About Recent Data Bits
Solution Smith’s Recent Data Bits is a running log of timely information focused primarily on marketing and SEO, published by Solution Smith. In the broader sense, it explores multi-channel marketing and the tools, methods, and observations involved in building and maintaining an online presence.
An RSS feed is available for Solution Smith’s Recent Data Bits, allowing users to subscribe with an RSS reader and receive updates whenever new entries are added. Entries may link to guides based on practical experience and tested information, and may occasionally discuss events affecting the online industry.
Published
The AI Bug Apocalypse Has Officially Started
There are a lot of security issues I do not like about hosting standards; that is one of the reasons I administer hosting accounts myself.
Published
Engineering the Death of Legacy SEO
Many SEO strategies are based on established assumptions about how search engines retrieve and rank documents. As AI increases the potential computational cost of deeper content evaluation, a layered approach could perform initial assessment first and defer more resource-intensive processing until a page meets a certain threshold.
Legacy SEO models may not adequately account for initial rankings that precede deeper content evaluation. This proposed model offers one way to interpret ranking volatility and the subsequent loss of search visibility sometimes described as the “Mount AI” pattern.
Published
AI Visibility, Mentions and AEO Source Attribution
Research, observations, and updates on AI visibility, mentions, and source attribution within Answer Engine Optimization (AEO), covering changes in AI search, AI-generated answers, entity representation, retrieval, citations, and the evolving relationship between search engines and AI systems.
Published
Quality Content Criteria for AI, NLP and AEO
This page examines quality content criteria for AI, NLP, and AEO, focusing on how AI systems can evaluate content beyond criteria traditionally associated with search engines. It explores factors such as clarity, ambiguity, content structure, unique information, gain of knowledge, explicit statements, and contextual interpretation, while considering where these criteria overlap with SEO. The goal is to identify characteristics that make information easier for both people and AI systems to understand and use when answering queries.
Published
Directories, SEO, and Search Everywhere
While many large directories no longer exist, Tripadvisor, Yelp, and other content hubs are still used functionally to help people discover businesses and other sites. In this sense, directories have evolved rather than disappeared.
Directories and Query-Based Search Engines looks at how query-based search reduced the importance of traditional directories, but did not eliminate them. Directories evolved into specialized and functional discovery platforms. In a multi-channel marketing strategy, the question is not whether something is technically a directory or whether competitors have a link there. The question is whether the platform provides a useful path to the people you want to reach. The page explores how audience relevance, link-following behavior, and emerging discovery platforms can inform directory selection.
Published
PageRank SEO Strategy: The Markov Chain Model
Looking at PageRank as a Markov chain can change how you understand PageRank.
A Markov chain is a mathematical way of modeling what happens when movement from one state to another is based on probability. Applied to the web, the states are pages and the links between them provide the paths a hypothetical random surfer could take.
PageRank uses the link graph to model how importance could be distributed across the web. By modeling how a reasonable surfer might follow those links, the system eventually arrives at a stable distribution. That distribution produces a PageRank score for each page, representing its relative importance within the link graph.
In this sense, PageRank is a mathematical prediction rather than a measurement of what people are actually doing. It predicts how importance would be distributed if users followed the link graph according to the model.
Published
About AEO Everywhere Philosophy
A Search-Everywhere philosophy should account for multiple AI answer systems, as well as AI agents operating with their own selection criteria. It also needs to account for situations in which an AI system discovers content that mentions or links to a brand or product. For that information to be useful to an AI system, the content needs to be understandable and optimized for AEO / LLM. Given this diversity, the SEO Heretic's position is that AEO is not the same as SEO.
Many major AI answer systems use search and retrieval systems to discover information when generating answers. This creates another form of discovery that differs from traditional document retrieval.
The distinguishing characteristic of these systems is that, rather than simply presenting a list of documents, they generate answers from information contained within documents. The challenge for AEO is therefore not simply to make information discoverable, but to make the information within those documents understandable, useful, and worthy of being selected for an answer.
Many platforms also incorporate AI systems that operate primarily within their own content ecosystems, including X, Reddit, and YouTube. To be discoverable through these systems, information generally needs to exist within the platform's own content environment. A Search-Everywhere approach therefore considers not only where information is published, but also which AI systems may discover, interpret, and use that information.
LinkedIn provides an example of an internal AI ecosystem rather than a standalone, user-facing AI answer engine. Its AI systems use semantic content matching and generative models to retrieve and pair content with users who may find it relevant. This illustrates how AI-based discovery can operate within an existing platform without presenting users with a conventional AI answer interface. AEO Everywhere therefore extends beyond optimizing for standalone AI answer engines to include AI systems that determine which information and individuals are surfaced within other platforms. Details about the August update: LinkedIn's BIG Update Just Changed Everything for Small Creators
Solution Smith views AI as an evolving disruptive technology. The AEO philosophy is therefore expected to evolve as new AI systems and discovery behaviors emerge. The State of AI is maintained to provide relevant information about this evolution.
Published 8-21-2026
"The SEO Heretic" is the brand persona and publishing identity of Wayne Smith, founder of Solution Smith. He uses the moniker across research articles, media releases, and community discussions on Reddit (/u/Website-Smith).
The SEO Heretic emerged from the evolution of search. Much of the informative advice about SEO is now obsolete or based on assumptions about how search works that are not reflected in the actual workings of search. The role of the SEO heretic is to question or challenge dogma and traditional knowledge when empirical observation contradicts it.
Solution Smith's "The SEO Heretic" uses simulations, testing, and observation to research modern SEO and develop SEO strategies based on what that research reveals about search. These strategies are shared in Solution Smith's AI-Aware Full-Stack SEO Guide.