My regular writing lives on Substack under the name Duane Forrester Decodes.
I cover the shift from traditional search to AI-driven discovery. How retrieval systems work. What’s changing for practitioners. Where the industry is headed and what to do about it. Occasional detours into career strategy, leadership, and the realities of building a professional reputation in a field that keeps reinventing itself.
New posts typically land weekly. Some are free, some are for paid subscribers.
Recent Posts
Your AI Visibility Strategy Doesn’t Work Outside English
The old model pushed outward from the brand. The new one was built inward from culture and that changes a lot.
April 12, 2026
If you find yourself drawn to the kind of thinking this series explores, my book The Machine Layer covers the broader landscape of how AI systems are reshaping visibility, trust, and discovery for brands and practitioners navigating this shift. It is a natural companion to the topics this newsletter continues to dig into. Its available on Amazon now.The English AssumptionThis series has been written in English, tested in English, and grounded in research conducted primarily in English. Every framework discussed here (vector index hygiene, cutoff-aware content calendaring, community signals, machine-readable content APIs) was conceived by an English-speaking practitioner, stress-tested against...
Your Owned Content Is Losing to a Stranger’s Reddit Comment
Why community consensus has become a core AI visibility signal and what brands need to do before the window closes
April 5, 2026
Do You Need an AI Search Consultant?The next time you ask an AI what product to buy, which agency to hire, or which software platform actually works, pay attention to where the answer comes from. Increasingly, it does not come from the vendor’s own website. It comes from a stranger’s Reddit comment written eighteen months ago, upvoted 847 times by people who tried the thing themselves.This is not an accident. It’s architecture.The Reddit EffectThe financial architecture behind Reddit’s presence in AI answers became public in early 2024. Google signed an initial licensing agreement with Reddit worth a reported $60 million...
llms.txt Was Step One. Here’s the Architecture That Comes Next
A four-layer framework for giving AI agents clean, authoritative access to your brand
March 29, 2026
My New Book: The Machine LayerThe Machine Layer, my new book with expanded frameworks for the GenAI era, is available now on Amazon, in print and Kindle versions.The conversation around llms.txt is real and worth continuing. I covered it in a previous article, and the core instinct behind the proposal is correct: AI systems need clean, structured, authoritative access to your brand’s information, and your current website architecture was not built with that in mind. Where I want to push further is on the architecture itself. llms.txt is, at its core, a table of contents pointing to Markdown files. That...
When the Training Data Cutoff Becomes a Ranking Factor
When model training cutoffs shape retrieval, your content’s timing becomes a visibility signal, not just a publishing detail
March 22, 2026
A Career Manual - The Machine LayerEvery AI system serving answers today operates with two fundamentally different memory architectures, and the boundary between them runs along a single invisible line: the training data cutoff. Content published before that line is baked into the model’s weights, always accessible, confident and unreferenced. Content published after that line only surfaces when the model retrieves it in real time, which introduces a different retrieval path, a different confidence profile, and, critically, different presentation behavior in synthesized answers. If you’re optimizing for brand visibility in AI-generated search, this distinction is not a footnote. It is...
The Content Moat Is Dead. The Context Moat Is What Survives.
Why commodity content lost its competitive advantage, and where to invest now.
March 15, 2026
Free Frameworks From My BookSo let’s say you spent six months building a resource library: guides, explainers, comparison pages, all well-researched and clearly written, structured for humans who are trying to make decisions. Your analytics show strong engagement, and your team is proud of the work.Then someone asks ChatGPT a question your library answers perfectly, and the response cites a competitor. Not because the competitor was more accurate or more thorough, but because they published original benchmark data that the model could not find anywhere else. Your content was correct; theirs was irreplaceable. That distinction now helps decide who gets...