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The search is “linkedin organic reach.” It is the unpaid distribution of a post in the LinkedIn feed: how many members see it without a boost. It is not a follower count, not Delhi-specific, and not a switch LinkedIn flipped off.

LinkedIn still shows posts. It matches them to people using meaning (profile + what they have actually engaged with), then ranks candidates with a sequential model. LinkedIn’s engineering blog names the actions that model is trained on: long dwells, likes, comments, and shares. That is the honest definition. The rest of this page is what you can still control.

If you want which formats tend to earn those signals, use Best-performing LinkedIn content (https://www.influensea.com/blog/best-performing-linkedin-content-2025). This URL stays on reach itself.

What “organic reach” is not

It is not impressions from ads. It is not “everyone who follows you.” A company page with 50,000 followers can still be almost invisible in the feed. Third-party samples of company pages (not your account) have long shown page posts occupying a small slice of a typical feed compared with posts from people. Treat that as a structural bias toward personal profiles, not a personal failure.

It is also not a 360Brew score. A 2025 research write-up about a 150-billion-parameter model named 360Brew spread through creator posts as “the new algorithm.” LinkedIn’s VP of Feed engineering, Tim Jurka, later answered the question in public: 360Brew is not used in ranking. It was an internal test with a small group; they shut it down. The withdrawn paper is not a ranking spec. Stop optimizing for a model that is not in production.

What LinkedIn has actually published

On 12 March 2026, LinkedIn Engineering described the production stack in Engineering the next generation of LinkedIn’s Feed (https://www.linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed) (Hristo Danchev):

Retrieval — a unified LLM embedding system that represents members and posts, so a post about “small modular reactors” can still match someone whose profile says electrical engineering. Cold-start members get guesses from the profile, not only from a long click history.
Ranking — a transformer Generative Recommender that treats your last thousand-or-so interactions as a sequence, not three independent clicks. A related paper on Feed SR (arXiv:2602.12354) reports that sequential ranker as the majority feed experience in online tests (+2.10% time spent vs the prior production ranker). That is LinkedIn’s A/B number, not a promise about your next post.

The retrieval write-up is explicit about what goes into a post prompt: format, author (name, headline, company, industry), engagement counts (as percentiles, not raw digits), and the post text. Member prompts include profile, skills, work history, and a chronological list of posts they engaged with — not every post they merely saw.

The ranker interleaves posts with actions. LinkedIn lists long dwells, likes, comments, and shares. They have not published the weight of each. Anyone selling “comments are 3.2× likes this quarter” is inventing a number.

They have published something more useful: impressed-but-ignored posts are hard negatives in training. A post that was shown and skipped is a signal. Scrolling past you is not free.

What that means on a Tuesday

You cannot “beat” a sequential recommender with a hashtag pack. You can make it easier for the model to place you next to the right people, and harder to skip you.

Match the person you claim to be. Headline, About, recent posts, and the topic of this post should be the same job. Retrieval encodes the author. A scatter of AI news, hiring memes, and a product dump reads as no topic.

Earn time on the post. Long dwell is a named training target. That is why a native document someone swipes, or a text post they actually finish, beats a link that sends them away in two seconds. Put outbound URLs in the first comment if you must share them. LinkedIn has not published a “70% penalty”; practitioners report link-in-body posts underperform. Test it on your own analytics rather than quoting a guru percentage.

Earn a real reply. A comment thread is an action the ranker sees. “Comment YES” is bait, and LinkedIn’s product posts about authentic conversation have been explicit about suppressing that pattern. Reply in the first hour because you want the thread to exist, not because a leaked “golden hour” formula exists.

Prefer a person over a page for the same idea. Publish from the founder or operator whose profile matches the topic. Use the company page for jobs and proof, not as the only organic channel.

Do not post twice to “game freshness.” Retrieval refreshes embeddings on a minutes-scale pipeline. Spamming the slot mainly competes with yourself.

A reach checklist that is not a 30-day challenge

Do these before you publish. If two fail, rewrite.

A stranger can say what you post about after reading the headline and this draft.
The value is in the post (native text, document, or native video). The blog URL can wait in comment one.
The first two lines earn a pause. No “In today’s fast-paced world.”
There is one question or claim a practitioner could answer with a sentence of their own, not a yes.
You will be at the keyboard when it goes live, to reply like a person.

Cadence: three good posts you can keep beat a daily streak you abandon. That is operations, not an algorithm secret. The printable version of the week lives in the LinkedIn content system (https://www.influensea.com/resources/linkedin-content-system).

How to read your own numbers

LinkedIn’s analytics show impressions, reactions, comments, and (on some accounts) dwell-ish proxies. They do not show “algorithm health.” Compare this post to your last ten, not to a creator with 200k followers.

Impressions much lower than usual, comments empty, people bounce: the post was skippable or off-topic for your graph.
Impressions down, comments and saves up: smaller, better match. That can still be a win.
Company page dead, personal profile alive: expected. Stop diagnosing the page as a bug.

Do not paste a competitor’s CTR into a strategy doc. We do not have their denominator.

Where a draft tool actually helps

A model can hold a shape (hook, point, takeaway, question) so you are not inventing structure every time. It cannot give you topical history, reply in the first hour, or guarantee impressions.

If you generate a draft, paste it into a LinkedIn-shaped preview (https://www.influensea.com/sign-up) with a real post body — line breaks, “see more,” the card — then edit until it sounds like you. A queue or an analytics graph is not that check. Influensea’s free plan includes 5 LinkedIn drafts a month; no card required.

Questions people type next

Why did my LinkedIn organic reach drop?

Often: thinner native posts, links in the body, a company page as the only channel, or a topic that does not match the profile. Sometimes the feed is simply more competitive. LinkedIn has not published a dated “reach cut” you can put on a calendar. Correct the 360Brew rumor before you rebuild the strategy around it.

Do company pages still get organic reach?

Yes, less of it than people posting the same idea. Use the page for the company record. Put the argument on a human profile whose headline matches the post.

Do I need to post every day to get reach?

No. A weekly batch you keep is the constraint. Daily posting with weak dwell is how you train skip.

Should I delete old off-topic posts?

Not as a magic reset. Consistency going forward matters more than archaeology. Pin three posts that prove the job.

Ready to ship a post that can be ranked

Organic reach is unpaid distribution under a semantic retriever and a sequential ranker. Write something a specific professional would finish and answer. Preview the body before it hits the live feed.

Generate a draft (https://www.influensea.com/sign-up), paste it into the LinkedIn preview, edit, then publish when you can stay for the first replies.

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