You Pasted the Output. You Felt It Immediately.
Something was off. The sentences were technically correct. Nothing was factually wrong. But reading it felt like chewing cardboard — structured, uniform, and completely lifeless.
That feeling has a cause. AI writing doesn't fail because it's inaccurate. It fails because it follows patterns so consistently that the patterns themselves become the tell. Once you know what to look for, you can't unsee it.
Here are the five most common ones.
1. Transition Addiction
AI models were trained on text that rewards logical flow. The side effect: they reach for connective tissue words constantly, even when the writing doesn't need them.
Before:
"Social media has changed how brands communicate. Furthermore, it has created new opportunities for direct customer engagement. Moreover, it allows for real-time feedback loops. In conclusion, brands that adapt will thrive."
After:
"Social media didn't just change how brands talk to customers. It handed customers a microphone and put it live. Brands that figured that out early built audiences. The ones that didn't are still wondering why their follower count is flat."
The fix isn't removing transitions entirely. It's earning them. If you need "furthermore" to connect two ideas, the ideas might not actually be connected.
2. Symmetrical Structure
This one is subtle but consistent. AI writing tends to balance everything. Three bullet points, each the same length. Paragraphs that mirror each other. Lists where every item follows the same grammatical form.
Before:
"To improve your content strategy, consider the following:
- Create high-quality content that resonates with your audience
- Distribute your content across multiple platforms strategically
- Analyze your performance data to optimize future efforts"
After:
"Three things actually move the needle on content: knowing exactly who you're writing for, getting that content in front of them consistently, and being honest when something isn't working. Most people nail the first two and ignore the third."
Human writers break rhythm. They use a short punch after a longer setup. They make a list of four things, not three, because that's how many there actually are. AI rounds everything to the nearest clean number.
3. Hedge Stacking
AI is trained to avoid being wrong, so it hedges. A lot. The problem is that hedges compound. One caveat is fine. Four in a row reads like the writing is afraid of itself.
Before:
"It's important to note that results may vary. It's worth mentioning that these strategies have worked for some businesses. It should be noted that your specific context will affect outcomes."
After:
"These strategies worked well for B2B SaaS companies in the 50-200 employee range. Different industry, different story."
The second version is more honest, not less. It tells you exactly what the claim is based on, instead of wrapping it in so many qualifiers that the claim disappears entirely.
4. Superlative Abuse
There's a specific vocabulary AI reaches for when it wants to sound impressive. You know the words. "Cutting-edge." "Powerful." "Transformative." "Streamlined." "Holistic approach." None of these words mean anything specific, which is exactly why AI uses them.
Before:
"Our platform offers a powerful, cutting-edge solution that streamlines your workflow and delivers transformative results for your team."
After:
"We cut the average onboarding time from 3 weeks to 4 days. For most teams, that's the difference between a tool people actually use and one that lives in the bookmarks bar."
The before version could describe any product in any category. The after version could only describe one product. That specificity is what makes it believable.
5. Missing Specificity
This is the pattern that underlies all the others. AI generalizes because it was trained on general text. It says "many experts" instead of naming one. It says "studies show" without citing anything. It says "users often struggle" when it means "I saw this exact complaint in three different product reviews from Q3 2024."
Before:
"Many content creators struggle with AI detection tools. Research suggests that AI-generated content is becoming increasingly prevalent online."
After:
"Originality.ai flagged a 2,000-word article I wrote as 94% AI. The fix took 20 minutes and three targeted rewrites. The patterns that triggered it were all structural, not vocabulary-based."
The second version has a source, a number, a timeframe, and a conclusion drawn from actual experience. That's what trust sounds like. General claims are what AI sounds like.
What You Can Do Right Now
Pick any piece of AI writing you've produced in the last week. Read it out loud. Listen for the moments where you'd never actually say the sentence out loud to another person. Those are your tells.
If you want a faster diagnostic, tclaw.dev flags these patterns automatically and shows you exactly where they appear in your text.
But honestly, reading it out loud works. Your ear catches what your eye skips. Trust it.
tags: writing, ai, content, productivity
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