Answer Engine Optimization is no longer a theory.
It is becoming a practical discipline shaped by search veterans, zero-click marketers, enterprise SEO leaders, AI search operators, startup builders, and agent-first thinkers.
The scale now justifies the urgency.
One 2026 AEO report estimates that ChatGPT handles around 2 billion queries per day. The same report claims AI-referred web sessions grew 527% year over year, based on more than 1 billion analyzed AI responses. Other 2026 AI search reports point to zero-click rates around 43% for AI-generated answer interfaces, with even higher no-click behavior in some AI search modes.
That means AEO and GEO are not side projects anymore.
They are becoming core visibility channels.
The most useful way to understand the field is to compare the practitioners shaping it:
- Jason Barnard gives AEO its origin story.
- Rand Fishkin explains the shift from rankings to answers.
- Amanda Natividad connects GEO to zero-click marketing.
- Lily Ray shows why traditional SEO discipline still matters.
- Rohit Singh separates AEO, GEO, AIO, and related terms.
- Ethan from Gauge explains the technical paths into LLM answers.
- Joseph pushes the field toward agent-first optimization.
My view from Kojable is that all of these perspectives are useful, but incomplete on their own.
The next phase of AEO will be operational.
Brands will need to measure share of answer, track AI citations, publish entity-rich content, and run feedback loops across ChatGPT, Gemini, Perplexity, Google AI Overviews, and emerging AI agents.
What is AEO?
Answer Engine Optimization, or AEO, is the practice of making a brand, product, person, or concept easy for answer engines to find, understand, trust, and cite.
In classic SEO, the goal was to rank in a list of results.
In AEO, the goal is to become part of the answer.
That distinction matters.
Search engines return documents. Answer engines synthesize claims.
A search engine might show ten blue links. An answer engine might produce one paragraph, three citations, and one recommendation. In an agentic workflow, it may choose one vendor or action without showing a list at all.
That is why AEO is not just “SEO with AI keywords.”
It is a shift from keyword visibility to entity trust.
Why these numbers matter
AEO used to sound abstract because AI answers felt like a future interface.
That is no longer true.
If ChatGPT is already processing billions of daily queries, and if AI-referred traffic is growing triple digits year over year, then answer engines are already influencing discovery, consideration, and purchase behavior.
The traffic number is only part of the story.
The more important point is that many AI interactions are zero-click. Users ask a question, read the answer, absorb the recommendation, and never visit the source page.
That means brands can lose influence even when their traditional SEO dashboards look stable.
A company may still rank on Google, but be absent from ChatGPT. It may appear in Perplexity citations, but not Gemini. It may be described accurately by one engine and incorrectly by another. It may be shortlisted by an AI agent, or ignored completely.
This is why AEO needs its own measurement layer.
The new question is not only:
“Do we rank?”
It is:
“Do AI systems understand, cite, and recommend us?”
Why practitioners matter in AEO
AEO and GEO are still early disciplines.
There is no single universal playbook yet. Different practitioners are defining the field from different angles.
That is useful because AEO is not one thing.
It sits at the intersection of:
- SEO
- Content strategy
- Entity optimization
- Brand authority
- Digital PR
- AI retrieval
- Generative search
- Agentic decision-making
- Measurement and analytics
The best way to understand the field is to compare the people shaping it.
The 7 practitioners: a quick comparison
| Practitioner | Main lens | Key number or signal | What they add to AEO/GEO |
|---|---|---|---|
| Jason Barnard | AEO origin story | Formalized AEO around 2018 | Frames AEO as the move from search results to direct answers |
| Rand Fishkin | Search evolution | Zero-click Google searches now around two-thirds in some studies | Explains why visibility is shifting from rankings to answers |
| Amanda Natividad | Zero-click and GEO mechanics | About two-thirds of Google searches end without a click | Breaks GEO into retrievability, extractability, credibility, and public evidence |
| Lily Ray | Enterprise SEO discipline | 15+ years in SEO; built and led a 35-person SEO team | Shows that technical SEO, E-E-A-T, audits, and authority still matter |
| Rohit Singh | Definitions and category clarity | Multiple competing AEO/GEO definitions still coexist | Separates AEO, GEO, AIO, and related AI search disciplines |
| Ethan from Gauge | LLM mechanics | About 14% of Ascend traffic came from ChatGPT queries | Explains pre-training and retrieval as two paths into AI answers |
| Joseph | Agent-first optimization | 33% of organic activity estimated as non-human AI activity; top performers capture 59.5% of AI citations | Pushes the field from answer visibility toward agent selection |
1. Jason Barnard: AEO as the original answer engine idea
Jason Barnard is important because he gives AEO its origin story.
Before ChatGPT, Gemini, Perplexity, and Google AI Overviews became everyday marketing topics, Google was already moving from search results to direct answers.
Featured snippets, knowledge panels, voice search, and answer boxes were early signs of the same transition.
Jason’s contribution is that he treated this shift as a distinct optimization problem.
The goal was no longer only to rank a page. The goal was to become the trusted source behind the answer.
A 2025 retrospective on Jason’s work says he formalized Answer Engine Optimization at BrightonSEO in April 2018. By 2026, that means AEO has had roughly 7–8 years to evolve from an early search concept into a broader AI visibility discipline.
That timeline matters.
AEO did not suddenly appear when generative AI became popular. It began when search engines started answering instead of only listing.
Generative AI has simply accelerated the shift.
What Jason adds
Jason gives AEO its root concept:
If machines answer questions directly, brands need to optimize for the answer layer, not just the ranking layer.
2. Rand Fishkin: AEO as the third phase of search
Rand Fishkin’s value is strategic.
He places AEO inside the bigger history of search.
The first phase was document ranking. Search engines organized the web and returned lists of pages.
The second phase was direct answering. Search engines began extracting snippets, panels, facts, and voice answers.
The third phase is generative synthesis. AI systems now remix information into full paragraphs, recommendations, comparisons, and decisions.
That third phase changes the marketer’s job.
In classic SEO, you could win by ranking a page that users clicked.
In AEO, you may win without a click.
Your brand may appear inside the answer itself. Your data may be cited. Your framework may shape how the model explains the category. Your company may become part of the default language of the market.
But the opposite is also true.
If the answer engine explains your category and leaves you out, you may lose influence even while your traditional rankings look healthy.
This is why Rand’s zero-click work matters.
SparkToro’s 2026 research reports that 68.01% of U.S. Google searches in the first four months of 2026 ended without a click. That is not just a search metric. It is a warning about the future of discovery.
If nearly two-thirds of searches do not send a user to a website, then brand visibility has to be measured before the click.
What Rand adds
Rand gives AEO its strategic framing:
Search is moving from ranked documents to synthesized answers, so marketers need to measure answer visibility, not just rankings.
3. Amanda Natividad: GEO in a zero-click world
Amanda Natividad’s work is valuable because she connects GEO with zero-click marketing.
Zero-click marketing starts with a simple reality: users often get value without visiting your website.
That used to happen on social platforms, newsletters, podcasts, and search results pages.
Now it also happens inside AI systems.
A user can ask ChatGPT, Perplexity, Gemini, or Google AI Overviews a question and receive a complete answer without clicking through to the original sources.
In a 2026 interview, Amanda notes that about two-thirds of Google searches now end without a click, aligning with the roughly 66–68% range commonly associated with SparkToro’s zero-click research.
That conversation appeared in episode 33 of The ChangeOver Podcast, but the theme is much older in her work: marketers have to create value where the audience already is, not only where attribution is easy.
Amanda’s GEO framework can be simplified into four mechanics:
- Retrievability
- Extractability
- Credibility
- Public evidence
These four mechanics are useful because they turn a vague AI search problem into practical work.
Retrievability
Can the AI system find your content?
This includes crawlability, indexation, sitemaps, internal links, stable URLs, and page accessibility.
If your best page cannot be retrieved, it cannot be cited.
Extractability
Can the model cleanly pull facts from your content?
This is where structure matters.
Clear headings, short paragraphs, tables, definitions, lists, and explicit claims make content easier for models to parse.
A beautiful page that hides its meaning behind vague brand language may perform badly in AEO.
Credibility
Why should the model trust you?
Author expertise, original data, customer proof, case studies, strong sourcing, and a history of useful publishing all matter.
AEO does not remove the need for trust. It increases it.
Public evidence
Does the wider web confirm your authority?
This is where off-site signals matter.
Podcasts, LinkedIn posts, YouTube interviews, third-party write-ups, partner pages, customer stories, public decks, and creator content all help answer engines corroborate your entity.
What Amanda adds
Amanda gives GEO its practical mechanics:
Be findable, extractable, credible, and publicly corroborated.
4. Lily Ray: traditional SEO still matters
Lily Ray’s perspective is important because it prevents overcorrection.
A lot of AI search commentary makes it sound like traditional SEO is dead.
It is not.
AEO and GEO build on SEO. They do not erase it.
Technical SEO still matters. Content quality still matters. Internal linking still matters. Authority still matters. E-E-A-T still matters. Structured data still matters.
Lily’s credibility comes from depth.
Her bio notes more than 15 years of SEO experience. Algorythmic’s about page says she built and led a 35-person SEO team at Amsive, serving clients from small businesses up to Fortune 50 brands. On LinkedIn, she has referenced almost 10 years of SEO and AI search decks available on Slideshare.
That background matters because AEO cannot be reduced to prompt tricks.
For mature teams, AEO should not be a disconnected experiment run by one content marketer. It should become part of the audit process.
A serious AEO audit should look at:
- Classic organic rankings
- Technical crawl health
- Indexation
- Internal links
- Content quality
- Structured data
- Author expertise
- Entity consistency
- AI Overview visibility
- ChatGPT mentions
- Gemini mentions
- Perplexity citations
- Competitor citation share
- Off-site corroboration
This is the bridge between old SEO and new AI visibility.
The teams that win will not abandon SEO. They will extend SEO into answer engines.
What Lily adds
Lily gives AEO its operational discipline:
AI search should be audited, measured, and improved with the same seriousness as traditional organic search.
5. Rohit Singh: AEO and GEO are distinct but connected
Rohit Singh’s contribution is definitional clarity.
AEO and GEO are often used interchangeably, but they should not be treated as identical.
AEO is broader.
It includes direct-answer systems such as featured snippets, voice search, answer boxes, AI Overviews, and AI-generated answers.
GEO is more specific.
It focuses on generative engines that retrieve, synthesize, and produce natural-language responses.
That distinction matters because different systems behave differently.
A featured snippet may depend heavily on classic ranking and passage extraction.
A generative AI answer may depend on retrieval, entity resolution, source diversity, training data, and synthesis patterns.
An agentic workflow may skip visible answers altogether and simply choose one tool, vendor, or action.
Rohit’s perspective is useful because the field is still messy.
Different practitioners use AEO, GEO, AIO, AI search optimization, LLM SEO, and agent optimization in different ways. That confusion is not a failure. It is a sign that the discipline is still early.
AEO in 2026 feels like SEO in 2006: important, commercially valuable, and not yet fully standardized.
What Rohit adds
Rohit gives the field category clarity:
AEO, GEO, AIO, and AAO are related, but they should be defined and measured separately.
6. Ethan from Gauge: two paths into LLM answers
Ethan’s GEO perspective is useful because it gets closer to how large language models actually produce answers.
In Ascend.vc’s GEO piece, Ethan notes that about 14% of their site traffic was already coming from ChatGPT queries when he started paying attention.
That is a useful signal.
Even for a startup or venture firm, LLM-driven discovery was no longer theoretical. It was already visible in analytics.
Ethan separates two paths into LLM visibility:
- The pre-training path
- The retrieval path
These paths have different timelines and different optimization strategies.
The pre-training path
In the pre-training path, the model answers from what it already learned during training.
This is a long-cycle game.
If a model has already absorbed consistent information about your brand, product, or framework, it may mention you without live search.
If it has not learned you yet, you may remain invisible until a future model update.
This creates a 3–12 month horizon for some AEO work.
Evergreen, entity-rich assets matter here because they may influence future model snapshots.
The retrieval path
In the retrieval path, the model searches or fetches information at answer time.
This is a short-cycle game.
The model may retrieve recent pages, compare sources, and synthesize an answer from a small document set.
This creates a 1–30 day horizon.
Freshness, links, source authority, clear structure, and entity clarity can change retrieval-driven answers much faster.
Why this matters
Many marketers treat AI visibility as one system.
It is not.
Some AI answers are shaped by long-term training data. Others are shaped by live retrieval. Some are a mix of both.
That means AEO needs two operating rhythms:
- Durable entity-building for future model snapshots
- Fast content iteration for retrieval-driven answers
What Ethan adds
Ethan gives AEO its time-scale model:
Optimize for both long-cycle model knowledge and short-cycle retrieval visibility.
7. Joseph: from AEO to agent-first optimization
Joseph’s contribution is that he pushes the field beyond answers and into actions.
That is where the next major shift may happen.
In SEO, the user searches and clicks.
In AEO, the user asks and reads.
In AAO, the user delegates and the agent acts.
AAO stands for Assistive Agent Optimization.
This matters because agents may not show users a list of options. They may choose one answer, one vendor, one booking, one route, one software tool, or one product.
That changes the funnel.
The goal is no longer just visibility.
The goal is selection.
Joseph’s LinkedIn commentary estimates that around 33% of what we call organic traffic is now non-human AI activity. In the same broader argument, he cites data suggesting that top performers capture 59.5% of all AI citations, up from 30.9% previously.
That is a power-law warning.
If AI citations concentrate around a small group of trusted brands, the early winners become easier to cite again. The brands that are absent become harder to discover.
Joseph’s ladder captures the progression:
| Acronym | Meaning | User behavior |
| SEO | Search Engine Optimization | User scans results and clicks |
| AEO | Answer Engine Optimization | User reads a direct answer |
| GEO | Generative Engine Optimization | User asks an AI system for synthesized guidance |
| AIO | AI Overview Optimization | User sees AI summaries inside search |
| AAO | Assistive Agent Optimization | User delegates a task and the agent chooses |
This is the most commercially intense version of AEO.
If an agent picks one winner, being second or third may not matter.
That makes entity trust, public evidence, integrations, pricing clarity, reviews, and machine-readable product information even more important.
What Joseph adds
Joseph gives AEO its agent-first future:
The next optimization target is not only being cited, but being selected.
The biggest difference between the 7 practitioners
Each practitioner is looking at a different layer of the same transformation.
Jason looks at the origin of answer engines.
Rand looks at the strategic evolution of search.
Amanda looks at zero-click distribution and GEO mechanics.
Lily looks at enterprise SEO execution.
Rohit looks at definitions and category boundaries.
Ethan looks at LLM mechanics and time horizons.
Joseph looks at agents and decision automation.
The mistake would be choosing only one lens.
AEO needs all of them.
If you only follow Jason and Rand, you understand the strategic shift but may miss the operational details.
If you only follow Amanda, you understand zero-click mechanics but may underweight technical SEO.
If you only follow Lily, you keep SEO discipline but may move too slowly for retrieval-driven AI answers.
If you only follow Rohit, you get better definitions but still need execution.
If you only follow Ethan, you understand model mechanics but still need brand and content systems.
If you only follow Joseph, you see the agent future but may skip the work needed to become trusted today.
The real AEO playbook combines all seven.
My opinion from Piush Vaish at Kojable
As a founder and engineer working on Kojable, I see these practitioners as mapping different sides of the same mountain.
Jason and Rand explain why the search interface is changing.
Amanda explains why zero-click visibility is now a serious marketing channel.
Lily shows why traditional SEO discipline still matters.
Rohit helps separate the language so teams can stop mixing every AI search concept into one vague bucket.
Ethan explains why some answers feel frozen for months while others change in days.
Joseph points toward the next commercial battleground, where agents choose one winner instead of showing ten results.
My own view is shaped by 10+ years in data science, including work with unicorn and Fortune 10 companies, and by putting LLM systems into production rather than only talking about them in theory.
That background makes me think the next phase of AEO is not just strategy.
It is measurement.
Marketing teams do not only need more AI search theory. They need systems that show how their brand appears across answer engines, where they are missing, which competitors are being cited, and whether content updates actually change the answer.
That is what we are building with Kojable.
Kojable is designed to help B2B marketing teams measure and improve their share of answer across AI search surfaces.
The goal is to make AI visibility trackable.
A team should be able to ask:
- Does ChatGPT mention us for our priority queries?
- Does Perplexity cite us or our competitors?
- Does Gemini understand our product category correctly?
- Are we appearing in Google AI Overviews?
- Which pages are being used as sources?
- Which entities are missing from our content?
- Did our latest article change the answer after 7, 14, or 30 days?
- Are agents likely to select us, ignore us, or choose a competitor?
That is where AEO becomes practical.
Not a buzzword.
Not a one-time content project.
Not a replacement for SEO.
AEO is becoming a measurable operating system for brand visibility in AI-generated answers.
The winners will be the brands that become clear, trusted, and well-corroborated entities across the machine-readable web.
They will not only optimize for keywords.
They will optimize for entities, evidence, citations, and selection.
That is the future of search visibility.
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