The uneven rise of AI-generated web content
Abstract
In less than five years, generative artificial intelligence (AI) has dramatically reshaped the public-facing web. Using a sample of more than 5 million crawlable webpages, we demonstrate that the share containing AI-generated text has increased precipitously, but unevenly. Our analyses reveal substantial differences in AI composition across language and domain categories (e.g., .edu, .com, or .gov). A higher fraction of English-language webpages contain AI-generated text compared to pages in other languages, and prevalence is substantially lower on .gov and .edu domains compared to .com. These distinctions persist even after accounting for compositional effects. We also find heterogeneity based on a page's position in the connectivity structure of the web. AI-generated text is most common and has increased most rapidly in pages that are the least central in the graph, which drives much of the overall growth in AI-generated text. A slower increase has taken place for more central pages, with the most central tier emerging as the least likely to contain AI-generated text. Methodologically, we use multiple AI detection tools, and generate estimates using a Bayesian hierarchical model that incorporates corrections for the detectors' statistical properties (both false positives and false negatives) and allows for different detector performance across groups, while propagating uncertainty in detector error rates into prevalence estimates. Our work suggests that AI is a prominent, but varied, contributor to public information and internet-based communication.
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BibTeX
@misc{visokay2026uneven,
title = {{The uneven rise of AI-generated web content}},
author = {Adam Visokay and Soumyakanti Pan and Molly Griston and Polina Iasakova and Mark Nepf and Denis Peskoff and Riya Sinha and Jikai Sun and Isabelle Thiara and Letao Wang and Yiwei Wu and Lauren Yan and Tyler H. McCormick},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36631},
eprint = {2610.36631},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36631},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The uneven rise of AI-generated web content
AU - Adam Visokay
AU - Soumyakanti Pan
AU - Molly Griston
AU - Polina Iasakova
AU - Mark Nepf
AU - Denis Peskoff
AU - Riya Sinha
AU - Jikai Sun
AU - Isabelle Thiara
AU - Letao Wang
AU - Yiwei Wu
AU - Lauren Yan
AU - Tyler H. McCormick
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36631
UR - https://arxiv.org/abs/2610.36631
AB - In less than five years, generative artificial intelligence (AI) has dramatically reshaped the public-facing web. Using a sample of more than 5 million crawlable webpages, we demonstrate that the share containing AI-generated text has increased precipitously, but unevenly. Our analyses reveal substantial differences in AI composition across language and domain categories (e.g., .edu, .com, or .gov). A higher fraction of English-language webpages contain AI-generated text compared to pages in other languages, and prevalence is substantially lower on .gov and .edu domains compared to .com. These distinctions persist even after accounting for compositional effects. We also find heterogeneity based on a page's position in the connectivity structure of the web. AI-generated text is most common and has increased most rapidly in pages that are the least central in the graph, which drives much of the overall growth in AI-generated text. A slower increase has taken place for more central pages, with the most central tier emerging as the least likely to contain AI-generated text. Methodologically, we use multiple AI detection tools, and generate estimates using a Bayesian hierarchical model that incorporates corrections for the detectors' statistical properties (both false positives and false negatives) and allows for different detector performance across groups, while propagating uncertainty in detector error rates into prevalence estimates. Our work suggests that AI is a prominent, but varied, contributor to public information and internet-based communication.
ER -