Key Highlights
Google Launched AI Mode at Google IO 2025, an AI based search experience built on a custom version of Gemini 2.5 Flash.
Google AI Mode helps with complex tasks and questions by breaking them down into smaller tasks using the query fan-out technique.
AI mode uses advanced reasoning to accurately cite sources. A needed evolution from Google AIO, which generates answers and cite sources after.
We believe Google will began to syphon long-tail searches towards AI Mode.
SEO's can optimize for AI Mode by optimizing for topics, addressing sub-topics in structured content.
SEO's should use tools like Azoma that track how they perform in AI Mode.
Introduction: What is Google AI Mode?
At Google I/O 2025, Google announced the nationwide rollout of AI Mode across the US, with international expansion planned for later this year. This represents the most significant evolution in Google since the introduction of RankBrain, fundamentally changing how users interact with answer engines.
AI Mode harnesses Gemini 2.5 Flash to handle complex questions and multi-faceted queries that traditional search and AI Overviews struggle to address effectively. Unlike the problematic AI Overviews launch, this new AI feature demonstrates significantly improved accuracy and reliability through its sophisticated query processing architecture.
As competition intensifies with ChatGPT and Perplexity, we predict Google will increasingly route complex, long-tail searches toward this platform, positioning it as the primary interface for nuanced information discovery and comprehensive research reports.
Google AI Mode Queries are 2 to 3 Times Longer
User behavior in this platform reveals a fundamental shift in types of queries. Searches are consistently 2-3 times longer than traditional Google queries, indicating users feel comfortable expressing complex, conversational intent rather than keyword fragments.
This behavioral change mirrors the success of platforms like Perplexity, where users naturally engage in more detailed questioning. The extended query length provides the system with richer context, enabling more precise intent understanding and comprehensive results.
Rather than conducting multiple iterative searches, users can now move from initial research to actionable insights in a single interaction. This efficiency represents a paradigm shift from traditional information retrieval to intelligent assistance that can save hours of research time.
Google AI Mode Cites Multiple Sources
This platform's source handling represents a crucial improvement over AI Overviews' citation methodology. Instead of generating responses and retroactively finding supporting sources, the system actively synthesizes information from multiple authoritative sources during the reasoning process.
The system evaluates source credibility, cross-references information across multiple publications, and presents synthesized insights with transparent attribution. This approach significantly reduces hallucination risks while providing users with verifiable, comprehensive results.
For complex questions requiring interdisciplinary knowledge, the platform excels at connecting insights from academic research, industry reports, and expert analysis, delivering nuanced understanding that single-source responses cannot match.
How Does Google AI Mode Process Queries?
This platform's processing architecture fundamentally differs from traditional algorithms. When users submit complex questions, the system employs advanced natural language understanding to parse intent, context, and desired outcome.
The integration with Gemini 2.5 Flash enables sophisticated reasoning capabilities, allowing the system to understand implicit connections, identify knowledge gaps, and structure comprehensive results. This processing power extends to analyzing up to 1,500 pages of content simultaneously while maintaining coherent context.
Project Mariner integration provides additional capabilities for task completion, enabling the platform to move beyond information retrieval toward actionable assistance, including trip planning, reservation booking, and comprehensive research reports synthesis.
Custom Gemini 2.5 Flash for Enhanced Performance
Google developed a specialized version of Gemini 2.5 Flash specifically optimized for applications. This custom model balances processing speed with reasoning depth, ensuring responsive performance while maintaining answer quality.
Gemini 2.5 Flash demonstrates exceptional performance across AI benchmarks, leading the LMArena leaderboard in all categories and showing significant improvements in coding, reasoning, and long-context understanding. This benchmark superiority translates directly to quality, with the model excelling at complex questions interpretation and multi-source synthesis.
The model's training emphasizes accuracy, source attribution, and contextual understanding—addressing the primary weaknesses identified in AI Overviews. Real-time web browsing capabilities ensure information freshness, while advanced reasoning prevents the logical errors that plagued earlier AI implementations.
Integration with Google's broader ecosystem—including Gmail, Chrome, and Google Workspace—enables personalized, context-aware responses that leverage users' existing data with explicit permission.
Google Deep Research: Advanced Analysis Capabilities
One of the most powerful new AI features introduced alongside the platform is Deep Research, available through the Google AI Pro plan. This capability takes the query fan-out technique to an unprecedented level, conducting hundreds of simultaneous searches to create expert-level, fully-cited reports in minutes.
Deep Research excels at handling complex questions that require extensive investigation across multiple domains. Instead of spending hours of research manually gathering information from various sources, users can request comprehensive analysis on topics ranging from market research to academic investigations.
The feature integrates seamlessly with docs and other Google Workspace tools, allowing users to export detailed reports directly into their workflow. This represents a significant evolution toward automated research assistance, positioning Google as a direct competitor to traditional research and consulting services.
Different from Overviews: Query Fan-Out Technique
The query fan-out technique represents the platform's core innovation, distinguishing it fundamentally from AI Overviews' flawed approach. When processing complex questions, the system simultaneously decomposes them into multiple focused sub-queries, each addressing specific aspects of user intent.
For example, the query "How does AI-based optimization affect SEO for businesses targeting global audiences?" becomes:
"AI impact on SEO strategies"
"Global SEO considerations for businesses"
"AI adoption trends by region"
"SEO tool adaptation for AI platforms"
Each sub-query runs through traditional algorithms simultaneously, ensuring comprehensive coverage while maintaining the accuracy and reliability of established ranking systems. Gemini 2.5 Flash then synthesizes results using advanced reasoning, producing coherent, well-attributed responses.
This parallel processing approach explains the platform's superior accuracy compared to AI Overviews, which generate responses first and find sources afterward.
Content Optimization Strategies for Google's New Platform
The shift to this new AI feature demands fundamental changes in SEO strategy, requiring a move from keyword-focused optimization to comprehensive topic coverage that addresses multiple user intents through the query fan-out process.
Topic Authority Over Keywords: Effective optimization involves creating in-depth content addressing primary topics and related subtopics, such as "AI impact on digital marketing strategies" with sections covering implementation, measurement, and future trends. Structure content to address various user intents within your topic area, ensuring the platform finds relevant information regardless of specific query phrasing. Develop content that demonstrates clear understanding of topic relationships, helping the system recognize your authority across related subject areas.
Sub-Intent Coverage and Structure: Since the platform decomposes complex questions into focused components through query fan-out, content succeeds when it addresses not just primary topics but anticipates related questions users might have. Address all logical sub-questions within your topic area with dedicated sections providing detailed, actionable information. For a topic like "AI's impact on SEO," include sections on implementation challenges, measurement strategies, tool adaptations, and future predictions.
Research Integration and Formatting: Incorporate insights from multiple authoritative sources, demonstrating the comprehensive research the platform values in source selection and synthesis. Use clear headings, logical flow, and scannable formatting that helps the system identify and extract relevant information efficiently during the fan-out process. This approach ensures your content appears relevant for both primary queries and component sub-queries generated during processing.
You can also look at the recommendations provided by Google themselves.
Conclusion
Google's new AI feature represents a fundamental shift in technology, moving beyond information retrieval toward intelligent assistance. As types of queries become longer and more complex, SEO strategies must evolve from keyword targeting to comprehensive topic coverage.
Success in this platform requires understanding query fan-out, creating structured content that addresses multiple user intents, and building topical authority rather than keyword density. Organizations that adapt quickly to these changes will maintain visibility in an increasingly AI-mediated landscape.
The transition is happening now. If you'd like to track your performance in AI mode, reach out to our team at Azoma here.
Frequently Asked Questions
Will AI Mode Replace Standard Google Search?
AI Mode complements rather than replaces traditional Google Search. While AI Mode handles complex, multi-faceted queries requiring synthesis and reasoning, traditional search remains optimal for simple, direct information retrieval. Google maintains both interfaces to serve different user needs and query types effectively.
How do I Track my Performance in AI Mode?
Currently, traditional analytics tools don't provide AI Mode-specific metrics. Specialized tools like Azoma are developing AI Mode tracking capabilities. Google AI Pro subscribers gain access to advanced analytics features, though comprehensive AI Mode performance tracking remains an emerging capability requiring dedicated monitoring solutions.
Will Standard SEO Strategies Work for This Platform?
Traditional SEO strategies require significant adaptation for success. While technical SEO fundamentals remain important, content strategy must shift from keyword targeting to topic authority. The platform prioritizes comprehensive results and well-structured content over keyword-optimized pages, demanding new approaches to content creation and optimization.
What is Google AI Mode and how does it work?
Google's platform is an advanced interface powered by a custom version of Gemini 2.5 Flash. It uses query fan-out techniques to decompose complex questions into multiple sub-searches, processes them simultaneously through traditional algorithms, then synthesizes results using AI reasoning. This approach provides comprehensive results with proper attribution while maintaining the accuracy of established systems.
Article Author: Max Sinclair