Prepared March 31, 2026, by Grant Simmons
Introduction
| Important note. This white paper does not claim to reproduce Waikay’s proprietary formula. The public materials describe the topical presence conceptually as a score built from depth, breadth, and concentration of topic associations. The mathematical framework in this document is an academically grounded operationalization of that concept, not a reverse-engineering of Waikay’s internal system. |
This document seeks to provide a rigorous whitepaper that explains why Waikay’s concept of Topical Presence in AI Systems is plausible, measurable, and useful through the lenses of distributional semantics, entity-relatedness, topic coherence, and information theory. [1][2][3][4][5][6][7][8]
Abstract
Topical presence is the measurable degree to which an AI system associates an entity with a defined set of relevant topics. This paper argues that the concept is scientifically defensible because it aligns with well-established research traditions: distributional semantics explains why co-occurrence and context produce semantic association; embedding-based similarity explains how those associations can be measured; topic modeling and topic coherence explain how topical neighborhoods can be evaluated; and Shannon entropy provides a principled way to measure concentration versus diffusion across topics. Building on those foundations, this paper proposes an operational framework for topical presence that comprises depth, breadth, and concentration. The result is not a claim about any single vendor’s secret formula, but a generalizable measurement model for AI-era brand visibility. [2][3][4][5][6][7][8]
References
[1] Waikay launch materials describing AI Topical Presence as a metric built from depth, breadth, and concentration, summarized in Newsworthy.ai and related coverage on March 26, 2026.
[2] T. Cohen and D. Widdows, ‘Empirical Distributional Semantics: Methods and Biomedical Applications,’ Journal of Biomedical Informatics, 2009.
[3] D. Jurafsky and J. H. Martin, ‘Vector Semantics and Embeddings,’ Speech and Language Processing draft chapter, Stanford University, 2023.
[4] N. Reimers and I. Gurevych, ‘Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,’ 2019.
[5] K. S. Brown et al., ‘Investigating the Extent to which Distributional Semantic Models Capture Semantic Similarity,’ Cognitive Science, 2023; and related semantic similarity literature.
[6] H. Rahimi et al., ‘Contextualized Topic Coherence Metrics,’ Findings of EACL, 2024; plus topic-coherence evaluation literature.
[7] C. E. Shannon, ‘A Mathematical Theory of Communication,’ Bell System Technical Journal, 1948.
[8] H. Lei et al., ‘Concentrated Document Topic Model,’ 2021, using entropy as a measure of topic concentration.
