The Impact of AI-Generated Text on the Internet
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Abstract (arXiv)
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative developments (sometimes subsumed under the Dead Internet Theory). What has hindered answering these questions is that it has not been understood just how much of the internet is actually AI-generated or AI-edited. To this end, we construct a representative sample of websites published on the internet between 2022 and 2025 using the Internet Archive, and apply a state-of-the-art AI text detector on them. We find that by mid-2025, roughly 35% of newly published websites were classified as AI-generated or AI-assisted, up from zero before ChatGPT's launch in late 2022. We also find statistically significant evidence for some of the identified hypotheses; for example, that increases in AI-generated text on the internet correlate negatively with semantic diversity and positively with the prevalence of positive sentiment. We do not, however, find statistically significant evidence supporting the hypothesis that an increased rate of AI-generated text on the internet decreases factual accuracy or stylistic diversity. Notably, this diverges from public perception, which we measure in a user study, where the majority of US adults turned out to believe in all four of the above-mentioned hypotheses. Individuals who do not use AI or use it infrequently tend to believe in these negative impacts more than those who use it frequently; similarly, individuals who hold negative views of AI tend to believe in these hypotheses more than those with favorable views of the technology.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 47 of its 80 references that are in the map. First posted on arXiv 14 Apr 2026.
Builds on
47 of 80 references.
References · 47
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35 more
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Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026Pangram 4 Technical Reportcited by 3
- 2026The Future of Facts: Tracing the Factual Generation-Verification Gapcited by 1
- 2026Iterative Finetuning is Mostly Idempotentcited by 0
- 2026Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attributioncited by 0
- 2026The Fairness Collapse Phenomenon: Bias Amplification in Language Models Trained on Synthetic Datacited by 0
- 2026You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come Fromcited by 0
- 2026You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come From21 Sep 2026
- 2026The Fairness Collapse Phenomenon: Bias Amplification in Language Models Trained on Synthetic Data4 Aug 2026
- 2026Pangram 4 Technical Report29 Jul 2026
- 2026Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution16 Jul 2026
- 2026The Future of Facts: Tracing the Factual Generation-Verification Gap26 May 2026
- 2026Iterative Finetuning is Mostly Idempotent1 May 2026