The Debate Around AEO and GEO
The discussion surrounding AEO (Agentic Engine Optimization) and GEO (Generative Engine Optimization) centers on whether these concepts constitute a distinct discipline, a subset of SEO, or merely a rebranding of traditional SEO. Determining where to “plant a flag” is challenging, as each argument presents a compelling case. What is indisputable, however, is that significant change is underway, and it may be prudent to focus on the intersections of these competing ideas.
Arguments Against AEO/GEO
Many SEO professionals contend that AEO and GEO do not sufficiently differentiate themselves to justify classification beyond standard SEO practices. Harpreet Singh Chatha of Harps Digital recently highlighted several misconceptions surrounding AEO and GEO in a tweet:
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LLMs.txt: The belief that a separate markdown file can control AI indexing is unfounded; no AI search engine currently uses such files.
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Paying for “chunk optimization”: Breaking content into chunks is merely improving readability, not a new optimization discipline.
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Claiming AEO/GEO is unrelated to SEO: Nearly all AI-related optimizations share foundational SEO principles.
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Saying SEO is dead: SEO fundamentals remain essential, regardless of AI adoption.
Critics note that a small but vocal segment of pro-AEO/GEO agencies often lacks practical SEO experience, promoting solutions that are at best redundant and at worst exploitative. As Greg Boser, a veteran SEO professional since 1996, observed:
“The core of our work has always been understanding how humans use technology to gain knowledge. We do not need a slew of new acronyms. If we redefine the ‘E’ in SEO from ‘Engine’ to ‘Experience,’ we can focus on meaningful work rather than debating terminology.”
Challenges in Articulating AEO/GEO
Perceptions that AEO and GEO are not distinct often stem from inadequate differentiation by proponents. Frequently, new tactics labeled as AI-specific are identified by experienced SEOs as established practices.
Microsoft, in an October 2025 blog post on optimizing content for AI, emphasized:
“While there is no secret strategy for being selected by AI systems, success starts with content that is fresh, authoritative, structured, and semantically clear.”
The post reiterates the importance of core SEO fundamentals—crawlability, metadata, internal linking, and backlinks—while noting that AI search delivers answers rather than ranked lists of pages. As a result, the focus shifts from ranking entire pages to determining which pieces of content are selected to form the AI-generated answer:
“In AI search, ranking still occurs, but it is less about ordering pages and more about which pieces of content earn a place in the final answer.”
Jesse Dwyer of Perplexity AI echoed this perspective:
“The major difference in AI search today lies in sub-document processing. Rather than indexing entire pages, AI systems focus on granular snippets—distinct from traditional featured snippets.”
Microsoft’s explainer, From Discovery to Influence: A Guide to AEO and GEO, focuses primarily on e-commerce, reflecting the sector’s significant opportunity in AI search. By contrast, informational sites face the risk of losing branding and contextual value, as agentic AI may treat them primarily as data sources.
Traditional SEO Practices That Resemble GEO
Several techniques promoted under the AEO/GEO umbrella have long been part of standard SEO:
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Answer-Oriented Content: Structured responses have been a core SEO tactic since the introduction of featured snippets in 2014.
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Content Chunking: Organizing content into concise, readable sections improves mobile experience and usability.
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Structured Content and Data: Headings, metadata, and schema markup disambiguate content and have been SEO best practices for years.
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Content Clarity, Semantic SEO, FAQs, and Freshness: These elements continue to be essential for effective search optimization.
Emerging Opportunity: Authoritative Citations
One notable divergence from traditional SEO is the emphasis on authoritative citations. AI systems increasingly prioritize content that is validated by credible sources. For informational sites, securing citations from reputable publications or organizations can enhance discoverability and trust in AI-generated outputs. This represents a relatively new opportunity for authoritative content providers, as AI search currently favors verified, FTC-compliant, and Google-friendly sources.
Google Maintains That SEO Remains Fundamental
Several Google executives, including Robby Stein (VP of Product), Danny Sullivan, and John Mueller, assert that SEO continues to be fully relevant. They note that AI-driven results often rely on underlying Google searches, drawing from top-ranked sites to synthesize answers and links. This perspective reinforces the idea that traditional SEO principles remain crucial even as AI-generated summaries become more prevalent.
Recent developments, such as OpenAI hiring a content strategist to leverage SEO rather than GEO, further suggest that even organizations focused on AI-driven search still recognize the enduring importance of conventional SEO practices.
Optimization Beyond Google
Manick Bhan, founder of the Search Atlas SEO suite, offers a nuanced view on why the industry may be moving toward a bifurcated path of SEO and GEO. He observes:
“SEO has always meant ‘search engine optimization,’ but in practice it has historically meant ‘Google optimization.’ Google defined the interface, the ranking paradigm, the incentives, and the entire mental model the industry used.
The challenge with calling GEO a ‘sub-discipline’ of SEO is that the LLM ecosystem is not a single ecosystem, and Google’s AI Mode is itself becoming a generative surface.”
Bhan emphasizes that each AI search and answer engine employs distinct methodologies, meaning that while tactics may share similarities with traditional SEO, the retrieval models, interfaces, and answer surfaces have fundamentally changed.
He elaborates:
“GEO is not just SEO with a fresh coat of paint. While the tactics remain in the universe of on-page and off-page signals, the machines we optimize for have changed. Today’s answer engines:
Retrieve differently,
Fuse and weight sources differently,
Handle recency differently,
Assign trust and authority differently,
Fan out queries differently, and
Incorporate user behavior into their RAG corpora differently.
Even seemingly small mechanics—like logit calibration and temperature—produce measurable semantic drift and citation divergence across engines. Consequently, we observe differences in retrieved sources, answer structures, citation patterns, semantic frames, and ranking behavior across LLMs, AI Mode surfaces, and traditional Google results.”
Bhan concludes that humility and experimentation are more important than dogmatic adherence to labels, as treating all AI-driven search as “just SEO” fails to acknowledge the substantive differences and rapid evolution of these systems.
Finding Clarity in the SEO vs. GEO Debate
The ongoing debate around AEO, GEO, and SEO largely reflects disputes over taxonomy rather than substantive disagreement about content strategy. The lack of consensus is less a failure to define GEO or AEO than a reflection of the early stage of this transition.
Key points of clarity include:
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There is no single GEO playbook, but measurable differences exist in content retrieval, citation, and answer synthesis.
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Natural-language delivery of answers distinguishes AI outputs from traditional search results.
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For content creators, the focus remains on producing clear, contextually rich answers capable of functioning as cited or synthesized passages.
Recognizing the evolution of optimization highlights that SEO is no longer aimed at a single search surface. Instead, it is adapting to a landscape where multiple systems, AI engines, and generative surfaces determine how content is discovered, evaluated, and presented. The term SEO itself is in transition, reflecting the need for strategies that address both traditional search and AI-mediated outputs.