LLM-Generated Content and Personal Voice
LLM-generated content has become a visible presence on professional social platforms. The author’s objection is not to using the tools themselves, but to letting a model replace the writer’s expression, often producing a style that readers can quickly recognize as unlike the person posting it.
The article focuses on LinkedIn. The author notes that platform features such as AI rewriting make these tools easy to use, but argues that many generated or rewritten posts are not merely mediocre. They carry repetitive, distracting patterns in both formatting and wording.
The Stylistic Signals Readers Notice
The author points to recurring signals such as abundant emojis, one-sentence paragraphs, familiar contrastive constructions, and em dashes used in ways that do not match the writer’s usual habits. None of these features proves that AI wrote a passage, but several appearing together can create a strong impression of machine-generated prose.
The effect goes beyond matters of taste. When readers sense that a post was not written by its apparent author, they may also question how much of its experience, judgment, and opinion is genuine. The article’s conclusion is blunt: when authorship becomes uncertain, readers may simply stop reading.
LLMs Work Better as Assistants Than Ghostwriters
The author does not reject the value of large language models. He considers them useful for brainstorming, understanding text, and serving as effective editors. This distinction suggests a more cautious workflow: use the tool to organize and test ideas that already exist, rather than wrapping an undeveloped viewpoint in polished-looking prose.
The Remaining Question of Authenticity
The argument is based on personal observation and a forceful critique of style; it provides no detection accuracy figures or reader survey. These signals therefore should not be treated as a reliable AI-authorship test. Platform norms, languages, and individual writing habits can all affect perception. A more useful lesson is to ask whether a post is specific, credible, and consistent with its author’s established voice, rather than searching for a fixed set of AI markers.
Keep the Credit for Expression
The debate ultimately points to a simple editorial principle: tools may improve expression, but they should not obscure the person expressing it. For professional content that depends on trust, preserving an individual tone, concrete experience, and genuine judgment may matter more than producing a flawless but interchangeable post. The author’s recommendation is to trust one’s own voice and write what one actually means.