Duane Forrester Decodes

AI is rewriting how brands get found, cited, and trusted. Duane Forrester breaks down what that means for practitioners and marketing leaders who can't afford to get it wrong. duaneforresterdecodes.substack.com

Duane Forrester Decodes is a technology podcast hosted by Unknown Host, with 17 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Duane Forrester Decodes, a technology show with 17 episodes published.

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  Duane Forrester Decodes

  AI Search Runs on Two Memory Systems. The Platforms Don’t Use Them the Same Way.

  Jun 7, 202615 minS0

  <p>Referenced in this episode:</p><p>When the Training Data Cutoff Becomes a Ranking Factor (Duane Forrester Decodes) https://duaneforresterdecodes.substack.com/p/when-the-training-data-cutoff-becomes The companion piece this episode builds on, where I first laid out the parametric-versus-retrieval distinction and what it means for timing.</p><p>How Perplexity finds and chooses its sources (Search Engine Journal) https://www.searchenginejournal.com/perplexity-ai-interview-explains-how-ai-search-works/565395/ Background on why Perplexity runs a live search on essentially every query rather than answering from memory.</p><p>Google's AI optimization guidance, and why AI Search is still Search (DemandSphere) https://www.demandsphere.com/blog/google-ai-optimization-guide-ai-search-is-still-search...

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  Duane Forrester Decodes

  You Can Finally Measure Content Alignment. That’s the Dangerous Part.

  May 31, 202619 minS0

  <p>References from this episode:</p><p>When I mentioned Gerard Salton's SMART system at Cornell, the foundational vector space model work from the 1960s, here's the background on that: https://en.wikipedia.org/wiki/SMART_Information_Retrieval_System</p><p>The Netflix study I referenced on cosine similarity producing arbitrary results in embedding models, that's the 2024 paper from Steck, Ekanadham, and Kallus: https://research.netflix.com/publication/is-cosine-similarity-of-embeddings-really-about-similarity</p><p>The MTEB benchmark leaderboard, where you can see the performance spread across current embedding models: https://huggingface.co/spaces/mteb/leaderboard</p><p>Goodhart's Law, the "when a measure...

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  Duane Forrester Decodes

  Data for Decisions and Evidence for your conversations.

  May 26, 202618 minS0

  <p>The Machine Layer - available at Amazon now.</p> <br/><br/>Get full access to Duane Forrester Decodes at duaneforresterdecodes.substack.com/subscribe

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  Duane Forrester Decodes

  You’re Using AI at the Execution Layer. The Value Is in the Judgment Layer.

  May 24, 202618 minS0

  <p>The tools are deployed. The licenses are paid. And if you’re a senior SEO or GEO practitioner right now, you’re probably using AI every day - for drafts, for summaries, for first passes at content that used to take twice as long. That’s real productivity, and it’s not nothing.</p><p>It’s also not the return the investment is capable of producing. And the gap between what you’re getting and what’s available isn’t a tool problem. It’s a mode problem.</p><p>A peer-reviewed study published at the 2025 ASIS&T Annual Meeting by...

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  Episode #4

  LLM Guidance Doesn’t Port the Way SEO Guidance Did

  May 17, 202618 minS1

  <p>For roughly two decades, the SEO discipline operated on a quiet assumption that turned out to be one of its most valuable features. Guidance from one search engine traveled. If Google said sitemaps mattered, Bing said sitemaps mattered. If Bing said structured data deserved real effort, Google said the same. Practitioners optimized for Google with reasonable confidence that the work would carry across the other engines, and most of the time it did. That portability was not luck. It was the product of a structurally large overlap layer that the major search engines had jointly built, brick by brick...

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