lightbulb The big idea
The feed doesn't favor a format. It weighs engagement: dwell time, comments, shares. Those signals feed a transformer ranker that reads more than a thousand of each member's past interactions before deciding who sees your post1. The carousel, document, and video multipliers creators repeat carry zero LinkedIn sourcing.
What's actually running
In March 2026, LinkedIn published its most detailed feed disclosure yet. The pipeline is a single LLM-based dual-encoder that finds candidate posts by nearest-neighbor search on embeddings, then ranks them with a transformer called the Generative Recommender1. Embeddings refresh within minutes; the pipeline runs in under 50 milliseconds for more than 1.3 billion members.
The ranker doesn't score posts one at a time. It reads a sequence of more than a thousand of a member's past interactions, alongside profile fields, device type, and affinity features1. Format is one input among many. LinkedIn discloses no boost or penalty tied to it.
The dwell-time signal has a longer paper trail. In 2020, LinkedIn's engineers disclosed two dwell measurements (on the feed and after the click) plus a P(skip) model predicting whether dwell time falls below a skip threshold. Adding dwell features lifted the ranker's offline AUC by as much as 10%2. The architecture has since been rebuilt; the doctrine hasn't. Clicks alone are noisy; time spent is the truer signal.
The multipliers nobody can source
LinkedIn's last on-the-record statement on format was a denial. In 2019, Pete Davies, then LinkedIn's Senior Director of Product for the feed, wrote: "Despite the rumors, the algorithm doesn't favor any particular format"3. Six years and two rebuilds later, LinkedIn has never reaffirmed or retracted that position.
Meanwhile, creators repeat the opposite claim: carousels earn about 1.9× the reach of other formats, documents outperform images by roughly 3×, video over three minutes reaches 1.21× the median. Every number traces to Buffer, Oktopost, or similar studies. None cite LinkedIn, and none reconcile with the 2019 denial. The 2026 engineering post names format as an input the ranker sees, not a lever that shifts distribution1.
Two things can be true. Format can correlate with reach without the algorithm rewarding format: a carousel holds the scroll longer, so its dwell time is higher, so its engagement is stronger. That's a signal effect, not a format multiplier. When a guide quotes 1.9× as fact, it's inventing a number LinkedIn has never confirmed.
The 2026 change most guides missed
In May 2026, LinkedIn changed how it distributes generic AI-generated content. Laura Lorenzetti, LinkedIn's VP of Product, laid out the policy. Posts the system flags as generic or inauthentic aren't removed. They stay fully visible to the poster's first-degree connections. What changes is reach: flagged posts are suppressed from recommendations beyond that circle4. In testing, LinkedIn says the system correctly identifies generic content 94% of the time. It doesn't say by how much reach drops.
Lorenzetti's post welcomes AI-assisted writing that carries an original angle or sparks conversation. What gets suppressed is content produced at scale with no added judgment. That is the same pattern LinkedIn's 2020 P(skip) model was already built to punish. The policy formalizes what the ranker was already doing.
The signals LinkedIn has named
LinkedIn names its positive signals outright. The engineering post lists likes and shares among the interaction types the ranker embeds and reads as sequences1; the public Help Center adds reactions, comments, reposts, and sends as the specific engagement types that shape what shows up in your feed5. The Help Center also names three negative signals: Not interested, unfollow, and Report post — each reduces similar content in that member's feed5.
What LinkedIn hasn't disclosed: how those signals weigh against each other, what counts as a "long dwell" in minutes, or any decay curve for how fast a post's reach fades. Claims like "the first 90 minutes decide reach" or "5–10% of your network sees it first" trace to creator blogs, not LinkedIn.
Two things to act on today
Two places to start:
- Write posts that hold attention long enough to earn a long dwell. Long dwells and comments are two of the four engagement signals LinkedIn names as ranker inputs. Chapter 3 walks through the post structures that keep a reader on the screen past the "See more" fold.
Read Chapter 3 arrow_forward - Check whether your Followable layer is the one costing you. The Scorecard's routing table flags the layer scoring lowest across the five: Findable, Followable, Verifiable, Credentialed, Introducible. Four minutes, no signup.
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Notes
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LinkedIn Engineering Blog — Hristo Danchev (Senior Staff TPM, LinkedIn Feed AI/Infra). "Engineering the next generation of LinkedIn's Feed," published March 12, 2026. Primary source for the LLM-based dual-encoder retrieval, the transformer "Generative Recommender" ranker reading >1,000 past interactions per member, sub-50ms end-to-end latency, embeddings refreshing within minutes, and the four named engagement inputs (long dwells, likes, comments, shares). Also confirms format is an input feature the ranker sees, with no disclosed differential boost by format type. linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed ↑ ↑ ↑ ↑ ↑ ↑
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LinkedIn Engineering Blog — Siddharth Dangi, Johnson Jia, Manas Somaiya, Ying Xuan. "Understanding Feed Dwell Time," published May 12, 2020. Discloses the two dwell-time measurements ("on the feed," starting once at least half a post is visible; and "after the click"), the P(skip) probability model as a ranking feature, and the offline AUC lift ("as much as 10%") from adding dwell-time features. Predates the March 2026 rebuild; treat the mechanism as durable doctrine, treat the specific 10% figure as describing the pre-2026 system. linkedin.com/blog/engineering/feed/understanding-feed-dwell-time ↑
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LinkedIn Pulse — Pete Davies (then LinkedIn Senior Director of Product Management, Feed). "What's in your LinkedIn feed: People You Know, Talking About Things You Care About," published June 25, 2019. LinkedIn's most direct on-the-record statement that feed ranking does not favor a particular post format. Predates two subsequent architecture rebuilds; LinkedIn has not issued a newer statement either reaffirming or retracting this position. linkedin.com/pulse/whats-your-linkedin-feed-people-you-know-talking-things-pete-davies ↑ ↑ ↑
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LinkedIn Pulse — Laura Lorenzetti (LinkedIn, VP of Product). "Keeping conversations real," published May 20, 2026. Announces the AI-content policy: generic AI-generated posts are not removed; their distribution is suppressed beyond the poster's direct connections and followers. Reports 94% detection accuracy in initial testing. Explicitly states AI-assisted content that carries original perspective or sparks meaningful conversation is not targeted. No disclosed magnitude for the reach reduction. linkedin.com/pulse/keeping-conversations-real-linkedin-laura-lorenzetti-9821e ↑
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LinkedIn Help Center — "Best practices to customize what you see on your LinkedIn feed" and "Suggested posts in feed" (undated, verified current at access, 2026). Official disclosure that Not interested, unfollow, and Report post register as negative signals that reduce similar content in a member's feed. Reactions, comments, reposts, and sends are named as positive signals. No disclosed weighting between positive and negative signals. linkedin.com/help/linkedin/answer/a528031 · linkedin.com/help/linkedin/answer/a1499047 ↑ ↑
Five primary-source citations, all Tier 1 by the project's evidence bar — two LinkedIn Engineering posts, one LinkedIn Pulse product-leadership post, one LinkedIn VP policy post, and the live Help Center. The multipliers career-advice creators quote (carousel 1.9×, document 3×, video 1.21×) are named because they circulate widely; they are cited to no one because LinkedIn has never published a number that supports them. The specific gaps we did not claim to know — a "first 90 minutes" window, a cold-start percentage, a numerical reach penalty for AI content — live in the underlying research bundle.