3.7×
as many AI-isms as news, pre-ChatGPT
Press releases wrote like this before any model did
1.61 against 0.44 uses per 1,000 words in pages from 2021 and earlier.
Entered for the defence · ScriptGrain SGR-007
The Court of Public Prose
In re Dead Writing Theory
- Case No.
- DWT-002
- Matter
- Where the AI-isms came from
- Filed
- Verdict
- Verdict for the defence
Not an invention. An inheritance.
- The question
- Before ChatGPT, which kinds of human writing already used the words and habits now called AI-isms most, and when did each become common?
- The sample
- 17,000 pieces of writing from before ChatGPT: 6,600 web pages in 16 kinds of writing, 103 homepages, 10,000 arXiv abstracts, 600 passages from pre-1928 books, Google Books 1800 to 2019
- Filed
- The study
- ScriptGrain SGR-007 Ours: made by the same studio that runs this site.
§ IFindings of fact
What the study found.
- Before ChatGPT, press releases used AI-isms such as "innovative", "pivotal" and "seamless" 3.7 times as often as news articles: 1.61 against 0.44 uses per 1,000 words in pages from 2021 and earlier (ScriptGrain, 2026).
- Words that studies of scientific writing found language models over-use, including "innovative", "advancements", "pivotal", "delve", "meticulous", "underscore", "comprehensive", "potential", were most common in press releases in writing from before ChatGPT.
- The sentence shapes associated with AI writing, such as "not X but Y" and reflexive lists of three, were most frequent in pre-1928 speeches (2.07 per 1,000 words) and sermons and religion (1.94), against 0.45 in UK news.
- "Delve" was rare in 2021 news everywhere: it appeared on 3 of 600 Nigerian news pages, 1 of 600 Kenyan, and none of 1,000 UK and US pages.
- In English-language books, "innovative" went from 0.14 to 12.12 uses per million words between 1950 and 1980; "transformative" rose 31 times between 1980 and 2019, long before ChatGPT.
- Before ChatGPT, "delve" appeared in about 1 in 5,287 arXiv abstracts (1,057,471 abstracts from 2015 to November 2022 with no later revision); only 3.5% contained any of the twelve words later identified as 2024 AI tells.
- "Delve" was already 3.0 times as common in English-language books in 2019 as in 1980 (Google Books).
- 40 of 43 words now called AI-isms were more common in English-language books in 2019 than in 1980.
- Of 26 kinds of writing measured, a Q&A forum (English Stack Exchange) used AI-isms least: 0.04 per 1,000 words, 37 times less than press releases.
§ IIThe court reporter's account
Where the AI-isms came from.
By Jack Stovell
Case 2 asks a nastier question. If the machine voice is a machine’s invention, where was it before the machine? Study SGR-007 went looking in 17,000 pieces of writing, every one from before ChatGPT.
The first finding is almost funny. Press releases used AI-isms 3.7 times as often as news did, 1.61 against 0.44 per 1,000 words. The corporate announcement was already talking like that, years before any chatbot learned to.
Then the sentence shapes. The ones everyone calls AI turned up most in pre-1928 speeches, at 2.07 per 1,000 words, and in sermons, at 1.94. UK news managed 0.45. So the “machine” rhythm looks a lot like something people were doing from a pulpit or a podium, to a crowd, long before anyone typed a prompt.
The words tell the same story. Of 43 words now called AI-isms, 40 were more common in books in 2019 than in 1980. They were already rising, quietly, in print.
Last, the “delve came from Nigerian English” story. It didn’t hold up: 3 of 600 Nigerian news pages used it, 1 of 600 Kenyan, 0 of 1,000 UK and US.
The verdict goes to the defence. The AI voice looks less like an invention than an inheritance: the vocabulary of corporate announcements laid over the shapes of older speeches and sermons. The machine, it seems, learned from us. And we weren’t exactly a great teacher.
§ IIIThe exhibits entered in this case
Prosecution v. Defence
Any tally counts the evidence items below; it is not a measure of truth.
Evidence for the theory
For the prosecution
0 exhibitsNo exhibit entered for the prosecution in this case.
Evidence against the theory
For the defence
4 exhibitsExhibit D-4Case No. DWT-002 Exhibit D-5Case No. DWT-002 2.07
per 1,000 words in pre-1928 speeches
The "AI sentence shapes" were most at home in old speeches and sermons
Sermons and religion 1.94; UK news 0.45.
Entered for the defence · ScriptGrain SGR-007
Exhibit D-6Case No. DWT-002 40 / 43
more common in 2019 than 1980
40 of 43 "AI-isms" were already rising in books
Google Books. "Transformative" rose 31 times between 1980 and 2019.
Entered for the defence · ScriptGrain SGR-007
Exhibit D-7Case No. DWT-002 3 / 600
Nigerian news pages used "delve" (2021)
The "delve came from Nigerian English" story did not hold up
1 of 600 Kenyan, 0 of 1,000 UK and US. Rare everywhere; the difference was within chance.
Entered for the defence · ScriptGrain SGR-007
§ IVThe finding
The finding
The "AI voice" looks less like an invention than an inheritance: the vocabulary of corporate announcements laid over the rhetorical shapes of older speeches and sermons.
§ VQuestions for the court
Asked and answered.
Where did the AI-isms come from?
From human writing. Before ChatGPT, press releases used AI-isms 3.7 times as often as news (1.61 against 0.44 uses per 1,000 words), and the sentence shapes called AI were most frequent in pre-1928 speeches (2.07 per 1,000 words) and sermons (1.94).
Were AI-isms rising before ChatGPT?
Yes. 40 of 43 words now called AI-isms were more common in English-language books in 2019 than in 1980. "Transformative" rose 31 times between 1980 and 2019.
Did "delve" come from Nigerian English?
The evidence does not support it. In 2021 news, "delve" appeared on 3 of 600 Nigerian pages, 1 of 600 Kenyan and none of 1,000 UK and US pages: rare everywhere, and the difference was within chance.
How was it measured?
17,000 pieces of writing from before ChatGPT: 6,600 web pages in 16 kinds of writing, 103 homepages, 10,000 arXiv abstracts, 600 passages from pre-1928 books and Google Books 1800 to 2019. Counted with deterministic code, never a language model.