The short answer: Google is half right. In its recent guidance on AI search, Google argues that AEO and GEO are “still SEO”: there is no secret llms.txt trick, no special schema, and no AI specific rewrite that substitutes for genuinely useful, original content. On the mechanics of getting cited, that is correct, and every vendor selling technical AI SEO hacks should make you skeptical.
But the guidance answers a narrow question: how do AI systems pick sources? It skips the bigger one: how do people use AI assistants in the first place? People do not use ChatGPT, Claude, Gemini, or Perplexity the way they use a search box. They arrive earlier, with a life situation rather than a keyword. “We are having our first baby and our two bedroom feels small” is not a query Google's keyword universe was built around, but it is exactly the kind of message an assistant sees, and the assistant's answer often decides which brands ever enter the consideration set. Optimizing for that conversation is a different discipline than ranking for “best 3 bedroom homes in Austin.” That is the part “GEO is still SEO” misses. For the mechanics side of the comparison, see our guide to GEO vs SEO.
What Google actually said
Google's AI search guide makes three points worth taking seriously:
- There is no technical shortcut. Google explicitly dismisses llms.txt files, AI specific content rewrites, and special “AI schema” as wasted effort. If a vendor's pitch is a technical trick that makes AI systems prefer you, the search engine that runs the largest AI surface in the world just told you it does not work.
- Unique, non commodity content wins. The guide's core advice is the same advice good SEOs have given for a decade: publish content that says something original, backed by real expertise and data no one else has. Commodity content that restates what ten other sites say gives an AI system no reason to cite you.
- The fundamentals transfer. Crawlability, clear structure, direct answers, and credible sourcing help you in classic search and in AI answers alike. You do not need a second, parallel website for machines.
We agree with all three points. Aethon's own content strategy follows them. If your AEO vendor is selling llms.txt files, ask hard questions.
What the “still SEO” framing gets wrong
People bring assistants moments, not keywords. Search happens after intent forms: you already know you want “CRM for small business” before you type it. Assistant conversations start earlier. Users describe situations: a divorce, a new baby, a restaurant opening, a company entering a new market. The assistant translates that life moment into needs, and then into brand recommendations. By the time a traditional keyword would have been typed, the shortlist may already exist, and if your brand is not in the assistant's answer, you never see the customer you lost. There is no search console for conversations you were left out of.
At Aethon we map more than 200 of these life moments per industry and track what assistants recommend at each one. That methodology is Contextual AI Presence Mapping©. The gap between a brand's keyword rankings and its moment level visibility is routinely enormous, because the two are measuring different funnels.
“Just do good SEO” is not an operating plan. Even where the mechanics overlap, marketers still need to know three things Google's guide does not tell them: where they currently show up across ChatGPT, Claude, Gemini, and Perplexity; which specific gaps are costing them recommendations; and what to change first. Monitoring dashboards answer the first question and stop. That is why Aethon pairs measurement with an Action Engine: each visibility gap maps to a concrete fix, such as the comparison page you have not written, the outdated pricing information a model keeps citing, or the third party listing that misdescribes you.
Attribution changes the conversation with your CFO. Classic SEO earned its budget by tying rankings to traffic to revenue. AI visibility must clear the same bar. Counting brand mentions in AI answers is a vanity metric unless it connects to pipeline. Whatever tooling you choose, insist on revenue attribution for AI referred visitors, or you will be defending the budget with screenshots.
The market is validating the category either way. The money has already voted on whether this space matters: Profound, the largest pure play AI visibility vendor, raised a $96M Series C at a $1B valuation in February 2026, with backing from Lightspeed, Sequoia, and Kleiner Perkins and a customer list that includes roughly one in ten Fortune 500 companies. You do not get a unicorn out of a discipline that is “just SEO.” The honest synthesis: the mechanics of being citable are SEO, and the strategy of being recommended inside conversations is new.
What to do this quarter (a practical checklist)
- 1. Baseline your assistant visibility. Ask the four major assistants the questions your buyers actually ask, including the messy, situational ones, and record which brands come back. Do it before and after any content push. If you want the baseline done for you, request a free AI visibility audit.
- 2. Kill the commodity content backlog. Google just told you it will not earn citations. Redirect that effort into original data, strong comparisons, and honest FAQ content.
- 3. Write for the moment, not just the keyword. For each priority segment, list the ten life situations that precede a purchase and make sure something on your domain speaks to each one directly.
- 4. Demand execution, not dashboards. Whether or not you use Aethon, require any AEO tool to tell you what to fix, not just what you lost.
- 5. Wire up attribution. Tag AI referred sessions and follow them to pipeline so the program can defend itself in revenue terms.
Frequently asked questions
Is GEO really just SEO?
Mechanically, mostly yes: AI systems reward the same crawlable, original, well sourced content that ranks in search. Strategically, no: assistants are consulted earlier in the journey, with situational prompts rather than keywords, so the surface you are optimizing for and the questions you must answer are different.
Should I create an llms.txt file?
Google's guidance says llms.txt does not influence its AI features, and no major assistant has committed to honoring it as a ranking input. It is cheap to add, but treat it as housekeeping, not strategy.
Do I need separate content for AI assistants?
No. You need content that answers real buyer questions directly, states facts a model can verify, and covers the situations that precede purchase. That content serves Google, assistants, and humans at once.
How do I measure whether AI assistants recommend my brand?
Systematically prompt the major assistants (ChatGPT, Claude, Gemini, Perplexity) with your buyers' real questions and situations, track which brands are recommended over time, and tie changes to the content and PR work you shipped. This is the core of what an AI visibility platform automates.
How is Aethon different from monitoring tools?
Aethon starts from life moments rather than keyword style prompts, covers 200+ moments per industry, and pairs every visibility gap with a recommended action through its Action Engine, with attribution for AI referred pipeline. Monitoring tools tell you that you are losing; Aethon is built to tell you what to do about it.