with AI, check that specific funder's current guidelines for an AI use or disclosure policy. Some, like NIH and the Spencer Foundation, already publish one; assume others might, even if they haven't advertised it.
• Never paste a client's donor list, financial records, or program participants' personal information into a public AI tool without confirming its data-handling policy first — the tool doesn't need to hallucinate anything to create a real privacy problem.
• Read the finished draft aloud before it goes out. The sections that still sound generic — usually the organizational track record and the programmatic vision — are the ones an experienced reviewer is most likely to flag, and the ones most worth rewriting in your own voice.
• If an AI tool hands you a list of funders, statistics, or citations without a source you can click through and check yourself, treat it as a set of leads, not a finished answer.

None of this is a reason to avoid AI in grant writing, and it isn't a reason to trust it blindly either. The research on citation hallucination — wide-ranging, model-dependent, and not measured directly in a grant-writing context — points to one plain conclusion that doesn't need a precise percentage to be true: an AI model can produce a specific, false, entirely plausible-sounding fact with full confidence, and the only real defense is checking it yourself, every time, before it reaches a funder.