Last reviewed by Robert Prime — June 2026
Readers have started asking AI what to read next. The obvious question for any author — does AI recommend my book? — turns out to have a measurable answer, and we built the instrument to measure it: the AI Book Discoverability Index, a continuously-updated public index of every source the AI engines cite when recommending books.
This guide walks through what the data shows — and what to do about it.
How the index works
We ask the four citation-transparent engines — ChatGPT, Claude, Gemini and Perplexity — the questions real readers ask: "best [genre] books", "books like X", "what should I read about…". Every source they cite gets logged. As of 10 June 2026 the index holds 91,000+ live citations across 916 audited books in 16 genres, and it grows with every audit (now 141,282+ live citations as of July 2026). Numbers trace to live citations, not model memory — and the methodology is published alongside the data: simulated-assistant runs are excluded, a domain must be cited for at least two different books to rank, and any domain sourced ≥70% from one title is excluded.
Finding 1: A quarter of books are invisible
27% of audited books are never named by any engine, for any reader question. The median AI Shelf Score is 17/100, and only 3% of books are recommended reliably by name. AI doesn't have a long tail — it has a short shelf, and most books aren't on it.
Finding 2: Reddit barely matters for books
Across the wider web, Reddit is among AI's most-cited domains. For book recommendations it's under 1% — rank #11 of 5,881 sources at the time of writing in our live index. The engines build book answers from Goodreads, Wikipedia, YouTube and specialist best-of lists instead. If you've been told to "win Reddit" for AI visibility, the data says: for books, put that effort into the sources AI actually cites.
Finding 3: Retailers aren't where AI learns
Every bookshop trails the reference and curation sources. AI cites where it learns about books — review sites, best-of lists, reference pages — far more than where readers buy them. A strong Amazon listing matters for conversion; it barely registers for AI discovery.
Finding 4: Every genre has a kingmaker
Each genre has niche sites that punch far above their weight — the hidden gatekeepers AI trusts for that shelf. In personal finance, a credit union's "best money books" list is cited across 54 different audited books. In true crime it's CrimeReads; in young adult, ReadBrightly. The per-genre pages name the top sources for every genre — that's an author's submission map.
What to do with this
- Check where you stand. The free 90-second check tests your book across 2 of the 5 engines; the full £29.99 audit runs all 5 (ChatGPT, Claude, Perplexity, Gemini and a labelled Amazon Rufus simulation).
- Target your genre's sources. Open your genre's index page and work the top-10 list — those are the places citations come from.
- Fix the basics AI reads. A recognisable author entity (Goodreads, Wikidata), structured data, and a plain-spoken description — covered in our AI book discovery guide.
The index updates continuously — explore the live data.
