Understanding Google’s AI Mode and the Impact of Query Fan-Out on SEO

Google’s latest search innovation, AI Mode, is reshaping how information is presented to users, and consequently, how publishers approach search engine optimization. Launched to U.S. users last month, AI mode employs a sophisticated technique called “query fan-out” to provide synthesized answers, prompting a re-evaluation of traditional SEO tactics. This shift, powered by Google’s Gemini large language model, is already causing ripples throughout the digital publishing landscape.

What is Query Fan-Out?

At the core of AI Mode lies the concept of query fan-out. Rather than responding to a single search query,the AI breaks down the user’s question into a network of related subtopics. According to Mike King, CEO of iPullRank, this process examines the underlying “subintents” driving the initial search.

As a notable example, a query like “best running shoes” could be dissected into subqueries such as “best running shoes for women,” “trail running shoes,” “running shoes for flat feet,” and “affordable running shoes.” This happens instantaneously, with the AI model gathering information from across the web to construct a comprehensive response. As Adithya Hemanth, SEO lead at Incubeta, explains, the AI proactively anticipates and delivers information the user might find valuable, even if not explicitly requested.

This mechanism also underpins Google’s AI Overviews,enabling the system to tackle complex queries like “plan a family vacation to Europe.” The query fan-out might then generate searches for “family-kind European cities,” “budget-friendly European travel,” and “European vacation packages for families.” Lily Ray, VP of SEO strategy at Amsive, notes that the system is designed to predict and address subsequent user needs, aiming to keep users engaged within the Google search experience.

How Does This Differ From Traditional Search?

Traditional search operates on a keyword-matching basis, delivering a single set of results for a single query. In contrast,AI Mode triggers multiple,distinct searches,yielding a multitude of results. Olaf Kopp, co-founder of Aufgesang, highlights that traditional search analyzes entire webpages, while query fan-out focuses on extracting relevant “chunks” or passages from various sources. Moreover, Google does not disclose the specific queries generated during the query fan-out process.

Mollie Ellerton, head of SEO at Hookflash, emphasizes that this represents a shift towards optimizing for the unknown. “We’re optimizing for the unknown,” she states, acknowledging the increased complexity of the SEO landscape.

Implications for Publishers

The rise of AI Mode signifies a departure from the traditional goal of ranking for individual keywords. Instead, publishers must focus on providing comprehensive information across a spectrum of related sub-queries. This shift, according to SEO leaders, also suggests a potential decline in referral traffic to publisher websites. The AI-generated summaries may satisfy user intent directly within the search results, reducing the need to click through to external links.

Ray observes that AI Mode is intentionally designed to discourage external linking.While some users may still seek more in-depth analysis and click through to publisher sites, Hemanth predicts that the majority of user intent will be fulfilled by the AI-generated responses, potentially impacting traffic to evergreen content.

The SEO community anticipates a decrease in search referral traffic for publishers, with Ellerton suggesting a greater emphasis on brand performance.

Optimizing for the New Landscape

Given the lack of data transparency from Google regarding query fan-out,SEO professionals are turning to tools like Google’s People also Ask feature,and also third-party platforms such as AlsoAsked and Profound,to gain insights.The focus is shifting from tracking click-through rates to measuring visibility within AI-powered search engines. However, dedicated tools for this purpose are still in their early stages of development.

King has developed a tool called Qforia to simulate the query fan-out process using Gemini prompts, generating 20-30 related queries for a given search question.beyond these emerging tools,the foundational principles of E-E-A-T (Experience,Expertise,authoritativeness,and Trustworthiness) remain crucial.

editorial Strategies for AI-Driven Search

To succeed in this evolving environment, content must be easily digestible by AI models, employing clear language, structured formatting, and bullet points.Kopp recommends keeping passages concise, ideally between two and four sentences. Editorial teams should concentrate on addressing a wide range of topics and subtopics relevant to a user’s search journey.

Publishers should identify potential keywords, assess their current rankings, and optimize passages that Google is likely to extract. This involves breaking up lengthy paragraphs into focused, topic-specific segments. Incorporating real-world experiences, such as product review videos, and original research can also enhance content value. King stresses the need to focus on the entire customer journey, anticipating potential sub-queries and search intents.

Ellerton suggests leveraging user-generated content platforms like Reddit and TikTok to identify emerging search trends, as Google does not currently share this data.

Will AI Mode fundamentally alter the relationship between search engines and content creators? What new strategies will publishers need to adopt to thrive in this evolving landscape? Share your thoughts in the comments below!