Read enough AI-written text and you start noticing the same moves over and over. Something always "delves" into a topic. A conclusion always "underscores" how important something is. Every list of three ends with the third item being the biggest and best one. This isn't a coincidence, and it's not just you noticing patterns that aren't there. Researchers have actually measured it, and there's a real reason behind it.
The word lists give it away first
In 2024, researchers looked at medical abstracts on PubMed and found the word "delve" shooting up right after ChatGPT launched. Words like "underscore," "meticulous," "boast," "showcase," and "tapestry" spiked too. A different study in 2026 looked at news writing across 34 languages and found the same thing happening worldwide. Words like "emphasize," "revolutionize," and "crucial" showed up far more often in AI-assisted writing than in writing without it.
Why these specific words? It likely comes down to how these models get trained. After a model learns to predict text, it goes through a second round of training, where humans rate its answers and the model gets nudged toward whatever people rated highly. Researchers who've studied this say it pushes models toward a certain tone: a bit more formal, a bit more polished, favoring words that sound thorough and confident. Nobody told the model to love the word "delve." That kind of writing just kept getting rewarded during training, so the model leaned into it.
It's not just the words, it's the shape
The bigger giveaway isn't any single word. It's the shape of the whole piece. AI writing tends to follow the same skeleton every time: an opening that restates the question, three sections that all feel about the same length whether the topic actually splits that evenly or not, and a closing paragraph that sums everything back up. Real human writing is messier than that. People spend more time on the point that actually matters and skip quickly past the one that doesn't. AI text often gives every point the same amount of space, whether it deserves it or not.
You'll also see too much summarizing. A paragraph makes a point, and then the next sentence restates that same point in slightly different words, like the model isn't sure you caught it the first time. Most human writers trust the reader more than that.
Repetition is a real, studied problem, not just a style complaint
There's actual technical research behind why models repeat themselves. Researchers have traced repetitive text back to specific patterns inside the model, and there are training tricks built specifically to cut it down. This matters because the repetitiveness people notice isn't just a matter of taste. It's a real, measurable behavior with a cause. That also means it's something a company can choose to fix or ignore.
The list of "AI words" keeps changing
Here's something worth knowing: once people started calling out words like "delve" online in early 2024, its use in new AI-assisted writing actually dropped, according to a 2025 study of arXiv paper abstracts. But other favorite words, like "significant," kept climbing anyway. The researchers behind that study describe it as a back-and-forth. Writers notice a tell, they adjust how they use AI output to avoid it, and the obvious signs shift to a new set of words. That means any list of "AI words to avoid" you read today will probably look a bit dated in a year. The real habit, leaning on a small set of safe, impressive-sounding words, sticks around longer than any single word on the list.
How editing actually fixes this
None of this means you should avoid AI-assisted writing. It just means you should edit it. A few things reliably help:
Cut the throat-clearing. If a paragraph opens with "it's important to note that" or "in today's fast-paced world," that sentence usually isn't doing any real work. The piece reads better without it.
Vary the rhythm on purpose. Read a paragraph out loud. If every sentence is roughly the same length, break that up. Mix in some short ones with some longer ones. Real writing has more variation than a model's default output does.
Cut the summary sentence. If a paragraph repeats its own point in the last line, you can usually delete that line without losing anything.
Ask for specifics, not vibes. A prompt like "make this more engaging" tends to get you more of the same generic polish. A prompt that asks for a concrete example, a specific number, or a particular counterargument tends to get you text that's harder to mistake for a template.
Read it next to something you wrote yourself. The fastest way to spot AI-sounding phrasing is to put it next to a paragraph you know is genuinely yours. The gap usually jumps out right away.
Why this matters beyond annoying phrasing
Sounding "AI-generated" isn't just an aesthetic issue. It affects trust. Readers who spot the tells, correctly or not, start discounting the content even when the information itself is accurate. That's a real cost for anyone publishing writing at scale, whether you're a student, a marketer, a journalist, or a company. As detection tools and regular readers both get better at spotting these patterns, generic AI phrasing turns from a shortcut into a liability.
It also points to where this might be heading. As more of the internet's writing becomes AI-assisted, and as future models get trained partly on text that earlier AI helped write, there's a real question about whether these patterns get reinforced instead of fading away. That's part of why human editing matters more, not less, as these tools spread. It's one of the few things actively pushing back against the sameness, instead of feeding it.