Generative AI floods and dilutes the market for books
Not on the map. Why?
Abstract (arXiv)
Generative AI can produce book-length works of fiction at near-zero cost. These books are often dismissed as low-quality ``slop'' that buyers will ignore, and are assumed to carry little commercial weight. We test that assumption with full-text AI detection across 14,419 self-published genre-fiction books sold on Amazon from 2023 to 2026, matched to daily sales records through June 2026. None of these books disclose whether or not they contain AI-produced content. We find that books for which we detected substantial AI text (> 25%) make up a large share of the catalog but a smaller share of sales. Even so, they reach commercial scale, winning a growing share of sales over time and taking more of the scarce top-rank positions once held by books with no detected AI text. Over this period, the number of books with observed sales in a quarter grew 19.2-fold, while quarterly revenue grew only 8.9-fold. The market therefore added selling books faster than it added revenue, and revenue per selling book fell across most genres. Books with no AI text lose the most ground in genres with high AI diffusion, and most of all where Kindle Unlimited availability is high. Among top-selling books, those with substantial AI text draw on more distinctive language from existing books than do books with no AI text; for these books overlap rises with revenue, a gradient we do not detect for books with no AI text. Generative AI can thus reshape a creative market through scale rather than quality. Our results bear directly on the market-effect question at the center of the fair use defense to copyright infringement.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 22 Jul 2026.
Builds on
11 of 27 references.
References · 11
- 2023Art or Artifice? Large Language Models and the False Promise of Creativitycited by 172
- 2024AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Textcited by 42
- 2025People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated textcited by 40
- 2025OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokenscited by 27
- 2025AI use in American newspapers is widespread, uneven, and rarely disclosedcited by 10
- 2026Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High-Quality Bookscited by 9
- 2026StoryScope: Investigating idiosyncrasies in AI fictioncited by 9
- 2025Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writerscited by 8
- 2025Measuring AI "Slop" in Textcited by 6
- 2026Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Modelscited by 6
- 2026AI Fiction in the Wildcited by 2
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Citing papers per year: 2026: 2.
2 papers.