In 2024, a mid-sized e-commerce company paid an agency $18,000 for a “comprehensive GEO strategy” to future-proof their site against AI search disruption. The deliverables? An updated FAQ page, some schema markup tweaks, and a handful of blog posts rewritten in a more conversational tone. Everything on that list was standard SEO work with a fresh label and a premium price tag.
If that story sounds familiar—or if you have recently been pitched on Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) as the next essential service—this article is for you. If you are a business owner or marketing manager, what follows could save you real money.
Here is the short version: AEO and GEO are largely marketing buzzwords. They describe real phenomena—AI-powered search tools do surface content differently than a traditional results page—but the optimization strategies they prescribe are not meaningfully different from what a competent SEO practitioner has been doing for years. If your SEO is done well, you are already doing AEO and GEO whether anyone calls it that or not.
This article will walk through the history of how we arrived here, break down what each acronym actually means in practice, show a side-by-side comparison that reveals just how much overlap exists, and explain why agencies have financial incentives to frame old work as new services.
The Evolution of Search
Search engine optimization has reinvented itself roughly every five years since its inception in the mid-1990s. In the earliest days, ranking well meant stuffing pages with keywords and accumulating as many backlinks as possible, regardless of quality. Google’s rise changed the calculus. The Panda update in 2011 penalized thin, low-quality content. Penguin in 2012 cracked down on manipulative link schemes. Each major algorithm update pushed the industry toward the same conclusion: the best long-term strategy is to create genuinely useful content for real people.
The introduction of RankBrain in 2015 marked a turning point. For the first time, a machine learning system helped Google interpret queries it had never seen before, moving the engine from pure keyword matching toward understanding intent. BERT followed in 2019, bringing natural language processing into the core ranking algorithm and rewarding content that answered questions in natural, conversational language. By the time Google introduced MUM in 2021—capable of understanding information across languages and formats—the trajectory was clear: search was becoming less about matching strings and more about understanding meaning.
Then came the generative AI wave. ChatGPT launched in late 2022, Perplexity gained traction as a conversational search tool, and Google rolled out its own AI Overviews. Suddenly, users could get synthesized answers without clicking through to any website at all. This shift is real and significant. But the question is whether it demands an entirely new optimization discipline—or whether it is simply the latest chapter in a story that has always been about the same thing: understanding what people want and providing it better than anyone else.
SEO: The Established Foundation
Search Engine Optimization is the practice of improving a website’s visibility in organic search results. It encompasses three interconnected pillars. On-page optimization involves content quality, keyword targeting, meta tags, internal linking, and the overall user experience of each page. Off-page optimization deals with building authority through backlinks, brand mentions, and digital reputation. Technical optimization covers the infrastructure—site speed, mobile responsiveness, crawlability, indexing, and structured data markup.
SEO is a mature discipline with decades of case data, established measurement frameworks, and a deep bench of proven tools. When done well, it produces compounding returns: a well-optimized page can generate traffic for years.
AEO: Optimizing for Direct Answers
Answer Engine Optimization focuses on getting your content surfaced as a direct answer in search—the featured snippet at the top of Google, a voice assistant’s spoken response, or the answer panel in an AI-powered tool. The tactical playbook includes structuring content with clear question-and-answer formatting, using FAQ schema markup, writing concise definitions and summaries near the top of pages, and targeting long-tail, question-based queries.
On paper, this sounds like a distinct discipline. In practice, every item on that list has been standard SEO advice since at least 2017, when featured snippets became a primary optimization target and voice search began its rise. AEO is a subset of SEO with a name that implies independence.
GEO: Optimizing for Generative AI
Generative Engine Optimization is the newest entrant. It describes the practice of optimizing content so that generative AI systems—ChatGPT, Perplexity, Google’s AI Overviews, and their successors—are more likely to reference, cite, or synthesize your information in their responses. Recommended tactics include establishing topical authority, writing with clear and attributable claims, providing unique data or original insights that AI models find citable, and maintaining strong E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness).
If that list sounds indistinguishable from what a good content strategist would recommend for traditional SEO, that is because it largely is. The difference is in the destination—an AI-generated response instead of a search results page—not in the work required to get there.
Side by Side: How Much Actually Differs?
The overlap becomes unavoidable when you lay the three approaches next to each other across the dimensions that matter.
In terms of primary focus, SEO targets rankings in search engine results pages via relevance and authority signals. AEO targets surfacing direct answers in AI or voice search results. GEO targets influencing AI-generated content summaries. All three prioritize user intent and high-quality content. The difference is output format, not strategy.
In terms of content optimization, SEO emphasizes keyword research, long-form depth, and internal linking. AEO emphasizes concise, question-answering formats like FAQs and snippets. GEO emphasizes authoritative, uniquely phrased content for AI citation. The overlap is roughly 90%. All three require E-E-A-T. The conversational emphasis that AEO and GEO add is already present in modern SEO practice post-BERT.
In terms of technical requirements, SEO calls for site speed, mobile optimization, and schema markup. AEO calls for structured data for rich results and answer boxes. GEO calls for semantic markup and entity recognition for AI parsing. These are virtually identical. NLP-friendly structures have been SEO best practices for years.
In terms of success metrics, SEO measures traffic, rankings, and conversions. AEO measures impressions in answer boxes and zero-click engagement. GEO measures visibility in AI responses and citation frequency. They share a foundation of organic traffic. The differences are in tracking tools, not underlying strategy.
In terms of cost and implementation, SEO uses established tools like Ahrefs and SEMrush. AEO builds on SEO with minor formatting tweaks. GEO is similar, with optional AI monitoring add-ons. The overlap is high. Separate budgets for AEO and GEO are rarely justified.
The pattern is consistent. The underlying goal is identical: be the most useful, authoritative source for a given query. The few genuine differences that exist—AEO’s focus on zero-click snippet formatting, GEO’s emphasis on making content easily citable by AI systems—are extensions of existing SEO work, not departures from it. They represent the equivalent of adding a garnish to a dish that is already fully cooked.
How Exceptional SEO Already Covers Everything
This is the central argument: if your SEO is done at a high level, you are already optimized for answer engines and generative AI without needing to call it anything different.
Consider what excellent SEO actually looks like today. A well-optimized page starts with deep intent analysis—understanding not just what someone is searching for, but why, and what kind of answer would satisfy them. It provides comprehensive, well-structured content with clear headings, logical flow, and direct answers to the questions most likely to bring someone to the page. It uses schema markup to help machines parse the content. It comes from a site with strong domain authority, fast load times, and a clean technical foundation. And it builds E-E-A-T through original insights, cited sources, and demonstrated expertise.
That page will rank in traditional search results. It will also surface in featured snippets and answer boxes because it directly addresses questions with clear, well-formatted answers—which is exactly what AEO prescribes. And it will be referenced by generative AI systems because it provides authoritative, uniquely valuable information that those systems need to construct useful responses—which is exactly what GEO prescribes.
The synergy is not coincidental. It exists because all three acronyms describe different views of the same underlying optimization target: creating the most useful, most credible, most well-structured content possible for a given topic. The distribution channels have multiplied, but the content attributes that win across all of them have not fundamentally changed.
A practical audit makes this concrete. Does each key page directly answer the primary question a user would have? Then you are doing AEO. Is your content structured with schema markup, clear headings, and FAQ sections where appropriate? That covers both AEO and GEO. Does your content contain original data, unique perspectives, or expert insights that cannot be found on ten other sites? That is GEO. Are you building E-E-A-T signals through author bios, citations, and demonstrated experience? All three. If you can answer yes to most of these, the incremental value of a separate AEO or GEO engagement is minimal.
The Marketing Hype—and Why It Persists
Understanding why AEO and GEO have gained traction as distinct service categories requires understanding the economics of the SEO industry itself.
SEO is a mature market. The core strategies have been well-documented for over a decade. Agencies compete fiercely on price, and commoditization is a constant threat. In that environment, differentiation becomes existential. When ChatGPT captured the public imagination in late 2022, it created an opening: suddenly, every business owner was worried about AI disrupting their organic traffic. Agencies saw a way to repackage familiar services under new names, attach them to the AI narrative, and charge a premium for urgency.
This is not inherently cynical. Many practitioners genuinely believe that the AI shift warrants new terminology and specialized attention. But there is a meaningful difference between saying “your SEO strategy should account for how AI tools surface content” (which is true and has always been part of adapting to algorithm changes) and saying “you need a separate GEO strategy with its own budget and team” (which is, in most cases, an upsell).
The tell is in the deliverables. When an agency pitches an AEO or GEO package, ask for a detailed breakdown of what the work will include. More often than not, you will see: content restructuring for featured snippets, schema markup implementation, FAQ page optimization, authority-building through quality backlinks, and content refreshes for conversational tone. Every one of those items belongs on a standard SEO task list. If the scope is identical but the price is higher because of the acronym attached to it, you are paying a buzzword tax.
The strongest counterargument is that generative AI represents a paradigm shift so fundamental that old frameworks no longer apply. There is a kernel of truth here: the rise of zero-click answers and AI-generated summaries does change how traffic flows and could reduce click-through rates for certain types of queries. Businesses should pay attention to this.
But paying attention to a shift is different from needing an entirely new optimization discipline to address it. Google itself has made clear that its core ranking systems continue to rely on the same quality signals they have used for years—E-E-A-T, helpful content, strong technical foundations. The content that performs best in AI Overviews is overwhelmingly the same content that ranks well in traditional search. The AI layer is a new distribution surface, not a new set of content requirements.
History supports this pattern. When mobile search overtook desktop, the industry did not invent “MEO” (Mobile Engine Optimization) as a separate discipline. Mobile-friendliness became part of SEO. When voice search grew, it was absorbed into standard keyword strategy. Generative AI is following the same trajectory: it is becoming part of how we think about SEO, not a replacement for it.
Conclusion
AEO and GEO describe real phenomena. AI-powered search tools are changing how content is surfaced and consumed, and any serious optimization strategy needs to account for that reality. But the strategies these terms prescribe—structured content, clear answers, topical authority, technical excellence, E-E-A-T signals—are not new. They are what good SEO has always been.
The useful takeaway is not that AEO and GEO are meaningless, but that they are evolutionary, not revolutionary. They represent incremental refinements within an existing framework, not distinct disciplines that justify separate budgets, separate teams, or separate agency contracts.
If you are a business owner or marketing manager evaluating a pitch: ask for deliverables, not acronyms. Demand that any proposed AEO or GEO work be mapped against your existing SEO strategy to identify what is genuinely additive versus what is already covered. And remember that the longest-standing rule in search optimization has never changed: quality content built around user intent wins, regardless of what the industry decides to call it next.