The uneven rise of AI-generated web content
Adam Visokay, Soumyakanti Pan, Tyler McCormick and colleagues (2026 Summer Institute in Computational Social Science at the University of Washington, UC Berkeley) estimate how much of the crawlable web contains AI-generated text, using more than 5 million pages from 2019 to 2026 and a Bayesian correction for detector errors.
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Prevalence. In August 2026 about 24% of crawlable webpages (95% credible interval 22.9 to 24.4) contained AI-generated or AI-edited text, up from near zero before 2022.
Acceleration. The rate of increase nearly doubled, from 4.6 points per year between early 2023 and early 2025 to 8.1 points per year since.
Where it grows. About four-fifths of the increase comes from domains on the periphery of the web graph. The most central tier is the least likely to contain AI text. Prevalence is higher on English pages than other languages and much lower on .gov and .edu than .com.
Method. A Bayesian hierarchical model combines several AI detectors with labeled benchmarks and pre-2022 control crawls, estimating prevalence jointly with detector sensitivity and specificity and propagating that uncertainty.
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.