The search marketing world is buzzing with acronyms like GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization). But do we really need separate frameworks for these emerging AI-powered search features? Google’s recent insights from Search Central Live Deep Dive Asia Pacific 2025 suggest the answer is a resounding “probably not.”
During this pivotal session, Google’s Cherry Prommawin and Gary Illyes addressed the burning question on every SEO professional’s mind: whether GEO and AEO require entirely new optimization strategies. Their response challenges the industry’s rush to create distinct frameworks for AI-powered search features.
AI Search Features: Same Engine, Different Interface
Cherry Prommawin made a crucial point that many marketers are overlooking: AI Mode, AI Overviews, Circle to Search, and Google Lens function remarkably similar to existing features like featured snippets and knowledge panels.
These cutting-edge AI tools don’t operate in isolation. Instead, they tap into the same ranking signals and data sources that power traditional search results. Think of them as sophisticated layers built on top of Google’s existing search infrastructure.
Gary Illyes reinforced this concept, explaining that AI-driven tools and classic search services share one unified infrastructure. This single system handles everything from indexing to ranking to serving results across all formats.
“AI Mode and AI Overviews are just features of Search, and built on the same Search infrastructure.”
When Google deploys new AI capabilities, they’re essentially integrating additional models into their existing framework. Circle to Search and Lens simply add their query-understanding modules on top of the foundation that’s been refined for decades.
The Three Pillars Remain Unchanged
Crawling: Same Bots, Same Process
Every AI Overview and AI Mode feature depends on the identical crawler that powers Googlebot. This system continues to visit pages, follow links, and gather fresh content exactly as it has for years.
While Gemini operates as a separate system with its own bots within Google’s ecosystem, it still feeds data into the same underlying models that inform search results.
Indexing: Proven Methods Enhanced
The core indexing process for AI search mirrors traditional search methods completely. Crawled pages undergo analysis and organization into Google’s index, where statistical models and BERT refine the data.
These statistical models aren’t new innovations—they’ve been supporting Google’s search quality for over two decades. Originally developed for features like “did you mean” suggestions and spam detection, they now enhance AI-powered results.
BERT adds natural language understanding to this established process, but it’s working with the same fundamental data structure.
Serving: Enhanced Query Understanding
The serving process begins with query interpretation, identifying stop words, extracting key terms, and parsing queries into meaningful components.
During the ranking phase, the system evaluates hundreds of potential results using various signals. Different content formats receive different weightings, but the underlying evaluation process remains consistent.
RankBrain applies machine learning to adjust these signals, while MUM brings multimodal understanding to complex queries. Both systems work within the established framework rather than replacing it.
Practical Implications: Leverage Your Existing SEO Knowledge
Given the deep integration between AI features and standard search, creating separate GEO or AEO programs might actually waste valuable resources. Here’s why sticking with proven SEO principles makes more sense:
Resource Efficiency: Instead of splitting your team’s attention across multiple frameworks, focus on enhancing your existing SEO strategies to work effectively across all search formats.
Skill Transferability: Your current SEO expertise directly applies to optimizing for AI-powered features. The same content quality, relevance, and authority signals matter whether your content appears in traditional results or AI Overviews.
Strategic Consistency: Maintaining a unified approach ensures your optimization efforts reinforce each other rather than competing for attention and resources.
Future-Proofing: As Google continues evolving its AI features, a solid SEO foundation will adapt more easily than rigid, feature-specific frameworks.
Cherry Prommawin and Gary Illyes concluded their enlightening session by emphasizing that AI represents another feature within the broader search ecosystem. SEO professionals can confidently apply their existing optimization expertise to both AI-enhanced and traditional search products.
Moving Forward: Evolution, Not Revolution
The key takeaway from Google’s perspective is clear: successful optimization in the AI era doesn’t require learning entirely new disciplines. Instead, it demands understanding how proven SEO principles apply across evolving search interfaces.
Focus on creating high-quality, authoritative content that answers user questions clearly and comprehensively. Structure your information logically, use natural language, and maintain the technical excellence that’s always driven search success.
Rather than chasing separate frameworks for every new AI feature, invest in strengthening your foundational SEO practices. This approach positions you to succeed regardless of how Google’s AI features continue evolving.
The search landscape is undoubtedly changing, but the core principles of providing value to users through well-optimized, relevant content remain constant. Your SEO expertise isn’t becoming obsolete—it’s becoming more valuable as the foundation for success across all search experiences.
