By Mari Luukkainen in Startups — 22 Jan 2026

Every AI writing tool sounds the same. Here's why

The market is flooded with tools that make everyone sound the same.

I've been building Shitpost Senpai which is an AI tool that helps people write content that doesn't sound like it was generated by a hostage.

In the process, I've researched every competitor in the space. Jasper. Copy.ai. Tweet Hunter. Taplio. Postwise. All of them.

And I found the same problem everywhere:

They all promise to "write in your voice." They all fail.

And - what if your voice is boring? They all fail.

The universal complaint

I pulled reviews from G2, Capterra, Reddit, Product Hunt. The pattern is:

"The Brand Voice does NOT carry through, it reverts back to AI speak."

"The AI post generator fails to accurately capture users' unique writing styles and tones, which almost always results in generic and inauthentic content."

"A free ChatGPT account is more powerful."

Most oof marketers report their AI voice features produce inconsistent, generic output.

Someone sent 247 cold emails using ChatGPT with a 6.5% response rate. Then posted AI-written LinkedIn content and someone commented "did AI write this?" within 8 seconds.

Why this keeps happening

Every tool follows the same broken playbook:

Analyze your sample content
Generate a prose description of your "voice"
Use that description to guide future generation

The problem? They reduce your voice to adjectives.

When Jasper analyzes your content and generates "Helpful, but not bossy" as your voice description, that description becomes the input and not your actual messy, specific, weird content.

I call this the pixelation problem: taking a high-resolution personality and compressing it into generic adjectives, then expecting the LLM to reconstruct the original from those adjectives.

It can't.

"Friendly and professional" describes approximately 50 million LinkedIn profiles. It describes none of them specifically.

What users actually mean by "sounding like me"

When I dug into what people want, it's not what the tools are optimizing for:

Imperfections matter. The messy sentences, the random thoughts, or the silly jokes which are the things that make a writer feel like a real person. Perfection signals inauthenticity.

Variable rhythm creates humanity. AI writes in uniform sentences that are roughly of the same length. Humans write with greater 'burstiness.' Some crisp, some meandering.

Emotional range proves authenticity. The angry ones, the frustrated ones, the ones with emotion. The emails where you actually said what you thought.

Users don't want their content polished. They want it to sound like them having a good day.

The tells everyone recognizes

Reddit users have catalogued the specific patterns that mark content as AI-generated:

Overuse of "delve," "game-changing," "transformative," "leverage"
The "rule of three" in staccato patterns: "You tried. You learned. You improved."
Excessive "Moreover," "Furthermore," "Additionally" transitions
"In conclusion" syndrome
Hedge words and passive voice everywhere
"Curious what others think" engagement bait

One user summarized it perfectly:

"Most AI content reads like a corporate press release married a TED Talk transcript and had a baby raised by SEO consultants."

The real problem: tools optimize for the wrong thing

Every AI content tool optimizes for:

Professional tone
Grammatical perfection
Engagement patterns from successful posts
Brand safety

None of them optimize for:

Your specific weird vocabulary
Your punctuation quirks
Your humor type
Your energy level
Your chaos tolerance

That's why they all sound the same. They're trained to regress to the mean. To find the safe middle ground. To produce content that's inoffensive to everyone and interesting to no one.

What would actually work

If I were building a voice tool from scratch (which I am), here's what I'd do differently:

Preserve actual patterns, not descriptions. Don't turn my writing into "friendly and casual." Keep track of my sentence length distribution, my punctuation habits, my capitalization quirks, my tendency to start sentences with "And" or "But."

Make voice dimensional, not descriptive. Instead of generating prose descriptions, measure things like: Formality (2/10), Chaos (7/10), Energy (8/10), Sass (6/10). Then let users adjust those dimensions directly.

Include anti-patterns. What does this person NEVER say? What words trigger the "this sounds like AI" alarm? Build avoidance into the system.

Let users dial chaos. Sometimes I want to be professional. Sometimes I want to post like it's 3 AM and I've had three energy drinks. Same persona, different energy. Current tools don't understand this.

Learn from what works, not what's safe. Track engagement. Surface patterns. "Your audience responds better when you're meaner on Wednesdays." Actually useful feedback.

The opportunity

54%+ of longer LinkedIn posts are now AI-generated. "AI slop" was Merriam-Webster's word of the year.