LinkedIn has shifted dramatically! 7 old tactics no longer work

If you are still running LinkedIn with the approach you used in 2024, it may be time to rethink your strategy.

In 2026, LinkedIn is making a significant upgrade to its recommendation algorithm.

Its new recommendation system uses LLMs and generative recommendation models to understand content topics, user interests and past interactions more deeply.

At the same time, the platform is targeting AI slop, automated comments and low-quality engagement.

That means:

Relying on templates, manufactured comments and sheer publishing volume to gain exposure is becoming harder.

I have distilled this year’s key changes into seven points, especially relevant to B2B SaaS, AI products, international marketing and founder-led personal brands.

Many people fall into this pattern on LinkedIn:

AI tools today.

Family life tomorrow.

A motivational leadership quote the day after.

The account looks active, but never develops a clear position.

In the recommendation mechanism announced in March 2026, LinkedIn emphasised using LLMs to understand posts and match them to users’ long-term interests.

The platform needs to understand not only what you publish, but who may genuinely care about it.

Instead of chasing a different trend every day, develop two or three connected areas consistently.

For an AI SaaS founder, these might be:

• AI agents and automation
• SaaS growth and acquisition
• Practical product lessons from building a company

These topics share a target audience.

Consistency helps people understand why they should follow you.

The same applies to your profile.

Your headline, About section and regular posts should support the same professional positioning.

The easier you are to understand, the easier it is for readers to recognise your expertise.

In September 2026, LinkedIn Chief Product Officer Hari Srinivasan announced a feed test.

The platform is experimenting with two tabs:

Network: your existing connections

Primarily content from people you are connected to or follow.

For You: personalised recommendations

Relevant content from within and beyond your network, based on professional interests.

This suggests that LinkedIn may support two distribution mechanisms:

One based on existing relationships.

The other based on matching content and interests.

It is a change creators should watch.

Previously, building a large network might have seemed necessary for consistent exposure.

Interest-led recommendations give professional content a chance to reach potential customers beyond that network.

The feature is still being tested and is not visible to everyone. It does not mean follower counts no longer matter.

My interpretation is:

Your network shapes your existing influence; content quality shapes your ability to keep reaching new people.

This is one of the most important changes this year.

LinkedIn introduced an interesting feedback option:

Seems like AI slop

Users can flag posts they consider mass-generated AI content without a genuine point of view.

In figures published in September, LinkedIn said:

• More than one million members had used the feedback feature.
• Overall views of posts identified as AI slop had fallen by approximately 40%.
• Some feedback was being added to creators’ private analytics.

This does not mean that using AI to write automatically reduces reach.

The target is primarily low-effort, repetitive, empty content lacking an individual perspective.

For example:

“Five habits successful people practise every day.”

“Seven truths every founder must know.”

“AI is changing the world. Are you ready?”

Such posts may read smoothly and look attractive.

Yet the reader finishes without learning anything useful.

My recommendation is:

AI can organise your language, but it cannot create your experiences, judgment or insight for you.

One post containing real data, a practical case and an original perspective is preferable to ten polished pieces of empty AI copy.

The scarce resource is not the ability to produce text, but professional insight worth sharing.

Many LinkedIn operators previously relied on automation tools.

Automatically liking dozens of posts each day.

Generating comments in bulk.

Even using browser extensions to reply to prospects automatically.

LinkedIn is prioritising action against these behaviours in 2026.

The company says it blocks hundreds of thousands of automated commenting attempts every day.

Two patterns are particularly relevant:

First, large-scale automated comments with little human involvement.

Second, replies that merely restate a post without adding information.

Suppose someone shares an article about SaaS growth.

You comment:

“Great insights. Thanks for sharing!”

That comment is not inherently a violation, but usually adds little value.

What if you could add:

“We tested a similar approach last year and found that the trial-to-paid bottleneck was initial product activation, not registration.”

Would that not create a better discussion?

An effective comment does not just show you were there. It shows you have thought about the issue.

A common belief is:

Social media users have no patience, so shorter is always better.

But LinkedIn’s feed does not simply reward short posts.

Its recommendation system predicts several behaviours, including longer reading dwell time, or Long Dwell.

This indicates that reading behaviour provides a useful signal.

However, dwell time is not the only ranking factor, and longer posts do not automatically receive more exposure.

The important question is:

Does the content keep delivering value that makes people want to continue?

I recommend this structure:

Opening: introduce a clear problem or counterintuitive perspective.

Second paragraph: explain why the problem matters.

Body: develop the analysis with cases, data and experience.

Ending: provide a clear conclusion or practical recommendation.

For example:

An ordinary opening:

“Today I am sharing five B2B SaaS marketing tips.”

A stronger opening:

“We published 30 SEO articles without gaining a single paying customer. Later, we realised SEO was not the problem.”

The second supplies a concrete situation and a question worth exploring.

The opening determines whether someone expands the post; the body determines whether they finish it.

Over the past two years, one recommendation has been popular among LinkedIn operators:

Create a PDF carousel if you want more reach.

Readers keep turning pages, potentially increasing dwell time.

The situation is not that simple now.

Some third-party tracking suggests that carousels’ advantage over other formats is narrowing.

Samples, account sizes and observation periods differ, so this does not establish a platform-wide demotion of PDF carousels.

The important principle is:

Format should not substitute for content quality.

An article without an original idea remains unhelpful when divided into ten PDF slides.

A clear text post grounded in real experience may perform well instead.

My recommendation is:

Use carousels for charts, processes and step-by-step explanations.

Use text for perspectives, cases and analysis.

Use video to demonstrate product workflows.

Decide what problem the content solves before choosing its format.

The final point may be the most important.

You have probably seen claims such as:

“Saves carry five times the weight of likes.”

“Comments longer than 15 characters increase recommendations.”

“You must reply to every comment within 30 minutes.”

“LinkedIn is now controlled by a 150-billion-parameter model called 360Brew.”

These claims spread easily.

They create an illusion:

Learn one hidden parameter and you can crack the algorithm.

The problem is that many precise weightings have not been officially confirmed by LinkedIn.

The engineering article published in March 2026 actually described an LLM-based feed system using generative recommendation models.

The system can analyse more than 1,000 historical interactions to understand changing interests and predict different behaviours.

These include:

• Reading dwell time
• Likes
• Comments
• Shares and other interactions

That is very different from saying a particular action always has a fixed multiple of another action’s weight.

A recommendation algorithm is a dynamic system, not a simple points table.

Instead of studying unverifiable secret weights, study your actual data.

For example:

Which topics reach more target customers?

Which posts attract more substantive comments?

Which content increases profile visits?

Which perspectives prompt potential customers to contact you?

Comparing these signals with your account’s history is more reliable than blindly following an algorithm weighting graphic.

Taken together, these seven points suggest a clear trend.

LinkedIn is trying to distinguish two kinds of creators:

People with genuine expertise, industry experience and independent perspectives.

And people who simply use AI and automation to keep producing content.

Content production alone will become less defensible as a competitive advantage.

Anyone can ask AI to generate 20 posts a day.

Not everyone can provide real industry data.

Not everyone has personally solved complex problems.

And not everyone can offer an idea worth serious consideration.

I believe LinkedIn operators in 2026 should focus on four things:

First, establish clear professional positioning.

Help people understand who you are, your expertise and the problems you solve.

Second, keep collecting real cases.

Turn experience, judgment, failures and methods into content.

Third, participate in substantive discussions.

Build genuine professional relationships instead of manufactured engagement metrics.

Fourth, measure content by business outcomes.

Look beyond views to relevant audiences, potential customers and eventual conversion.

For B2B SaaS and AI founders, LinkedIn’s greatest value has never been follower growth alone.

It is helping future customers know, understand and trust you before they need your product.

Algorithms keep changing. Genuine expertise, real experience and long-term trust remain the hardest advantages to copy.