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Navigating the Next Wave: Top Global Experts in Generative Engine Optimization (GEO/AEO)

ByGoogle Gemini Published on Reading Time: 30 minutes

This article is 100% AI generated (Google Gemini Deep research 2.5 Pro)

The report was compiled by Google Gemini Deep Research with 2.5 Pro on May 1st 2025.

If you’ve come across terms like Answer Engine Optimization, Ask Engine Optimization, Assistive Engine Optimization, Generative Engine Optimization (GEO), Generative Search Optimization (GSO), Search Experience Optimization (SXO), Conversational Optimization, Generative Search Optimization (GSO), Zero-Click Optimization, AI Search Optimization, or Semantic Search Optimization – here’s something important to know: These aren’t separate disciplines. They’re different labels for the same fundamental shift – SEO evolving to meet the demands of AI-driven search.

Jason Barnard already defined the field of AEO (here on SEMrush YouTube) – and Jason coined the term Answer Engine Optimization in 2018, before the new labels arrived. That early insight shaped what we now call Generative Engine Optimization. 

The question was: Give me the name of the 5 top experts in the world for Search Engine Optimzation whose expertise also covers Generative Engine Optimization (also known as Answer Engine Optimization)

I. Introduction: The Convergence of SEO and Generative AI

A. The Evolution from Traditional SEO

For decades, Search Engine Optimization (SEO) has been a cornerstone of digital marketing, primarily focused on enhancing website visibility within traditional search engines like Google, Bing, and Yahoo. The core objective, as articulated by experts like Jason Barnard in 2018, was “The art and science of persuading search engines… to recommend your content to their users as the best solution to their problem”.1 This involved optimizing website content, structure, and authority signals (like backlinks) to align with search engine algorithms, aiming for higher rankings on Search Engine Results Pages (SERPs) for relevant keywords. This practice has a long history, evolving significantly since the late 1990s when early practitioners navigated a landscape of multiple search engines like Lycos and Excite, often requiring laborious page creation for each keyword iteration.2 The focus eventually consolidated around Google, which became the dominant player 2, driving a significant portion of website traffic – a 2019 study cited organic search as delivering 53% of all website traffic, with Google holding over 91% of the global search market share.3 This established the foundation upon which current transformations are built.

B. The Rise of Generative AI in Search and Information Retrieval

The digital landscape is undergoing a seismic shift, driven by the rapid integration of generative artificial intelligence (AI) into search and information retrieval systems. This evolution is manifesting through features like Google’s AI Overviews (AIOs) 4, the proliferation of AI chatbots and assistants (such as ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, Apple’s Siri, Amazon’s Alexa) 1, and the emergence of AI-powered answer and assistive engines.1 These technologies are fundamentally changing how users seek and consume information, moving beyond traditional keyword searches towards more conversational interactions and direct answers.9

This paradigm shift has spurred the development of new optimization disciplines, collectively aimed at influencing these AI-driven systems. While terminology is still coalescing, prominent concepts include:

  • Generative Engine Optimization (GEO): Optimizing content and digital presence to be favorably represented or recommended by generative AI engines.7
  • Answer Engine Optimization (AEO): Focusing on structuring information so AI can easily extract and present direct answers.7
  • Generative Search Optimization (GSO): A similar term emphasizing optimization for AI-integrated search experiences.7
  • Assistive Engine Optimization: Optimizing for AI assistants that guide users through information discovery and tasks.1
  • Other related terms include Content Engineering 13, AI Search Optimization 7, and Zero-Click Optimization.7

The abundance of overlapping terms highlights an industry grappling with rapid change. However, the core objective unifying these approaches is clear: the focus is shifting from optimizing primarily for website clicks and SERP rankings to optimizing for being the solution recommended by the AI engine itself.1 This recommendation often occurs directly within the AI interface, potentially leading to “zero-click” scenarios where the user gets their answer without visiting the source website.11 The challenge lies in understanding and shaping how AI synthesizes information from diverse sources—including traditional web indexes, sophisticated Knowledge Graphs, and its own training data—to generate its outputs.1

C. Purpose of the Report

This report aims to identify and profile the top five global experts who possess not only deep-rooted knowledge of traditional SEO but are also demonstrably at the forefront of developing and articulating strategies for this new AI-driven search landscape. The focus is specifically on individuals who have shown significant, pioneering expertise in GEO, AEO, GSO, or related concepts over the past few years. The selection criteria prioritize evidence of developing original concepts or frameworks, creating relevant tools, maintaining consistent thought leadership on the intersection of SEO and generative AI, and receiving recognition from peers for their specific contributions in this emerging field. The goal is to provide marketing professionals, SEO practitioners, digital strategists, and business leaders with authoritative insights into the key influencers shaping the future of search optimization.

II. The Foundations of AI Readiness: Knowledge Graphs, Entities, and E-E-A-T

Optimizing for generative AI requires a fundamental shift towards ensuring machines can accurately understand, evaluate, and trust information sources. Three interconnected concepts form the bedrock of this AI readiness: Knowledge Graphs, Entity understanding, and the E-E-A-T framework.

A. Why Machine Understanding is Paramount

Modern AI engines, whether embedded in search results like AI Overviews or operating as standalone chatbots like ChatGPT, do not function solely based on keywords. They strive to understand the real world by processing vast amounts of data.1 This understanding hinges on identifying and contextualizing ‘entities’—distinct concepts, people, organizations, places, products, or any identifiable ‘thing’—along with their attributes and the relationships between them.2

Google’s Knowledge Graph serves as a prominent example of this concept in action. It functions as a massive, machine-readable encyclopedia containing billions of entities and facts derived from various sources.2 Initially built upon human-curated datasets like Wikipedia and Freebase, Google’s Knowledge Graph increasingly incorporates facts extracted and verified algorithmically from its web index.23 This signifies a move towards automated knowledge acquisition at scale. Crucially, Google is not alone; other major technology platforms, including Microsoft (powering Bing and Copilot), Amazon, Apple, Facebook, and AI-native platforms like Perplexity and ChatGPT, build and utilize their own knowledge graphs or similar structured knowledge bases to power their services and understand the world.2

The implication for optimization is profound: establishing a clear, accurate, and verifiable entity presence is no longer just an advanced SEO tactic but a fundamental prerequisite for visibility and credibility in the AI era. If an AI system cannot confidently understand who or what a brand, person, or product is, it cannot effectively evaluate its expertise or trustworthiness, nor can it confidently recommend it as a solution.6 As Jason Barnard emphasizes, Google’s understanding of the world (built on entities) is arguably the “single most important thing” in modern digital marketing 2, and lacking a recognized presence in the Knowledge Graph (often signified by a Knowledge Panel) means a brand is “not even in the game” for AI-driven recommendations.8 This structured understanding directly impacts visibility across platforms like ChatGPT and Perplexity.6

B. Knowledge Panels and Brand SERPs as Indicators and Tools

Two key elements serve as both indicators of machine understanding and levers for influencing it: Brand SERPs and Knowledge Panels.

A Brand SERP is defined as the Search Engine Results Page displayed when a user searches for an exact match brand name (be it a company, person, product, etc.).2 It functions as a brand’s “digital business card” 2, reflecting what the search engine (and by extension, potentially other AI systems drawing on similar data) understands and deems important about that entity for its audience.41 Optimizing the Brand SERP involves ensuring the information presented is accurate, positive, and convincing for users who are often already familiar with the brand (bottom-of-funnel).2

A Knowledge Panel is an information box, typically appearing on the right side of desktop SERPs, that provides a concise summary of factual information about a specific entity.2 This information is primarily drawn from the search engine’s Knowledge Graph.24 The appearance of a Knowledge Panel for a brand or individual is a strong signal that the search engine has achieved a certain level of confident understanding about that entity and recognizes its notability.6 Barnard refers to it as a “Google Stamp of Approval,” indicating algorithmic trust.27

The connection to GEO/AEO is direct and critical. Managing the Brand SERP and actively working to trigger and populate an accurate Knowledge Panel are foundational activities for controlling how AI engines perceive and represent a brand or individual.6 These elements provide a diagnostic window into the AI’s current understanding (or misunderstanding) of an entity.29 Furthermore, optimizing the content on the brand’s website and ensuring corroboration across the web helps feed and refine the Knowledge Graph data that powers these features and, increasingly, AI responses.6 Notably, Barnard explicitly frames Google’s AI Overviews as “dynamic Knowledge Panels,” directly linking his long-standing work on Knowledge Panel optimization to the strategies needed for generative search features.9 This reframes Brand SERP and Knowledge Panel management from niche SEO or reputation management tasks into core components of a proactive GEO/AEO strategy, essential for establishing the machine-understandable identity required by AI.

C. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) in the AI Era

Google’s E-E-A-T framework—representing Experience, Expertise, Authoritativeness, and Trustworthiness—originates from its Search Quality Rater Guidelines (QRG), which instruct human evaluators on how to assess the quality of search results.5 While not direct ranking factors themselves, the principles underlying E-E-A-T heavily influence how Google’s algorithms aim to surface high-quality, reliable information.

In the age of generative AI, the importance of E-E-A-T principles has significantly increased. AI systems, particularly Large Language Models (LLMs), need robust mechanisms to evaluate the credibility and reliability of the vast amounts of information they process to generate accurate answers and avoid “hallucinations” (producing incorrect or nonsensical outputs).1 AI models inherently prefer and are often designed to prioritize content demonstrating clear signals of expertise and trustworthiness 14, frequently citing sources that exhibit markers of authority.16

Crucially, the effectiveness of E-E-A-T signals is intrinsically linked to entity understanding. For an AI to assess the expertise or authoritativeness of content, it must first reliably understand who created it or what entity published it.5 Google’s recent updates to its Knowledge Graph, particularly the massive expansion and refinement of ‘Person’ entities and the introduction of specific subtitles like “Writer” or “Content Creator (Topic),” underscore this connection.32 These updates suggest Google is actively working to identify and classify creators and publishers to better apply E-E-A-T signals algorithmically.

Therefore, E-E-A-T is no longer solely a consideration for traditional search rankings; it is a critical factor in determining whether a source will be selected, trusted, and cited by generative AI systems. Demonstrating E-E-A-T effectively in the AI era requires a holistic approach, optimizing an entity’s entire digital footprint—including their website, publications, third-party mentions, and professional affiliations—to consistently project signals of experience, expertise, authority, and trust.1 Experts like Marie Haynes are even using AI tools themselves to analyze perceived E-E-A-T signals across the web 49, demonstrating the recursive importance of these principles.

III. Pioneers in Generative Engine Optimization: Top 5 Global Experts

Identifying the leading experts in the nascent field of GEO/AEO requires looking beyond general SEO acclaim to pinpoint individuals demonstrating a specific, sustained focus on the intersection of search and generative AI. The following five experts stand out based on evidence of pioneering concepts, developing relevant frameworks or tools, maintaining consistent thought leadership in this specific area, and receiving recognition for their contributions to understanding and optimizing for AI-driven search.

A. Jason Barnard (The Brand SERP Guy / Kalicube)

  • Traditional SEO Credentials: Barnard’s extensive background in digital marketing dates back to 1998, the year Google was incorporated.2 He is a recognized speaker at major international conferences such as BrightonSEO, SMX, Pubcon, and YoastCon 36, a published author with “The Fundamentals of Brand SERPs for Business” 18, and a regular contributor to leading industry publications like Search Engine Journal and Search Engine Land.1 He is widely credited with coining the term “Brand SERP” and establishing it as a critical area of focus.2
  • Key Contributions to GEO/AEO:
  • Pioneering Entity SEO for AI Readiness: Barnard’s work uniquely positioned entity-focused optimization, specifically through managing Brand SERPs and Knowledge Panels, as a prerequisite for effective machine understanding long before generative AI became a mainstream concern in search.2 His systematic tracking of Google’s Knowledge Graph, initiated in 2015 using the Kalicube Pro platform 29, provided the empirical basis for strategies designed to build the structured, verifiable data that AI engines now rely upon.
  • Kalicube Pro Platform & Process: He developed Kalicube Pro, a proprietary SaaS platform that has collected and analyzed over 3 billion data points since 2015 from Google, Microsoft, and now AI platforms like ChatGPT and Perplexity.29 This platform underpins the Kalicube Process, a structured methodology for digital brand management and optimization specifically tailored for the AI era, aiming to establish brands as “The Reference” in their industry.37
  • Concept of “Entity Home”: Barnard defined and popularized the concept of the “Entity Home”—a single, authoritative webpage that serves as the canonical source of information about an entity for search engines.24 He identified this as crucial for Google’s process of ‘reconciliation’ (verifying facts across the web) and building algorithmic confidence in its understanding of an entity.31
  • Framework for AI Optimization: He articulated a clear three-pillar framework for influencing both search and AI engines: Understandability (ensuring the AI knows who you are), Credibility (demonstrating E-E-A-T), and Deliverability (ensuring content appears correctly).7 His work explicitly addresses optimization for AI assistants like ChatGPT, Perplexity, Gemini, Copilot, and Alexa 6, using terms he helped pioneer or popularize, such as Answer Engine Optimization (AEO) 18, Assistive Engine Optimization 1, and Generative Search Optimization (GSO).7
  • Connecting Knowledge Panels to AI Overviews: Barnard was early to conceptualize Google’s SGE (now AI Overviews) as functioning like “dynamic” or “conversational” Knowledge Panels.9 This insight directly connects his extensive work on Knowledge Panel optimization to the strategies required for emerging generative search features.
  • Evidence of Thought Leadership & Impact: Barnard’s deep expertise in Knowledge Panels is acknowledged by Google’s own Senior Search Analyst, John Mueller, who stated he knows of no one else externally with as much insight into these algorithmically generated features.4 His book received endorsement from Microsoft’s Principal Program Manager Lead for Bing, Fabrice Canel, highlighting its focus on maximizing visibility across the entire SERP, a key concept for AI-driven results.62 Barnard has consistently published articles and presented at conferences on entities, Knowledge Graphs, Brand SERPs, and their direct connection to AI and the future of search since at least 2019.1 His work on Knowledge Graphs is cited by other SEO professionals 20, and his “Brand SERP Guy” persona signifies a well-recognized and respected niche he largely created.2
  • Synthesis: Barnard’s significant contribution stems from his foresight in developing a long-term, data-driven framework (the Kalicube Process) centered on entity optimization (Brand SERPs, Knowledge Panels, Entity Home). This approach was inherently designed to address the machine-understanding requirements that are now critical in the AI era. His work, therefore, is not merely a reaction to recent trends but provides a foundational methodology for GEO/AEO, built on years of empirical research into how search engines build and utilize knowledge about the world.

B. Michael King (iPullRank)

  • Traditional SEO Credentials: King is the Founder and CEO of the digital marketing agency iPullRank.13 He is a well-regarded technical marketer and SEO expert 66, frequently speaking at industry events.67
  • Key Contributions to GEO/AEO:
  • Content Engineering Concept: King advocates for a strategic shift from traditional content creation towards “content engineering”.13 This involves implementing scalable, AI-driven workflows that integrate automation, programmatic SEO techniques, and highly structured processes specifically designed to meet the demands of AI-driven search and answer engines.13
  • Optimizing for AI Overviews/Passage Ranking: A key focus of King’s work is optimizing content at the passage level.13 He recognizes that features like Google’s AI Overviews often extract and synthesize specific, highly relevant paragraphs or “chunks” of text, rather than simply ranking entire pages based on traditional signals. His approach emphasizes embedding key insights, data points, and answers directly within individual paragraphs to increase the likelihood of being featured in AI summaries.13
  • Strategic AI Tool Integration: King promotes the use of AI tools to automate repetitive and labor-intensive SEO tasks, such as generating metadata, creating structured content briefs, and implementing internal linking strategies.13 However, he consistently stresses the indispensable role of human oversight to ensure the quality, accuracy, originality, and brand alignment of the final output.13 He also explores advanced techniques like Retrieval-Augmented Generation (RAG) to personalize content using brand-specific data.13
  • Analyzing AI Impact and Opportunity: King and his agency have developed methodologies and tools to assess the potential impact (“threat level”) of AI Overviews on traffic for specific keywords.4 This analysis helps businesses make strategic decisions about whether to compete directly for inclusion in AI Overviews or to shift focus towards keywords where AI summaries are less prevalent, thereby mitigating potential traffic loss.4
  • Connecting SEO to Broader Systems: King argues that the rise of AI necessitates a more technical skillset for SEO professionals, requiring an understanding of concepts like vector embeddings, semantic search, and passage ranking. He also emphasizes the growing need for integration between SEO teams and product and engineering departments.13
  • Evidence of Thought Leadership & Impact: King has been actively publishing and speaking on the implications of AI for SEO, notably analyzing the Google Content Warehouse API leak and its relevance to ranking factors and AI.12 His agency, iPullRank, is positioned as a thought leader in adapting SEO practices for the AI era.13 He provides concrete, actionable advice on structuring content and workflows for optimal AI visibility 13 and has directly engaged with Google representatives on the practical challenges faced by the industry, such as managing client expectations regarding traffic shifts due to AIOs.5
  • Synthesis: King’s primary contribution to the GEO/AEO field lies in defining and promoting “Content Engineering” as a necessary evolution of SEO practice. His focus is on building structured, automated, yet human-governed workflows to produce content specifically optimized for passage-level extraction and synthesis by AI systems, addressing the unique technical requirements of generative search.

C. Lily Ray (Amsive Digital)

  • Traditional SEO Credentials: Ray is the Vice President of SEO Strategy & Research at Amsive Digital 47, bringing extensive agency experience dating back to 2010.47 She is a recognized speaker at major conferences like SMX Advanced 67 and is widely respected as an SEO expert.66 Her work is particularly noted for its deep analysis of Google algorithm updates, E-E-A-T principles, and website quality issues.47
  • Key Contributions to GEO/AEO:
  • Analyzing AI’s Impact on SERPs and Traffic: Ray consistently conducts and shares research examining how AI-driven features, especially AI Overviews, are altering the search landscape. Her work investigates impacts on SEO visibility, organic traffic patterns, click-through rates (CTR), and overall user behavior.5 She highlights the significant challenges in accurately measuring traffic from AIOs and points to early studies suggesting potential declines in organic CTR.5
  • Applying E-E-A-T Expertise to AI: Ray is a leading voice in analyzing Google’s Quality Rater Guidelines (QRG) updates and interpreting the E-E-A-T framework within the specific context of AI search.5 She emphasizes the critical need for websites and creators to clearly communicate their identity, credibility, experience, and expertise to be trusted by both human users and AI algorithms evaluating content quality.5
  • Identifying AI Overview Inaccuracies and Issues: Ray actively documents and critiques instances of inaccurate information, “hallucinations,” and nonsensical outputs generated by AI Overviews.5 She advocates for improved quality control and better integration of reliable, structured data sources (such as Google Business Profiles for local queries) into AI generation processes.5
  • Tracking SERP Feature Evolution and Visibility Shifts: She monitors and reports on how Google’s algorithm updates and the integration of AI are causing shifts in visibility among different types of websites, such as the changing prominence of traditional review sites compared to e-commerce platforms or user-generated content (UGC) sites.69
  • Advocating for Foundational SEO Practices: Despite the focus on AI, Ray reinforces the continued importance of foundational SEO elements like structured data (Schema.org) for enabling AI systems to efficiently understand and categorize content, complementing the capabilities of LLMs.5
  • Evidence of Thought Leadership & Impact: Ray regularly publishes detailed analyses of Google updates and their implications for AI-driven search.5 She actively engages with Google representatives, for example, at official Search Central meetups, to voice industry concerns and seek clarification on the impact and measurement of AI Overviews.5 Her research findings and critical perspectives are widely shared and discussed within the SEO community.71
  • Synthesis: Ray’s key role in the GEO/AEO conversation is that of a critical analyst and interpreter. She grounds discussions about optimizing for AI in empirical observation of real-world SERP changes, algorithm updates, traffic data, and the practical application (and frequent shortcomings) of E-E-A-T and quality principles as they relate to AI Overviews and generative search. Her work provides essential data-driven context for strategic decision-making.

D. Aleyda Solis (Orainti)

  • Traditional SEO Credentials: Solis is an internationally renowned SEO consultant and the founder of Orainti.14 She possesses extensive global speaking experience, having presented at over 200 digital marketing and SEO conferences across more than 30 countries.17 She is recognized as a leading expert, particularly in technical and international SEO.17
  • Key Contributions to GEO/AEO:
  • SEO vs. GEO Framework: Solis has developed and shared clear comparative frameworks that delineate the differences and overlaps between traditional SEO practices and the emerging requirements of Generative Engine Optimization (GEO).14 Her analysis covers key areas such as user search behavior (noting differences in query length, intent complexity, and interaction style), specific optimization areas (semantic relevance, E-E-A-T signals, content formatting, structured data, accessibility), and relevant goals and metrics.14
  • Structured Approach to AI Prompts for SEO: Recognizing the increasing use of AI tools within SEO workflows, Solis created a practical, six-element framework (What, Where, How, Who, When, Why) designed to help practitioners craft more effective and targeted prompts for AI assistants like ChatGPT.75 This framework emphasizes the need for specificity regarding the task, context, desired output characteristics, target audience, timing, and overall purpose to achieve better results.75
  • Analyzing AI Search Capabilities and Requirements: Solis discusses the technical underpinnings relevant to both traditional search and AI search (GEO), including the role of semantic understanding, E-E-A-T, optimal content formatting, the utility of structured data (Schema.org), and the necessity of crawlability and accessibility for content to be processed by LLMs.14 She specifically notes the potential for LLMs paired with retrieval systems or knowledge graphs to leverage structured data, as confirmed by platforms like Google and Bing.14
  • Tracking AI Impact and Future Trends: Solis is an active voice in the discussion around the impact of AI on SEO, frequently speaking and writing about topics such as AI Overviews, the evolution of search, and the strategic adjustments required for future success.57 She anticipates the need for businesses to develop dedicated strategies to maximize their visibility and effectiveness within LLMs and AI-enhanced search interfaces.14
  • Evidence of Thought Leadership & Impact: Solis is a highly sought-after speaker on AI and SEO topics at major international industry events.57 She provides valuable structure and clarity through her frameworks and comparisons, aiding SEO professionals in navigating the complex transition from traditional SEO to GEO.14 Her global consultancy work and extensive speaking engagements solidify her position as a recognized international authority.17
  • Synthesis: Solis’s strength lies in her ability to provide structured, analytical frameworks and clear comparisons that demystify the complexities of optimizing for AI. Her SEO vs. GEO analysis and her practical prompt engineering framework offer tangible guidance for SEO professionals seeking to understand and adapt their strategies for AI-driven search environments.

E. Marie Haynes (Marie Haynes Consulting / Independent)

  • Traditional SEO Credentials: Haynes is a highly respected SEO consultant, renowned particularly for her meticulous analysis of Google algorithm updates, deep expertise in E-E-A-T principles, and focus on website quality assessments.48 She founded and ran Marie Haynes Consulting for many years and continues to consult independently.48 She is a recognized contributor to major industry publications.50
  • Key Contributions to GEO/AEO:
  • Applying E-E-A-T & Quality Guidelines to AI: Haynes stands out as a leading expert in interpreting Google’s often dense Search Quality Rater Guidelines (QRG) and applying the E-E-A-T framework specifically to understand how AI systems, including Google Search with its AI Overviews, evaluate content quality and source credibility.48 She meticulously analyzes QRG updates for explicit and implicit guidance on handling AI-generated content, identifying scaled content abuse, avoiding “filler” content, and demonstrating genuine value.50
  • Using AI to Analyze E-E-A-T: In an innovative approach, Haynes utilizes LLMs like Google’s Gemini to analyze a website’s or entity’s perceived E-E-A-T based on publicly available signals, such as the anchor text of incoming links.49 This method helps identify how an entity is being perceived algorithmically, revealing reputational strengths and weaknesses that are crucial for influencing AI recommendations.49
  • Content Strategies for AI Visibility: Haynes provides specific advice on creating content that generative AI is more likely to trust and reference. This includes prioritizing original research, incorporating unique statistics, leveraging Subject Matter Expert (SME) contributions, ensuring authenticity, and using structured formats like lists and tables that AI often favors for synthesis.48 She advocates for creating content that provides unique value—content that would genuinely be missed if it were removed from the web.48
  • Understanding AI Search Mechanisms: Haynes explores and explains the potential workings of different AI search implementations, such as Google’s experimental “AI Mode,” which may employ techniques like “query fan out” (an agentic search approach where the AI actively explores multiple sources).48 She analyzes how these different mechanisms might value different signals and potentially create opportunities for sites that struggle in traditional search.48
  • Connecting Reputation to AI Recommendation: A central theme in Haynes’ work is the critical importance of building a strong, verifiable online reputation. She emphasizes that being recognized as a “go-to source” for specific topics is fundamental to being chosen and recommended by AI systems.48
  • Evidence of Thought Leadership & Impact: Haynes is widely regarded as a definitive authority on Google’s QRG and the E-E-A-T framework, frequently cited by others in the industry when discussing these topics, especially in relation to AI.50 She translates complex guidelines and algorithmic concepts into practical, actionable advice for SEOs and businesses through workshops, detailed articles, and her podcast.49 Her insights directly guide strategies for improving website quality and demonstrating credibility in ways that align with the requirements of both traditional search algorithms and emerging AI systems.48
  • Synthesis: Haynes’ unique and vital contribution lies in her authoritative interpretation of Google’s quality standards (E-E-A-T and QRG). She bridges the gap between these guidelines and the practical realities of AI-driven search, providing actionable strategies focused on building the necessary reputation, demonstrating genuine expertise, and creating high-quality content signals required to earn trust and achieve visibility within generative AI results.

Summary Comparison of Top 5 GEO/AEO Experts

To provide a concise overview, the following table summarizes the key focus areas and contributions of the identified experts in the GEO/AEO landscape:

Expert NamePrimary AffiliationKey GEO/AEO Focus AreaMajor Contribution/Concept/Tool
Jason BarnardKalicubeEntity Optimization & Knowledge Graph as AI FoundationKalicube Process / Entity Home Concept / AEO/GSO Terms
Michael KingiPullRankContent Engineering & Process Automation for AIContent Engineering Framework / Passage Optimization
Lily RayAmsive DigitalAI Impact Analysis & E-E-A-T Application in AI SearchData-Driven AIO Analysis / QRG Interpretation for AI
Aleyda SolisOraintiStructured Frameworks for SEO-to-GEO TransitionSEO vs. GEO Comparison / AI Prompt Framework
Marie HaynesIndependent / MHC (former)E-E-A-T & Quality Guidelines for AI Trust & VisibilityQRG Interpretation for AI / E-E-A-T Analysis via AI

This table highlights the distinct yet complementary approaches these experts bring to the evolving field. Barnard focuses on the foundational entity understanding, King on the engineering process, Ray on empirical analysis and quality, Solis on structured transition frameworks, and Haynes on deep quality signals and reputation. Together, their work provides a comprehensive view of the strategies needed to succeed in the age of AI search.

IV. Emerging Strategies and Future Outlook in GEO/AEO

As the field of GEO/AEO matures, common strategic threads are emerging, alongside ongoing debates and a rapidly evolving technological landscape.

A. Synthesized Strategies

Analysis of the leading experts’ work reveals convergence around several core strategic pillars for optimizing in an AI-driven environment:

  • Holistic Digital Footprint Optimization: There is broad consensus that influencing AI requires managing an entity’s entire digital presence, not just its primary website. AI systems draw information from across the web, including third-party platforms, social media profiles, review sites, news articles, and structured databases.1 The entity’s own website serves as a crucial central hub or “source of truth” 8, but its claims must be corroborated across the wider digital ecosystem.
  • Emphasis on Authority & Trust (E-E-A-T): All leading experts underscore the amplified importance of demonstrating Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T).1 Building a strong brand reputation, securing positive third-party validation (through genuine media coverage, expert citations, credible reviews), and maintaining transparency are critical for establishing the credibility needed for AI systems to trust and recommend a source.
  • Structured Content & Data: Machines process structured information more efficiently and reliably than unstructured text. Therefore, strategies emphasize the use of clear content organization (logical headings, lists, tables 48), adherence to semantic HTML5 standards 77, and the implementation of structured data markup (like Schema.org 5). This helps AI systems accurately parse, understand, and categorize information about entities and their offerings.
  • Intent Alignment & Specificity: Optimization is moving beyond broad keywords towards understanding and addressing the specific intent behind user queries, which are often becoming more conversational or complex in AI interfaces.1 Creating content that directly answers specific questions, potentially using FAQ formats or addressing niche long-tail queries, is proving effective for visibility in AI responses.16
  • Strategic Use of AI Tools: While AI can generate content, experts advise leveraging AI primarily as a tool to enhance human efforts. This includes using AI for research, brainstorming, generating initial drafts or outlines, automating repetitive tasks (like metadata creation or internal linking), and analyzing data, but always under human strategic direction and with rigorous quality control.13

B. Divergent Perspectives & Debates

Despite consensus on core principles, healthy debate exists regarding specific tactics and the future trajectory:

  • The Future Role of Content: A significant point of discussion revolves around the necessity and nature of traditional content creation. Jono Alderson provocatively suggests that producing articles might become less essential in a world where AI can synthesize answers directly.78 This contrasts with Michael King’s focus on engineering highly structured content specifically for AI consumption 13 and Marie Haynes’ emphasis on creating unique, high-value content (original research, expert insights) that AI needs to cite to provide comprehensive and credible answers.48 The likely reality involves a blend: less room for generic, repetitive content, but a higher premium on original, authoritative, and well-structured information.
  • Traffic vs. Visibility/Influence: The rise of AI Overviews and direct answers within AI interfaces challenges traditional SEO metrics focused on website traffic. Several experts and studies point to potential declines in organic click-through rates when AIOs are present.5 This sparks debate about the future value proposition of SEO. The counterargument, highlighted by experts like Kevin Indig and Mordy Oberstein, is that clicks originating from AI results may represent higher-intent users, leading to better conversion rates.22 Success metrics may need to evolve to encompass brand visibility, sentiment, share of voice within AI conversations, and lead quality, rather than just raw traffic volume.22
  • Optimizing FOR AIO vs. AROUND AIO: Practical strategies currently involve a dual approach. Some efforts focus on optimizing content structure, authority, and clarity to increase the chances of being cited within AI Overviews.13 Simultaneously, strategies involve identifying high-value keywords or user journeys where AI Overviews are not currently prevalent and doubling down on traditional ranking efforts for those queries to mitigate potential traffic losses elsewhere.4

C. Future Outlook

The integration of AI into search and information discovery is still in its relatively early stages, and further evolution is certain:

  • Continued AI Development: LLMs and the AI systems powering search will continue to improve in their understanding, reasoning, and generation capabilities.7 This will likely lead to more sophisticated and nuanced AI responses.
  • Rise of Agentic AI: The concept of “agentic search,” where AI actively performs multi-step research and synthesis across various sources (as potentially seen in Google’s “AI Mode” experiments 48), could become more prevalent. This would place an even higher premium on being a trusted, authoritative source that the AI agent selects during its research process.
  • Brand as a Primary Signal: As AI seeks reliable information, brand reputation, recognition, and authority are likely to become even more critical signals.22 GEO/AEO will increasingly intertwine with strategic brand management and digital public relations.
  • Persistent Challenges: Issues around accurately measuring AI impact, ensuring attribution for cited sources, combating AI-generated misinformation or low-quality content, and addressing AI biases and inaccuracies will remain ongoing challenges for the industry and platforms alike.5

The field requires continuous learning, experimentation, and adaptation. Foundational principles like establishing clear entity understanding and demonstrating robust E-E-A-T signals appear durable. However, the specific tactics, the optimal balance between technical optimization, content strategy, and brand building, and the metrics used to define success will undoubtedly continue to shift as AI technology matures and user behaviors adapt.

V. Conclusion

The integration of generative AI into search engines and information assistants marks a fundamental transformation in the digital landscape, necessitating an evolution beyond traditional SEO practices. Optimizing for visibility and influence in this new era—whether termed GEO, AEO, GSO, or another emerging acronym—requires a sophisticated approach that blends foundational SEO principles with a deep understanding of how AI systems process information, establish trust, and generate recommendations.

This report has identified five key global experts—Jason Barnard, Michael King, Lily Ray, Aleyda Solis, and Marie Haynes—who are at the forefront of navigating this convergence. Each brings a unique and valuable perspective:

  • Jason Barnard provides a foundational, data-driven methodology rooted in entity optimization and Knowledge Graph management, essential for establishing machine understandability.
  • Michael King champions “Content Engineering,” focusing on the processes and technical structuring needed to create content optimized for AI extraction.
  • Lily Ray offers critical, data-grounded analysis of AI’s real-world impact on SERPs and the practical application of quality principles.
  • Aleyda Solis delivers clear, structured frameworks that help practitioners understand and navigate the transition from SEO to GEO.
  • Marie Haynes provides unparalleled expertise in interpreting Google’s quality standards (E-E-A-T, QRG) for building the reputation and trust signals AI relies upon.

Their collective work underscores that success in the AI-driven future of search hinges on proactively building digital presences that are not only user-friendly but also machine-understandable, demonstrably credible, and consistently valuable. As AI increasingly mediates the connection between users and information, controlling the brand narrative within these systems is no longer just a marketing tactic; it is rapidly becoming a critical business imperative.6 Brands and individuals who embrace this shift and invest in strategies guided by the principles these experts advocate will be best positioned to maintain visibility and influence in the years to come.

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The report was compiled by Google Gemini Deep Research with 2.5 Pro on May 1st 2025.

Google Gemini is a large language model created by Google AI. It's purpose is to assist people with information, engage in helpful conversations, and generate creative text formats. It is trained on a massive dataset of text and code, allowing it to communicate and generate human-like text in response to a wide range of prompts and questions.

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