How does the LinkedIn algorithm actually work in 2026?
Nobody outside LinkedIn knows precisely, and anyone claiming certainty is selling something. What is well-supported: the feed optimizes for dwell time and meaningful interaction; early engagement (especially comments, and your replies to them) expands distribution; posts pushing readers to external links reach substantially fewer people; and coordinated fake engagement is now detected and penalized. The largest practitioner study (van der Blom's Algorithm Insights Report) additionally finds AI-sounding posts earn roughly 30% less reach and 55% less engagement. The deeper truth for founders: only around 7% of members post regularly, your buyers see far fewer creators than you assume, and the durable strategy is being consistently worth reading, not chasing this quarter's ranking quirks.
Last reviewed: July 2026
First, the epistemics
LinkedIn does not publish its ranking system, and it changes continuously. So every "how the algorithm works" article is one of three things: official-but-vague platform statements, practitioner research that measures outputs at scale, or confident fiction. This page uses the first two and labels which is which. The largest ongoing practitioner study is Richard van der Blom's Algorithm Insights Report, built on thousands of tracked posts yearly; its numbers are estimates from observation, not LinkedIn documentation, and we cite them as exactly that.
What is well-supported
Dwell and interaction decide expansion. A post gets an initial test distribution; how long viewers stay and whether they interact meaningfully decides expansion. Comments are the heavyweight interaction, and van der Blom's data supports replying to comments in the first hour as one of the few author actions that measurably widens distribution.
Link-forward posts reach fewer people. Posts engineered to push readers off-platform are throttled; van der Blom's estimate runs as high as a 70% reach reduction for link-led posts. Direction: very well-supported. Exact number: practitioner estimate.
Generic and machine-sounding text underperforms. Van der Blom's 2025 data finds AI-style posts earn roughly 30% less reach and 55% less engagement, and Originality.ai's independent analysis finds human-written posts winning clearly in trust-dependent categories. Whether the mechanism is the model or the readers is unknowable from outside, and does not matter: the cost is real either way.
Fake coordination is detected. LinkedIn has publicly described detecting coordinated engagement patterns and limiting pod-boosted content's distribution. This one is official.
The myths to drop
Precise "post at 8:47am Tuesday" timing rules, hashtag counting, the idea that editing a post kills its reach, and SSI-boosts-distribution folklore: none of these trace to documentation or robust measurement. Timing matters weakly (post when your buyers are awake); ritual does not.
Why the algorithm matters less than founders think
Two numbers reframe the whole topic. Only about 7% of LinkedIn members post regularly, per van der Blom's data, so the supply of consistent, credible voices in any niche is thin: showing up at all puts you in a small minority. And your goal is not the feed's average viewer; it is the few hundred buyers in your market. A post that reaches 900 of the wrong people lost to a post that reached 90 of the right ones, whatever the impressions counter says. Algorithm literacy is worth one afternoon; after that, every hour spent chasing ranking quirks is an hour not spent saying something true to the people who might buy.
Slingapult's read: we optimize for the metric the algorithm can't see: who engaged, whether they fit your ICP, and what happened next. The gate keeps your posts human (the one 'algorithm hack' with data behind it), and the capture loop makes reach accountable to pipeline.