The AI tell is installed during fine-tuning. Humanizer tools edit the output instead.

In December 2024 two Florida State University linguists, Tom Juzek and Zina Ward, tried to answer a question that had become a joke among heavy readers of model output: why does ChatGPT say "delve" so much ^[1]. Their method was elimination rather than assertion, and the candidate left standing has direct consequences for anyone running a rewrite pass over machine text before it ships.

First they tested whether these words are simply frequent in the training corpus. Ruled out ^[2]. Then whether something in the architecture or the next-token mechanics favours them. Also ruled out ^[3]. What remained, after comparing a base version of Llama 2 against the same model fine-tuned with human feedback, was the fine-tuning stage itself: somewhere in humans rating outputs as good or bad, "delve" started winning ^[4].

The size of the resulting shift has been measured separately. A team led by Dmitry Kobak at the University of Tübingen ran over 15 million PubMed abstracts from 2010 to 2024 through a method borrowed from epidemiology, excess mortality repurposed as excess vocabulary, to separate ordinary frequency drift from anomalies ^[5]. "Meticulously" rose 137% year over year, "intricate" 117%, "commendable" 83% ^[6]. Kobak's group estimates at least 13.5% of 2024 biomedical abstracts show signs of LLM involvement ^[7], roughly one abstract in seven ^[9], a larger vocabulary shift than COVID produced ^[8]. Worth keeping the two studies separate: Kobak measures the shift, while the fine-tuning explanation for it remains the FSU team's own finding, unreplicated so far ^[10].

It is also decaying. A follow-up from the same FSU team found "delve", "boast" and "meticulous" turning up more often in ordinary spoken language, in podcasts and YouTube talks, as people who read a lot of AI text absorb its vocabulary ^[11].

Now look at what the tooling does. The most-starred project in this space has over 36,000 stars and is a single markdown file ^[12]. It does real work: it protects ordinary formal vocabulary from being flagged, refuses to invent facts mid-rewrite, and matches the user's own writing sample rather than imposing one house style ^[13]. Underneath, it checks a passage against roughly three dozen fixed patterns ^[14], which works out to about a thousand stars per pattern ^[15]. A second tool opens by citing false-positive research on detectors and says its signals are "worth acting on; not worth ruining someone's day over" ^[16], which is honest framing over the same genre of banned-word list.

At the other end, one tool instructs its model that "if even one of these words appears, the text immediately flags as machine-written" ^[17], with robust, scalable, integrated and proactive on the list, words that are often simply correct in technical writing ^[18]. Another caps em dashes at "Maximum ONE per 500 words" with no stated methodology, and tells the model to apply its rules silently, never mentioning them to the writer ^[19][20]. Word lists are cheap to build and fast to go stale ^[21].

Watch for a replication of the FSU fine-tuning result, and for whether the spoken-language bleed keeps widening. If the vocabulary keeps migrating into human speech, every list-based detector and every list-based evader degrade together.