Introduction to AI Search Visibility
The founder of Lorelight, Benjamin Houy, has decided to shut down the platform due to his conclusion that most brands do not require a specialized tool for tracking their visibility in AI search engines like ChatGPT, Claude, and Perplexity. Houy’s decision was based on his review of hundreds of AI answers, which revealed that the most frequently mentioned brands shared certain characteristics, including high-quality content, mentions in reputable publications, a strong reputation, and genuine expertise.
What Makes a Brand Visible in AI Search
According to Houy, there is no distinct "GEO strategy" or "AI optimization" separate from traditional brand building. He believes that AI models are trained on the same content that helps build a brand’s reputation elsewhere. In a blog post, Houy explained that customers appreciated the insights provided by Lorelight, but often stopped using the platform because the data did not lead to changes in their tactics. He argued that users tended to focus on the same fundamentals, regardless of whether they used GEO dashboards or not.
Debate on AI Search Visibility
The decision to shut down Lorelight has sparked a debate among marketers, with some professionals applauding Houy’s back-to-basics message and others arguing that AI search visibility is a significant metric. Some experts, like Lily Ray, praised Houy for his honesty, while others, such as Randall Choh, disagreed, citing the growing importance of AI search and its potential to drive conversions. Karl McCarthy pointed out that quality content, authoritative mentions, and reputation are essential for AI search visibility, but these are not features that can be provided by a tool.
Measuring AI Search Visibility
Measuring AI search visibility is still a challenging task, as assistants work differently from traditional web search engines. Assistants can surface brands in two main ways: by citing and linking sources directly in answers or by guiding users into familiar web results. Referral tracking can be done through direct links, copy-and-paste, or branded search follow-ups. However, attribution can be messy, and teams often rely on a combination of UTM tagging, branded-search lift, direct-traffic spikes, and assisted-conversion reports to estimate "LLM influence."
Why AI Search Visibility Matters
The main question is whether AI search requires its own optimization framework or if it primarily benefits from the same brand signals that drive traditional search engine optimization (SEO). If Houy is correct, standalone GEO tools might only provide engaging dashboards without influencing strategy. On the other hand, if advocates of AI search visibility are correct, overlooking this metric could mean missing out on profitable opportunities between traditional search and LLM-referred traffic.
What’s Next for AI Search Visibility
It is likely that SEO platforms will continue to incorporate "AI visibility" into existing analytics rather than creating a separate category. The safest approach for businesses is to focus on brand-building work that assistants already reward, while testing assistant-specific measurements where they are most likely to pay off. By doing so, companies can ensure they are well-positioned to capitalize on the opportunities presented by AI search.
Conclusion
In conclusion, the debate surrounding AI search visibility highlights the need for businesses to understand the role of AI in driving brand visibility and conversions. While some experts argue that AI search requires its own optimization framework, others believe that traditional brand-building strategies are sufficient. As the landscape continues to evolve, it is essential for companies to stay informed and adapt their strategies to maximize their online presence and reach their target audiences. By focusing on high-quality content, building a strong reputation, and leveraging authoritative mentions, businesses can improve their visibility in AI search and drive long-term success.