AEO vs GEO: being extracted is not the same as being recommended

For twenty years, search engines indexed links and left the synthesis to the buyer. Ten results, ten tabs, one human stitching together an opinion. Generative engines now do that stitching themselves, and they hand the buyer a finished answer, often with a shortlist attached.

That shift has produced two labels that most teams use interchangeably: Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO). When I wrote about Answer Engine Optimisation in May, I used AEO the way much of the industry does, as a catch-all for “being the answer”. That shorthand works on a board slide. It is a poor basis for a budget.

The two disciplines optimise for different machines, reward different assets and need different scorecards. AEO is about being extracted. GEO is about being synthesised, and ideally recommended. Treat them as one and you will over-invest in formatting and under-invest in the evidence and reputation that actually decide who makes the shortlist.

How an answer engine extracts

An answer engine runs a familiar pipeline: index the web, rank pages for a query, then lift one passage into a featured snippet, a People Also Ask box, a knowledge panel or a voice response. It is close to winner-takes-the-box. The engine rewards a concise definition directly under a matching heading, a clean list or table, structured data, and a page that already ranks.

Crucially, the engine is choosing your words. Get selected, and your sentence appears, with your URL beside it.

How a generative engine synthesises

A generative engine starts from a conversational prompt that carries context: “We’re a 40-person Series B SaaS business in Sydney on HubSpot; which product analytics tool should we shortlist?” It fans that prompt out into sub-queries, retrieves passages from many sources, weighs them against each other and writes a fresh answer in its own words.

It also brings a second input that AEO never touches: what the model already believes from training, a compressed memory of what the web has said about your category and your brand. So a generative answer draws on two things:

The consequence is uncomfortable. In synthesis, your page is evidence, not the answer. A model can cite your page and still recommend a competitor, or recommend you without citing you at all. Extraction rewards the best paragraph; synthesis rewards the most credible, most corroborated brand.

Ross Hudgens, founder and CEO of Siege Media, has just published Generative Engine Optimization: The Definitive Guide to AI SEO with Wiley. The most useful idea in his framing is that GEO works on the synthesis layer, not merely the retrieval layer. Siege compresses it neatly: SEO earns rankings; GEO earns citations.

I would add a product marketer’s corollary. Citations are necessary but not sufficient. The prize is the recommendation, and the sentence that comes with it: how the model describes you to the buyer.

The information gain imperative

A generative engine has, by definition, already read the consensus. A post that restates the top ten results adds nothing the model does not have, so there is nothing to cite. Information gain is the question of what your page contributes that the model cannot get anywhere else.

Siege’s own research reports that companies publishing new, proprietary data earn 45% more AI citations than peers working only in traditional formats. Treat any vendor figure with appropriate scepticism, but the mechanism is sound: novelty is the only thing a synthesiser needs you for.

Assets that carry genuine information gain:

• Original data: benchmarks from product usage, customer surveys, pricing indices.
• Named frameworks with a clear structure the model can reference.
• Defensible opinions with the reasoning shown, not hedged into mush.
• First-hand evidence: case results with real numbers, methodology and dates.
• Local data: ANZ is thin in global datasets, so an Australian benchmark often has almost no competition.

Structuring for machine comprehension

Retrieval works on passages, not pages. Write so that any section survives being lifted out of context and placed beside a competitor’s claim.

• Question-shaped headings, answer first. One or two declarative sentences, then the evidence.
• Self-contained passages. Name the entity (“Acme’s pricing”, not “our pricing”), restate the subject, avoid “as mentioned above”.
• Scope and date your claims. “As of October 2026, plans start at A$49 a month for teams of up to ten.” Precise, dated claims are harder to blend with stale or mismatched facts.
• Tables for comparisons, with the criteria stated explicitly.
• One set of facts, everywhere. The same category descriptor, ICP, pricing and proof numbers on your site, LinkedIn, review profiles and press kit. Conflicting facts get averaged, or dropped.
• Schema is hygiene, not a strategy. It is a strong AEO lever; for GEO it helps comprehension but will not carry a weak page.
• Make sure the machines can read you. Check that AI crawlers are not blocked and that key content is in the HTML rather than dependent on JavaScript, which many crawlers do not render reliably.

Brand consensus and digital PR

Ask a model “which is the best X for Y”, and it reaches for what the web agrees about. Your own site is one vote in that tally, and an obviously biased one. The votes that move the answer come from elsewhere: editorial roundups, review platforms, comparison sites, Reddit and other forums, analyst notes, podcasts and partner pages.

Two implications that most teams underestimate:

• Unlinked mentions count. Practitioners such as Reboot Online’s Oliver Sissons argue that context-rich brand mentions matter to models, with or without a backlink. The surrounding sentence matters as much as the name.
• Sentiment becomes positioning. If five Reddit threads call you “the expensive option”, the model will repeat that faithfully. Your positioning is now partly written by the crowd, and the model is its editor.

This is why Siege runs GEO across SEO, digital PR, affiliate and community teams together, and tells clients to commit to a horizon of six months or more, because citations compound rather than spike. The marketing team’s job is to make the sentence you want repeated true, quotable and widely distributed.

Comparative intent is the real prize

“X vs Y”, “alternatives to X” and “best X for Y” were always the highest-intent queries. Having run insurance distribution across five comparison sites, I learned early that the comparison table is where the decision happens.

Generative engines will build that comparison whether or not you take part. The only question is whose evidence they use. So publish honest comparison and alternatives pages, concede competitor strengths where they are real, and state the criteria that favour you. One-sided pages get discounted by models and buyers alike. In practice, these pages are your battlecards, made public and made citable.

A 90-day sequence for product marketing and content teams resetting their 2026 search strategy.

AEO is a formatting discipline for a shrinking box. It is cheap, it still works for the queries that keep their clicks, and you should keep doing it.

GEO is a positioning discipline for the new shortlist. The engines have taken over the buyer’s research, which turns organic discovery into reputation management at the level of the sentence. The brands that win will have something new to say, say it clearly, and be corroborated by people who are not them.

That is not a search tactic. It is product marketing, finally being read by a machine as closely as it always should have been read by the buyer.

Jarrod Ward

Senior product marketing manager with 15+ years in B2B SaaS across EdTech, FinTech and AI, and the founder of Briefly Daily, built solo. He advises SaaS teams on positioning, pricing and packaging, GTM, and all things Product Marketing.

Email me LinkedIn