AI Aware SEO vs Legacy SEO
Legacy SEO often reflects models of search engine behavior associated with the 2010–2020 era. In a simplified model, a web page is indexed and ranked using available information, with ranking signals contributing positively or negatively to its position. Rankings for the same keywords could be tracked over time, often with an expectation of relative stability. A limitation of some legacy SEO models and beliefs is that they do not account for the possibility that ranking signals may remain unevaluated or be deferred for later processing.
A three-state model represents positive, negative, and zero states, using zero to represent either a neutral condition or a signal awaiting evaluation. Although this is not a new computational concept, many legacy SEO models and beliefs fail to account for deferred evaluation. CPUs and GPUs can represent and process these values using conventional binary hardware. Signed numerical representations distinguish positive and negative values, while zero is neither positive nor negative. These capabilities have existed in computer hardware long before the internet.
Consider how NavBoost may fit this model:
User interaction data may initially be unavailable or insufficient for evaluation. Within this model, that condition can be represented as zero (0). Subsequent evaluation of user interaction data may contribute to a positive boost (+1) or a negative ranking effect (-1). This provides a framework for considering how user interaction data could contribute to either positive or negative ranking effects.
Consider Mount AI
Pages are initially indexed and ranked. After this initial ranking, further content evaluation may occur, followed by declines in ranking and traffic. This pattern is consistent with a system that defers deeper evaluation until content reaches a certain threshold, although the observed pattern alone does not establish that this is the mechanism responsible.
Consider observable initial ranking volatility
A page may appear in search results shortly after indexation, only to move to a different ranking position days or weeks later. This pattern is consistent with a system that performs an initial, relatively shallow assessment before conducting deeper content analysis at a later stage.
In some cases, a page may initially appear only for a direct search using quotation marks around the query, with broader search visibility developing later. In other cases, the initial ranking may reflect a prediction based on the expected performance of similar pages on the same site. If subsequent evaluation indicates that the page does not meet those expectations, its ranking may decline.
Deferred evaluation increases efficiency in layered AI-Aware SEO
Deeper content analysis, including neural inference, can require significantly more computational resources than initial indexing. From an efficiency perspective, there may be little benefit in performing resource-intensive analysis on content hosted on a site with minimal traffic or on a page that receives few searches.
As an example, consider how positive, negative, and zero values could be used to evaluate subheading tags:
- Each subheading is assigned a value of -1, 0, or +1. Within this model, zero indicates that the subheading has not yet been evaluated, allowing deeper evaluation to be deferred until further processing is warranted.
- Subheadings are not scheduled for deeper evaluation until the URL meets a specified traffic threshold.
- Once evaluated, the subheadings are assessed according to how well they match the search intent associated with the page's search traffic.
- Following evaluation, each subheading receives a positive or negative score based on the outcome. If the evaluation finds no positive or negative effect, the subheading receives a neutral score. In this model, a neutral result after evaluation is distinct from a zero value representing an unevaluated subheading.
Under this evaluation model, rigid legacy SEO does not adequately explain ranking volatility because its doctrine treats subheadings containing a target keyword as positive SEO factors. The observable pattern is that pages can rank and peak before subsequently falling as further evaluation occurs. This traffic pattern is often referred to as a Mount AI.
A legacy SEO approach might attempt to qualify the rule by stating that all subheadings must be relevant. However, relevance depends on the search intent being considered. Alternatively, the requirement could be shifted to the inclusion of LSI keywords, but the presence of such keywords does not, by itself, establish their relevance to a specific search intent. The issue is not whether LSI keywords can be statistically relevant, but how their relevance to a particular search intent is established. In modern search, that determination may involve AI/LLM-based interpretation, as no authoritative dictionary of LSI keywords establishes their relevance to a given search intent.
Google's statements regarding search learning from earlier searchers support considering deferred evaluation
For a system to evaluate and learn from prior interactions, it must be able to distinguish between positive outcomes, negative outcomes, and outcomes that remain undetermined. These outcomes can be represented using three states, although this is not the only possible implementation. Statements made by Google before Congress regarding Chrome and its use in Google's search systems provide context for examining how user interactions may inform search results. Google's public statements, the computational demands of AI scaling, and observed ranking volatility provide context for considering how deferred evaluation might operate. What remains proprietary is whether, how many, and under what conditions SEO signals utilize deferred states.
Created
by Wayne Smith – Raising the Standards
This article is part of Solution Smith's State of AI, an ongoing research series that examines how modern AI systems process information and how those behaviors affect search, Answer Engine Optimization (AEO), and traditional SEO.
AI has fundamentally changed information retrieval. In addition to disrupting many traditional SEO practices, it has introduced Answer Engine Optimization (AEO), where content is optimized not only for search rankings but also for selection, citation, and use within AI-generated responses.