GEO vs. SEO: Why the Debate Is Missing the Point

What 25 years of SEO experience and real AI Overview rankings tell us about what actually matters in 2026

The marketing industry has a talent for taking straightforward things and making them complicated. GEO versus SEO is the latest example.

I’m Duncan Lauder, President of Marketing Practicality, and I’ve been doing SEO since before Google ran ads. I’ve watched this industry coin new acronyms for old concepts more times than I can count. GEO — Generative Engine Optimization — is the current one. AEO — Answer Engine Optimization — is another. Both are generating significant industry debate, significant consulting revenue for people selling “AI optimization” services, and significant confusion for business owners who are trying to figure out whether they need to throw out everything they’ve been doing and start over.

They don’t. And the evidence is sitting right in front of us.

The blog posts on this site that rank #1 or #2 in Google for their target keywords are also consistently cited in Google AI Overviews for those same searches. We didn’t do anything special for AI. We wrote specific, authoritative content built on real data and documented client results. That content ranks in traditional search. It also appears in AI-generated answers. Because at Google — which handles the overwhelming majority of searches, AI-assisted or otherwise — it’s the same system.

What the GEO vs. SEO Debate Is Actually About

To be fair to the people advancing the GEO/AEO framework, there is a real phenomenon they’re trying to describe. The way people find information is changing. More searches are returning AI-generated summaries rather than a list of ten blue links. ChatGPT and Perplexity are being used for research queries that previously went to Google. AI Overviews appear at the top of a growing percentage of Google results pages, often without the user needing to click anything.

These are real changes. The question is whether they require fundamentally different optimization strategies — or whether they require better execution of the strategies that have always worked.

The answer, overwhelmingly, is the latter.

When SEO first emerged, we didn’t talk about “traditional search” or “alternative ways of searching.” SEO simply meant optimizing for how people found information. In that sense, optimizing for AI search today is still SEO, because SEO has always evolved with how discovery works.

That’s the clearest framing of the debate I’ve seen — and it matches 25 years of watching this industry rename old concepts with new labels every time the technology changes.

What Google Actually Says About AI Search Optimization

In May 2026, Google published a new documentation page specifically addressing optimization for its generative AI features, including AI Overviews and AI Mode. The guidance is unusually direct.

On the GEO/AEO terminology question, Google states plainly: “From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.”

That’s not a nuanced position. That’s Google telling you directly that the new acronyms describe the same thing they’ve always asked you to do.

The guide also lists tactics that are specifically not necessary for Google’s AI features:

• llms.txt files — not needed, receive no special treatment
• Content chunking — not needed, Google’s systems understand multiple topics on a page
• Rewriting content for AI systems — not needed, AI systems understand synonyms and general meanings
• Special AI schema markup — not needed, no special schema.org markup required
• Seeking inauthentic mentions — not helpful, core ranking systems focus on quality

This matters because a growing industry of consultants and agencies is selling all of these things as essential AI optimization services. Google’s own documentation says they aren’t necessary for Google Search.

The Proof: Real AI Overview Rankings From Real SEO

The most compelling argument against the GEO-as-separate-discipline position isn’t theoretical. It’s empirical.

The blog posts and service pages on marketingpracticality.com that rank #1 or #2 in Google for their target keywords — criminal defense digital marketing, bankruptcy attorney marketing, family law digital marketing — consistently appear in Google AI Overviews for those same searches. We didn’t write those posts with AI optimization in mind. We wrote them with SEO in mind: specific, authoritative content built on real data, documented case study results, and 25 years of direct client experience in those verticals.

This is not a coincidence. It’s how Google’s system works. AI Overviews draw from Google’s index using the same quality signals as traditional search. Content that Google’s algorithm has determined to be authoritative, specific, and trustworthy for a given query is the content that gets cited in AI-generated answers for that query. The mechanism is different. The inputs are the same.

Non-Commodity Content: The Only Thing That Actually Changed

Here’s the part of the AI search conversation that is genuinely worth paying attention to — and it’s not GEO or AEO. It’s what Google calls “non-commodity content.”

Google’s May 2026 AI Search guide draws a specific contrast:

• Commodity content: “7 Tips for First-Time Homebuyers” — generic advice available from any source
• Non-commodity content: “Why We Waived the Inspection and Saved Money: A Look Inside the Sewer Line” — specific experience, real situation, unique insight

The distinction is whether your content says something only you can say — based on what you’ve actually done, measured, and experienced. Generic advice is commodity. Documented results from real client engagements are not.

This matters more in an AI search environment because AI systems are particularly good at synthesizing commodity content. If ten sites all say roughly the same thing about criminal defense marketing, an AI system can generate a summary of that content without citing any of them specifically. There’s no differentiation to cite.

But if one site has a documented table showing a specific firm’s cost per lead dropping from $787 in 2015 to $15 in 2025 — with year-by-year conversion rate data — that’s non-commodity content. The AI system can’t generate that from general knowledge. It has to cite the source.

What Non-Commodity Content Looks Like in Practice

• Real CPL data from real campaigns — not “most firms see 30-50% improvement” but “$787 per lead in 2015, $15 per lead in 2025, here’s the year-by-year table”
• Documented case study trajectories — not “our clients get results” but specific before/after data with timeline
• First-person professional perspective — not “experts recommend” but “in 25 years of managing criminal defense campaigns, here’s what I’ve actually seen”
• Market-specific observations — not “DUI season is busy” but “DUI arrests spike 155% on New Year’s Eve based on NHTSA data — here’s the exact campaign adjustment that captures those leads”

This is not a new SEO principle. It’s E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — applied to the content level. Google has been rewarding this type of content in traditional search for years. The AI search environment makes it more important, not different.

What You Can Safely Ignore

Given that a growing industry is selling AI optimization services with urgency, it’s worth being specific about what you can ignore — at least for Google Search, which is where the overwhelming majority of searches happen.

• llms.txt files — Google doesn’t give these special treatment. They may have relevance for non-Google AI platforms but won’t improve your Google AI Overview visibility.
• Content chunking — Breaking your content into small pieces for “AI readability” is counterproductive. Google’s systems understand nuanced, multi-topic content on a single page. Chunking reduces the depth and authority signals that help content rank.
• AI-specific rewriting — You don’t need to rewrite your content in a special way for AI systems to understand it. Write clearly for humans. AI systems are better than ever at understanding natural language.
• Special AI schema markup — No special schema.org markup is required for Google’s AI features. Continue using Article, FAQPage, and LocalBusiness schema as part of standard SEO practice.
• Inauthentic mention campaigns — Some GEO guides recommend getting your brand mentioned across forums, blogs, and social platforms to increase AI citation frequency. Google’s spam systems are designed to detect and discount inauthentic mentions. Focus on earning genuine citations through quality content.

What Actually Matters for AI Search Visibility

With the noise cleared away, here’s what actually moves the needle for AI search visibility — and none of it requires a new acronym:

1. Be Indexed and Eligible for Snippets

Pages must be crawled, indexed, and eligible for snippets to appear in Google AI Overviews. This is the technical baseline. Check your robots.txt, verify indexing in Google Search Console, and confirm you haven’t accidentally excluded pages from snippet eligibility with a nosnippet tag.

2. Non-Commodity Content That Only You Can Write

As covered above — real data, documented experience, specific outcomes. The content that gets cited in AI answers is the content that provides information the AI system can’t synthesize from generic sources. As we’ve shown with our own legal marketing content, posts built around real CPL data and documented case study results rank in traditional search and appear in AI Overviews simultaneously.

3. Clear Semantic HTML Structure

Proper heading hierarchy (H1, H2, H3), semantic HTML elements, and a logical content structure help both traditional crawlers and the AI systems processing page content. This is not new — it’s been standard SEO practice for years. The emphasis on it in AI search contexts is warranted because AI systems that retrieve content need clear structure to identify the most relevant section of a long page.

4. FAQ Sections Optimized for Direct Extraction

FAQ sections with clear question-and-answer pairs serve a dual purpose: they qualify for FAQPage schema rich results in traditional search, and they provide clean, extractable content for AI systems assembling answers to specific queries. Write FAQ answers as complete, standalone paragraphs that fully answer the question without requiring surrounding context. Answers of 80-200 words hit the right balance of completeness and concision for AI extraction.

5. Mobile Performance and Page Experience

Page experience signals — Core Web Vitals, mobile usability, load speed — remain ranking factors in traditional search and are required for AI feature eligibility. A page that fails Core Web Vitals thresholds is at a disadvantage regardless of content quality.

6. Google Business Profile for Local Visibility

Google’s AI Search guide explicitly recommends Google Business Profiles for visibility in AI responses for local businesses. For attorneys, trades contractors, and other local service businesses, GBP optimization is the single most direct action available for improving AI-powered local search visibility — the same action that drives traditional map pack rankings.

What This Means for Local Businesses Specifically

For the legal practices and trades businesses that make up the majority of Marketing Practicality’s client base, the GEO vs. SEO debate simplifies even further.

When someone searches “criminal defense attorney near me” or “HVAC contractor [city]” — whether through traditional Google Search or through an AI-assisted interface — the results they get are driven primarily by local search signals: Google Business Profile completeness, review volume and recency, proximity, and location-specific content. These signals haven’t changed. Their importance has increased.

The local business that dominates traditional local search — strong GBP, consistent review generation, location-specific service pages — will also dominate AI-generated local recommendations. The mechanism is the same. The signals are the same. The outcome is the same.

What this means in practice:

• Criminal defense attorneys — The same local SEO system that gets you into the map pack for “criminal defense attorney near me” is what gets you cited in AI-generated answers for that query. See our criminal defense local SEO guide for the complete framework.
• Bankruptcy attorneys — Content built around real CPL data, specific case study outcomes, and documented results for your vertical ranks in traditional search and appears in AI Overviews. See the bankruptcy case study for documented results.
• HVAC, plumbing, and roofing contractors — GBP optimization, review generation, and local content that signals genuine geographic expertise are the highest-impact actions for both traditional and AI-assisted local search visibility.

Where GEO Is Legitimately Different From SEO

Having made the case that GEO and SEO are essentially the same for Google Search, it’s worth acknowledging where the distinction has genuine validity.

Non-Google AI platforms — ChatGPT, Perplexity, Claude, and others — may weight signals differently than Google’s index. These platforms don’t all draw from Google’s search index when assembling answers. Some have their own web crawlers. Some rely on retrieval augmented generation from sources other than traditional search results. Some weight brand mentions across forums, social platforms, and community sites in ways that differ from Google’s approach.

GEO becomes important after the point where an AI system decides which sources to synthesize into a response. For non-Google AI platforms, the factors influencing that decision may differ meaningfully from traditional SEO signals.

For most businesses in most markets, this distinction is currently academic. Google handles the vast majority of searches — AI-assisted or otherwise. Optimizing for Google visibility is optimizing for the channel where your audience actually is. As non-Google AI platforms grow in usage and search share shifts, this distinction will matter more. For now, the practical priority is clear: get the Google fundamentals right, and AI visibility largely follows.

The businesses that should pay the most attention to non-Google AI platforms are those targeting sophisticated B2B buyers who are more likely to use AI assistants for research — not the local service businesses and legal practices that represent the majority of businesses competing for local search visibility.

The Bottom Line

Twenty-five years ago, the debate was whether businesses needed to optimize for Alta Vista, Yahoo, and Google separately or whether one approach covered all three. The answer then was the same as it is now: build content that genuinely serves the person searching, make it technically accessible, and demonstrate real expertise through specific and documented claims. The platform changes. The principle doesn’t.

GEO and AEO are new labels for the same principle applied to a new interface. The businesses spending money on separate AI optimization strategies — llms.txt files, content chunking, AI-specific schema — are buying solutions to problems they don’t have, from agencies selling urgency rather than results.

The businesses investing in non-commodity content built on real data and direct experience, maintaining strong technical SEO fundamentals, and building genuine local authority through GBP optimization and review generation — those businesses are already optimized for AI search. They just call it SEO.

Frequently Asked Questions About GEO vs. SEO