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The Naming Wars of an Industry That Doesn’t Know What to Call Itself
Six acronyms. Two years. Zero consensus.
GEO. AIO. AEO. AIEO. AIVO. LLMO. Pick your poison. Every conference panel, every LinkedIn thought leader, every martech vendor has a favorite.
Ask five people at a marketing meetup what we call “that thing where you make sure AI mentions your brand.” You’ll get six different answers. And some fisticuffs.
TL;DR
- Six acronyms: No agreement. All of them make sense, partially. The industry can’t agree because nobody knows how AI platforms will stabilize monetization yet.
- A tangible option: AI Visibility Optimization. Great for explaining it to the clients and managing their expectations.
- The numbers: Just ChatGPT handles 18% of Google’s query volume but delivers just 0.6% of Google’s referral traffic. Usage up. Clicks aren’t following, but the industry is emerging. Just needs to be sold differently.
I’ve spent fifteen years in search engine optimization. Built an agency, survived Matt Cutts <3. Created tools that other SEOs use. For the past year, I’ve been elbows deep in AI visibility – building a platform that tracks and optimizes how brands appear across ChatGPT, Claude, Grok, Gemini, Perplexity, and Google’s AI Overviews. All that and I am still not 100% sure my answer is the right answer to this question, but hell if I don’t try to explain my logic.
The naming chaos isn’t stupidity. It isn’t marketing departments failing at basic coordination. It’s something more interesting and more revealing about where this industry actually stands.
Why Nobody Can Agree (And Why That’s Rational)
Think back to when SEO became a real discipline.
It wasn’t when someone figured out how to stuff keywords into meta tags. It wasn’t when backlinks became currency. SEO solidified as an industry when Google’s business model became predictable.
Google figured out ads adjacent to organic results. Advertisers learned that organic clicks have measurable value. Agencies could calculate ROI. Budgets could be justified. Apps could be built. Services could be sold.
The money flows stabilized. Everything else followed.
Now look at AI platforms in 2025:
- ChatGPT runs on subscriptions. Will that change? OpenAI keeps hinting at ad-supported tiers.
- Perplexity experimented with sponsored answers before backing off.
- Google’s AI Overviews sit awkwardly above the traditional ad real estate.
- Claude stays subscription-only. For now.
Nobody (or at least we the plebs) knows how AI answers will be monetized in three years. Which means nobody knows what “optimization” even means in this context.
Are we optimizing for citations that drive subscriptions? For placement in ad-adjacent AI responses? For API calls that cost someone money? For training data influence that compounds over years?
The acronym proliferation reflects genuine uncertainty about what we’re optimizing for. Until the platforms figure out their business models, expecting the optimization industry to standardize terminology is like expecting architects to agree on building codes before anyone decides whether we’re constructing houses or submarines.
This isn’t confusion. It’s rational hesitation.
The Shift Nobody Named Properly
The deeper problem: old mental models don’t map onto the new reality.
Traditional SEO tackled what can be called a selection problem. Google has XY relevant pages. It picks 10 for page one. It ranks them. User clicks one. Victory.
The game was position. The metric was rank. The win condition was getting picked.
AI systems work differently. They don’t select your page from a list. They interpret your content, blend it with dozens of other sources, synthesize something new, and serve that synthesis to the user. Your words might appear – paraphrased, combined with competing perspectives, stripped of context, embedded in an answer you had no hand in constructing.
This isn’t selection. Its interpretation.
That changes everything about what “optimization” actually means.
In the old game, you competed for position. In the new game, you compete for influence. Whose framing shapes the answer. Whose data gets cited. Whose narrative structure survives the synthesis.
Your content doesn’t win by being selected anymore. It wins by being trusted, understood, and preferred as raw material for someone else’s answer.
That’s a fundamentally different optimization problem. None of the existing acronyms quite captures it.
The Acronym Breakdown: What Each Term Gets Right (And Wrong)
Let’s walk through what’s actually out there. Not the marketing spin. The real trade-offs.
Now let’s dig into each one.
GEO: Generative Engine Optimization
This one has academic backing. A Georgia Tech research paper in 2024 established GEO as a legitimate discipline with measurable variables. That matters. When you can point to peer-reviewed research, you’re not just making stuff up.
GEO captures the core shift accurately. We’re optimizing for engines that generate new content, not just retrieve and rank existing pages. The term is specific enough to guide strategy without being so narrow it excludes important tactics.
The problem? Try explaining it to a CMO who just learned that ChatGPT is eating their organic traffic.
“We need to implement Generative Engine Optimization.”
“Is that… AI SEO? Can’t our SEO team just handle it?”
The term is technically correct. It requires a vocabulary lesson before the conversation can even start.
Best for: Technical teams, research discussions, specialist conferences.
LLMO: Large Language Model Optimization
This is the implementation layer. How you structure content so LLMs can chunk it, vectorize it, retrieve it, and cite it cleanly.
Think of LLMO as the technical specification beneath GEO:
- Semantic HTML
- Schema markup
- Clear entity references
- Text chunking patterns that work well with embedding models
- Architectural decisions that make your content machine-readable at a deep level
LLMO speaks directly to developers and data scientists. It’s precise. It’s actionable. And it absolutely requires explaining what a large language model is to anyone in the C-suite.
Best for: Developer documentation, technical implementation guides, engineering teams.
AEO: Answer Engine Optimization
AEO has history. It emerged in the early 2010s when Alexa and Siri started pulling “direct answers” from the web. Featured snippets. Voice search results. Position zero.
The term still has legs because some AI systems – particularly voice interfaces – do still operate in answer-selection mode. They pick a single source rather than synthesizing multiple sources.
But the term feels dated. It implies a system that selects an answer, not one that constructs a new answer from many sources. It’s tactical rather than strategic.
You’ll still use AEO techniques – FAQ schemas, question-phrased headers, concise answer blocks – but as components of a larger approach, not as the central structure.
Best for: Voice search optimization, featured snippet targeting, FAQ schema implementation.
AIO: Artificial Intelligence Optimization
I see this one everywhere. And I wish I didn’t.
AIO is too broad to mean anything specific. Are we talking about optimizing AI tools for marketing workflows? Using AI to create content? Optimizing content for AI consumption? Training machine learning models? All of the above?
The term collapses half a dozen distinct disciplines into one vague category. Worse, it can create confusion with Google’s “AI Overviews” product – a specific feature, not a general optimization category.
When someone asks, “Do we need AIO?” – Yes. And then print an invoice. They 100% need something from AIO, everybody does :)
Best for: Nothing, really. Internal catch-all references at best.
AIEO and AIVO Variants
AIEO (AI Engine Optimization) showed up in vendor pitches around early 2024. No research backing. No consistent definition across sources. It’s “AIO” and “AEO” mashed together, inheriting the confusion of both.
AIVO (AI Visibility Optimization) is different. I’ll explain why in a moment.
Best for: AIEO – skip entirely. AIVO – keep reading.
The Visibility Problem Nobody Discusses
Here’s what keeps me up at night.
A client’s brand appears in 80% of ChatGPT responses about their category. Accurately described. Properly positioned. Mentioned ahead of competitors.
Traffic increase: zero.
The numbers tell the story clearly
ChatGPT processes about 2.5 billion prompts daily. OpenAI confirmed this, and a 2025 research paper from Harvard, Duke, and OpenAI tracked 18 billion messages per week – roughly 2.57 billion per day. Google handles over 5 trillion searches annually, which works out to around 13.7 billion per day.
Usage ratio? ChatGPT sits at roughly 18% of Google’s daily volume. Not bad for a platform that’s three years old competing against one that’s had 25+ years to become a verb.
But here’s where expectations crash into reality
Similarweb tracked referral visits in June 2025. AI platforms collectively sent about 1.13 billion referral website visits. Google Search sent 191 billion. That’s a 0.6% ratio.
Read that again. AI platforms generate less than one percent of the referral traffic that Google does.
This is why naming matters so much. Call this work “AI SEO” and clients expect SEO-like traffic reports. They’ll measure success by clicks, sessions, conversions attributed to AI sources. They’ll be disappointed.
The usage gap is closing. The traffic gap isn’t – not yet, maybe not ever. Anyone selling AI visibility services needs to set expectations accordingly, or they’re building a retention problem into their business model.
Why so few?
Users got their answer without leaving the chat interface. They never clicked through.
In traditional SEO, visibility and traffic go hand in hand. Rank higher, get more clicks. Simple.
In AI, that relationship breaks down completely. You can have massive visibility – dominant presence in AI responses – and see small traffic movements in your analytics. Conversions are a different story.
This is the measurement crisis hiding behind the terminology crisis. We don’t just disagree on what to call this work. We disagree on what success looks like.
If you sell AI optimization services to clients expecting SEO-style traffic reports, you’re setting yourself up for a retention nightmare. The metrics that matter here are different:
- Citation frequency across platforms
- Sentiment in AI-generated descriptions
- Share of voice in category-level queries
- Narrative alignment with brand positioning
- Accuracy of information being surfaced
These are brand metrics. PR metrics. Reputation metrics. Not traffic metrics.
All that led me to my current preferred term.
Why “AI Visibility Optimization” Makes Sense
I want to be clear. I’m sharing the reasoning that led me to this personal preference – again, informed by a decade and a half of SEO grind, a lot of learning by building an AI monitoring and optimization tool, and many conversations with clients trying to figure out what they need and what they’re even buying.
Here’s why I lean toward AI Visibility Optimization – or AIVO (maybe even AVO?:), if we need something shorthand.
It describes the outcome, not the mechanism
When a CMO asks “what does this AIVO do?”, I can answer in one sentence:
“We make sure you show up when people ask AI for recommendations about your industry.”
No explanation needed. No vocabulary lessons. No PowerPoint slides explaining what a generative engine is.
Compare that to GEO: “We’re optimizing content for generative AI engines that synthesize answers from multiple sources.”
Same service. Same work. One lands immediately. One requires a tutorial.
SEO succeeded partly because the term is terrible at describing the mechanism (nobody cares about “engine optimization”) but excellent at implying the outcome (people hear “show up in SEARCH”). AIVO works the same way. The mechanism is complex. The outcome is obvious.
It fits existing mental models
Marketing budgets have visibility line items. Brand visibility. Search visibility. Social visibility. The concept needs no introduction.
More importantly, “visibility optimization” positions this work correctly. It’s not just another SEO task. It’s a distinct discipline that might live closer to PR and brand management than to technical SEO.
That framing helps with internal resource allocation. This isn’t necessarily your SEO team’s job. Or it’s your SEO team’s job plus your content team plus your communications team working together.
It captures what actually matters
Go back to that nightmare scenario – high AI presence, zero traffic. If you’ve been selling “AI SEO” or “Generative Engine Optimization,” you have an expectation problem. Clients expect SEO-like outcomes from SEO-like terminology.
If you’ve been selling “AI Visibility,” the win condition is already framed correctly:
- Are we visible?
- Are we accurately represented?
- Are we positioned well relative to competitors?
Those questions have answers that don’t depend on click-through rates.
It bridges the technical and strategic
At Four Dots, we’ve built tools specifically for this space. FAII tracks brand mentions across ChatGPT, Claude, Grok, Gemini, Perplexity, Google AI Overviews – the major AI surfaces where visibility matters. We monitor Chat Intelligence (how brands appear in conversational AI) and SERP Intelligence (how brands appear in AI-enhanced search), AI website visits and use that analysis to identify content gaps that need filling.
When I explain this to technical teams, I talk about citation frequency, schema, crawlability, entity recognition, content chunking, and retrieval patterns. When I explain it to executives, I talk about AI visibility.
Same work. Different vocabulary. Different frames. Both accurate.
A good term should accommodate both conversations. AIVO does.
The Only Thing That Actually Matters
Cross my heart, I’m not campaigning for AIVO to become the industry standard. I don’t even have a landing page for AI visibility optimization on our website! Which acronym wins isn’t the point.
What matters: my clients understand what I’m selling them. My team can communicate across departments without a glossary.
GEO works. LLMO works. “AI SEO” works (and it’s still catchy). Pick whatever lands with your audience.
The terminology will keep evolving. The platforms will keep changing. The underlying shift – from competing for clicks to competing for influence – is real and accelerating.
The brands that figure this out early will compound advantages. The ones that ignore it will find themselves invisible in the exact places where their customers are increasingly making decisions.
Whatever you call it, that work matters. And it’s starting to gather momentum.
A Practical Structure That Works
Here’s a practical model that emerged from these conversations.
External communication: Use AI Visibility Optimization (AIVO). It’s accessible, outcome-focused, and requires zero technical education for partners and executives.
Internal technical work: Use Generative Engine Optimization (GEO) and Large Language Model Optimization (LLMO). They’re precise, actionable, and respected in technical communities.
Organizational responsibility: Treat AI visibility as a shared function – co-owned by:
- SEO (technical foundation)
- Content (creation and optimization)
- PR/Comms (reputation and narrative control)
This dual-track approach gives you clarity without sacrificing precision. You meet partners where they are. You maintain the technical rigor needed for results. And you don’t have to explain what a generative engine is every time you request budget.
The Timeline Reality
One more thing the acronym debates tend to ignore: the work itself has different time horizons depending on what you’re trying to influence.
Retrieval visibility (appearing in AI responses that pull from indexed content) can shift in weeks to months. You publish authoritative content, it gets indexed, RAG systems retrieve it, you start appearing in answers.
Training data influence (shaping what AI models “know” at a foundational level) takes a long time, years potentially. We’re talking about content that exists, gets cited, becomes authoritative, gets included in future training runs, and gradually shapes model knowledge. You plant seeds now. You harvest later.
Most clients want the first thing but don’t realize they need the second thing too. Retrieval visibility is necessary but not sufficient. If AI models fundamentally misunderstand your brand – if the training data shaped their “knowledge” incorrectly – no amount of RAG optimization will fully fix it.
This is why AI visibility optimization is closer to brand-building than to tactical SEO. You’re playing a long game. The terminology should reflect that.
Where This Goes From Here
I don’t know what the industry will call this work in three years. I don’t know which platforms will survive, how they’ll mutate, which business models will stabilize, which metrics will emerge as standards.
Here’s what I do know – with necessary perspective.
Three years. That’s all it took for ChatGPT to reach 18% of Google’s daily query volume. Google needed over two decades to build what OpenAI is already nipping at. The growth trajectory is undeniable.
But remember the traffic gap we covered earlier. Usage and referral value aren’t the same thing. AI platforms punch at 18% of Google’s weight in queries – and still deliver less than 1% of its referral traffic. That ratio might shrink. It might not.
This isn’t a replacement story. It’s an emergence story.
A new traffic and conversion source is forming. One that operates on interpretation rather than selection. One that’s growing fast from a small base. One that rewards different content strategies than traditional search. One that might never generate Google-level clicks – because users get answers without leaving the chat.
The brands that figure this out early won’t abandon their SEO efforts. They’ll add a new channel while the field is open. They’ll establish positions before competitors realize this matters. They’ll build authority with AI systems now, when the cost of entry is low and the compounding advantage is high.
The ones who wait? They’ll scramble to feed perpetually hungry training crawlers. They’ll wait for the next model update hoping their content finally gets indexed. They’ll watch ROI timelines stretch into months or years because catching up is harder than keeping pace.
This isn’t about choosing between Google and AI. The old game still pays better. The new game is where the growth is happening.
Whatever you call it, that work matters. And it’s worth getting right while the field is still open.
