Common Mistake: Chasing Cited Pages Instead of Owning Them

E963: I sit down with TJ Robertson to discuss how AI search experiences are influencing SEO strategy, content creation, and brand visibility.

This conversation looks at how large language models, AI Overviews, and conversational search interfaces are becoming an additional layer of discovery that SEOs and founders need to understand.

We break down practical tactics, areas of disagreement, real experiments, and the difference between being cited by AI systems and actually being recommended within responses.

The discussion covers both opportunities and limitations, where AI introduces new dynamics, and how teams are adapting workflows.

Topics covered: - The difference between ranking in search results and appearing inside AI-generated responses - Whether AI search experiences will complement or change traditional search behavior - Hyper-specific content and how AI systems interpret granular queries - The relationship between classic SEO fundamentals and generative engine optimization - Citation vs recommendation and why that distinction matters for conversions - Bottom-of-funnel content strategies that perform across both search and AI contexts - Parasite properties, secondary sites, and long-term visibility assets - Tracking prompts vs tracking keywords and the current limitations of measurement - Using branded content, FAQs, and reviews to shape how AI systems understand a company - Content templates that tend to get surfaced inside AI responses - Claude workflows and building a structured brand knowledge base for content creation - The role of directories, foundational links, and early authority when starting from zero - Where social platforms influence AI visibility and where they appear to have little impact - Publisher monetization challenges and potential adaptations as AI summaries become more common - Common misconceptions and speculative GEO advice circulating in the industry

We also discuss failures, experiments that did not work, and areas where the industry still lacks clarity - including how much training data matters compared to real-time search retrieval.

If you work in SEO, content, SaaS, or growth, this episode offers a practical look at what the generative engine optimization landscape looks like as it continues to evolve.

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⭐️ TJ Robertson on LinkedIn - https://www.linkedin.com/in/tj-robertson-seo/ ⭐️ TJ Robertson on 𝕏 - https://x.com/SeoRobertson/ ⭐️ TJ Robertson on YouTube - https://www.youtube.com/@TJRobertsonDigital ⭐️ TJ Robertson on Instagram - https://www.instagram.com/tjrobertsondigital/ ⭐️ TJ Robertson on TikTok - https://www.tiktok.com/@tjrobertson52 ⭐️ TJ Robertson's Agency - https://tjrobertson.com/

💎 Compact Keywords - My SEO Course - Get paying customers through SEO - Clear step-by-step video breakdowns - SEO templates to be copied and adapted for your products and services: https://compactkeywords.com/

00:00 17 Years in SEO 00:29 Starting an AI-First SEO Agency 01:35 SEO vs GEO 03:20 Ranking in AI 04:54 How Fast to Publish 07:03 180-Day Playbook 09:15 Tracking LLM Visibility 10:09 Biggest LLM Citation Mistake 13:16 Parasite Properties & Second Sites 16:42 Content Templates That Win in LLMs 19:13 Fact Density, Brand Voice, & Brand Ambassadors 32:22 Reviews, Press Releases, and Branded Content 40:19 Branded FAQs 42:08 FAQ Hub Pages to Control the Narrative 44:02 AI SEO Opportunity 44:50 Setting Up Claude as a Brand Ambassador 46:50 Claude AI SEO Content Workflow 49:27 Images That Convert 54:20 Opus 4.6, Claude Cowork, and AI Agents 56:29 Vibe-Coded SaaS Strategy 59:36 The Skill SEOs Need 01:02:05 GEO Hot Takes: Schema Hype, "Post Everywhere," and Citation Obsession 01:05:31 Zero-Click Search 01:08:59 Rebuilding Visibility from Zero 01:13:22 Biggest SEO Failures & GEO Wins 01:19:41 Training Data vs Query Fan-Out 01:21:26 Wrap-Up

The Edward Show. The #1 search engine optimization podcast: https://edwardsturm.com/the-edward-show/

#generativeengineoptimization #answerengineoptimization #searchengineoptimization #digitalmarketing