The AI writing tics that hurt engagement: A study

The web has strong opinions about what “AI-written” content looks like, and even stronger ones about what’s supposedly wrong with it. Scroll any content marketer’s LinkedIn feed, and you’ll find confident claims that em dashes and other AI “tells” signal bad, automated writing.

The problem with these debates is that they often confuse taste with performance. What counts as “bad writing” will always be subjective. But if the goal for content marketers is to communicate clearly and compete in the information marketplace, the practical question should be: which LLM habits actually turn readers off?

To find out, we analyzed a large dataset of content marketing pages to identify which AI writing “tics” we see most often called out to understand which are turning off readers — and the ones we may be calling out for no reason.

How we built our ‘AI tics’ study

At this point, you’ve probably all seen them, too:

• “In today’s fast-paced digital landscape…”
• “It’s important to note that…”
• “Not only… but also” (repeated over, and over and over…)
• “In conclusion” (even when nothing has been concluded)

The second you notice them, it’s hard not to see them everywhere an LLM has helped produce copy. Many readers report hating these LLM patterns. But how exactly are they impacting user engagement?

To find out, we gathered a list of the most common AI writing tells we and others have noticed. These include:

• “Not only… but also” constructions: “Not only does X do Y, but it also does Z.”
• Sentence starts with “then,” “this,” or” that”: “Then you should…” “Then the system…” “This shows…” “This means…” 
• Introductory filler: “In this article,” “We’ll explore,” and “Let’s take a look”. 
• “Conclusion” starters: “In conclusion,” or other AI equivalents of clearing your throat.
• Em dashes: The most infamous punctuation mark in today’s content marketing.

From there, we built a dataset of:

• 10 domains of varied site size and monthly traffic, in a wide array of industries including tech, ecommerce, healthcare, education, analytics, and more
• 1,000+ content marketing URLs, built from a mix of workflows including posts that were either fully human-written, written collaboratively by humans and AI, or completely AI-generated.

Then we standardized our dataset by: 

• Aligning shorter posts and cornerstone content by standardizing every writing tic as occurrences per 1,000 words. Since longer articles naturally contain more of, well, everything, a 3,000-word guide would otherwise look “worse” than a 600-word post simply because it has more sentences.
• Excluding any page under 500 words. Very short pages don’t give enough room for stylistic patterns to emerge, and their engagement metrics are likely driven more by intent than by engagement alone.
• Prioritizing engagement rate as…