The Words That Instantly Signal AI-Generated Text

You can usually spot AI-generated text in the first two sentences. Not because the grammar is wrong or the facts are off, but because certain words keep showing up. Words that no working writer would actually reach for, but that AI models have learned to treat as sophisticated.

Here is a list of the biggest offenders, why they happen, and what to swap them with.

Why AI keeps reaching for the same words

Language models are trained on enormous amounts of text, much of it from academic papers, corporate reports, and content marketing. Those genres have their own verbal tics. Words like "delve," "foster," and "underscore" appear constantly in that training data, so models reproduce them constantly.

It is a frequency problem. The model has seen "delve into" thousands of times in authoritative-sounding contexts, so it keeps reaching for it when it wants to sound authoritative. The fix is not to sound less authoritative. It is to use the words a confident writer would actually use.

The most recognizable words and phrases

"Delve"

Nobody uses "delve" in conversation or in good prose. It sounds like a word chosen to imply depth without actually going there. "We will delve into the topic of..." tells you nothing about whether the depth is real.

Swap it for: "look at," "examine," "dig into," "cover."

"Testament" and "tapestry"

"This is a testament to human ingenuity." "A rich tapestry of perspectives." Both phrases land with a hollow thud. They feel like something a motivational poster would say.

Real writers use concrete evidence instead of calling something a testament. If human ingenuity produced something impressive, describe what it produced.

"Seamless" and "robust"

These are product-marketing words that AI has imported into general prose. A "seamless experience" means nothing without specifics about what would otherwise have been awkward. A "robust framework" is not more convincing than "a framework" unless you explain what makes it strong.

"Leverage," "unlock," and "elevate"

All three arrived in business writing years ago and have been slowly draining of meaning ever since. AI has accelerated that drain. "Leverage your skills to unlock new opportunities and elevate your career" is a sentence that contains zero information.

"In today's world" and "in today's fast-paced environment"

These are AI's favorite throat-clearing phrases. They exist to set up a contrast ("in today's world, things have changed") but they rarely deliver on that setup. The observation that the world exists is not an insight.

Cut it. Start with the actual point.

"It is worth noting that..."

This phrase is almost always filler. If it is worth noting, note it. The announcement that you are about to say something noteworthy does not make the thing more noteworthy.

Words that soften when specificity is needed

AI hedges with words like "various," "numerous," "several," "certain," and "many" when a number would be cleaner. "Various studies suggest" is weaker than "Three studies from the past two years suggest." The vaguer phrasing sounds like the model is not sure of its facts (and often it isn't), so it covers with broad strokes.

A before / after example

Here is a paragraph straight from an AI writing assistant, lightly edited only to remove the client name:

Before: "In today's competitive landscape, it is crucial to leverage your brand's unique strengths to foster authentic connections with your audience. By delving into consumer behavior, you can unlock new opportunities and deliver a seamless experience that serves as a testament to your commitment to quality."

That paragraph has eight AI tells in four sentences.

After: "Your brand has specific things it does well. Figure out which of those things your audience actually cares about, then build your messaging around those points. Study what your buyers do, not what they say they do, and you will find the gaps worth filling."

The rewrite is shorter, says more, and reads like a person wrote it. None of the words are impressive on their own, but that is exactly the point.

The checklist: words to search before you publish

Run a find-and-replace on these before any AI-assisted piece goes out — the whole list takes under two minutes:

• delve / delving
• tapestry
• testament
• landscape (when used figuratively, as in "the marketing landscape")
• seamless
• robust (unless you are describing a physical structure)
• leverage (as a verb)
• unlock (as a metaphor)
• elevate (as a metaphor)
• foster
• garner
• underscore (as a verb meaning "emphasize")
• navigate (when used figuratively)
• pivotal / crucial / vital (pick one, and only when genuinely true)
• in today's world / in today's [adjective] environment
• it is worth noting
• serves as / stands as
• not only... but also
• various / numerous / several / certain (replace with a number when possible)

This is not an exhaustive list. But clearing these from a piece will remove the majority of the signals that readers and editors pick up on. For a complete editing pass, the free humanizer prompt at /humanizer-prompt runs through these and more in a single step.

What makes these words cluster together

One observation that surprises people: AI-written text is often identifiable not just by individual word choices but by how those words combine. "Delve" rarely appears alone. It shows up with "into the intricacies," or "into the nuances," or "into the complexities of." Same with "testament": it almost always pairs with "to the power of" or "to human ingenuity" or "to the importance of."

These pairings are statistical. The model learned them as a unit. So if you find one member of the pair in your draft, search for the second.

This clustering is part of why the 18 signs a piece of text was written by AI go beyond word choice alone. Sentence structure, paragraph rhythm, and the way ideas connect are all part of the signal.

Other patterns worth knowing

The em dash habit

AI uses the em dash at a rate that no human writer would. It has learned that em dashes signal sophisticated prose, so it inserts them far more than any editor would let through. We cover this in detail in why AI loves the em dash and how to spot it, but the short version: if your draft has more than two or three em dashes in a 500-word piece, count them and cut aggressively.

The rule-of-three compulsion

AI structures conclusions as triplets. "Faster, smarter, and more reliable." "Clear, concise, and compelling." These triplets feel organized but they are almost always filler. Real conclusions make a single strong point, not three adequate ones. See the rule of three: AI's favorite sentence pattern for the full breakdown.

Hedged authority

Human experts say "I don't know" when they don't know. AI models hedge differently: they use phrases like "it is important to consider" or "one might argue" to sound measured without actually committing to a position. If your draft is full of "it is important to note that," you are reading the AI's uncertainty in disguise.

FAQ

Why does AI use these specific words so much?

Because they appear constantly in the authoritative writing the models were trained on. Academic and corporate text is full of "leverage," "foster," and "underscore." The model learned to associate those words with sounding credible. The problem is that overuse has made them into signals of AI writing rather than signals of quality.

Will removing these words fool an AI detector?

That is not really the right goal. AI detectors are unreliable. They flag human writing and miss AI writing at rates that make them unsuitable for high-stakes decisions. Removing AI buzzwords will make your writing clearer and more credible to human readers, which is the actual goal. What happens with a detector is a separate question with a different answer.

Do all AI models use the same words?

Broadly, yes, because they are trained on overlapping datasets and share similar objectives. Claude, ChatGPT, and Gemini will all reach for "delve" more than a human writer would. The specific frequency varies, but the tendency is consistent enough that editors who work with AI output daily recognize it across models.

What if I actually like some of these words?

Some of them are fine words used badly. "Navigate" is a useful verb when someone is literally moving through something. "Robust" works when you are describing a wine or a physical system. The problem is not the word itself but its metaphorical overuse. If you reach for it because it fits, keep it. If you reach for it because it sounds smart, cut it.

Is there a fast way to run these checks without reading the whole article again?

Yes. Paste your draft into a find tool (most word processors have one) and search for the words on the checklist above. Then use the humanizer prompt to run a structured revision. It will flag these and other patterns in one pass, which is faster than reading for them manually.

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The list above will not catch everything. Good writing has too many variables for a word list to be sufficient on its own. But it will eliminate the easiest tells, and eliminating the easy tells is where most people need to start.