What the terms actually mean
AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) both describe structuring content so it is accurately retrieved, cited, and summarized by AI systems — a related but distinct goal from traditional SEO's focus on ranking position in a list of links. In practice the two terms are used inconsistently across the industry, often interchangeably, and neither has a single authoritative definition the way "SEO" eventually settled into one. What's consistent across most usage: both describe optimizing for a zero-click or low-click outcome, where the goal is being the source an AI system cites or summarizes rather than the link a human clicks.
What Google says about optimizing for its own AI features
Google publishes official guidance on this specifically, and it's worth reading directly rather than through secondhand summaries, because it directly contradicts some widely repeated GEO advice. Google's core position is that its AI features — AI Overviews and AI Mode — are built on retrieval-augmented generation (RAG) using Google's standard Search ranking systems, meaning traditional, fundamentals-first SEO directly supports AI visibility rather than requiring a separate discipline. Google's concrete recommendations: publish unique, non-commodity content with a genuine expert point of view and firsthand experience; avoid mass-producing content variations to try to manipulate AI responses, which it treats as a violation of its spam policies; include quality images and video; and maintain standard technical SEO fundamentals — crawlable and publicly accessible content, semantic HTML, sound JavaScript SEO practices, reduced duplicate content, efficient crawl budget use, and strong page experience. For business-specific visibility, Google points to Google Merchant Center feeds, complete Google Business Profiles, and conversational tools like its "Business Agent" for brand interactions.
Just as notable is what Google explicitly says doesn't help: an llms.txt file (a proposed standard for signaling content to AI crawlers) and special markup aimed specifically at AI systems are ignored by Google Search; content "chunking" into AI-friendly segments isn't required; writing in a special AI-targeted style provides no measurable benefit; and pursuing inauthentic mentions or citations doesn't help. That list matters because several of those exact tactics show up as recommended best practice in third-party GEO guides.
What third-party GEO guidance recommends for other AI engines
Practical guides aimed at visibility in AI engines beyond Google — Perplexity, ChatGPT Search, and similar — converge on a fairly consistent tactical checklist. Common recommendations include: leading each section with a direct, self-contained 40–60 word answer before elaborating; semantic chunking, meaning one clear concept per section rather than sprawling multi-topic pages; a clean H2/H3 heading hierarchy with sections in the 200–400 word range, supported by bullet points and comparison tables; frequent, specific statistics with clear attribution to named, authoritative sources rather than vague claims; structured data markup (Article, FAQPage, and BreadcrumbList schema); consistent, unambiguous definitions of key terms to support entity recognition; a deliberate pillar-and-cluster content structure to build topical authority; content freshness, since AI systems appear to favor more recently updated material; question-and-answer-framed headings that mirror how people actually query AI systems; and measurement via referral-source filtering in analytics (traffic from chat.openai.com, perplexity.ai, and similar) combined with manual or automated citation monitoring across multiple AI platforms.
Where the two disagree, and how to read that honestly
The tension here is real and worth naming plainly rather than blending into one unified checklist: Google explicitly states that llms.txt files, AI-specific markup, and content chunking don't affect its own AI features, while several popular GEO guides recommend those same tactics as best practice for visibility in other AI engines. Both things can be true at once — Google's statement is specifically about Google's systems, and other AI providers (OpenAI, Perplexity, Anthropic) haven't published equivalent guidance confirming or denying the same claims about their own retrieval pipelines, so it's not established that what's true for Google is true for all of them. The honest position is skepticism rather than either blanket acceptance or blanket dismissal: treat semantic chunking, clear headings, and direct answers as good writing and good technical structure that serves human readers and traditional SEO regardless of any AI benefit, and treat AI-specific claims that lack a named source's confirmation — particularly ones like "AI engines prefer content some precise percentage fresher than average" — as marketing claims from GEO tool vendors until an AI provider itself confirms the mechanism. This site's own editorial approach applies here as much as anywhere: no recommendation should be adopted just because it appears on a checklist, without a technical justification behind it.
A working example: this site's own entity data
Rather than only describing structured, AI-consumable publishing in the abstract, SearchEngines.Net practices it directly. This site publishes an EntityMap — a machine-readable index, built to the open EntityMap v1.0 specification, that describes the site's key entities (concepts, organizations, people, metrics) as structured records with extractive, sourced text chunks and explicit relationships between them, published as both human-readable HTML and machine-readable JSON. It's a distinct approach from schema.org markup or an llms.txt file, and it's a genuine, current example of entity-first structured publishing rather than a hypothetical — worth examining directly if the underlying idea of "structuring content for machine consumption" is one you're evaluating for your own site.
Practical recommendations
Distilled to what has actual technical justification behind it rather than checklist appeal: get the SEO fundamentals right first, since Google's own guidance confirms they directly support its AI features; write content that leads with a clear, direct answer to the question its heading asks, because that serves human readers, traditional SEO, and any AI system's retrieval step simultaneously; use structured data (schema markup) for what it has always been good for — helping any automated system, human or AI, understand what a page is about — without expecting it to be a shortcut around content quality; measure AI-referral traffic in analytics so claims about AI visibility are based on your own data rather than industry-wide averages; and treat any single-source, unconfirmed statistic about AI ranking behavior (from any vendor, including GEO-specific tool vendors) with the same skepticism you'd apply to an unconfirmed SEO ranking-factor claim a decade ago, because the underlying epistemics are identical: a third party is guessing at a system's internal behavior from the outside.