Episode Details

E1127: Google's biggest SEO leak gave marketers more information about how search works than almost any event in the industry's history.

But according to Mike King, most SEO strategies, tools, and reporting systems barely changed after the leak.

Mike explains what the leaked Google API documentation confirmed, what it revealed about links and ranking systems, and why the SEO industry has failed to act on some of its most important findings.

We break down Google's different index tiers, why a backlink's value may depend on where the linking page sits in the index, and why common third-party metrics such as Domain Authority and Domain Rating may not tell you what Google actually values.

Mike also explains how Google uses vector embeddings to understand pages, passages, websites, authors, brands, and entities. This changes how marketers should think about relevance, authority, mentions, links, and content creation.

We also go deep into AI search and how platforms such as ChatGPT retrieve and evaluate information.

Topics covered include: - What Google's API leak confirmed about ranking systems - Why Mike believes the SEO industry failed to adapt - How Google separates content into different index tiers - Why links from pages with rankings and traffic may carry more value - Why Domain Authority and Domain Rating can be misleading - How vector embeddings help Google understand relevance - Why brand mentions can sometimes matter more than backlinks - The difference between optimizing for prompts and query fan-outs - How ChatGPT breaks a prompt into multiple search queries - Why comprehensive content can create more chances to appear in AI answers - How to inspect the searches ChatGPT performs behind the scenes - Why AI search should not be treated as another version of traditional SEO - How brands should measure AI visibility, citations, accuracy, and performance - Why Reddit, YouTube, LinkedIn, earned media, and your website all matter - How content format affects whether AI systems select your page or video - Why starting your own subreddit may help protect your brand's position - The biggest mistake companies make with AI-generated content - Why publishing an article from a single prompt often produces weak results - How human review, original sources, structured prompts, and subject experts improve AI content - Why Google may rely more on user behavior than unreliable AI-content detection - Which older SEO tactics are returning because of AI search - What "relevance engineering" means - Why future marketing teams may need SEO, PR, UX, content, data, and engineering skills - Why SEOs should learn to build tools and test AI platforms directly

Mike also shares a specific AI search case study involving 499 response codes.

His team found that ChatGPT was abandoning page requests because the client's website took too long to respond. After identifying the problem in the site's log files and improving response speed, the client's AI search visibility increased by roughly 300% over three months.

Mike's practical recommendation is simple:

Check your server logs for 499 errors. Identify the affected URLs. Improve page speed, especially time to first byte. According to Mike, fixing this problem can improve AI search performance in 30 days or less.

This conversation is for SEOs, founders, content teams, PR professionals, and marketing leaders trying to understand how discovery is changing across Google, ChatGPT, Claude, Bing, YouTube, Reddit, and other search surfaces.

⭐️ Mike King's agency, iPullRank - https://ipullrank.com ⭐️ Mike King's music - https://verywellversed.com ⭐️ Mike King on LinkedIn - https://www.linkedin.com/in/michaelkingphilly/ ⭐️ Mike King on 𝕏 - https://x.