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Proposal pack · RO-LD09-03 v1

Is the diversity of what reaches the market falling, or only diversity inside experiments?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-09 debate on 15 October 2026, not a finding. Not yet reviewed. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld09-03/v1/.

Not yet measured A PhD project Needs a budget (medium or higher) Partly addressed

Research question

In markets for creative and intellectual work, such as self-published fiction, has the diversity of published works, of works that sell and of works that reach the top ranks changed since generative AI spread, beyond what changes in volume explain?

Why it matters

Experiments show that work made with AI is more alike within a task, and one platform study shows average novelty falling while peak novelty rises. Readers and buyers meet a market, not an experiment. If markets select for distinct work, pool-level convergence may not reach the shelf; if AI-assisted work floods the catalogue and the top ranks, market diversity could fall even where individual works are rated well.

The debate lists this among what we still do not know: Does market-level diversity (books, apps, published papers) fall, or only diversity within experiments?

Evidence that motivates it

  1. On a large art-sharing platform, artists who adopted text-to-image AI raised their output by 25% and the favourites their works received per view by 50%; average content novelty and visual novelty fell over time, while peak content novelty rose and favourites became less concentrated among adopters. (LD09-SL-C11 v1.0)
    On an art platform, adopters' average novelty fell while peak content novelty rose and favourites became less concentrated.
    Cites: Zhou & Lee (2024), “Generative artificial intelligence…”
  2. In a preregistered online experiment, writers given one or up to five story ideas from GPT-4 wrote stories rated 5.4% and 8.1% more novel and 3.7% and 9.0% more useful than writers without AI, with the largest gains for less creative writers; but their stories were 5.2% and 5.0% more similar to the AI ideas and more similar to each other. (LD09-SL-C10 v1.0)
    AI-assisted stories were rated better and were more alike in an experiment.
    Cites: Doshi & Hauser (2024), “Generative AI enhances individual…”
  3. Kaggle competition code from 2019 to mid-2026 has become more alike in its literal syntax, but the author finds no evidence of homogenization in the approach or intent of the code. (LD09-SL-C33 v1.0)
    Code converged in syntax but not, by one measure, in approach.
    Cites: Burtch (2026), “The Hitchhiker’s Guide to Monoculture: AI…”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Build a panel of self-published genre fiction from before 2022 to 2026 with full text, release dates, genre and sales rank. Measure diversity at three levels (all releases, books with sales, top-ranked books) on plot and theme (structured summaries coded and validated by blind readers) and on style (embeddings), separately. Compare the trend in each genre with the genre's estimated share of AI-written text, holding the number of releases fixed by resampling.

Data needed

Full texts or long samples of published books with sales data; a validated distribution-level estimator of AI text; reader coding of themes.

Feasibility

A PhD project. Medium; data access and reader coding are the main costs. Copyright limits sharing the texts; the analysis can share derived features only. AI detection at the book level is uncertain and should be treated as a share, not a label.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 15 October 2026; not yet checked in depth).

The nearest study measures dilution of sales and revenue per book by AI-written books in self-published fiction, and language overlap with existing books, but not the diversity of themes and plots across the catalogue or the top ranks.

Where the search could not reach. Screened by two web searches and an arXiv record lookup on the date shown; cultural-economics journals were not searched, and no study of app markets was found. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld09_03_v1,
  author = {{SocioLens project}},
  title = {Is the diversity of what reaches the market falling, or only diversity inside experiments?},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld09-03/v1/}},
  note = {Research opportunity RO-LD09-03, version v1}
}

@misc{ro_ld09_03_nearest_1,
  title = {Chakrabarty, Liu, Ginsburg \& Dhillon, "Generative AI floods and dilutes the market for books," arXiv:2607.20349 (2026), working paper under review},
  url = {https://arxiv.org/abs/2607.20349},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_03_nearest_2,
  title = {Zhou \& Lee, art-platform study (LD09-SL-C11)},
  url = {https://doi.org/10.1093/pnasnexus/pgae052},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld09_03_nearest_3,
  title = {Slendebroek \& Metaxa, "The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization," AIES (2026)},
  url = {https://ojs.aaai.org/index.php/AIES/article/view/43312},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_zhou_lee_pnas_nexus_2024,
  title = {Zhou \& Lee, “Generative artificial intelligence, human creativity, and art,” PNAS Nexus 3, pgae052, 2024},
  url = {https://doi.org/10.1093/pnasnexus/pgae052},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_doshi_hauser_science_advances_2024,
  title = {Doshi \& Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances 10, eadn5290, 2024},
  url = {https://doi.org/10.1126/sciadv.adn5290},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_burtch_monoculture_arxiv_2607_13077,
  title = {Burtch, “The Hitchhiker’s Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semantics,” arXiv:2607.13077, 2026},
  url = {https://arxiv.org/abs/2607.13077},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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