GEO Is Mostly Snake Oil — Here's What Actually Works According to Google
Lorena Ly
Founder
GEO (Generative Engine Optimization) is the latest acronym agencies are selling at premium rates. The pitch: AI is changing search, so you need entirely new tactics — llms.txt files, "content chunking," AI-specific rewriting, mention farming.
There's one problem. Google has published detailed documentation on optimizing for AI features, and it contradicts most of what the GEO industry is selling.
The GEO Sales Pitch
The typical GEO package includes:
- llms.txt files — A robots.txt-style file that supposedly tells AI crawlers how to read your site
- Content chunking — Restructuring pages into discrete, AI-digestible blocks
- AI-specific rewriting — Reformatting content so AI models can "understand" it better
- Mention farming — Seeding your brand across forums and directories so AI models "learn" about you
- Structured data optimization — Adding schema markup specifically to appear in AI features
- Long-tail query targeting — Creating pages for every conversational query variant
An important distinction: The problem isn't caring about AI visibility -- brands absolutely should understand what AI platforms say about them. The problem is the specific tactics being sold under the GEO label. Monitoring your AI presence and tracking competitors is intelligence. Creating llms.txt files and farming mentions is snake oil.
Google Doesn't Use the Terms "GEO" or "AEO"
Google's own guide is titled "Optimizing for generative AI features on Google Search" -- not GEO, not AEO. These terms were invented by the marketing industry, not the platforms.
More importantly, that guide opens with a critical statement: optimizing for AI features is fundamentally the same as optimizing for traditional search, because AI features use retrieval-augmented generation on the same ranking systems. Not a separate AI index. Not a different algorithm. The same one.
Google Debunks the Core GEO Tactics
Google's AI optimization guide directly addresses -- and dismisses -- the most popular GEO tactics.
| GEO Tactic | What Google Says |
|---|---|
| llms.txt files | No Google-recognized llms.txt standard exists. Googlebot follows robots.txt and standard web protocols. If a vendor charges you for an llms.txt file, they're charging for something Google doesn't read. |
| Content chunking | AI features pull from the existing search index. Content that ranks well in traditional search is what AI features draw from. "AI chunking" is just good content structure -- an SEO best practice for over a decade. |
| AI-specific rewriting | No special writing format is recommended. Modern LLMs parse natural human writing -- that's what they were built to do. Write clearly and comprehensively; if AI can't understand it, humans probably can't either. |
| Mention farming | Not just ineffective -- potentially dangerous. Google's spam policies address inauthentic engagement, and Quality Rater Guidelines evaluate reputation based on independent, credible sources. Manufactured mentions are noise. |
| Structured data as GEO | Schema markup is genuinely useful, but not required for AI features. Google's docs confirm AI features pull from pages without any structured data. It's one signal among many, not a magic ticket. |
| Excessive long-tail pages | Google warns against this. AI features use query fan-out (generating sub-queries), so one comprehensive page serves multiple query variants. Separate pages per variation is doorway abuse under Google's spam policies. |
How Google's AI Features Actually Work
Google's AI features (AI Overviews, AI Mode) use Retrieval-Augmented Generation:
- User asks a question
- AI generates related sub-queries (query fan-out)
- Sub-queries run against Google's existing search index using the same ranking systems as traditional search
- Top-ranking results are retrieved as context for the AI model
- AI generates an answer with citations from those results
The critical insight is step 3. The same ranking systems -- RankBrain, BERT, MUM, Neural Matching, PageRank -- power both traditional results and AI features. If you rank well in traditional search, AI features will draw from your content. If you don't, no amount of llms.txt files or content chunking will change that.
What Actually Works -- Straight From Google
Google's documentation is refreshingly straightforward about what matters:
- Non-commodity content. Original research and data that only your organization can produce. Unique perspectives from practitioners with real experience. First-hand reporting that adds new information. Notice what's absent from Google's guidance: no mention of writing for AI models, formatting for machine consumption, or using any "AI-friendly" structural template. The emphasis is entirely on substance.
- E-E-A-T signals (Trust is central). Experience, Expertise, Authoritativeness, Trustworthiness. When an AI model needs to choose which sources to cite, it draws from results that already passed through ranking systems weighing these signals heavily. Google's Quality Rater Guidelines make Trust the central element -- a site can have expertise and authority, but without trustworthiness, its quality rating drops. For YMYL topics (health, finance, safety, legal), the trust bar is even higher, and AI features are especially cautious about which sources they cite.
- Comprehensive topic coverage. One authoritative page on "CRM for small business" outperforms twenty thin pages targeting slight phrasings. Query fan-out means the AI generates sub-queries and looks for content addressing multiple angles -- a comprehensive page naturally answers many of them. Twenty thin pages each answer one, poorly.
- Technical SEO fundamentals. Mobile-first design, Core Web Vitals, crawlable architecture, clean URLs, HTTPS. If Googlebot can't access your content, AI features can't use it either. These aren't GEO tactics -- they're the same practices that have mattered for years, often neglected by agencies too busy selling shiny new packages.
- Independent reputation signals. News coverage, Wikipedia mentions, expert reviews, industry awards, Better Business Bureau ratings. The opposite of mention farming -- Google evaluates reputation based on credible, independent sources that validate your expertise. You can't manufacture this. You earn it by doing good work and building a legitimate reputation.
The Spam Risk Nobody's Talking About
GEO tactics don't just fail -- some actively risk penalties. Google's spam policies identify scaled content abuse as a top concern: using automation (including AI) to generate large volumes of content to manipulate rankings without adding value. Mass-producing pages for query variations is doorway abuse. AI-rewriting hundreds of pages without adding genuine value is scaled content abuse. Sites hit by spam actions lose rankings entirely, including from AI features.
Google's guide on AI-generated content is clear: method of creation doesn't matter, quality is the only criterion, and mass production without value equals spam. Sections 4.6.5 and 4.6.6 of the Quality Rater Guidelines confirm that low-effort AI content created primarily for search visibility gets flagged as low quality. For GEO services relying on AI content at scale, this is a ticking time bomb.
So What Should You Actually Do?
If you're an agency owner or marketing leader being pitched GEO services, here's a practical filter: ask one question about any proposed tactic. "Would this still be good practice if AI search features didn't exist?"
If the answer is yes -- better content, stronger E-E-A-T signals, cleaner technical SEO, genuine reputation building -- it's worth doing. It will help you in traditional search AND AI features, because they use the same ranking systems.
If the answer is no -- llms.txt files, AI-specific formatting, mention farming, content mass-production -- you're paying for something that either doesn't work or actively risks a spam penalty. The real competitive advantage in AI search is the same advantage it's always been: produce content so good, so original, and so trustworthy that Google's systems consistently choose to cite you.
The Real Problem GEO Should Be Solving
The underlying concern driving GEO adoption is valid. When someone asks ChatGPT for recommendations and your product isn't mentioned, that's a real business problem. The issue isn't awareness of the shift -- it's the response.
The right response combines two things:
First, visibility and diagnosis. Know what AI platforms actually say about your brand today -- not what you hope they say, not what your marketing team assumes. The actual responses, across multiple platforms, for the queries your buyers use. Most brands are flying blind here. Beyond monitoring, understand why -- when a competitor gets recommended and you don't, what sources is the AI drawing from? What content do they have that you're missing? Tracing AI citations back to their sources and identifying specific evidence gaps is where the real insight lives.
Second, targeted action using fundamentals. The fix isn't a GEO trick. It's creating original content that fills your evidence gaps, building the E-E-A-T signals you're missing, and strengthening specific citation sources where you're weak. The strategies are fundamentals -- the same work Google has always rewarded -- but the targeting based on diagnosis is what makes it efficient. You're not doing generic SEO and hoping for the best. You're closing specific gaps that your diagnosis uncovered.
The brands that will win in AI search aren't buying GEO packages. They're doing the hard, legitimate work of becoming the source AI platforms want to cite. That starts with knowing where you stand. You can't fix what you can't see.