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Understanding the AI Visibility Index

In a significant move to quantify the dynamics of how leading artificial intelligence (AI) engines handle sensitive content, Everything-PR has released the first installment of the Restricted Category AI Visibility Index. This groundbreaking study highlights the complexities and biases inherent in AI when it comes to searching and discovering adult content and other restricted areas like cannabis, gambling, and telehealth. The study’s findings suggest that while adult brands may surface when explicitly named, they tend to disappear altogether when users pose the same inquiries in a generic manner.

Key Findings from the Study

This study lays out five critical findings that shed light on the behavior of AI engines like ChatGPT, Claude, Perplexity, and Google AI:

1. The Naming Threshold: This phenomenon indicates that AI engines are more likely to reveal restricted brands when they are specifically named in the query. Conversely, when users frame their questions generically, these brands disappear from the results. For example, a search for “best creator subscription platforms 2026” yields nameable platforms like Patreon, while the query “OnlyFans alternatives” rolls out options like Fansly and ManyVids, demonstrating two completely different sets of branded responses from a single AI engine.

2. The Wellness Passport: Specific brands that focus on sexual wellness and related products have a unique standing in the AI index. Brands like Maude, Dame, and We-Vibe feature prominently when users search for "best vibrators" but fail to appear when trying to discover broader platforms. This inconsistent visibility underscores the complexities of AI engagement with sensitive subjects.

3. The Role of Institutional Authority: The study notes that the visibility of brands in AI retrieval is highly dependent on their presence across trusted institutional sources such as Wikipedia, mainstream media, and wellness publications. This raises a crucial question about which sources are considered authoritative by AI training data.

4. Factual Accuracy: The research indicates that while AI engines retrieve factual information reliably when it engages with established brands like Pornhub, the problem lies primarily in the initial retrieval process itself—brands may not emerge at all unless prompted correctly.

5. Crisis Cycles: Lastly, the study reveals that events like payment-processing issues or regulatory actions have lasting impacts on how AI performs retrieval. These crises become ingrained in the training data, thus affecting visibility for brands over extended periods.

Methodology Behind the Study

Conducted in May 2026 with six specific metrics, the researchers evaluated retrieval responses from four major AI engines. Each engine was tested in controlled environments with personalization disabled, ensuring the accuracy of the findings. The six metrics that were evaluated included Citation Share, Refusal Rate, Factual Accuracy, and more, forming composite scores for each brand and engine combination.

Looking Forward

The Restricted Category AI Visibility Index aims not just to analyze existing biases but to pave the way for an ongoing exploration of how AI engines handle some of the world's most rigorously regulated industries. A second volume is anticipated for release in January 2027, expanding the scope to include more brands and diverse prompt categories.

This pioneering work will be crucial for marketers, policymakers, and industry participants who need clarity on navigating brand visibility in this complex landscape. As AI continues to evolve, understanding its biases will be vital to ensuring all brands receive equitable treatment in the digital space.

For more detailed insights, the full report can be accessed at Everything-PR.