In a recent LinkedIn post, Justin Oberman discusses the challenges posed by LinkedIn’s current approach to detecting AI-generated content, arguing that attempting to “beat the detector” is a futile effort. Instead, Oberman proposes that content creators should focus on human-driven editing as the primary strategy to ensure their posts maintain authenticity and reach.
Oberman’s central thesis is that LinkedIn’s reliance on AI to police AI-generated content is inherently flawed. He highlights the unreliability of AI detectors and the lack of transparency in the process.
“The weird part is they’re using AI to do it. And everyone already knows AI detectors are shaky. Which means some solid posts are going to get buried because a machine guessed wrong.”
This system, according to Oberman, is problematic because it lacks clear feedback mechanisms for users. There are no warnings, labels, or appeals processes, leading to a quiet loss of reach for content that might be unfairly flagged.
The Futility of ‘Beating the Detector’
Oberman strongly advises against trying to outsmart the AI detection algorithms. He likens the situation to trying to win a game where the score is never revealed.
“So stop trying to figure out how to ‘beat the detector.’ Because you can’t beat something that never shows you the score.”
Instead of focusing on circumventing the detection system, Oberman suggests a more effective approach: improving the inherent quality of the writing through meticulous human editing. He emphasizes that this is where true value lies in content creation, especially in the age of AI.
Elevating Content Through Human Editing
The core of Oberman’s strategy revolves around the principle that “writing is 75% editing.” However, he clarifies that this refers to human editing, not AI-assisted editing. He asserts that no human-facing content generated by AI should be published without significant human oversight and refinement.
Key Editing ‘Hunts’
To assist creators in this process, Oberman has developed a framework he calls “The Copy Hunter Field Guide for Writing and Editing in the Age of AI.” This guide identifies specific areas, or “hunts,” to focus on during the editing process. These include:
- Identifying and correcting repetitive sentence patterns.
- Refining overly polished or unnatural transitions.
- Eliminating “empty intensity” and predictable phrasing.
- Removing words or structures that sound inauthentic or “assembled, not lived-in.”
Oberman acknowledges that these methods do not offer a guarantee against LinkedIn’s AI detection but posits that they significantly increase the odds of a post being perceived as genuinely human-authored.
“But they will give you a better shot.”
He explains that these “hunts” are designed to imbue writing with a more natural, human-like quality, making it less likely to be flagged by an “unintelligent machine,” as he describes LinkedIn’s detection system.
The Copy Hunter Field Guide
Oberman is offering his guide as a course, structured into modules. The first module, covering the initial ten “hunts,” is already available. He plans to release subsequent modules as they are developed. The full course will eventually be priced at $200, but he is offering an early-bird presale price of $52 using the code PRESALE. He encourages early adopters to provide feedback during the course’s development.
In essence, Oberman’s message to content creators on LinkedIn is to shift their focus from gaming AI detection systems to mastering the art of human editing, thereby producing more authentic and resonant content.
📝 About This Content
This article is based on insights shared by Justin Oberman on LinkedIn.
📅 Originally posted on June 30, 2026 | View original post on LinkedIn →