AEO vs GEO vs SEO: what changes when buyers search with AI

Search did not die. It split. Traditional SEO still decides who gets crawled, trusted, and ranked. But now buyers also ask ChatGPT, Perplexity, Gemini, and Claude for answers. That means your pages need to both rank and be quotable.

The short version

What still matters from classic SEO?

Most of the old rules still matter because answer engines still need source material. If your pages are thin, slow, hidden behind JavaScript, or semantically vague, they are weak candidates for both Google and AI retrieval. Static crawlable HTML, obvious page intent, descriptive titles, clear internal linking, and original topical depth still win.

That is why the AlexDLY blog ships as static HTML instead of a client-rendered content shell. Bots can read the whole page on first load. That matters for both ranking and citation.

What changes for AEO and GEO

A search result only needs a clickable title. An answer engine needs a clean extract. That pushes content toward direct definitions, obvious section headers, FAQ-style phrasing, structured data, and concise summaries near the top of the page. The goal is not stuffing keywords. The goal is making the page easy to quote accurately.

• Lead with the answer, then expand.
• Use headings that mirror real buyer questions.
• Publish original frameworks, not generic recap copy.
• Keep claims specific enough that a model can cite them without guessing.
• Use schema so machines can identify the page type, author, topic, and FAQs quickly.

How do AI answer engines decide what to trust?

AI answer engines favor pages that are crawlable, recent enough, topically aligned, and easy to compress into a faithful summary. No one outside the model vendors sees the full weighting, but that visible pattern is clear. They also like pages with a distinct point of view. If ten sites say the same bland thing, the one with the clearest structure and the strongest framing is more likely to be cited.

What does an answer-ready section look like?

An answer-ready section puts the question in the heading and the answer in the first sentence below it. Compare two versions of the same section on a bookkeeping firm's site:

• Before: a heading that says "Our approach" followed by three sentences about the firm's values before any fact appears.
• After: a heading that says "How much does monthly bookkeeping cost for a 10-person business?" followed by "Most of our clients that size pay a flat monthly fee based on transaction volume, and we quote it after a 20-minute review of last month's statements."

The second version matches the words a buyer types or speaks, and a model can lift the first sentence without rewriting it. Keep the depth below that first sentence: the edge cases, the limits, what changes the answer. That is what makes the page worth citing over a one-line competitor.

How do you check whether AI assistants cite you?

Run a monthly spot check by hand. Write down ten questions your buyers ask right before they buy, ask each one in ChatGPT, Perplexity, Gemini, and Claude, and record whether your site is named or linked. Answers vary from run to run, so treat it as a trend, not a score. If a competitor keeps getting cited for a question you answer better, open their page and look at how the answer is structured. Usually the difference is a plainer heading and a direct first sentence, not a better product.

Common mistakes that hurt both rankings and citations

• Marking up FAQs in schema that do not appear on the visible page. Structured data should describe content a reader can see.
• Loading the main content with JavaScript after the page renders, so a crawler sees an empty shell.
• Publishing 30 short posts that each answer a slice of the question instead of one page that answers all of it.
• Repeating the keyword in every heading. Models read for meaning, and so do people.
• Leaving dates, prices, and claims stale. An outdated number is a reason not to quote you.

The practical playbook for founder-led teams

If you are not a giant media company, your edge is not publishing more. It is publishing pages that answer the exact questions your buyers ask right before they buy.

• Build one pillar page per important question cluster.
• Turn each service promise into a proof-backed explainer.
• Use blog posts to support money pages with internal links and shared vocabulary.
• Write comparison, framework, and implementation pages because those are easier for AI systems to summarize than vague brand essays.
• Refresh winners instead of spraying dozens of low-signal posts.

What we changed on this blog

For AlexDLY, the right move was not a giant CMS rebuild. It was tightening the static blog layer: richer metadata, clearer schema, a keyword-led pillar page on AEO, GEO, and SEO, stronger internal linking, a maintained sitemap, and an llms.txt file that points answer engines at the most useful URLs.

That same pattern applies to most founder-led sites. Clean structure beats complexity. Machines reward clarity.

Where this connects to the product

AlexDLY exists because visibility without execution is incomplete. A site can surface in search and still lose because follow-up is slow, qualification is fuzzy, and the next action dies in the handoff. That is why the same blog that teaches AI SEO also points to operational plays like safe agentic follow-up and agentic sales workflows for solo founders.

Sources

• Google Search Central, AI features and your website. Supports that classic SEO practice still applies to AI Overviews and AI Mode.
• Google Search Central, Understand JavaScript SEO basics. Supports that Google sees only rendered HTML and that server-side or pre-rendering helps crawlers.
• Google Search Central, General structured data guidelines. Supports that markup should describe content visible to readers.
• Aggarwal et al., GEO: Generative Engine Optimization, arXiv 2311.09735, November 2023 (KDD 2024). The paper that named GEO and defined generative engines as systems that synthesize answers from multiple sources.
• Jeremy Howard, The /llms.txt file, September 2024. The original llms.txt proposal.