What the Fuck Are SEO, AEO, GEO, AI Search, LLMO, and Every Other Acronym?

I've been spending more time on LinkedIn lately. Professional opportunities. Business discussions. The occasional opportunity to watch somebody turn an ordinary business concept into a revolutionary new service.

Recently, I came across a post separating SEO, AEO, and GEO into three different games.

SEO gets you found.

AEO gets you chosen as the answer.

GEO gets you recommended by artificial intelligence.

Well, shit.

Then somebody in the comments introduced DIO: Digital Identity Optimization.

Because what the world desperately needed was another fucking acronym.

There is legitimate work behind some of these labels. Generative AI changes how information can be retrieved, assembled, and presented. Businesses should understand that.

But a new label does not establish a new service, and a new service does not establish a return on the money.

This article is for the person signing the fucking check, not the person inventing the name of the service.

I've worked on search visibility, advertising, and customer acquisition for businesses, including legal practices in competitive Southern California markets.

The business problem was getting discovered by relevant people, giving them a reason to contact us, and determining whether the resulting customers justified the acquisition expense.

I've also sat through enough agency meetings to know how much time you can spend discussing what somebody intends to do without establishing what the fuck they've accomplished.

That is what irritates me.

Specialists should get paid. Useful work costs money.

Renaming the invoice is not the work.

Search Technology and Search Optimization Are Different Things

Search technology discovers, processes, retrieves, and presents information.

Search optimization is the work of making your information accessible and useful within those systems.

A change to the technology can require different work. It does not automatically require a separate retainer for every new interface.

Start with the system.

Google didn't invent search. Its early research helped show how the web's link structure could contribute to evaluating pages. PageRank was part of that approach, not the entirety of search and certainly not a complete description of Google's current ranking systems. [1]

The useful point: a search engine can consider more than whatever a business says about itself.

Google describes conventional Search in three stages: crawling, indexing, and serving results. In ordinary language: access pages, process their information, and select results for a search. Following the requirements does not guarantee any particular page will be crawled, indexed, or shown. [2]

Eligibility is not visibility.

And visibility is not a customer.

What Generative AI Actually Changed

A language model and a search engine are not interchangeable.

The 2017 paper Attention Is All You Need introduced the Transformer architecture that became foundational to modern language models. That history matters because generating language and retrieving web information are distinct capabilities that products can combine. [3]

A model can generate an answer using learned patterns and the information available in its current context. A retrieval system can supply sources for that answer.

Google describes using retrieval-augmented generation—RAG—and query fan-out in its generative search experiences. RAG brings retrieved information into answer generation. Query fan-out runs related searches to investigate parts of a question. Google says these experiences remain rooted in its core Search ranking and quality systems. [4]

Imagine someone asks, “Who is a good personal injury attorney in Pasadena?”

A conventional search experience might present firm websites, a map, directories, advertisements, reviews, and other webpages.

A generative experience might retrieve information from several of those sources and construct a response discussing particular firms and the factors a person should consider.

The underlying information can overlap substantially even though the presentation is different.

Your page might become one supporting source inside an answer assembled from several sources. The reader may learn something without visiting your website. A citation might help them investigate further, or it might never become a click.

Those possibilities create real work around source selection, accurate representation, and measurement.

They do not create evidence that every newly named optimization service works.

A Short History of the Acronyms

The names followed changes in how people encountered information.

Search engine optimization developed around improving visibility in search engines. As search products displayed more direct answers and featured information, marketers increasingly used labels such as answer engine optimization to describe work aimed at those experiences.

The 2017 Transformer paper helped establish the architecture behind many modern language models. Google introduced its Search Generative Experience experiment in 2023, using generated snapshots and links to supporting web content. [3, 7]

That same year, researchers submitted GEO: Generative Engine Optimization, which formalized a framework for measuring and improving source visibility in generated responses. [6]

Labels including LLMO, AIO, and GAIO are now used to package related work. They can describe meaningful differences in product behavior, access, retrieval, representation, and measurement.

They are not universal service standards.

The acronym tells you what somebody named the package.

The scope tells you what you are buying.

So What the Fuck Do the Acronyms Mean?

These are practical definitions for this article. Several industry labels overlap, and sellers use them differently. Ask what a provider means before assuming its package matches the label.

SEO — Search Engine Optimization

Work to improve discoverability in search engines.

Local SEO — Local Search Engine Optimization

Search work focused on a business's relevant geographical markets.

AEO — Answer Engine Optimization

A label for visibility in direct-answer experiences.

GEO — Generative Engine Optimization

A research term and service label for visibility in generated answers.

LLMO — Large Language Model Optimization

In marketing, a label for visibility or representation in LLM-based systems. Define the scope.

AIO or GAIO — AI Optimization or Generative AI Optimization

Broad labels. AIO can also mean AI Overviews in search discussions. Context matters.

SXO — Search Experience Optimization

A label connecting search visibility with the experience after discovery.

DIO — Digital Identity Optimization

The label used in the discussion I encountered. It does not establish a standard deliverable.

AI Search

A product or technology category, not a particular service package.

SGE — Search Generative Experience

Google's earlier experimental search branding, not another current optimization requirement.

AI Overviews and AI Mode

Google Search features that provide generated summaries and an experience for more involved questions and follow-ups.

RAG — Retrieval-Augmented Generation

Using retrieved information to support generated answers.

LLM — Large Language Model

A kind of model used to process and generate language.

llms.txt

A proposed website information convention that can guide agents to useful content. It is not a universal ranking credential.

Google's documentation describes its features and terminology; the GEO paper and llms.txt proposal provide the research and proposal context. [4–7, 11]

GEO is not simply an acronym somebody invented for a sales deck. Aggarwal and colleagues published GEO: Generative Engine Optimization as a preprint in November 2023. They tested methods for improving visibility in generative responses and reported gains in their experimental setting. [6]

That deserves attention.

It does not establish that the same intervention will produce lasting recommendations, customers, or profit across every commercial AI product.

Research evidence has a scope. So should the sales claim.

Ask the Platform Before Buying the Package

Google explicitly treats optimization for its generative Search experiences as part of SEO. [4]

Its AI-features guidance says a supporting page needs to be indexed and eligible for a Search snippet. It does not require special AI schema or a new machine-readable file. [5]

But “the fundamentals still apply” is not permission to ignore newer controls.

As of this article's October 12, 2026 review, Google documents a Search Console control for including or excluding a site's links and content from specified generative Search features. Google says it rolled out worldwide by August 31, 2026. Inclusion is the default, subject to inherited property settings; it is separate from the control for AI training. [8]

That is a concrete thing to check.

Now look outside Google.

ChatGPT: OpenAI distinguishes OAI-SearchBot, used for its search features, from GPTBot, which crawls content that may be used for training. Those controls are independent. Allowing search access does not require allowing the training crawler. [9]

Perplexity: Its documentation identifies PerplexityBot for surfacing and linking websites in search results, and separately describes user-triggered page access. It also identifies robots.txt and firewall configuration as access considerations. [10]

These are documented operational differences.

“Upload this file and every AI will recommend you” is a very different claim.

Access, retrieval, citation, recommendation, and sale are different outcomes.

A competent specialist should be able to tell you which one the work addresses.

llms.txt: Congratulations, You Created Another Text File

Let's be fair to the file.

Jeremy Howard proposed llms.txt in 2024. The proposal, now in its second version, describes a concise guide that helps agents locate useful information, often including software documentation. [11]

That can be useful.

A well-maintained documentation guide has a purpose. Someone may reasonably charge to create and maintain it.

But Google says llms.txt has no special role in Google Search and does not help or harm Search visibility or rankings. Its guidance allows that other systems may use such files. [4]

So ask:

Which system is expected to use it?

What problem does it solve?

How will we know whether it helped?

If the answer is “AI likes it,” keep asking.

A useful file and a demonstrated acquisition strategy are not the same purchase.

And the filename does not explain a monthly retainer.

The FAQ Advice Needs a Fucking Date on It

Clear answers can help customers. Structured data can support eligible search features.

But the advice has to match the feature that actually exists.

Google restricted FAQ rich results in 2023. Its documentation changelog now says that feature stopped appearing in Search on May 7, 2026, and its FAQ rich-result documentation was removed in June. [12]

An agency selling FAQ rich results using an old presentation needs a new presentation.

That does not mean you should remove useful customer questions from your website. It means an FAQ page and Google's former FAQ display are different things.

Google also says there is no special schema required for its AI features. [5]

Answer the question because the customer needs the answer.

Do not pretend exactly fifty words and a piece of markup constitute proof of a new ranking mechanism.

A Dentist in Los Angeles Isn't Selling Software to the Entire World

Now we get to the business.

Suppose someone wants a dentist near Pasadena. Being well known in Beverly Hills does not automatically make a practice convenient for that customer.

Google's local guidance describes relevance, distance, and prominence: whether the business fits the search, where it is, and how well known it is. Reviews and links can contribute to prominence. None of this guarantees placement. [13]

That is Google's local guidance, not a published formula for every AI recommendation.

The underlying customer problem still matters wherever discovery happens.

Can you provide the service this person needs, where they need it, and give them a reason to choose you?

It also helps to understand which surface you are discussing.

Organic results can show webpages. Local results can show businesses and maps. Paid search can buy eligible placements through an advertising auction. A generated response can synthesize information from several sources and cite some of them.

Those discovery paths can influence one another without becoming the same product.

For a local service business, I would begin here:

• Maintain an eligible, accurate Business Profile: real business details, the right categories, services, hours, contact information, and appropriate location or service-area information.
• Verify the website in Search Console and investigate whether important pages can be accessed and indexed. Verification is a diagnostic step, not a ranking boost. [14]
• Explain the services you actually deliver. Make the next step easy: call, book, inquire, or get the information needed to decide.
• Maintain relevant professional listings and earn authentic customer feedback where appropriate.
• Participate in the real market. Community involvement, professional relationships, useful resources, and credible local coverage can matter to people before they ever matter to a retrieval system.
• Check which inquiries become paying business and what that business contributes after its costs.

A directory your customers use can be valuable. Fifty directories nobody uses are not automatically fifty improvements.

A paid placement may earn its keep through referrals. The payment itself does not demonstrate an improvement in organic ranking.

And publishing 47 interchangeable city pages is not the same as expanding an operation. Google identifies doorway abuse and scaled content created primarily to manipulate rankings as spam. Useful service-area pages are not automatically spam; purpose and substance matter. [15]

If the business opens another legitimate location, it has created more than another page. It has created an operating commitment: people, hours, service capacity, customer experience, and a place or service area it can accurately represent.

Build the capacity to serve the market you claim to serve.

For a geographically dependent business, I favor establishing a functioning local operation before expanding. That's my operating preference, not a prerequisite for national search visibility.

An online company may serve the country from day one.

A dental practice cannot clean somebody's teeth through a fucking Zoom call.

Products and SaaS Have a Different Problem

Imagine a fictional company selling practice-management software.

Its customers may be spread across the country. The distance from the buyer to the company's headquarters may matter very little.

They care whether the product solves their problem.

Does it work?

What does it cost?

What does it integrate with?

What happens when something breaks?

Why this instead of the alternative?

My starting point would be accurate product information, useful demonstrations, documentation, credible comparisons, authentic feedback, and a clear path to evaluating the product.

The SaaS company might develop its reputation through customer usage, product documentation, software directories, independent comparisons, professional communities, and support discussions. That is a different operating problem from convincing someone that a dental office is relevant and accessible near Pasadena.

For e-commerce, practical buying information also matters: availability, delivery, returns, and the actual cost to the customer. Keep applicable product feeds and listings aligned with what the customer can buy.

Third-party discussion can give people evidence beyond your own claims. It may also give retrieval systems information to use. Neither outcome guarantees a recommendation.

A product being discussed is not necessarily a product being endorsed.

And visibility in the wrong use case can send you customers you cannot satisfy.

Maybe Give People a Reason to Talk About You

Before buying another optimization package, consider whether your customers have somewhere useful to ask questions and share experiences.

That might be Reddit. It might be an existing industry forum, a user group, or a support community.

It might not need to be a community at all.

Nobody owes your company a discussion.

Creating a subreddit and filling it with advertisements is not the same as building one.

A useful community requires participation, moderation, and a reason to return. That is work, and it has a cost.

Evaluate it for customer understanding, support, retention, and referrals. Treat any search benefit as something to investigate, not a guaranteed bonus.

Do not buy fake enthusiasm and call it public recognition.

Google's guidance explicitly warns against pursuing inauthentic mentions as an AI-search shortcut. [4]

Real customers discussing a real problem can teach you something even if an AI assistant never cites the conversation.

The Agency Still Has to Earn Its Money

Imagine a business considering an additional $2,000 monthly GEO package.

These numbers are hypothetical. They illustrate a simple break-even decision, not an industry benchmark or a complete causal-attribution model.

Over a three-month test, the fee is $6,000.

Suppose each additional completed sale produces $500 in contribution after the variable costs of delivering it, before this package and fixed overhead.

Twelve additional sales cover the fee.

Eight produce $4,000 in contribution. Subtract the $6,000 fee and the package leaves a $2,000 shortfall over that period.

Twenty produce $10,000. Subtract the fee and there is $4,000 left before fixed overhead and any other incremental costs.

Notice the word additional.

Customers who would have arrived anyway do not become incremental customers because a reporting tool assigns them to a new channel.

And the break-even threshold rises if the package requires more internal labor, software, or content spending.

A longer buying cycle or credible repeat-purchase value may justify a different measurement window. Define that before the result, rather than inventing future value after the numbers disappoint.

If the objective is awareness or correcting misinformation, say so and price the project against that objective. Don't sell it as immediate acquisition and defend it afterward as unmeasurable brand value.

An impressive citation report can coexist with an investment that loses money.

So can a useful technical improvement that takes time to pay back.

The job is to distinguish them.

The Strongest Counterargument: There Really Is New Work

Yes. There is.

An AI product may misrepresent your service. Relevant pages may be inaccessible to its crawler. A citation may lead to obsolete pricing. Your analytics may miss a discovery path customers actually use.

Those are concrete problems worth investigating.

Specialists can also help with large sites, migrations, product feeds, multilingual content, retrieval testing, and measurement.

If a provider identifies a product-specific problem, explains the intervention, and demonstrates useful results, I do not care whether it calls the work SEO, AEO, or GEO.

Charge for the work. Explain the work. Show what changed.

Overlap with SEO does not make the work worthless.

It makes the scope and billing worth examining.

Here's the Test That Could Prove Me Wrong

If I am skeptical of an additional package, I should be able to say what evidence would change my mind.

Before the work begins, agree on:

• The problem: Which product, audience, location, and search or buying situation are we targeting?
• The intervention: What will change, why should it help, and what is already included in the current contract?
• The baseline: Record representative questions, page access, existing visibility, and business outcomes before the change.
• The test: Repeat observations over time using varied, realistic questions. Record the product or model where visible, date, location, and whether live search was used. Use a comparable untreated group or phased rollout where practical.
• The decision: Set the budget, measurement window, and conditions for continuing, adjusting, or stopping.

One flattering screenshot is not a trend.

A fixed prompt list is not the whole market.

A before-and-after increase is not automatically proof that your change caused it.

Use available platform reporting as well as your own analytics. Google now documents a Generative AI performance report in Search Console. Bing's AI Performance reporting tracks citation activity across supported Microsoft and partner experiences; citations are not clicks or sales, and its trends do not by themselves establish causation. [16, 17]

Connect the evidence to qualified inquiries, purchases, contribution, or the other outcome you agreed to pursue. Record the attribution limits instead of pretending every customer journey is fully visible.

If the provider can demonstrate a worthwhile improvement beyond the existing work, my objection to the additional fee fails.

Good.

The objective is to buy useful work, not win an argument about what to call it.

The Technology Changed. The Business Still Needs Customers.

Generative AI deserves serious technical attention.

So does the invoice.

Begin with what you sell, who needs it, how they discover it, and what they need to understand before choosing you.

Then examine the systems standing between that customer and your business.

Make the information accessible.

Make it accurate.

Give people a reason to choose you.

Measure what happens.

When something specific to an AI product needs fixing, fix it.

But before you pay another fucking agency to optimize your company for an acronym you learned fifteen minutes ago, make sure the package addresses an actual problem.

If people can't understand what you do, your intake doesn't work, or the sale loses money, changing the label on your marketing will not solve it.

GEO may be part of the work.

It is not a substitute for understanding the fucking business.