Why ChatGPT Makes Everyone Sound the Same (And What to Do About It)

AI doesn’t write bad content.

It writes average content.

Perfectly, mathematically, statistically average content.

(And that’s the problem.)

Last month I ran an experiment that’s been keeping me up at night. Hired 5 freelance writers on Upwork—none of whom know each other—and asked them to record Loom videos showing their process for writing a 500-word article about meditation benefits. No ChatGPT allowed. I wanted to see them actually write it, screen recording and everything, so I knew they weren’t just firing up their favorite LLM when the camera was off.

Then I took the same topic and asked ChatGPT to generate 5 different versions using slightly varied prompts.

The human videos were fascinating. Watched people:

• Start with personal stories

• Google specific studies, then summarize in their own words

• Delete paragraphs and start over when the flow felt wrong

• Talk out loud to themselves about structure

• Reference their own meditation practice

The human posts had vocabulary diversity (measured by unique words per 100). They varied in structure—one story format, one Q&A, one essay-style, two hybrid approaches. Different metaphors. Distinct tones you could identify blind.

The AI outputs? Indistinguishable.

All 5 defaulted to numbered lists. Four opened with “In today’s stressful world...” All mentioned “reducing stress” and “improving focus” in the first 100 words. The vocabulary overlap across all 5 versions? 73%.

I couldn’t tell the AI posts apart. Could immediately distinguish each human writer.

Same tool, similar prompts.

Same output.

This isn’t a bug in the system. It’s math—and the math is working exactly as designed.

The Statistical Average Machine

Here's what AI actually does when you ask it to write:

Scans millions of similar examples in its training data. Calculates probability: "What word is most likely to come next based on all the patterns I've seen?"

Not "What word would this specific person use?"

Not "What would be interesting here?"

What's most probable.

The result? AI gives you the statistical center of everything it's processed on that topic. The average phrase. The average structure. The average tone.

Average is the enemy of memorable.

The Math Nobody Wants to Talk About

Let's say you prompt AI: "Write an opening sentence about time management."

In its training data, thousands of time management articles. These phrases appear most frequently:

• "Time is our most valuable resource" (appears 847 times)

• "In today's fast-paced world" (appears 623 times)

• "Effective time management is crucial" (appears 591 times)

AI picks the high-probability phrase. Because probability equals safety.

Now say you'd actually write: "I wasted three hours yesterday watching TikToks about productivity instead of actually being productive."

That exact phrase? Maybe twice in the training data. Low probability. Gets filtered out.

Your authentic voice is an outlier. AI is trained to eliminate outliers.

That's the convergence problem.

Ten different humans enter. One averaged voice exits.

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What This Actually Reveals About AI

That experiment haunts me because I realized something: most of my early AI-assisted content probably looked exactly like those 5 indistinguishable posts.

The issue isn’t that AI is bad at writing. It’s that AI is too good at being average.

When you ask it to write about meditation, it doesn’t pull from one meditation article. It synthesizes thousands of them. Every phrase that appears frequently gets weighted higher. Every unique approach that only appeared once or twice gets filtered out as noise.

The result? The statistical center of “meditation content.”

Not the best meditation content. Not the most interesting. The most common.

Why This Happens: The Training Data Problem

AI learns from what exists online. Specifically:

• Blog posts

• Articles

• Social media

• Books

• News

But here's the catch—most of that content is already mediocre.

The internet is full of:

SEO-optimized posts written to game Google, not help humans

Content mill articles produced for pennies per word

Generic "best practices" recycled from other generic posts

Corporate communications sanitized by legal teams

When AI learns from this data, it learns mediocrity.

Then reproduces mediocrity.

You feed it the average of the internet. It gives you back the average of the internet.

The Feedback Loop That Should Terrify You

More people use AI to write → that AI content gets published → future AI trains on that content → produces even more averaged output → which trains the next generation.

We're creating a convergence spiral.

AI trains on human + AI content → produces more averaged content → that content trains the next AI → even more averaged content.

Nicholas Carr calls this "the great homogenization."

I call it The Great Blandening—where every piece of content inches closer to the same beige, forgettable middle until nothing stands out anymore. We're not just making content similar. We're actively erasing the possibility of difference.

(And we're all—myself included—complicit in building this loop every time we post generic AI content without editing it.)

Turns out the fastest way to sound like everyone else is to ask AI to “make this better.”

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Three Signs You're Caught in the Convergence Trap

Sign #1: The Synonym Shuffle

You generate something with AI. Feels generic. So you prompt: "Make this more engaging."

AI swaps "use" with "utilize." Changes "help" to "facilitate." Replaces "show" with "demonstrate."

Not more engaging. Same genericness wearing a thesaurus.

Sign #2: The Format Clone

All your content starts looking identical:

Hook sentence

Three paragraphs of context

Numbered list (always 5-7 items)

Brief conclusion

Call to action

This isn't your structure. It's AI's most probable structure based on what appears most frequently in training data.

Sign #3: The Voice Void

Read three pieces of your AI-assisted content aloud.

Can't tell which is which? They all sound like they came from the same content generator?

You've converged.

Breaking Free: The Anti-Convergence Framework

You can't fight probability with more probability. Can't ask AI to "be more unique" and expect miracles.

You need to inject divergence deliberately.

Left side: “Do everything for me.” Right side: “Do it with me.” Only one of these doesn’t produce digital cardboard.

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Step 1: Identify Your Outlier Patterns

What makes your writing yours? Usually the stuff that doesn't follow "best practices."

Maybe you:

• Start with a story, never a statistic

• Use sentence fragments. Like this.

• Ask rhetorical questions but never answer them

• Reference specific cultural moments only certain people would get

• Deploy analogies from your specific profession/experience

Write these down. These are your anti-convergence patterns.

Step 2: Feed AI Your Outliers FIRST

Don't ask AI to write like you. Show it examples where you're most unlike standard content.

Here are three examples of my writing. Notice:

- I never use corporate jargon
- My paragraphs are usually 1-3 sentences max
- I tell personal stories about failure more than success
- I use em dashes—like this—instead of parentheses
- I reference 90s video games as metaphors

[Paste your most distinctive writing here]

Now write a post about [topic] that matches these outlier patterns, NOT standard blog post patterns.

Step 3: The Deconvergence Edit

After AI writes, do a deconvergence pass.

Find every phrase that sounds "like content." Replace with something specific to you.

Generic → Specific:

• "Boost productivity" → "Stop checking email every 4 minutes"

• "In today's fast-paced world" → "Last Tuesday when my calendar had 8 meetings back-to-back"

• "Unlock potential" → "Actually finish the project instead of starting three new ones"

See how specificity breaks convergence?

The Collaboration Method That Actually Works

My exact process for working with AI without joining the convergence:

Sunday evening:

Outline the post. Key points, stories I want to tell, specific examples.

This part is 100% human. AI can't know my specific experiences.

(I start my weeks on Sunday because I like flashing a subversive middle finger to organized religion's Monday-first tyranny. Also because weekends are social constructs and my brain doesn't care what day the calendar says it is.)

Sunday night:

Use AI to expand my outline with these constraints:

Using my voice patterns [reference previous samples], expand this outline:

[My outline]

Requirements:
- Keep my specific examples intact
- Add connective tissue between points 
- DO NOT add generic transitions like "furthermore" or "moreover"
- DO NOT use business jargon
- Match my sentence rhythm

Monday morning:

Edit the output. Ruthlessly.

Remove anything that sounds like "content." Replace with something that sounds like me.

Usually means:

• Cutting 30% of the words

• Adding 2-3 more personal stories

• Replacing abstract concepts with concrete examples

• Breaking up any paragraph longer than 4 lines

The result:

Content that benefits from AI's speed but doesn't sound like AI's average.

Using AI solo is how you get content that reads like a corporate PowerPoint had a baby with a refrigerator manual.

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What Happens If You Don't Fix This

Two years from now.

You're still posting 3x per week. Still using the same generic prompts. Still copying output without much editing.

Your engagement has dropped 60%. Used to get 50-100 comments per post. Now you get 8-12, mostly from bots.

Your audience isn't angry. They're just... gone. Unfollowed quietly because your content started sounding like everyone else's. Why follow you when they can get the same information from 47 other creators who also just asked ChatGPT "write a post about productivity tips"?

Your credibility has shifted. People used to see you as a thought leader. Now they see you as someone who phones it in. The worst part? They're not wrong.

Your business has stalled. That course launch you planned? Half the projected signups. The consulting inquiries? Down 70%. Turns out generic content attracts generic interest, which converts into generic revenue: not much.

Meanwhile, the creators who learned to collaborate with AI strategically—who preserved their voice while scaling their output—they're thriving. Growing audiences. Building authority. Converting readers into customers.

Same tools. Different outcomes.

The AI revolution doesn't reward people who use AI the most.

Rewards the people who use AI the best.

Your Action Plan

This week:

Take three pieces you created with AI

Read them aloud

Mark every phrase that sounds "generic"

Rewrite those phrases in your specific voice

Next week:

Before asking AI to write anything, give it three examples of your most distinctive writing: "Write in THIS style, not in standard blog post style."

This month:

Build a "voice bank"—collection of your writing that's most unlike standard content. Reference it every time you work with AI.

The convergence problem is real.

But it's solvable.

You just have to decide: sound like everyone else, or sound like you?

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Discussion Question: What's the most generic AI-written phrase you've seen this week? Drop it in the comments. (Bonus points if you can guess the topic just from the phrase.)

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Crafted with love (and AI),

Nick "Fighter of the Great Blandening" Quick

PS…Want more on collaborating with AI without losing your voice? Subscribe for new posts every Sunday and Wednesday.

Next up: The Voiceprint Method for training AI on your specific voice patterns.