Modern AI feeds now read for meaning, not keywords — here's what that changes

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Content platforms like LinkedIn have shifted from keyword-based pipelines to large language model retrievers that assess the semantic meaning of posts before ranking them. An LLM first determines whether a piece of content is relevant enough to be considered, while a separate transformer model handles the final ranking. This means traditional engagement tactics such as keyword stuffing, timed posting, and like-trading pods are far less effective than before. The article also cautions that widely circulated claims about LinkedIn's system being powered by a model called '360Brew' appear to be misinformation unsupported by LinkedIn's own published research. The practical takeaway for writers is that clarity and specificity now serve both human readers and algorithmic retrieval equally well.

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