Words that sound like AI: 40 words and the plain alternative

Because a model's default word is the one that fits the most contexts. A word like "pivotal" can describe a meeting, a study, a year or a hire without the writer deciding what made it so; a word like "turning point" at least implies a before and after. Weighted abstractions are safe choices for a system that has to be plausible on every subject, and that is why they cluster in generated text.

The statistical version of this was measured by Liang and colleagues in 2023. Seven detectors flagged 61.22 percent of 91 human-written TOEFL essays as AI, the flagged essays had significantly lower perplexity, meaning more predictable word choice, and a vocabulary enrichment prompt cut the false positive rate to 11.77 percent (Liang et al. 2023, arXiv 2304.02819, read 9 November 2026). Predictable vocabulary is what detectors read; the words below are what predictable vocabulary looks like to a person.

Every word is quoted because each is the subject here, not a recommendation. The alternative column gives the plain word or the kind of fact that should replace it.

Three absences deserve a line. Several words readers associate with chatbots are not in the table because this site's own style rules keep them off the page in any context; the blader README's example list covers them. Their absence here is a publishing constraint, not a judgment that they read as human.

More than one, and in a short span. A single "pivotal" in a 600-word article is a word choice. Four importance words, two field metaphors and "underscore" in the same 150 words is a pattern, and a reader will feel it before naming it. The blader README's rule for weak tells applies: a pattern counts "only when several tells share a passage, because a careful writer may use any one of them on purpose" (read 9 November 2026).

GPTZero's technology page describes a premium feature to "detect AI vocabulary" alongside its sentence-by-sentence classifier and says its model shields against paraphrasing meant to make AI text look human (GPTZero, technology page, read 9 November 2026). So at least one commercial detector does keep a word list. Its main instrument is still statistical, which is why swapping the 40 words is a readability edit first and a detector edit a distant second.

The fact the word was standing in for. Most of the 40 are placeholders: "pivotal" holds the place of the consequence, "landscape" holds the place of the market, "underscore" holds the place of the evidence. The plain alternative in the table is the minimum; the better edit is to answer the question the weighted word avoided.

Work through the groups in order. Importance words first, because cutting them forces you to say why something matters or admit it does not. Metaphors next, replacing each with the thing it pictured. Verbs of avoidance last, since "shows" for "underscores" is a one-word swap with no meaning risk. The manual humanizing checklist places this vocabulary pass last among its 20 edits for the same reason: it moves the least meaning for the time it takes.

If you want a tool for the word pass, the free humanizer list sets out which ones accept how many words without an account, with every limit checked on 8 November 2026. Give the tool text whose facts are already in place, then read what comes back against the original, because a word swapper cannot tell "significant" the statistic from "significant" the adjective.

No detector score should be the only evidence in a misconduct case, and a word list is weaker evidence than a detector score. The guides hub holds the rest of this series.