GEO, AEO, AIO: let’s bring some order to the chaos of SEO acronyms!
GEO, AEO, SXO, LLMO: every week, a new acronym pops up promising to be the “new SEO.” You’re faced with a jungle of terminology fueled by budget anxiety and the need to sell existing processes as something new. Acronyms are convenient because they help you find your way around, but they come with a risk: they become a mental shortcut and give you the illusion of controlling change just because you’ve managed to pigeonhole it.
But as you focus on labels, you risk losing sight of the big picture just when you need to decide how to invest your time and budget. Because what matters is understanding how your work changes if responses are shortened and sources are selected before you even click. And the truth is that all these “new disciplines” only describe different parts of the same process. Your goal hasn’t changed, despite AI: to be found, to be chosen, and to respond to a need better than others, regardless of the interface used by the user.
For us at SEOZoom, acronyms are used to read reality, without pretending to rewrite the rules of visibility. If you consider them to be separate compartments, language takes over strategy. If you use them correctly, they become tools for interpreting the market clearly.
SEO remains the infrastructure that feeds artificial intelligence
Generative artificial intelligence systems do not create information out of thin air. This is the key point to keep in mind. Every word you read in a synthetic response has its roots in a document, book, or web page, because the machine reworks and synthesizes existing materials that someone has made accessible and, above all, decodable.
The real problem is that this information root is fed by two profoundly different channels: behind every generated response, there are two “intelligences” working with opposing logics—one lives in the past, the other in the present. This is also why confusion arises and acronyms proliferate.
- The crystallized past: the memory of LLM models
Imagine Large Language Models (LLMs) such as ChatGPT or Gemini in their basic mode as a giant paper encyclopedia, ‘frozen’ at the moment of printing. This is the so-called Knowledge Cut-Off: AI queries an image of the world that was formed during training, comparing and assimilating already established semantic relationships.
At this stage, what AI knows about your brand is crystallized: it knows the facts, entities, and reputation you have built up to yesterday, but it is blind to what is happening today. It is like a student who has learned from dozens of books: it gives you the notion, but it does not tell you (except rarely) which page it took it from. If it quotes you, it is not “choosing” your page in real time, it is simply repeating what it has learned. These mentions represent a milestone of established authority: they indicate that you have been recorded in the memory of the models as an essential reference. We are not talking about SEO in the classic sense, but about brand governance: building an identity so consistent and authoritative that it becomes “native” information for the machine, ready to re-emerge in every future update.
- The dynamic present: Answer Engines and RAG
At the opposite end of the spectrum are Answer Engines such as Google’s AI Overview or Perplexity. They do not “know” the answer, they find it in real time by synthesizing web results through the RAG (Retrieval-Augmented Generation) system. It is a new layer that comes between you and traditional search: AI searches online for you, interprets the question, and activates a query fan-out process, breaking down the query into multiple simultaneous searches and then synthesizing a single answer, citing only the most recurrent and consistent sources.
Here, the nature of the exchange has changed radically. Users no longer type “best product X,” but ask “what is the best product X for me?”, expecting a summary, not a list of links to check. In this context, visibility is no longer exclusively a matter for Google, but rather the ability to be chosen as a source in that millisecond when AI decides who deserves to be mentioned.
The new code of visibility
This duality between past and present clarifies why generative models do not replace search, but use it as nourishment. To construct discursive responses, they still draw on the web index, but read it with new eyes.
At SEOZoom, we have codified this transformation into SEO for AI, the operating protocol necessary to make your content visible, digestible, and “validatable” by machines.
In this vision, classic SEO remains the condition of access (the basis for controlling keywords and the index), while GEO (Generative Engine Optimization) is the force that legitimizes you as a source. SEO puts you in the running, GEO convinces artificial intelligence that your brand is the only authoritative reference to be synthesized in the final response.
Citations in memory (LLM) are not obtained through a technical trick: they are the effect of your overall reputation. If your brand is associated with competence and reliability, it remains so in the model; if you produce poor content, it influences the responses until the model is retrained. Citations in Answer Engines (RAG), on the other hand, depend on your technical ability to provide solid, verifiable, and structured answers.
SEO for AI is all about striking this balance: teaching the past who you are, so that you can be found by the present when needed. First, you work on positioning (RAG), then you consolidate your reputation (LLM). Only if you are already strong on Google can you compete for visibility in generative engines.
The acronyms of visibility today, the definitive map
It’s time to bring order to the jungle of acronyms that has invaded digital marketing.
Each acronym isolates specific functions and occupies a precise space in the new search architecture: it defines when content should be found, when it should be chosen as a source, and when it should be used to construct a response.
Your basic need remains to understand what strategy the brand needs now that factual search has taken over from documentary search and understanding language matters more than code scanning. If you still confuse GEO with AEO, you are ignoring whether your brand is failing because it is invisible in the memory of machines or because your content is technically incapable of providing ready-to-use data in real time.
- SEO
acronym for Search Engine Optimization, which in Italian means “Search Engine Optimization”.
Born in the early 1990s as a game of cunning against engines such as Yahoo! where quantity won over quality, optimization became a scientific profession with the arrival of Google in 1998, shifting the focus to links and authority. In practice, SEO represents the set of technical and semantic activities aimed at ensuring that a website is correctly interpreted by crawlers through the optimization of the source code, information architecture, and external authority signals.
Now forget your nostalgia for the days of AltaVista, when all you had to do was repeat a keyword endlessly to fool rudimentary crawlers: today’s SEO is the invisible infrastructure of knowledge, the backend without which any ambition for visibility falls flat. If information does not enter Google’s database with surgical precision, it does not exist for the retrieval systems that feed the synthetic responses of artificial intelligence.
Today, doing SEO means guaranteeing your right to a digital existence: you provide the necessary accessibility foundations so that generative models can locate your source among billions of documents.
Manage the scanability and indexability of your site so that Google not only finds your pages, but recognizes them as authoritative nodes within an index that now serves as food for AI. It is the foundation on which every other acronym rests: without solid SEO, your artificial intelligence strategy has no raw material to work with.
The evolution has been radical: we have gone from convincing an algorithm to rank us to certifying our knowledge so that artificial intelligence chooses us as its primary source. Optimization affects every bit of your digital ecosystem through three pillars: technical SEO for speed and architecture, on-page SEO for metadata and semantic breakdown, and off-page SEO to validate your authority through external signals. It applies to every reality, from personal blogs to e-commerce giants, because if you fail at this stage, you are out of the game before AI even begins to generate its response: the machine cannot cite what it is not allowed to see and catalog.
- SEO for AI
acronym for Search Engine Optimization for Artificial Intelligence
Stop writing to capture clicks and start designing to be understood by machines. SEO for AI is not simply a technical variation of traditional SEO, but a definitive paradigm shift that transforms web pages into information nodes of a Knowledge Graph, already codified by Ivano Di Biasi in the book of the same name published in July 2024 and then incorporated into our approach and strategic protocol at SEOZoom.
It represents the culmination of twenty years of search evolution, where the goal is to become the answer itself provided to the user by generative engines such as ChatGPT, Gemini, and Perplexity. Optimizing for SEO for AI means overcoming the barrier of human reading to enter the field of algorithmic processability, structuring content by breaking down information into entities and rigorous logical relationships. This approach ensures that AI models can correctly interpret the deep meaning of the content, using it as primary material for synthetic responses in real time.
This involves surgical data preparation that facilitates extraction by algorithms and reduces the risk of hallucinations, making the brand an unassailable source of truth for the machine. In practice, when you apply SEO for AI, you are building a digital identity so transparent and logical that artificial intelligence cannot help but choose it as a factual reference. The future of visibility depends on this ability to dialogue with models: SEO still positions you in the index, but it is the SEO for AI protocol that allows you to emerge from anonymity in the SERP to preside over the new layer of generated responses.
- GEO
acronym for Generative Engine Optimization
GEO identifies the new phase of visibility, which lies in the critical transition between the classic SERP and generated responses, and represents the technical response to the need to oversee AI-based response engines. The term was introduced in November 2023 by a joint study between researchers at Princeton, Georgia Tech, and the Allen Institute for AI, which isolated for the first time the statistical criteria that drive a linguistic model to prefer one source over another. The data showed that including authoritative citations, numerical data, and precise technical language can increase the likelihood of a content being cited by up to 40%.
The probabilistic foundations provided by academic research have paved the way for marketing, and in fact, today GEO is the most popular term used to generically describe the activity of optimizing content from a simple web page to a source that can be cited by generative models and engines such as ChatGPT, Gemini, and Perplexity. Operationally, GEO means taking care of three fundamental pillars: clarity of concepts, expressed in such a way that the machine recognizes them as authoritative; informational consistency, to allow for accurate and verifiable synthesis; and thematic authority, which transforms you into a constant reference for an entire topic and not just for a single query.
From an SEOZoom perspective, however, GEO is the verification and optimization of the historical and static memory of LLM language models. It concerns all the information that the machine has consolidated during its training phases and which constitutes its native knowledge. If you query a model without it consulting the index and it spontaneously cites you as an industry leader, you have achieved a GEO result: you have built a presence so deeply rooted that it is part of the algorithm’s very identity.
- AEO
acronym for Answer Engine Optimization
AEO governs the ability to appear as the source chosen by AI in response to user queries, shifting the focus from simply monitoring SERPs to achieving immediate responses.
While traditional SEO works to position a link that the user must click on, AEO works to transform your content into the solution that the system selects to satisfy the search intent before any other source. And if GEO presides over the model’s memory, AEO is the operational lever for dominating performance in the here and now. It is the process that allows your data to be extracted and presented as absolute truth within a generative box or voice assistant.
In reality, the concept of AEO has its roots in 2018, when Jason Barnard realized that Google was ceasing to be a simple search engine and becoming an answer engine. Even then, the fragmentation of SERPs into featured snippets and Knowledge Graphs indicated that optimization was no longer just about climbing to the top ten positions, but about the ability to be decisive through concise and technically flawless answers. Today, in the era of RAG systems, this ability has become a prerequisite for survival: Google’s AI Overview or Perplexity’s answers are nothing more than the extreme evolution of that process that began with featured snippets.
Operationally, doing AEO means dismantling the classic narrative structure to organize knowledge into logical units that are easily extractable. This means favoring a dense information structure, where each paragraph unequivocally answers a specific question and a direct intent. Only through a technical architecture that facilitates data scanning and synthesis can you ensure that AI chooses your source as the most reliable for resolving the user’s doubt in that precise millisecond. In SEOZoom, AEO is the tool with which you transform your brand’s authority into a present, constant, and dominant response.
- AIO
acronym for Artificial Intelligence Optimization
It is the term that the market has adopted to define the frontier of editorial efficiency. It originated as a “trench” term between the end of 2022 and the beginning of 2023, exploding in parallel with the spread of ChatGPT. While GEO and AEO describe the visible result in machines, AIO focuses exclusively on the method. It is the transition from the craft of words to the engineering of content: the goal is not simply to generate text, but to build an information infrastructure that is inherently “AI-ready.”
The term is currently suffering from an identity crisis, divided between those who reduce it to optimization for Google’s AI Overview and those who use it as a synonym for automated writing. In the technical reality of professional production, AIO governs the integration of artificial intelligence to analyze vast data sets, identify semantic gaps, and automate repetitive technical tasks. Doing AIO means using computational power to scale the accuracy of facts and ensure that the output produced is technically compatible with the reading standards of intelligent agents. You don’t delegate strategy to the machine, but leverage its speed to make the entire information structure of the site easily mappable.
Your operational priority in the AIO approach is to transform mass production into surgical distribution of information. You must stop producing isolated pages and start designing content ready to be processed, reused, and validated by generative engines. AIO is the pillar of what we call “conscious automation”: the necessary bridge to reduce friction between the publication of data and its understanding by retrieval systems.
- AISO
acronym for Artificial Intelligence Search Optimization
AISO is an acronym that emerged in the SEO debate to identify optimization specifically for search engines based entirely on artificial intelligence. Unlike its predecessors, it has a more pragmatic and market-related matrix: its popularity exploded when the acronym began to appear massively in job offers on platforms such as Indeed, a sign of companies’ need to give a name to a new skill, distinct from traditional SEO.
AISO identifies the need to monitor those ecosystems defined as “AI-native” (such as Perplexity, ChatGPT Search, or Claude) that do not use the classic search engine index as their sole source and where the user is not looking for a list of links, but a definitive and immediate answer. Optimizing for AISO therefore means shifting the focus to the ability of content to “dialogue” with artificial intelligence systems, ensuring that information is extracted, synthesized, and presented correctly without requiring further steps on the part of the user.
In operational terms, AISO focuses on creating content that prioritizes coherence of discourse and verifiability of facts. Since these systems do not necessarily reward classic Domain Authority, but rather the solidity of the data provided in real time, AISO imposes extreme informational rigor. The goal is to eliminate any gray areas or ambiguities that could lead the model to hallucinate, ensuring that the brand is chosen as the primary source at the exact moment the engine builds the summary for the user.
- LLMO
acronym for Large Language Model Optimization
LLMO represents the most technical layer of the new visibility and concerns the data engineering behind models such as GPT, Claude, or Gemini. Emerging in early 2023 with the mass diffusion of Large Language Models, this discipline focuses on “how” to structure information so that it is perfectly understandable and usable by generative engines. LLMO involves applying a surgical breakdown of texts to eliminate any linguistic ambiguity, making it easier for models to retrieve accurate information. The aim is to reduce the statistical uncertainty of the model: the more a text is technically configured to be digested, the more likely it is to be chosen to feed the synthetic response.
In practice, LLMO optimization requires a deep understanding of semantics to enable models to use data efficiently. This process relies on techniques such as chunking — the fragmentation of texts into coherent logical units — and the organization of information according to concept proximity logic. It is the fundamental standard for interacting with RAG systems, where the machine “reads” external sources in real time to generate responses. Preparing content for these models means ensuring that information is easily extracted, guaranteeing the brand a leading role in automated response flows.
You should consider LLMO as the technical barrier that separates ignored content from selected content. If the AI understands the hierarchy of your information exactly, it will use you to respond; if the data is unstructured or overly verbose, you will be discarded in favor of a better-structured source. The goal is not creativity, but informational transparency: LLMO ensures that your content is configured to minimize the risk of hallucination, making your source the most “secure” for the model generation process.
- SXO
acronym for Search Experience Optimization
SXO marks the definitive move away from purely technical optimization to embrace User Experience (UX) as a pillar of positioning. Originally coined by Christian H. “Knee” Webber in 2015, this concept has taken center stage with the integration of Core Web Vitals into Google’s ranking in 2021 and is now becoming vital with artificial intelligence eroding organic traffic through direct answers: it is no longer enough to enter SERP, it is necessary to guarantee a quality experience that satisfies the search intent and transforms every single visit into a concrete action, whether it be a conversion or a subscription. In practice, SXO measures visitor satisfaction, making user perception a determining factor for maintaining ranking over time.
Operationally, the application of SXO requires obsessive attention to the fluidity of interaction and the perception of value that the site offers on both desktop and mobile. Google has made this link explicit through behavioral metrics such as INP (Interaction to Next Paint), which measures how quickly the site responds to user commands. It is not just a matter of aesthetics, but of breaking down all friction: loading speed and visual stability have become signals of algorithmic quality. While SEO guarantees visibility, SXO ensures that visibility is not wasted, resolving the user’s information needs in a matter of seconds and without distractions.
Today, visibility also means the ability to guide the user towards a smooth and positive solution, which will increasingly influence your position in the market. In every sector, SXO is the bridge that transforms residual traffic into real business (conversions, leads, sign-ups). Optimizing for search engines makes no sense if you don’t first optimize for the human being who queries them: in 2026, a technically perfect but frustrating to navigate website is destined to disappear from AI responses and positions that matter.
- Search Everywhere Optimization
A strategic reinterpretation of SEO applied to digital ubiquity
Search Everywhere represents the definitive overcoming of the boundaries of traditional SERP. It is a profound reinterpretation of SEO linked to the radical evolution of search: the idea that visibility does not begin and end on Google, but extends to every system capable of extracting and using content. From the spread of voice assistants to marketplaces and social media, the goal is to optimize so that information can be easily retrieved wherever the user decides to query the network. Today, this concept has been absorbed into the global entity strategy: artificial intelligence uses signals distributed across every platform to validate a brand’s reliability. If the data on TikTok, Amazon, or LinkedIn confirms the authority of your website, your algorithmic “trust score” grows on every channel.
Although the market has shifted its focus primarily to aspects purely related to generative AI, the basic principle of the need for integrated multichannel oversight remains central. In practice, this means generating consistent brand signals at every digital touchpoint to feed the memory of language models and real-time response systems. AI does not trust a single source; it cross-references data to verify the consistency of your leadership. You need to be present wherever a conversation or search takes place to ensure that machines, by aggregating external information, always find confirmation of your undisputed authority. Search is no longer a confined act, but an activity spread across every digital environment: optimizing for every type of search means ensuring that your knowledge is ready to be queried anywhere on the network, transforming the brand into an omnipresent and verifiable entity.
The structural difference between GEO, AEO, AIO, and LLMO
Let’s face it: after reading all the definitions of the acronyms, you probably have some doubts. It is not easy to perceive the differences between the acronyms at first reading, as they are used interchangeably or with slightly different meanings, creating confusion. Even worse, many give different names to the same thing, or conversely, the same name to different things.
Take GEO, for example. In the common version, GEO is used as an umbrella term to describe everything related to optimization for generative AI, as if optimizing for LLM were the same as aiming for visibility in live searches. But in reality, the strategies and techniques are completely different. GEO describes optimization that works primarily on the historical memory of models, where content is consolidated over time and becomes a reference source for AI. Optimization for LLM and visibility in live searches (such as those for RAG) require distinct techniques and objectives.
A similar phenomenon occurs with AEO: initially, it represented optimization for direct responses in search engines. But over time, AEO has also become the term that describes the work of achieving visibility in generative systems. Previously, the focus was solely on optimization for zero-click responses, but now the focus is also on being chosen by generative systems as a source of truth.
Another example of ambiguity concerns AIO: initially, it was the generic acronym for all AI optimization activities, but it has now taken hold as AI Overview Optimization, the specific optimization work for Google’s generative response systems.
And look at LLMO: the idea that it is a separate discipline now seems an exaggeration, because it is more correct to think of it as an integrated part of the SEO infrastructure.
The AISO case also tells you that the market is tired of acronyms and that the evolution of job requests and services are not keeping pace. After the glut of acronyms in recent years, many brands are returning to simply asking for “SEO,” but with a focus on the inclusion of AI. This trend marks an important point: while acronyms have served to describe specific nuances of the SEO process, the market is asking for a unified vision that integrates all these phases into a single operational flow.
SEO for AI as a solution
We don’t know which acronyms will survive, but the different needs they describe will certainly remain:
- The need to be in memory (GEO)
- The need to appear in live responses (AEO)
- The need to speak the language of machines (LLMO)
- The need to monitor new engines (AIO)
The point is that in practice, the actions on content are the same. The difference often lies in how the term is marketed.
SEO for AI is our response to this fragmentation. It is a unique and integrated approach that encompasses all these concepts, without confusing the end user with acronyms and separate categories that then overlap. SEOZoom has chosen to use a descriptive phrase to encompass all these needs under a single umbrella, responding to modern visibility requirements.
Because SEO has not changed, it has evolved to adapt to the new world of generative engines, while maintaining its central role in ensuring brand visibility and reputation.
Archeology and the future: the evolution of search from 2005 to today
This unified vision is based on a rigorous method, the analysis of historical data that distinguishes the SEOZoom method.
To manage the current chaos, we thought it would be useful to look back and study the algorithmic foundations laid in the 2000s, because understanding how Google learned to index the world provides the key to mastering the moment when machines began to generate it. The evolution of search is a continuous flow, and SEOZoom’s technology stems precisely from the observation of these deep mechanisms: only by studying how the engine learned to “read” the web twenty years ago can we now control the way artificial intelligence “writes” it.
In the early years, at least until 2010, search engines operated as a sophisticated Information Retrieval system. The logic was binary: the algorithm scanned the index looking for an exact match between the string typed by the user and the characters on the page. There is no real understanding of meaning: the engine maps the presence of words, evaluates the authority of links (the intuition of Google’s PageRank), but remains “blind” to the real context of the content. SEO is therefore an essentially mechanical and deterministic activity, focusing on structure, accessibility, and links: if you optimize—working on backlinks, exact matches in the text, (partially) technical structure of the site, and even more extreme and black hat tactics—you can climb the rankings.
Between 2010 and 2012, there were some watershed moments: Google cracked down on spam (this was the time of the Panda and Penguin algorithms, which began to wipe out low-quality sites and pages) and, above all, launched the Knowledge Graph, starting to think in terms of “things” and entities and no longer just strings. It is the infrastructure that makes today’s SEO for AI possible: mapping the relationships between concepts (people, places, objects, and ideas) allows the engine to begin building that “native knowledge” that today allows LLMs to cite facts without necessarily having to scan a web page in real time.
With the progressive evolution of Google’s semantic understanding capabilities, SEO has broken the linearity of ranking—computational power is shifting from words to context. With Hummingbird (2013), the algorithm begins to process the entire query rather than fragmenting it into individual keywords. In 2015, RankBrain introduced machine learning into the heart of the ranking system: for the first time, the machine uses mathematical vectors to interpret queries it has never seen before, trying to guess the search intent based on similar behavioral patterns. Optimization ceases to be mechanical and becomes semantic, requiring content that can respond to user needs in a more sophisticated way. The user experience becomes a key factor for ranking, and this is where the concept of SXO comes from.
At the same time, with the expansion of featured snippets and zero-click answers, SEO adapts to a new paradigm: it is no longer enough to be visible, you have to be chosen to respond directly to the user’s question. This is the emergence of the concept of AEO, where visibility is atomized into immediate, synthesized, and non-clicked responses.
The introduction of BERT in 2019 represents the definitive technological leap towards current AI. Google integrates the Transformer architecture to analyze natural language bidirectionally, understanding nuances, prepositions, and complex relationships within the sentence. The engine finds documents and goes one step further, understanding their content with almost human precision. It is the technical foundation on which all modern LLMs are built.
Today, we are witnessing the final convergence. On the one hand, search extends beyond Google and also moves to social media and other channels; above all, generative models build responses from pre-existing content, because technologies developed to understand text (NLU) are used to generate it (NLG).
SEO for AI is the answer to this transition: we no longer need to optimize solely for an index that retrieves links, but for models that use those same entities and semantic relationships—mapped since 2012—to build concise answers. AI has not eliminated SEO, it has only raised the bar: to be cited by a machine that can write, you need to provide it with data that it can read without ambiguity.
SEOZoom as the hub for online visibility
The risk of this terminological fragmentation is believing that you need five different strategies to manage visibility, or worse, five different consultants. Instead, we have tried to demonstrate how the acronyms of modern SEO — GEO, AEO, AIO, LLMO — describe different stages of a single process of online visibility optimization. There is no such thing as “content for Google” separate from “content for AI” — there is content that can be indexed, semantically understood, and ultimately cited.
And you don’t need ten different tools to manage your brand’s online visibility; you need to oversee the three stages that transform data into a response. SEOZoom is exactly the integrated platform that allows you to tackle the complexity of new search habits and achieve visibility where the user is looking for you, whether it’s Google, generative engines, voice assistants, or social platforms.
- The foundation: context control
Get it out of your head that AI can save you if your basic SEO is leaking. Generative machines (especially those that do RAG) don’t invent facts, they read them from those who have authority on Google. For this reason, the first step remains to secure the structure. Go to SEOZoom and use the SEO Spider to ensure that crawlers (even AI ones) don’t find dead ends. Use Page Performance to decide what to keep and what to prune, because AI hates background noise. And when you write with the Editorial Assistant, you’re not just optimizing for keywords: you’re building the semantic completeness that Google needs to trust you and AI needs to use you as a source. If you fail here, neither GEO nor AEO will start.
- The AI level: identity verification
Once the content is there, you have to ask yourself: does the machine understand it? Here you enter the territory of GEO and LLMO. With the GEO Audit, you stop looking at the technicalities and start looking at your identity: you understand what values, what buyer personas, and what “character” the models associate with your brand. It’s an algorithmic reputation check. And before publishing, you use the AI Engine, the simulator that tells you straight up whether your article has the vector characteristics to compete in synthetic responses or whether it will be discarded because it is too ambiguous.
- The action: monitoring the response
Finally, there is pure performance: the citation. This is where AEO and AISO come into play. You need to know if your content is ready for immediate response: the AEO Audit serves this purpose, removing any friction between the user’s question and your information. At the same time, you use the AI Prompt Tracker to monitor how models react to your inputs and whether you are extracted correctly in conversational flows. And to close the circle, stop guessing about results: with the AI Overview Insight feature, you can see exactly where, how much, and how you are quoted in Google’s generative responses.
Synthesis is the method
You don’t need a dictionary for acronyms, you need a method to govern them. SEOZoom’s SEO for AI is the acknowledgment that the search engine has changed. If you work well on the fundamentals (classic SEO), take care of data identity (GEO), and optimize synthesis (AEO), you’ve covered the entire ecosystem. Stop chasing the latest acronym on LinkedIn and focus on the only thing that matters: remaining the primary source of information, regardless of who—or what—asks the question.
