Sometimes the modern web feels as though thousands of unrelated pages hired the same copy editor.
The words recur. The paragraph rhythms recur. The same polite transitions and tidy three-part explanations appear in product pages, newsletters, essays, documentation, and corporate announcements that have no obvious connection to one another.
Large language models offer one plausible mechanism for some of that sameness.
A 2025 paper in the Proceedings of COLING examined unusually rapid changes in scientific English and identified a set of words whose increased frequency was consistent with large-language-model use. The most famous example was “delve,” alongside words such as “intricate” and “underscore.” See Why Does ChatGPT “Delve” So Much?.
The interesting part is not that one word supposedly exposes a robot. It does not.
Shared generators can create shared habits
Language models do not choose every valid phrase with equal probability. Training, fine-tuning, system prompts, preference optimization, and common user instructions can all push output toward certain constructions.
Then publishing scale amplifies the bias.
If one person uses a model to draft one article, a recurring phrase is trivia. If thousands of publishers use closely related systems to generate millions of pages, small stylistic preferences can become visible across the web.
This produces a strange cultural effect: sites built by unrelated organizations can begin to sound related.
The result resembles standardization. Introductions converge. Explanations arrive in similar shapes. Conclusions become reassuring. Certain adjectives become fashionable almost overnight.
Style is evidence of influence, not proof of authorship
This is where detection gets slippery.
Humans imitate fashionable language. Editors impose house styles. Search optimization rewards structures that competitors then copy. Templates produce repeated phrasing. A human who reads a great deal of AI-assisted prose may begin using some of the same language without ever pressing a generate button.
So finding “delve” on a page does not establish that a machine wrote it. Neither does a tidy em dash, a three-item list, or a sentence beginning with “It is important to note.”
The defensible claim is broader: widespread use of similar generative systems can introduce measurable lexical patterns into published language.
That matters to Dead Internet Theory because people often describe the modern web as feeling strangely homogeneous before they can explain why.
Some of that feeling may come from business consolidation, search optimization, platform templates, and shared cultural trends. Some may come from millions of writers using the same handful of models as invisible collaborators.
The web does not have to be fake to become stylistically flatter.
It only needs enough people—and machines—to keep reaching for the same words.