Dan Sherrard-Smith Explains Algorithm Shift Impacting LinkedIn Reach

In a recent LinkedIn post, Dan Sherrard-Smith discusses a significant shift in the platform’s algorithm and its impact on user reach, asserting that a decline in visibility is not necessarily due to content quality but rather to a fundamental change in how LinkedIn distributes posts.

Sherrard-Smith highlights the introduction of a new AI system, which he terms “360Brew,” as the primary driver of these changes. According to his analysis, this AI actively reads posts and user profiles to determine audience relevance.

“Your LinkedIn reach didn’t drop because your content got worse. It dropped because LinkedIn rebuilt the entire algorithm, and you’re now being shown to the wrong people.”

The core of Sherrard-Smith’s argument centers on how the algorithm now categorizes users and their content. He explains that if a user primarily engages with peers within their own industry, such as consultants interacting mainly with other consultants, LinkedIn’s AI interprets this as the user’s target audience being those peers.

This misinterpretation, as Sherrard-Smith points out, leads to content being shown to the wrong demographic. Instead of reaching potential clients or decision-makers, users find their posts distributed to individuals who are not likely to convert into business opportunities. This phenomenon, which he labels the “Bubble Effect,” is identified as a key reason for the widespread drop in reach experienced by many users.

The Algorithm’s New Mechanism

Sherrard-Smith elaborates on the mechanics of this algorithmic overhaul. He states that the new AI system evaluates not only what a user writes but also their engagement patterns. This holistic approach means that both content creation and interaction behavior play crucial roles in shaping content distribution.

“LinkedIn’s new AI system (360Brew) actually reads your posts and profile. It decides who you’re ‘for’ based on what you write AND who you engage with.”

He further quantics that this has led to a substantial decrease in median reach across the platform, estimating it at a 47% drop. To understand and combat this, Sherrard-Smith and his team have conducted extensive research, consulted with LinkedIn insiders, and analyzed data from over 500,000 profiles.

Strategies for Algorithm Retraining

Addressing the “Bubble Effect,” Sherrard-Smith outlines a strategy for “retraining” the algorithm to ensure content reaches the intended audience. This involves a deliberate and phased approach to content creation and engagement, aiming to signal to LinkedIn’s AI that the user’s primary audience consists of potential clients rather than just industry peers.

He suggests that this requires a strategic shift in how users approach the platform, moving beyond simply posting content to actively managing their visibility. Sherrard-Smith’s research has culminated in a guide, “The LinkedIn Growth Blueprint: 2026 Strategy,” which details his findings and proposed solutions.

Key Components of the Strategy

According to Sherrard-Smith, the guide covers several critical areas, including:

• The underlying reasons for LinkedIn’s algorithm changes.
• A detailed breakdown of the algorithm’s four-stage content distribution process.
• The “Bubble Effect” and its implications.
• A three-phase system designed to retrain the algorithm.
• Analysis of effective content formats backed by performance data.
• The 80/20 rule for optimizing distribution towards buyers.
• A structured posting schedule and a comprehensive strategy checklist.

Sherrard-Smith emphasizes the importance of adapting to these new algorithmic realities to regain and enhance reach on the platform. He offers this blueprint as a solution for founders and professionals seeking to connect with decision-makers effectively.