Most founders I talk to have the same workflow: open ChatGPT, paste a rough idea, ask for a LinkedIn post, tweak two words, hit publish. Then they wonder why reach is falling off a cliff.

The problem isn’t AI. The problem is which parts you’re outsourcing. Here’s the actual teardown of my stack — what the model writes, what I rewrite by hand, and the specific tells I strip before anything goes live.

The AI tells LinkedIn (and readers) are pattern-matching on

You don’t need a leaked memo to know the platform is dampening obvious AI writing. Users are muting it themselves. Skim your own feed and count how many posts you scrolled past in under a second. They almost all share the same fingerprints:

Contrast-construction hooks: “It’s not X. It’s Y.” “Not a bug. A feature.”
Em dash cascades: three em dashes in four sentences.
Three-word sentences stacked like poetry. On their own line. For emphasis.
Corporate opener phrases: “In today’s landscape”, “In an era where”, “Let’s dive in.”
Closers that summarize the obvious: “And that’s the truth.” “That’s the whole game.”
Symmetry: every paragraph the same length, every sentence the same rhythm.

None of these are wrong on their own. Stacked together, they’re a signature. If your last three posts hit four or more of these, you’re writing in Default GPT Voice — and that voice is now a demotion signal, both algorithmically and socially. I broke down the underlying reach mechanics in The AI Slop Tell.

The split: what AI does, what I do

My rule is simple. Automate the boring part. Defend the voice. Here’s exactly where the line sits.

What Claude/ChatGPT does (the boring part)

Research pulls: summarize a 40-minute podcast, pull three stats from a report, list every objection in a Reddit thread.
Outlines: take a raw voice memo and turn it into a 5-beat structure.
Rough first drafts of long-form (blogs, newsletters) where I’m going to rewrite 60%+ anyway.
Reformatting: turning a blog into a carousel outline, a reel script into a LinkedIn post skeleton.
Fact-checking my claims against sources I paste in.

What I do by hand (the voice part)

Every hook. Written out loud, into a voice memo, then typed.
Every personal story. No model has the specific texture of what actually happened in my Tuesday.
Every opinion or take. If it’s my POV, it comes from my brain.
The last pass. I read the post out loud. If it sounds like a robot pitching a TED talk, I burn it and start over.

The actual n8n + Claude pipeline

Here’s the workflow I run. It’s boring on purpose.

Capture (Otter or Apple Voice Memos): I talk for 3-8 minutes about a specific thing that happened this week. Not a topic. A moment.
Transcribe + tag (n8n webhook to Claude): the transcript goes into a Claude call with a system prompt that pulls out (a) the one-line insight, (b) the story beats, (c) any specific numbers or names.
Outline only: Claude returns a 5-line outline. Not a draft. I explicitly forbid a full draft at this stage — that’s the trap most founders fall into.
Write the hook by hand in a plain Notion doc. No AI open.
Write the body by hand, using the outline as a checklist. Voice memos open next to me for exact phrasing.
Slop check pass: a second Claude call, but only to detect, not to rewrite. Prompt: “List every em dash, every it’s-not-X-it’s-Y construction, every three-word stacked sentence, every generic opener. Do not rewrite. Just flag.”
I fix each flag manually.
Dog test: read the post out loud. If my tone shifts into fake-authoritative, it goes back to step 4.

The context I feed the model matters more than the prompt itself. If you want to understand why the same prompt produces slop for you and gold for someone else, read Context Engineering. And if your voice prompt is still generic, Voice Prompt Is the Moat has the exact structure I use.

The slop detector prompt (steal this)

Paste your draft into Claude or GPT with this. Do not let it rewrite.

Flag every em dash and suggest whether it should be a period, comma, or colon.
Flag every “it’s not X, it’s Y” construction.
Flag every sentence under 4 words used for emphasis.
Flag any opener that could appear in a McKinsey report.
Flag any closer that summarizes what the post just said.
Flag any paragraph where every sentence is the same length.
Return a numbered list. Do not rewrite anything.

The reason “do not rewrite” is bolded in my actual prompt: the second you let the model touch the fix, it puts the slop back in a new outfit. Anthropic’s own prompt engineering docs are clear that constraint prompts (“do X, do not do Y”) outperform open-ended ones — use that.

What actually moved for me

When I switched from “AI writes, I edit” to “AI researches, I write, AI audits,” a few things changed. Comments got longer. DMs shifted from “great post” to actual questions about the specific thing I wrote about. Reach on hand-written posts ran roughly [X]x my AI-drafted ones over a [30-day] window.

Not because AI is bad. Because the part I was outsourcing — the voice — was the only part anyone was reading for.

The one-line version

Automate research, outlines, reformatting, and detection. Defend hooks, stories, opinions, and the final read-aloud. If a post could’ve been written by any founder in your category, it will perform like a post written by any founder in your category. If you want to go further on sequencing what to automate first, Automate In This Order lays out the full stack order.

Read your next post out loud. If your dog looks concerned, rewrite it.

AI Slop Workflow Teardown: What to Automate, What to Rewrite