Duane Forrester Decodes

Show Notes This week: the web is filling up with machine-written content just as machine readers are about to become its dominant audience, and the two trends feed each other. Retrieval systems already lean toward AI-written text, which quietly narrows the pool of sources behind AI answers while accuracy still looks fine, a state researchers call deceptively healthy. We walk through the mechanism...

Show Notes Microsoft Just Proved a Point About Search Today Announcing Microsoft Web IQ — Bing Search Blog https://blogs.bing.com/search/June-2026/Announcing-Microsoft-Web-IQ Introducing AI Performance in Bing Webmaster Tools (public preview) — Bing Webmaster Blog https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview New AI Visibility Insi...

Show Notes: 81.8% of My “AI Assistant” Traffic Was Fake Episode summary Over two weeks, a brand-new website with zero promotion behind it logged thirty-three visits from AI assistants. Only six were real. The rest were lying about who they were, and the Googlebot numbers were worse. In this episode I walk through exactly what I found in my own server logs, how I proved each finding past the point...

Resources and references Query length, prompts vs. searches: SimilarWeb data on how much longer AI prompts run than Google queries: https://officechai.com/ai/chatgpt-queries-17x-longer-than-google-searches-6x-longer-than-googles-ai-mode-similarweb-data/ Clickstream analysis on the gap between the typed prompt and the search the model actually fires: https://martech.org/chatgpt-growing-as-a-traffic...

Referenced in this episode: 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. How Perplexity finds and chooses its sources (Search Engine Journal...

References from this episode: 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 The Netflix study I referenced on cosine similarity producing arbitrary results in embedding models, that's the 2024 paper from Steck, Ekanadham, and Kallus: h...

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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. It’s also not the return the investment is capable of producing. And the gap between what you’re getting and what’s availa...

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 ac...

When a brand stops appearing in ChatGPT, or when its share of voice in Perplexity drops by half over a quarter, the typical response from the marketing org is to write more content. Sometimes a lot more. The thinking goes that if AI systems aren’t surfacing the brand, the fix is to feed them more material to work with. That instinct is a misdiagnosis. It’s a retrieval-layer fix being applied to wh...

Search engines were designed to do several things at once: rank a field of options, route the user to one of them, and keep the human inside the decision so the engine never owned the choice. That last part was not an accident. It was the liability architecture. LLMs were built without any of it. They were built to answer the question directly, which is a different job entirely, and the design cho...

Hiring managers are watching something uncomfortable happen in interview rooms right now. Candidates arrive with the right credentials, the right vocabulary, the right tool stack on their résumés, and then someone asks them to reason through a problem out loud, and the room goes quiet in the wrong way. Not in the thoughtful kind of way but the empty kind that tells you the person across the table...