Do not index
A founder asked me last week, why does my LinkedIn content sound like everyone else's? She had done everything the playbook told her to do. She bought the AI tool, fed it her bullet points, posted five times a week. The output was clean. It was also indistinguishable from the forty other founders in her space running the exact same process.
Here is the answer she did not want to hear. The sameness is not a quality problem you can prompt your way out of. When everyone uses the same models trained on the same corpus, the models converge on the same safe, competent, forgettable middle. Inc. reported on this directly, quoting Islam Midov: "Generic AI content does not work anymore. People still want authenticity, perspective, and trust." The credibility your content was supposed to build erodes the moment your reader realizes they have read your post a hundred times under a hundred other names.
This matters most if you are a founder running personal-brand content to win business, the kind doing $200k to $2M in revenue who treats LinkedIn as a pipeline and not a hobby. It matters if you are an agency owner or a ghostwriter charging $5k to $30k per month to produce that content for clients, because sameness is now the thing your buyers can feel even when they cannot name it.
This is not for people posting for fun or for reach alone. Skip this if your goal is volume and you measure success in impressions. If you are still convinced that the answer to a flat feed is simply more output, more posts, a bigger calendar, this article will not change your model. The problem is not that you are posting too little. It is that what you are posting carries no fingerprint.
Here is what I would actually do. Run every draft through what I call the Fingerprint Test. One question: could anyone else in my industry have published this exact post? If the answer is yes, it is sameness, and sameness gets scrolled past. A post passes the Fingerprint Test when it contains something only you could have written. A specific number from your own business. A client situation no one else witnessed. An opinion you would defend in a room full of people who disagree. The model can imitate structure. It cannot manufacture the time you lost $40k on the wrong hire, or the exact reason you fired a client who paid on time.
Why specificity beats polish
Most founders optimize the wrong layer. They sand the writing until it is smooth and lose the texture that made it worth reading. I have watched accounts post 200 times in a year and grow nothing, because every post was a competent summary of advice already in circulation. The accounts that compound are the ones where the writing is rougher but the substance is unrepeatable. A reader does not trust you because your sentences are clean. They trust you because you said something true that cost you something to learn.
There is a cheap test for this that has nothing to do with AI. Take your last ten posts and strike every sentence a competitor could have written word for word. If you are left with two sentences out of forty, you have found your real content, and the rest was filler the model was happy to generate for you. Most founders are shocked by how little survives. That surviving fraction is the entire reason anyone should follow you, and it is the first thing automation sands away.
This is also why the AI-sameness problem hits founders harder than it hits commentators. A commentator can survive on takes. A founder is selling judgment, and judgment shows up in specifics. When you outsource the specifics to a model, you are quietly telling your buyer that your judgment is generic too.
Where AI belongs in the process
I am not anti-AI. I run a content team and AI lives in our workflow every day. It belongs in the research layer and the drafting-support layer, the parts that move information around. It does not belong in the voice layer, the part where your actual point of view enters the post. The split is simple. Use the model to organize what you already know. Never use it to decide what you think. The founders getting flattened are the ones who let the tool cross that line, handing over the one thing that was supposed to be theirs.
If you want a deeper version of this, I have written before about how founders should position on LinkedIn as a practitioner first and a thought leader never, and the same principle applies here. Position from what you actually do, and the sameness problem mostly solves itself, because no two practitioners do the work the same way.
The trajectory worth noticing is this. As AI content gets cheaper and more uniform, specificity gets more valuable, not less. The founders who treat their lived experience as the product, and the model as a clerk, will own a moat that compounds every quarter. The ones who keep automating their way toward the middle will find the middle is where business goes to be ignored. The feed is going to keep filling with competent sameness. Your only durable advantage is the part of you a model has never seen.
