Google's AI Search Guidance Confirms GEO Is Still SEO

Google's new guidance on optimizing for generative AI features says the quiet part out loud: for Google Search, generative engine optimization is still search optimization. That might sound obvious, but it is worth saying plainly because the GEO conversation has gotten noisy fast.

The market has been filling up with GEO checklists that make AI visibility sound like a separate discipline: write in special chunks, create special files, chase mentions, add special schema, rewrite every page for answer engines. Some of those tactics are harmless. Some are useful in the right context. A lot of them are just a distraction from the actual work.

The center of the work is still strategy, content, technical accessibility, authority, and consistency. In other words: good digital marketing.

Google's Position Is Straightforward

Google says its generative AI features in Search are rooted in its core ranking and quality systems. The guide explains that AI Overviews and AI Mode use techniques like retrieval-augmented generation and query fan-out, but they still depend on relevant pages from Google's Search index.^[1]

That is the important operational point. If a page cannot be crawled, indexed, understood, trusted, or selected by the normal Search system, a thin GEO layer will not rescue it.

Google also addresses the terminology directly. AEO and GEO are common labels, but from Google's point of view, optimizing for generative AI search is optimizing for the search experience. That means SEO.

GEO Is Not A New Bag Of Tricks

There are tactical details worth knowing. Schema still matters. JavaScript SEO still matters. Page experience still matters. Internal links still matter. Merchant data and local business profiles can matter. But these are not AI-only hacks. They are ways to make the business, site, and content easier for users and search systems to understand.

Google's mythbusting section is useful because it gives teams permission to ignore a lot of noise. You do not need special AI markup to appear in generative AI search. You do not need to break every article into tiny chunks. You do not need to rewrite pages in a strange answer-engine dialect. You do not need to chase inauthentic mentions. And structured data is not required for generative AI search, even though it remains useful as part of a broader SEO strategy.^[1]

That last point is the balanced version. Schema is relevant. It can clarify page type, entities, breadcrumbs, products, FAQs, articles, organizations, and software. It can support rich-result eligibility. But schema cannot fake authority, originality, or usefulness.

The Best AI Search Strategy Is A Better Marketing Strategy

Google's content guidance puts the emphasis on valuable, non-commodity content. That means content with a point of view, first-hand experience, expert judgment, useful organization, and enough specificity to help a real person make progress.^[1]

That is not a new SEO trick. It is a marketing strategy problem.

If your positioning is generic, your content will be generic. If your product pages do not explain who you are for, AI systems have less to work with. If your comparison pages avoid tradeoffs, buyers and search systems both get weaker signals. If your blog only restates what already exists, it becomes commodity content. If nobody maintains the site, even strong pages decay.

AI search raises the cost of vague marketing. It rewards companies that can publish clear answers backed by real expertise, real products, real customer knowledge, and real evidence.

What Teams Should Actually Work On

The practical workflow is not "optimize for AI." It is "make the site a better source."

Workstream	Why it matters for AI search	What to ship
Strategy	AI systems need clear entity, audience, category, and use-case signals.	Positioning pages, use-case pages, comparison pages, audience pages.
Content	Non-commodity content gives retrieval systems something distinctive to cite.	Expert guides, practical workflows, original examples, maintained source pages.
Technical SEO	Google's AI features still rely on crawlable and indexable content from Search.	Crawl fixes, indexability checks, canonical cleanup, JavaScript SEO review.
Structure	Clear page organization helps humans and systems parse the answer.	Descriptive headings, internal links, tables, summaries, visible FAQs.
Authority	Trust is not created by markup alone.	Named authors, evidence, citations, real examples, consistent brand/entity data.
Schema	Structured data can clarify page meaning and rich-result eligibility.	Article, Organization, Breadcrumb, SoftwareApplication, Product, FAQ where appropriate.

This is exactly why AI visibility belongs inside the main SEO operating system. It touches crawlability, content planning, brand positioning, competitive research, page updates, and publishing governance. Treating it as a side project creates scattered fixes. Treating it as strategy creates compounding assets.

Where llms.txt Fits

Google says you do not need llms.txt or other special machine-readable files to appear in generative AI search. That should stop teams from presenting llms.txt as a Google ranking requirement.

But that does not mean llms.txt is useless. A clean llms.txt can still be a practical site map for agents, tools, and humans. The right framing is simple: use it as discovery infrastructure, not as a magic visibility lever.

The same rule applies to most GEO tactics. If the tactic helps you make the site clearer, more accessible, more consistent, or easier to maintain, it may be worth doing. If the tactic only exists because someone claims an AI model prefers it, be skeptical.

What Colma Should Help Teams Do

Colma's opportunity is not to sell GEO tricks. It is to help teams do the work that Google is describing at operational scale.

That means building the company context first: what the business sells, who it serves, what differentiates it, which competitors matter, what the buyer needs to understand, and which pages deserve investment.

Then the system can turn that context into work:

• Audit the foundation. Find crawl, indexability, technical, schema, and page-quality issues.
• Map the strategy. Connect topics, personas, competitors, keywords, and buying questions.
• Improve source pages. Make important pages clearer, more useful, more specific, and better internally linked.
• Keep schema honest. Add structured data where the visible content supports it.
• Maintain the system. Re-run audits, refresh pages, preserve decisions, and keep the work moving.

That is stronger than a checklist because it matches how search visibility actually compounds. One-off hacks decay. A durable SEO workflow keeps improving the site.

The Takeaway

Google's AI search guidance does not make SEO obsolete. It makes weak SEO harder to hide.

The companies that win AI visibility will not be the ones with the cleverest acronym. They will be the ones with clear positioning, useful source pages, crawlable sites, original expertise, consistent entity signals, relevant structured data, and a process for turning strategy into published work.

That is the same digital marketing work that has always mattered. AI search just makes the standard more visible.

References

1. Google Search Central - Optimizing Your Website for Generative AI Features on Google Search