Introduction to Query Fan-Out
Google’s VP of Product for Search, Robby Stein, recently shared new information about how query fan-out works in AI Mode. Although the existence of query fan-out has been previously detailed in Google’s blog posts, Stein’s comments expand on its mechanics and offer examples that clarify how it works in practice.
What is Query Fan-Out?
When a person types a question into Google’s AI Mode, the system uses a large language model to interpret the query and then "fan out" multiple related searches. These searches are issued to Google’s infrastructure and may include topics the user never explicitly mentioned. For instance, if you’re asking a question like things to do in Nashville with a group, it may think of a bunch of questions like great restaurants, great bars, things to do if you have kids, and it’ll start Googling basically.
How Query Fan-Out Works
Stein described the system as using Google Search as a backend tool, executing multiple queries and combining the results into a single response with links. This functionality is active in AI Mode, Deep Search, and some AI Overview experiences. The system can issue dozens or even hundreds of background queries and may take several minutes to complete.
Scale and Scope of Query Fan-Out
AI-powered search experiences, including query fan-out, now serve approximately 1.5 billion users each month. This includes both text-based and multimodal input. The underlying data sources include traditional web results as well as real-time systems like Google’s Shopping Graph, which updates 2 billion times per hour. Stein referred to Google Search as "the largest AI product in the world."
Deep Search Behavior
In cases where Google’s systems determine a query requires deeper reasoning, a feature called Deep Search may be triggered. Deep Search can issue dozens or even hundreds of background queries and may take several minutes to complete. For example, Stein used it to research home safes, a purchase that involved unfamiliar factors like fire resistance ratings and insurance implications.
AI Mode’s Use of Internal Tools
AI Mode has access to internal Google tools, such as Google Finance and other structured data systems. For example, a stock comparison query might involve identifying relevant companies, pulling current market data, and generating a chart. Similar processes apply to shopping, restaurant recommendations, and other query types that rely on real-time information.
Technical Similarities to Google’s Patent
Stein described a process similar to a Google patent from December about "thematic search." The patent outlines a system that creates sub-queries based on inferred themes, groups results by topic, and generates summaries using a language model. Each theme can link to source pages, but summaries are compiled from multiple documents. This approach differs from traditional search ranking by organizing content around inferred topics rather than specific keywords.
Looking Ahead
With Google explaining how AI Mode generates its own searches, the boundaries of what counts as a "query" are starting to blur. This creates challenges not just for optimization, but for attribution and measurement. As search behavior becomes more fragmented and AI-driven, marketers may need to focus less on ranking for individual terms and more on being included in the broader context AI pulls from.
Conclusion
In conclusion, query fan-out is a powerful feature in Google’s AI Mode that allows the system to generate multiple related searches and provide more comprehensive results. With its ability to access internal Google tools and real-time data, AI Mode is changing the way we search and interact with information online. As AI-powered search experiences continue to evolve, it’s essential to understand how they work and how they can be used to improve our online experiences. By leveraging the power of query fan-out and AI Mode, we can unlock new possibilities for search and discovery, and create a more intuitive and user-friendly online environment.