How The LinkedIn Algorithm Really Works Now
LinkedIn now uses one large AI model to rank content, so old hacks fail. Learn how it reads your posts, your profile, and your behavior to shape visibility.
You have probably noticed this already. The more people talk about “cracking” the LinkedIn algorithm, the less anyone seems to agree on how it actually works.
One person swears by posting at 8:07 in the morning. Another tells you to chase comments in the first 30 minutes. Someone else says you need carousels, long posts, or only one line posts. None of them can explain why last week’s post with 20 likes reached thousands of people, but yesterday’s post with 60 likes went nowhere.
If you feel like you are doing everything “right” and still feel invisible, you are not alone. Many smart people are posting more than ever and getting less reach, then walking away thinking, “Maybe my content is just bad” or “LinkedIn must be punishing me.”
Here is something you most likely do not know. LinkedIn has moved from a collection of smaller ranking systems toward a single, very large AI model that helps decide what you see, what you do not see, and who sees you. It does not behave like the old “if likes, then boost” type of logic that most advice is still built on.
That change matters for you, even if you never want to read a research paper in your life. It changes how your profile is read. It changes how your posts are grouped with other posts. It changes how your past behavior affects what you see next.
It also means two uncomfortable things. First, nobody outside LinkedIn has a full blueprint of the algorithm. Second, even inside LinkedIn, no single engineer can look at your post and tell you exactly why it did or did not reach a certain person.
The good news is that you do not need a secret formula. You just need a clear mental picture of what the system is trying to do and where you can still influence it. Your profile, your content, and your daily behavior are all inputs the model learns from.
My goal here is simple. I want to give you a plain language map of how the new LinkedIn “brain” works, why old hacks keep failing, and how you can work with the system without burning out or turning into a full time content machine. Once you see the logic behind it, the platform feels less like a mystery and more like a tool you can actually use.
From Simple Signals To One Large Model That Reads Your Content
For years, people thought the LinkedIn feed worked like a basic scoreboard. If your post got likes fast, it moved up. If people commented early, even better. The advice was simple. Post at the perfect time. Ask a question. Use a hook. Repeat every day.
That mindset worked a little because older systems leaned heavily on surface level signals. They used many small models, each looking at a narrow piece of your activity, and engineers had to maintain all of them. It was a giant patchwork of rules, signals, and hand built features. When something broke or became outdated, another rule was added on top.
LinkedIn replaced that structure with one foundation model called 360Brew. It is a huge decoder only model with one hundred fifty billion parameters. It understands text at a deeper level than the older systems. Instead of counting keywords or waiting for early likes, it reads the language of your post, your profile, and your past interactions to understand what your content is about and who it fits.
This shift matters more than any posting trick. You are no longer dealing with a system that reacts only to engagement. You are dealing with an AI that tries to understand meaning. It looks at the ideas in your post, the clarity of your writing, and the people who tend to respond to you. Then it predicts who might find your content valuable.
Think about what that means for your strategy. You do not need to chase hacks. You need to treat your post like a short conversation with a smart reader. If the message is confused, the system will not know who should see it. If your story is clear and relevant, the system has a better chance of passing it to the right people.
What The Algorithm Actually Reads When It Looks At Your Content
When people talk about the LinkedIn algorithm, they often imagine a machine that counts likes, scans for keywords, or checks how fast people respond. That was closer to how older systems worked. The new model does something very different. It reads.
360Brew is a text based model. It treats almost everything as language. Your post. Your profile. Your comments. The viewer’s history. Even the way two people interact can be represented as text. The paper explains that signals once engineered by hand are now expressed as natural language prompts.
Once you understand that the feed is built on top of a language model, writing becomes much simpler. You are writing for a real reader and a machine that reads like one.
1. Your Words Are The Main Source
The model sees your post first through your text. If your message is scattered or moves across several topics, the model has a harder time deciding who it fits. If your post is clear and focused, it becomes easier for the model to match the topic with readers who cared about similar content in the past.
Think about this like a brief conversation. If you tell a friend, “Today I learned something interesting about leadership,” they know what to expect. If you say, “Today I learned something weird about work and AI and morning routines and productivity,” they have no idea which part matters.
Language models behave in a similar way. Clarity helps.
2. Your Profile Is Part Of The Story
Many people underestimate how much their profile shapes the reach of their posts. Since 360Brew uses text from your profile as part of your identity, the way you describe yourself matters. It acts like a reference page that gives the model context about your background, interests, and possible areas of expertise.
If your About section is vague or filled with buzzwords, you give the model less useful information. If your experience is a list of duties instead of outcomes, the model cannot easily infer what you know or who your content might help.
Clear narrative in your profile helps the model build a clearer picture of who you are. That picture becomes part of how your posts and viral posts are interpreted.
3. Your Interaction History Becomes Training Data
One of the biggest changes in the new system is many shot personalization. Instead of a few recent clicks or likes, the model looks at a long sequence of your past behaviors written in text form. This is how the model learns your patterns and interests.
Here is what that means in practical terms.
If you often comment on thoughtful posts about your field, the model sees those topics as relevant to you.
If you engage with low quality threads, quick dopamine posts, or generic motivational content, that becomes part of your pattern too.
If you regularly ignore certain types of content, the model learns that you probably will not interact with similar posts.
Your behavior shapes what you see, but it also shapes who sees you. The system tries to connect creators and viewers who share patterns of interest.
4. The Model Connects Everything Into One Context
Because everything is represented as text, 360Brew can combine several layers of meaning.
What your post is about.
What the reader usually engages with.
How the interaction history between you and the reader has looked.
Whether similar posts from you performed well with similar people in the past.
This is more than counting likes. It is an attempt to understand relevance at a deeper level. This approach allows the model to generalize faster, adapt to new topics, and rank content with less manual tuning.
5. Why This Matters For Your Writing Style
If the model is reading like a smart but extremely busy reader, you need to help it understand the essence of your message quickly.
A few simple habits can make a huge difference.
Put the main idea in the first line.
Stick to one topic per post.
Use short paragraphs so each idea stands on its own.
Avoid vague claims and focus on concrete insights or experiences.
Use plain language that sounds like a human speaking, not a slogan.
Your goal is not to trick the system. Your goal is to give it enough clarity to know who might find value in what you wrote. When the meaning is clear, the model is more confident in predicting relevance. Higher confidence usually leads to better distribution.
Why Old LinkedIn Hacks Stop Working And Can Even Hurt You
People still share tips that worked years ago. Post at the perfect time. Add a question at the end. Use a hook that feels dramatic. Get thirty comments in the first hour. Join groups that promise engagement swaps. Many of these tactics were built for an older system that relied heavily on surface level signals. They no longer match how the new ranking model works.
The new system reads your content, reads the viewer’s history, and uses long patterns of behavior to predict relevance. This makes old hacks weak at best and damaging at worst.
The System Is Built To Generalize Past Simple Patterns
Large language models are trained on enormous amounts of text and interactions. The 360Brew paper shows that the model was designed to replace many hand tuned features with a general method that handles different tasks using the same architecture.
When a model can generalize this well, small shortcuts do not work for long. If the model notices that a posting trick consistently produces shallow engagement, it adjusts. These systems evolve through continual updates and evaluation. A tactic that worked last month can fade quickly.
This is why you sometimes hear creators say, “My trick stopped working.” The model learned around it.
Hacks Collapse Because The Model Reads Meaning, Not Just Signals
Older ranking systems could be influenced by early likes because they did not understand meaning in a deep way. They reacted to patterns in the numbers. Today, meaning is central. When your post reaches the ranking stage, the model reads your words as text and compares them with the viewer’s history.
This shift makes certain tactics weaker.
Keyword stuffing does not help because the model understands context.
Overly dramatic hooks do not score better if the post does not deliver value.
Asking for engagement can look unnatural if the content does not merit it.
In short, the model is looking for usefulness, not theatrics.
Engagement Pods Can Backfire Hard
Pods have always been risky, but the new system makes them even weaker. When people join a pod, they exchange predictable, shallow interactions. Comments look similar. Timing is predictable. The same faces appear again and again. The behavior is mechanical. Even if you’re using the same tools as LinkedIn influencers, you might still run into problems.
A language model that reads patterns can pick up on this. When the model sees the same group of users leaving low value comments on every post, it starts to view those interactions as less meaningful. The more predictable the pattern, the more likely the model will discount it.
This does not require a manual penalty. It is simply how predictive systems behave when they see data that does not match typical organic activity.
Forced Engagement Breaks Your Profile Signal
Your profile is part of your identity. The research explains that many shot personalization uses a long sequence of past behaviors to learn your preferences.
If you regularly comment on content you do not care about, the model learns a mixed pattern. If you engage with low quality threads, that becomes part of your signal. This confuses your profile and lowers the chance that your posts will reach people who actually care about your topics.
You end up teaching the model the wrong story about who you are and what you value.
Some creators also believe posting ten times a day increases reach. With the new system, quantity without clear identity makes it harder for the model to understand what your content represents. If you jump from AI to motivation to leadership to personal updates to memes, the system struggles to predict who your audience should be.
When a model cannot confidently predict relevance, it reduces exposure to avoid showing people content that might not matter to them. Low confidence usually results in lower distribution.
You do not need high volume. You need a consistent pattern of meaning.
Tricks Fade Because the System Is Updated All the Time
This is the part many people forget. The LinkedIn feed infrastructure is under constant improvement. The 360Brew paper describes ongoing refinement of prompts, features, and the quality of the model itself. Updates happen often, sometimes weekly.
A trick that exploits a minor ranking behavior will not live long. The moment the model or the infrastructure changes, the trick can disappear. Some hacks even stop working overnight because a prompt or scoring function was improved.
Chasing tricks is a losing race because the system evolves faster than people can adapt to hacks.
And if the model is reading meaning and learning from long term patterns, then the only sustainable strategy is to support that process with consistent, clear behavior.
Write posts that reflect a real interest or insight.
Comment on content you truly care about.
Build a profile that explains who you are in plain language.
Maintain steady posting habits instead of extreme spikes.
These are slow habits, but they build a strong identity signal that helps the system recognize your content more accurately.
Does The LinkedIn Algorithm Promote Men’s Content More?
Short answer, I cannot see LinkedIn’s code, so I cannot prove what the algorithm does. I can only look at three things: the official research, LinkedIn’s public statements, and what independent experiments and audits are showing.
Right now, all three point in different directions, which is why this topic feels so messy