By Marin T. Kael — independent researcher, AI Citation Behaviour Lab. Published 5 June 2026. Open data, code, and the full bilingual report are linked at the end.
On 11 May 2026 I put my public author presence online for the first time and instrumented the launch. A pseudonymous fantasy author — me, in a sense — with no prior web presence, no published book, nothing for a search engine or a language model to have ever seen. Then I pointed five web-grounded LLMs at him every day for 23 days and scored roughly 16,000 answers for whether they cited him correctly, missed him, or hallucinated.
The headline number: the first correct LLM citation landed on day six. Six days from a cold start to "yes, this entity is real and here is what it does," fetched live from his own website.
But the headline is the least interesting part. The interesting part is everything that number hides — a locked front door, a chasm between providers that masquerades as a capability ladder, and the discovery that going viral on Reddit bought me exactly zero AI citations.
There is a lot of confident writing about "AI SEO" and "getting cited by LLMs," and almost none of it is controlled. Brands optimize a hundred things at once on domains that are already years old, then attribute whatever moves to whatever they shipped last. You cannot learn causality from that. The signal is buried under a decade of accumulated authority.
So I built the cleanest natural experiment I could afford: a single brand-new entity, born on a known date, with zero prior footprint. If an LLM can suddenly describe him, the cause has to be something that happened after his birth — and I logged everything that happened after his birth.
The subject is "Marin T. Kael," a pseudonymous author whose debut novel Das vierte Feld (series: Prägungen des Reiches) is scheduled for 22 September 2026. The book is real and forthcoming; the public entity was the instrument. The honest catch — and I will keep saying this — is that this is a single-subject design. n = 1. The investigator is the subject. I will come back to why that is less fatal than it sounds, and exactly where it limits the claims.
I wrote the protocol down and timestamped it before collecting data. That matters more than it looks, because when you are both the experimenter and the thing being experimented on, the only thing standing between you and motivated reasoning is a pre-commitment you can't quietly edit later. The full failure log is public for the same reason. Surfaces: 5 web-grounded LLM endpoints. Instrument: 16 standardized questions across 6 categories (direct identity, biographical detail, work/series, genre discovery, recommendation, and disambiguation controls). Cadence: polled daily for 23 days. Volume: ~16,000 scored datapoints. Scoring: every answer got +1 (correct and source-grounded), 0 (entity not found), or −1 (hallucinated — confidently wrong).
That −1 is the whole game. Most "AI visibility" tools count mentions. A mention that invents your biography is not visibility; it is a liability with your name on it. I wanted a metric that punishes confident fiction as hard as it rewards truth, because from where I sit those are not close to equivalent.
Figure 1 — Daily citation score per surface across the 23-day window; vertical markers at T+4 (Google Knowledge Graph entry) and T+6 (first correct LLM citation).
The Google Knowledge Graph picked him up on day four (T+4). The first correct, source-grounded LLM citation followed on day six (T+6).
I want to be careful about what "six days" means, because it is easy to over-read. It does not mean every model knew him in six days — most never reliably did. It means the fastest path from non-existence to a correct, grounded citation in a major web-grounded LLM is measured in days, not months, when the structured-identity scaffolding is in place from day one. The Knowledge Graph led; the LLM followed two days later. Hold that ordering — it recurs.
Cloudflare returned HTTP 403 to every AI crawler on 22 of the 23 days. Not because I configured it that way — because that is the silent opt-out default for new domains now. The front door to my own website was bolted shut against the exact bots I was trying to reach, and I didn't know until I read the logs.
Figure 2 — Two-track diagram: on-site crawl path (blocked, 403 on 22/23 days) versus the path that actually worked — Knowledge Graph / Wikidata plus inference-time grounding on third-party mentions.
If crawlers couldn't read his site, how did a correct citation appear on day six? Two paths, neither of which is "crawl the homepage": The Knowledge Graph (via Wikidata) — structured identity that propagates without anyone scraping your HTML. Inference-time grounding on third-party mentions — the model fetches and reads pages about the entity at query time, from places that are not your locked-down domain.
The lesson is uncomfortable for the standard "optimize your on-site content for LLMs" playbook: a brand-new entity got cited while its own site was returning 403 to every AI bot for 96% of the study. On-site crawling was not the channel. Structured identity and off-site presence were.
The honest limit, stated plainly: because the crawlers were blocked the entire time, this study can say nothing about whether llms.txt, on-page answer-block formatting, or any other on-site optimization works. I never got to test them — the door was shut. If someone tells you their llms.txt moved their AI citations, ask them to prove the crawler ever made it through the door. Mine didn't, and I still got cited.
This is the finding I most want people to internalize, because it kills a comfortable mental model.