Three acronyms, one budget, and a lot of agencies pretending the distinction is bigger than it is.
SEO, AEO, and GEO are not three competing disciplines. They are three layers of the same problem: getting a machine to choose your content when someone asks a question.
What has changed is which machine, and what it does after it chooses you. A search engine hands the user a link.
An answer engine hands the user a sentence. A generative engine hands the user a synthesised paragraph assembled from five sources, and your brand either appears inside it or it does not exist for that query.
This guide breaks down what each term actually means, where the differences are real and where they are marketing, what strategies move the needle in each layer, and which tools are worth paying for.
We run this work daily for SaaS and ecommerce clients, and we operate our own publishing network, so most of what follows comes from watching citation behaviour on properties we control.
The short answer
- SEO optimises for ranking. The goal is a position in a list of links, and the win condition is a click.
- AEO optimises for extraction. The goal is having your content pulled out and used as the direct answer, in featured snippets, People Also Ask, voice results, and AI Overviews. The win condition is being the answer, whether or not anyone clicks.
- GEO optimises for selection. The goal is being one of the sources a large language model reaches for when it composes a response. The win condition is a citation, a named mention, or a recommendation inside the generated answer.
In practice, AEO and GEO overlap almost completely, and both sit on top of SEO fundamentals. The useful question is not which acronym to adopt. It is which layer your visibility is currently failing at.
- 84% of AI citations come from earned media, not owned content (Muck Rack, May 2026, across 25 million cited links)
- 0.3% share of AI citations going to paid and advertorial content in the same study
- 97% of llms.txt files received zero requests in May 2026 across 137,000 sites analysed by Ahrefs
- 900M weekly active ChatGPT users reported by OpenAI in February 2026, roughly double the prior year
Where the terms actually came from
Understanding the origin stories explains why the definitions are so inconsistent across the industry.
SEO needs no introduction. It has been a working discipline since the late nineties, and its mechanics are well documented: crawlability, indexation, relevance, authority signals, and technical health.
AEO emerged from the featured snippet and voice search era. When Google started answering questions directly on the results page, and when smart speakers started reading a single result aloud, practitioners needed a word for optimising toward the answer box rather than the blue link. AEO predates generative AI entirely.
It got a second life when AI Overviews arrived, because the underlying job is the same: make your content easy to lift out and present as a standalone answer.
GEO has an unusually precise origin. The term was introduced in a November 2023 paper titled “GEO: Generative Engine Optimization” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, and Ameet Deshpande, with authors affiliated with Princeton University, the Allen Institute for AI, Georgia Tech, and IIT Delhi. It was later presented at ACM SIGKDD 2024. The paper introduced GEO-BENCH, a benchmark of roughly 10,000 queries used to test nine content modification strategies against generative search systems.
That paper matters because it is the only widely cited piece of controlled research underneath a category that has since generated an enormous amount of unverified advice. Its headline findings are worth knowing:
- Adding statistics, quotations, and citations from credible sources lifted visibility by more than 40% on the paper’s position-adjusted metrics, depending on the strategy and domain.
- Fluency optimisation performed nearly as well as adding new information. Clear, well-constructed prose was easier for the model to parse and attribute, with no new facts added.
- Lower-ranked sources gained the most. Content sitting around position five saw the largest relative visibility increases, while position-one content saw comparatively little change.
- Keyword stuffing did not work. The tactics that moved generative visibility were about evidence and clarity, not density.
A caveat worth stating plainly
The Princeton figures come from a controlled benchmark on English-language datasets against the generative systems available at the time. Treat the percentages as directional, not as guarantees you can quote to a client.
The mechanism (evidence, clarity, and attribution improve citation odds) has replicated well. The exact numbers have not been re-validated against every current engine.
AEO vs GEO vs SEO: the comparison
Here is the practical breakdown across the dimensions that change how you actually work.
| Dimension | SEO | AEO | GEO |
|---|---|---|---|
| Optimising for | Ranking position | Extraction as the answer | Selection as a source |
| Primary surfaces | Google and Bing organic results | Featured snippets, People Also Ask, voice, AI Overviews | ChatGPT, Claude, Gemini, Perplexity, Copilot, AI Mode |
| Win condition | A click to your site | Your content is the displayed answer | Your brand is named or cited in the generated response |
| Content unit that matters | The page | The passage or answer block | The claim, and the entity behind it |
| Core levers | Technical health, backlinks, keyword and intent match, internal linking | Direct answers up front, question-shaped headings, schema, clean structure | Entity clarity, third-party coverage, original data, consistency across the web |
| Where authority comes from | Links to your domain | Ranking eligibility plus structural clarity | What other credible sources say about you |
| Measurement | Rankings, impressions, clicks, organic sessions | Snippet ownership, AI Overview presence, zero-click impressions | Citation share, share of voice in answers, sentiment, prompt-level presence |
| Feedback speed | Weeks to months | Days to weeks | Volatile, answers can shift week to week |
| Biggest failure mode | Ranking for terms that do not convert | Burying the answer under 400 words of preamble | Being invisible off-site while publishing constantly on-site |
Where AEO and GEO genuinely differ
Most published comparisons treat these as two distinct disciplines. The honest position is that the practical overlap is close to total, and the terminology has no industry consensus.
GEO carries academic and ecommerce associations because of the Princeton paper and the AI shopping wave. AEO is the preferred term among B2B and SaaS marketers because it inherits the older answer-box vocabulary.
That said, two real distinctions survive scrutiny:
1. AEO is about the passage. GEO is about the entity. An answer engine needs one clean, liftable block of text. A generative engine needs to have formed a view of what your company is, what category it belongs to, and whether it is credible enough to name. You can win a featured snippet with one well-structured page. You generally cannot get ChatGPT to recommend you in a category query with one page.
2. AEO is mostly on-site work. GEO is mostly off-site work. This is the distinction that changes budget allocation, and it is the one most teams get wrong.
The finding that should reshape your strategy
Muck Rack’s May 2026 edition of its ongoing “What Is AI Reading?” study analysed more than 25 million links cited by ChatGPT, Claude, and Gemini across 17 industries. The headline result:
- Earned media accounted for 84% of all AI citations, including journalism, academic research, government sources, encyclopedic sites, and third-party corporate content.
- Paid and advertorial content accounted for 0.3%.
- Journalism alone made up 27% of all cited links.
- The pattern has held across three editions since July 2025, with earned media ranging from 82% to 89%. This is structural, not a model-update artefact.
Citation behaviour also varies sharply by engine. In the same study, ChatGPT cited sources in roughly 96% of responses, Gemini in 82%, and Claude in just 55%, though Claude returned more sources per response when it did cite. If your buyers live in one of these tools, that difference changes your priorities.
AirOps research points the same direction from a different angle, finding that roughly 85% of brand mentions in AI answers originate from external domains, making brands substantially more likely to earn a citation through third-party coverage than through their own properties.
What this means in practice
If your AEO or GEO plan is entirely a content calendar for your own blog, you are working on roughly 15% of the problem. The largest single input to whether an AI system recommends you is what credible third parties have already written about you. That collapses the old boundary between SEO, content, and PR into one function.
The counter-argument, stated fairly
The “just do more PR” reading of that data is too simple, and it is worth naming the pushback. Earned media dominating the citation pool does not mean owned content is irrelevant.
Owned content is what makes you coverable in the first place. Journalists cite original data, product documentation, and named expertise. Without those assets on a domain you control, there is nothing for earned coverage to be about, and nothing for a model to verify your claims against.
The correct reading is sequencing, not substitution: publish the substance on your own site, then get it validated off it.
Strategy: how to actually run all three layers
Treat this as a stack. Each layer depends on the one below it, and skipping a layer is why most AI visibility programmes stall.
SEO: the eligibility layer
You cannot be cited by a system that cannot crawl, parse, or trust you. Nearly every AI surface either draws from a search index directly or from a retrieval pool that heavily reflects one. Google folds AI Overviews and AI Mode into ordinary Search, which means the work that earns rankings is largely the work that earns those citations.
- Crawlability and speed. Slow pages get crawled less often and drop out of citation pools faster. Server-rendered content beats client-side rendering for AI crawlers that do not execute JavaScript reliably.
- Crawler access policy. Decide deliberately which AI user agents you allow in robots.txt. Blocking GPTBot, ClaudeBot, or PerplexityBot is a legitimate business decision, but it is also a decision to be invisible in those tools.
- Topical depth on one domain. One property, deep coverage of the topics you are actually qualified to own. Not a microsite network.
- Real backlinks. Domain-level authority remains one of the strongest observed correlates of citation frequency in every third-party analysis published so far.
AEO: the extraction layer
This is where you make your content easy for a machine to lift. Most content fails here for a boring reason: the answer is buried.
- Answer first, context second. Put a direct, self-contained answer in the first two or three sentences under the relevant heading. Then expand. A passage that only makes sense with the four paragraphs above it cannot be extracted.
- Question-shaped headings. Use the phrasing your buyer actually uses. H2s and H3s framed as questions map cleanly onto the prompts people type.
- One idea per block. Short paragraphs, clean lists, and tables for comparative data. Structure is not decoration here, it is the parsing hint.
- Valid schema where it fits. Article, FAQPage, Organization, Product, BreadcrumbList. Generated server-side from real fields, validated, not bolted on. Schema does not cause citations, but it removes ambiguity about what your page is.
- Specificity over hedging. “Plans start at $29 per month for 50 tracked prompts” is extractable. “Affordable pricing for growing teams” is not.
GEO: the selection layer
This is where the work stops looking like SEO. You are no longer optimising a page. You are managing how a model understands an entity.
- Entity clarity. The model needs a consistent answer to “what is this company?” Your site, your LinkedIn, your Crunchbase entry, your G2 profile, and your press coverage should describe the same category, the same use case, and the same audience. Contradictory positioning across the web is the single most common reason a model refuses to place a brand in a category answer.
- Original data. The Princeton work found statistics addition among the highest-impact strategies, and the earned media data explains why: original research is the most citable asset you can produce. Benchmarks, surveys, and internal aggregate data give both journalists and models something to point at.
- Third-party validation. Review platforms, comparison sites, industry publications, and community discussion. If a category query returns your competitors, look at which sources the answer pulled from and work out why you are absent from those specific pages.
- Comparison and alternatives coverage. Buyers ask models comparative questions. Those answers get assembled from comparison content, most of it third-party. Being accurately represented on the comparison pages that already rank is often faster than publishing your own.
- Freshness and consistency. Stale pricing and outdated product claims propagate into answers and stay there. Keep the canonical facts current everywhere they appear.
On the ranking question
Published studies disagree, and you should know that before quoting one. Some analyses find that a large share of AI Overview citations rank in the top ten organic results, suggesting strong overlap with classic SEO.
Others find that a majority of AI Overview citations come from URLs outside the top twenty.
The reconcilable version: ranking well makes you eligible and improves your odds, but it does not determine selection. A page can rank first and never be cited, and a page can be cited without ranking at all.
Tools: what is worth paying for
The AI visibility tooling category matured quickly, and there is now a credible option at most budgets.
Almost all of these tools measure the problem rather than fix it. That is fine, as long as you buy them knowing the dashboard is diagnostic, not a deliverable.
Pricing below reflects publicly listed rates verified in mid-2026. This category re-prices constantly, so confirm against the vendor page before you commit.
| Tool | Best for | Indicative entry price | Notes |
|---|---|---|---|
| Profound | Enterprise depth and agentic crawl data | From around $499 per month, rising to custom enterprise tiers | The category reference point. Broadest engine coverage. Sells annual commitments rather than casual trials. |
| Peec AI | Mid-market and agencies, source-level gap analysis | From around 89 euros per month | Strong depth-to-price ratio, unlimited seats, agency workspaces, multi-language tracking. |
| Otterly.ai | The cheapest serious entry point | From around $29 per month | Daily tracking across several engines plus a GEO audit and analytics integration. Monitoring only, no content tooling. |
| Semrush AI Toolkit | Teams already inside Semrush | Around $99 per month standalone | Shallower than the specialists, but bundled into a contract you already pay for. Fastest way to get a baseline. |
| Ahrefs Brand Radar | Teams already inside Ahrefs | Around $199 per month per AI index, higher for full coverage | Useful if you want AI mentions sitting next to your existing backlink and ranking data. |
| Scrunch AI | Journey framing and content diagnostics | Enterprise, billed annually | Combines citation and trend reporting with content-level diagnostics. |
The tools you already have
Before buying anything, exhaust these. Most teams discover their real problem in week one without spending a cent.
- Manual prompt testing. Write 20 prompts across four intent types: branded, category, comparison, and problem-led. Run them across ChatGPT, Claude, Gemini, Perplexity, and Google AI Mode. Log which brands appear and which sources the answers cite. Repeat monthly. This single exercise produces more actionable insight than most dashboards.
- Google Search Console. Impressions rising while clicks fall flat is the signature of answer-surface visibility without click-through. That is an AEO signal, not a failure.
- Server log files. Check whether GPTBot, ClaudeBot, PerplexityBot, and Google-Extended are reaching your important pages at all. Crawl access problems are common and invisible in analytics.
- GA4 referral segmentation. Build a channel group for AI referrers. Expect the numbers to understate reality, because copied links strip the referrer and Google bundles AI Mode traffic into standard organic.
- Schema validators. Google’s Rich Results Test and the Schema.org validator. Free, and they catch the structured data errors that silently break eligibility.
What does not work
This category has produced more confident advice than evidence. A few things worth deleting from your plan.
llms.txt as a visibility lever
This is the clearest case. An Ahrefs study of 137,000 domains found that 97% of llms.txt files received zero requests in May 2026.
A separate SE Ranking analysis across 300,000 domains found no statistical correlation between having the file and AI citation frequency.
Google’s own AI optimisation guidance, updated in June 2026, states that you do not need machine-readable files, AI text files, special markup, or Markdown to appear in Google Search including its generative capabilities. Gary Illyes has said Google does not support llms.txt and is not planning to, and John Mueller has compared the idea to the meta keywords tag.
The nuance: llms.txt has a real use case, just not this one. Coding agents and agentic browsers do read it, which is why developer-tooling companies ship it.
If your product has documentation that AI coding assistants should navigate, build one. If a vendor’s headline AEO deliverable is “we will add an llms.txt file,” that is a few minutes of work being sold as a strategy.
Serving separate Markdown pages to AI crawlers
Both Google and Bing have indicated that serving bot-only alternate versions of your pages runs into cloaking policy. The risk is asymmetric: minimal upside, real policy exposure.
Wire-service press release distribution
Press releases do appear in AI answers, but overwhelmingly in industry-trend queries rather than product or comparison queries, and syndicated wire distribution barely registers in citation data. The placement matters far more than the distribution.
Volume as a strategy
Publishing more pages does not increase citation odds in any pattern we have observed across our own properties or client accounts. Generative engines select sources, they do not count them. Ten pages with original data outperform a hundred pages of synthesised commentary, because the hundred pages contain nothing a model could not have generated itself.
How to measure each layer
Standard KPIs for this work do not exist yet, and anyone presenting a single “AI visibility score” as an industry benchmark is inventing it. Measure per layer, and accept that the GEO layer is noisier than you are used to.
| Layer | Primary metrics | Review cadence | What good movement looks like |
|---|---|---|---|
| SEO | Rankings, organic sessions, indexed pages, crawl stats, referring domains | Monthly | Steady growth in qualified organic sessions and referring domains |
| AEO | Featured snippet ownership, AI Overview presence, impressions relative to clicks | Monthly | Rising impressions on question queries, snippet capture on target terms |
| GEO | Citation share, prompt-level presence, share of voice against competitors, sentiment, cited source list | Weekly to monthly, tracked as a trend | Appearing in more of your 20 tracked prompts, and appearing earlier in the answer |
| Business | AI-attributed sessions, assisted conversions, branded search volume, direct traffic trend | Quarterly | Branded search and direct traffic rising alongside citation growth |
You will never fully measure this channel with referrer data. Users copy links out of chat interfaces, which strips the referrer into direct traffic. Google bundles AI Mode and AI Overviews into standard organic with no clean way to isolate them in GA4.
And a large share of AI influence never produces a click at all, because the user got what they needed inside the answer.
Watch branded search volume and direct traffic as proxies, and treat citation share as the leading indicator rather than sessions.
A 30-day starting plan
If you are beginning from zero, this is the sequence we use.
Week 1: baseline
- Write 20 prompts across branded, category, comparison, and problem-led intent. Run them across the major engines and log every brand and every cited source.
- Check server logs for AI crawler access to your top 50 pages.
- Audit robots.txt and make an explicit, documented decision on each AI user agent.
Week 2: diagnose the gap
- For every prompt where a competitor appeared and you did not, identify the exact sources the answer used.
- Sort those sources into three buckets: pages you could be added to, publications you could be covered by, and pages you would need to create.
- Check entity consistency across your site, LinkedIn, review platforms, and any directory listings. Fix contradictions first, because they are cheap and they compound.
Week 3: fix the extraction layer
- Take your 10 highest-intent pages and rewrite the opening of each section to answer the question in the first two sentences.
- Add or validate schema on those pages.
- Replace every vague claim with a specific, verifiable one.
Week 4: start the off-site work
- Identify the three third-party pages that appear most often in your category’s answers, and pursue accurate inclusion on each.
- Scope one original data asset you can produce from information you already hold.
- Re-run your 20 prompts and record the delta. This becomes your monthly baseline.
Want to know where you actually stand?
We run this diagnostic for SaaS and ecommerce brands as a 30-day AEO Starter Sprint: prompt baseline, citation gap analysis, entity audit, and a prioritised fix list.Talk to us about AI visibility
The verdict
Stop treating these as three budgets. The acronyms describe different failure points in one pipeline, and knowing which one you are failing at is the entire value of the distinction.
If AI systems cannot crawl or trust your domain, that is an SEO problem, and no amount of answer formatting will fix it.
If they crawl you fine but never lift your content into an answer, that is an AEO problem, and it is usually solved by putting the answer first. If you are extracted regularly but never named when someone asks who the best option in your category is, that is a GEO problem, and it lives almost entirely off your own website.
The uncomfortable part for most content teams: the highest-leverage work in the third layer does not involve publishing anything. It involves being accurately described by people who are not you.
Frequently asked questions
Is AEO the same as GEO?
Functionally, close to it. Both describe making your content structured, credible, and citable by AI systems, and the tactical overlap is nearly total. The terminology differs by audience: GEO comes from the 2023 Princeton research paper and is more common in academic and ecommerce contexts, while AEO grew out of the featured snippet era and is preferred by B2B and SaaS marketers. If a vendor charges you separately for AEO and GEO, ask them to describe the difference in deliverables rather than definitions.
Is SEO dead?
No, and the framing is wrong. AI surfaces largely draw from search indexes and reflect the same authority signals. SEO is now the eligibility requirement rather than the finish line. What has genuinely changed is that ranking well no longer guarantees the click, because a growing share of queries resolve inside the answer.
Which one should a startup prioritise?
Fix the layer you are failing at, not the one with the newest acronym. A startup with a thin domain and no coverage should build SEO foundations and third-party validation in parallel, because both take months. A company with strong rankings but no presence in AI answers has a GEO problem and should redirect budget toward original data and earned coverage rather than more blog posts.
Do I need an llms.txt file?
Not for visibility in AI answers. Large-scale studies found almost no bot requests for these files and no correlation with citation frequency, and Google has stated directly that no such file is required. Build one only if AI coding agents need to navigate your product documentation, which is a genuine and different use case.
How long does it take to see results?
The extraction layer moves fastest, sometimes within days of restructuring a page, because indexing and re-crawling drive it. The selection layer is slower and noisier: entity signals and third-party coverage typically take one to three months to show up consistently in answers, and answers can shift week to week even when nothing on your side changed. Track trends across at least four weekly measurements before drawing conclusions.
Does schema markup improve AI citations?
Schema does not directly cause a citation, and Google has said no special markup is required to appear in its generative results. What valid schema does is remove ambiguity about what your page is and what entity it belongs to, which supports the entity clarity that generative systems rely on. Treat it as hygiene with a low cost, not as a lever with a measurable citation return.
Should I block AI crawlers?
It depends on your business model. Publishers monetising pageviews have a real argument for restricting crawlers that take content without sending traffic back, and crawl-to-referral ratios remain heavily lopsided for most AI companies. For SaaS and ecommerce brands where citation drives consideration and eventual purchase, blocking usually costs more visibility than it protects. Make it a documented decision per user agent, not a default.
What is the single highest-impact change most teams can make?
Move the answer to the top. In practice, a large share of B2B content buries the substance under context-setting, and both answer engines and generative engines struggle to extract from it. Put a direct, self-contained answer in the first two or three sentences under every heading, then expand underneath. It costs nothing, and it improves both layers at once.
