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
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.
Published 8-13-2026
Headlines and subheadings for AI-aware SEO
Traditional SEO often treats headlines as locations for keywords... AI systems treat headlines as scoping qualifiers that provide critical information through structure. The headline hierarchy aids AI systems to locate, interpret, mention, or cite information from a page for AEO.
Published 8-09-2026
LLMs, Data Compression, and the Effect on AEO and SEO
LLMs compress data by consolidating repeated knowledge spread across the internet. Pages that rely solely on common knowledge are unlikely to earn AI citations and often struggle for long-term search visibility.
This page explores the "Mount AI" traffic pattern—where content built strictly on common knowledge loses visibility as search engines evaluate it for low information gain.
Published 7-31-2026
Gain of Knowledge Through Specificity
Providing specific details about the subject or entity being discussed is fundamental to LLM optimization. As additional information is introduced, the important words and phrases associated with that subject naturally appear more often. The resulting increase in keyword density is therefore a byproduct of expanding the reader's knowledge rather than the objective itself. The goal is not to write more text, but to communicate more useful information about the subject.
For the subject of an office glass door, that knowledge may include the types of glass available, standard sizes, safety requirements, replacement options, installation considerations, pricing, labor costs, and how quickly the entrance can be returned to service. Each additional detail provides the LLM with richer contextual data, allowing it to extract precise answers and accurately map the topic's full semantic scope.
Published 7-30-2026
Traditional SEO often treated keyword density as an optimization target. The AEO perspective presented in this guide argues that keyword frequency is largely a byproduct of explicitly communicating facts, attributes, and relationships about an entity. As a document provides more useful knowledge, important entities are naturally referenced more often.
The article explores why this interpretation differs from traditional SEO guidance. Rather than optimizing for keyword density itself, it argues that the real objective is reducing the amount of inference an AI system must perform by making important facts and relationships explicit. Keyword frequency remains useful, but primarily as a signal that often accompanies a document with a high gain of knowledge.
Published 7-23-2026
LLM Advancements Affecting Scalability and Performance
Many people look at systems such as Google's AI Overviews and assume that current AI capabilities represent the limits of what large language models can achieve. In reality, both hardware and software are advancing rapidly, enabling models to run faster, scale to larger parameter counts, and operate on hardware that would have been impractical only a short time ago.
Recent advances include Mixture of Experts (MoE) architectures, low-bit quantization, and improved memory management. Together, these techniques reduce computational requirements, shrink storage footprints, and improve the scalability and efficiency of large language models.
Running a 744-billion-parameter large language model (LLM) locally on a laptop using only a CPU and 25 gigabytes of RAM, with no connection to a data center, may seem impossible. In fact, it has been demonstrated, although the resulting performance is far below what would be considered practical for everyday use.
Published 7-18-2026
SpamBrain; AI-Based Keyword Stuffing Detection
An examination of Google's SpamBrain and AI-based keyword stuffing detection using publicly available information, observable behavior, and controlled testing. The objective is to better understand how modern search systems distinguish naturally written content from manipulative keyword repetition.
It is not intended to recreate, simulate, or reverse engineer SpamBrain. Instead, it presents an open and transparent model that explains the system's observable behavior while clearly distinguishing documented facts from informed inference. SpamBrain and AI based search systems has fundamentally changed how keyword density should be viewed.
Published 7-16-2026
AEO for Mentions and Citations
For an AI system to mention or cite a source, the relevant information must be available when the answer is generated. The model first relies on its persistent native knowledge, which can be thought of as background knowledge about a subject. Because this knowledge is already part of the model, it is typically presented without attributing it to a specific source.
When the model requires information that is more current, specific, or authoritative than its native knowledge, Google's hybrid search system retrieves relevant documents and places them into the context used to generate the answer. Assertions contained within these retrieved documents become available for the model to synthesize and, when appropriate, cite. From an AEO perspective, this retrieval step is the primary opportunity for a website, organization, product, or person to receive a mention or citation.
To become part of that context, a page must rank highly enough for the primary query or one of its semantic fan-out queries to be retrieved. The AI must also clearly understand the assertions being made. Content that only implies an answer provides less confidence than content that states the information explicitly. Assertions that agree with the current consensus (commodity content), or clearly explain why they differ, are more likely to be incorporated into the generated answer. Content that extends beyond the commodity knowledge by providing explicit, relevant, and well-supported information is more likely to be cited as a source.