Every emerging field goes through a naming war. AI search optimization is in the middle of one, and the acronyms are not interchangeable. The name you choose determines what you measure, what you optimize, and what you ignore.
The three contenders
SEO (Search Engine Optimization). The incumbent. Optimize pages to rank in organic search results. Measure: position, impressions, clicks. The field is two decades old, the playbook is mature, and the signals are well-understood.
AEO (Answer Engine Optimization). Optimize content to appear in AI-generated answers. The emphasis is on "answer": the goal is to be the source an AI cites when it synthesizes a response. The term gained traction in 2024-2025 as ChatGPT, Perplexity, and Google AI Overviews started generating answers instead of linking to pages.
GEO (Generative Engine Optimization). Optimize for generative AI systems broadly. The term comes from the peer-reviewed paper by Aggarwal et al. accepted to KDD 2024, which introduced GEO-Bench and measured the effect of content edits on visibility in generative engine responses. The paper defined GEO as optimizing for "generative engines," which includes any AI system that generates responses from retrieved content.
AIO (AI Overviews). Specifically Google's AI Overviews. This is the narrowest term and the one most tied to a single platform. When people say "AIO optimization," they mean getting cited in Google's AI-generated summaries specifically.
What each name optimizes for
The distinction is not semantic. It changes the strategy.
If you optimize for SEO, you target keywords, build backlinks, and rank pages. The unit of measurement is the ranking position. This is still the largest channel by traffic volume, and the WashU study confirms that standard SEO signals (indexability, content quality, authority) remain the foundation for AI features.
If you optimize for AEO, you structure content to be quotable by AI. The unit of measurement is the citation. You care about whether your content appears as a source in AI-generated answers, not whether you rank #1 for a keyword. The strategy emphasizes clear, self-contained answers, first-hand experience, and non-commodity content.
If you optimize for GEO, you optimize for the generation step itself. The unit of measurement is visibility in the generated response. This includes citations but also includes mentions, paraphrasing, and implicit influence. The Aggarwal et al. paper showed that content-level edits (adding citations, statistics, quotations) can boost visibility by up to 40%.
If you optimize for AIO, you target Google's AI Overviews specifically. The unit of measurement is AIO impressions, which Google now reports in Search Console. The strategy is narrower because it only applies to one platform, but it is also the platform with the largest reach (2 billion monthly users per Google's own numbers).
Which one should you use?
The honest answer is that all four describe real, measurable phenomena, and most sites should be thinking about at least two of them.
SEO is the foundation. If your site is not crawlable, not indexed, and not ranking for anything, it will not appear in AI Overviews either. The WashU study found that AIO-cited domains score higher on credibility than average first-page results. Standard SEO signals are prerequisites, not competitors, to AI optimization.
AEO or GEO is the layer on top. Once the fundamentals are in place, the question becomes whether your content gets cited by AI systems. This is where content structure, specificity, and non-commodity signals matter. The data from CXL (55% of citations from first 30% of page), Ahrefs (38% overlap with top 10, down from 76%), and the Aggarwal paper (40% visibility boost from content edits) all describe this layer.
AIO is the measurement layer. Google's Search Console now reports AI Overview impressions separately. If you want to track whether your content is appearing in AI Overviews specifically, AIO is the term that describes that metric.
The naming trap
The trap is using the wrong name for the wrong layer. If you call everything "GEO," you may end up optimizing GEO-specific signals (llms.txt, Schema density) while ignoring SEO fundamentals (crawlability, rendering, content position). If you call everything "SEO," you may rank well organically but miss the AI citation opportunity entirely.
The Ahrefs data makes the distinction concrete. YouTube is the #1 cited domain in AI Overviews with 22.9% of citations. Reddit is #2 with 18.5%. Neither of these domains is "optimized for GEO." They are platforms with massive content volume, first-hand user experience, and structural advantages (video transcripts, threaded discussions) that happen to align with what AI retrieval systems surface.
What I use
In geo-optimizer-skill, I use "Answer Engine Optimization" as the primary term because it describes what the tool measures: whether your content can be found and cited by AI systems that generate answers. The tool checks SEO fundamentals (crawlability, rendering) and AEO signals (content structure, citation-worthy passages) because both layers are necessary.
I avoid "GEO" as a product label because the term has been co-opted by agencies selling tactics that the data does not support. The academic definition from the Aggarwal paper is rigorous. The commercial usage is not.
The bottom line
The acronym debate is not about branding. It is about what you measure. If you measure rankings, you optimize for SEO. If you measure citations, you optimize for AEO. If you measure generated visibility, you optimize for GEO. If you measure AIO impressions in Search Console, you optimize for Google's specific surface.
Most sites need SEO as the foundation and AEO as the layer on top. The data supports both. The data does not support most of what is sold under the GEO label. Choose your acronym based on what you can actually measure, not what sounds most innovative.
Top comments (2)
The distinction between citation and mention is the one I find hardest to measure in practice. When I tested a few small sites, being cited as a source and merely being paraphrased looked very different depending on whether the engine was doing live retrieval or answering from training data. Do you track those separately, or just count any appearance in the answer as visibility?
Thanks for this, it is the hardest part to measure in practice, not just in theory.
Yes, I track them separately. In geo-optimizer-skill I measure citability (AEO), not any appearance. The distinction you noticed between live retrieval and training data is exactly why I keep them apart. When an engine does live retrieval, it tends to give explicit citations you can verify and count. When it answers from training data, it paraphrases without attribution, and there is not much you can optimize there with content signals because the content is already in the model.
That is why I picked AEO as the primary term in the article instead of GEO. GEO includes mentions and paraphrasing in its academic definition, but in practice those signals are nearly impossible to measure reliably. A citation is a binary event: it is there or it is not. A mention is a spectrum, and as you found, it shifts depending on the engine mode.
What I do not track, and I think it is worth being honest about, is implicit influence. If a model saw your content during training and uses it to shape a response without citing or paraphrasing you directly, no tool can detect that. It is visibility in the broadest GEO sense, but it is also invisible by definition.