LinkedIn’s Paradox: 60% Traffic Loss Meets Triple-Digit AI Growth

In a candid disclosure that highlights the volatility of the new search landscape, LinkedIn has revealed that Google’s AI Overviews (AIOs) have gutted its non-brand B2B awareness traffic by as much as 60%. Despite maintaining stable organic rankings, the “answer-at-the-top” nature of AIOs has severed the traditional link between visibility and clicks for many high-funnel B2B topics.

The revelation comes from LinkedIn’s internal organic growth team, which has been tracking the evolution of AI search since the early 2024 pilot phases. The data suggests that for informational queries—where users are looking for definitions or “how-to” advice—Google is increasingly satisfying the intent directly on the SERP, leaving zero reason for a user to click through to a LinkedIn article or profile.

The “Visibility Currency” Strategy

In response, LinkedIn has abandoned the traditional “Search → Click → Website” funnel. Instead, the company has pivoted to a new framework focused on Influence over Acquisition:

“Be seen, be mentioned, be considered, be chosen.”

LinkedIn argues that even if a click doesn’t occur, being the primary source cited in an AI Overview builds mental availability. When that user is eventually ready to make a purchase weeks or months later, they are more likely to return via a branded search—a phenomenon LinkedIn calls the “Dark Funnel.”

By the Numbers: LinkedIn’s AI Footprint

While awareness traffic has plummeted, LinkedIn’s performance in the AI-native ecosystem tells a different story:

• Citations: According to 2025 Semrush data, LinkedIn is the #2 most-cited domain in Google’s “AI Mode,” appearing in roughly 15% of all responses.
• Referral Growth: LinkedIn reported triple-digit growth in traffic from LLM interfaces (such as ChatGPT and Perplexity).
• Qualified Intent: While these LLM referrals currently account for less than 1% of total traffic, LinkedIn noted they convert at a significantly higher rate than traditional search visits.

The AI Search Taskforce Playbook

To combat the traffic drain, LinkedIn established a cross-functional AI Search Taskforce. Their playbook focuses on technical and reputational “signals” rather than keyword density:

1. Modular “Chunking”: Re-structuring content into self-contained, logical blocks with clear headings (H2/H3) to make it easier for LLMs to extract and cite specific answers.
2. Expert Attribution: Prioritizing “Human-in-the-Loop” content. AI systems favor content with clear timestamps and verifiable expert authors over generic corporate copy.
3. Entity Correction: Actively monitoring and correcting misinformation about LinkedIn’s products or services when they appear in generative AI responses.
4. Semantic Hierarchy: Moving beyond keywords to ensure a page’s HTML structure explicitly defines the relationship between different concepts.

The Measurement Dilemma

The biggest challenge for LinkedIn—and for B2B marketers at large—remains attribution. When a user consumes your brand’s expertise inside an AI Overview and then disappears, traditional analytics show a “zero-click” failure.

LinkedIn is now urging marketers to look at Branded Search Lift and Citation Share as the primary KPIs. In a world where search is becoming a “pinball machine” of cross-platform touchpoints, the “click” is no longer the only metric of success.