Argument and explanation — why AI visibility must be measured as a statistical quantity, how the methods work, and where common practice falls short. Built on public evidence.
WhitepaperBA-W-2026-042026
Commercial AI-visibility tools have converged on a recognizable form: a daily score per tracked query, a trend line of those scores, a cited-or-not verdict per topic, and per-engine coverage badges. This paper argues the genre, as a class, cannot meet the disclosure floor a measurement requires. Walked against the ten minimum-disclosure items of BA-C-4, the common form omits the load-bearing ones: a daily single run is one Bernoulli draw per query, so the score carries no usable sample size; a line of bare points hides the day-to-day drift that is its own variance; one frozen query per topic measures the wording, not the need; API collection cannot support a claim about what users see; refusals and engine-wide updates go unmarked. The paper states what a conforming tool would show instead, and closes with five questions a buyer can ask. No tool is named or assessed.
WhitepaperBA-W-2026-032026
A web page is read by four kinds of machines: search crawlers, the retrieval pipelines behind AI answer engines, assistive technology, and autonomous browser agents. Three practices — search engine optimization, generative engine optimization (GEO), and web accessibility — each optimize for one or more of these readers. Working from primary documentation, specifications, regulations, peer-reviewed studies, and, for one 2022 statement lacking an official transcript, a flagged trade-press transcription (sampled 2026-07-09), this paper states each practice as a reader–objective–evaluator tuple, maps twelve page-level signals against the four readers, and audits the circulating claim that accessibility improves AI visibility. Five signals have documented consumers in multiple reader categories. But both load-bearing links of the proposed accessibility-to-GEO mechanism are verified documentary absences, Google states accessibility is not a direct ranking factor, and agent systems split between accessibility-tree and screenshot perception. It closes with five falsifiable propositions that no public study yet measures.
WhitepaperBA-W-2026-022026
When a person asks an AI assistant a question — which schools to consider, which vendor to trust, which clinic to visit — the answer names some organizations and omits others. Being named is AI visibility, and it is not the same as ranking on a search results page. This introduction defines the phenomenon and explains why it must be measured as a probability rather than checked once. It distinguishes the two ways an entity becomes known to an assistant — live retrieval of sources at answer time, and parametric memory formed during training — and summarizes the public evidence that answers are unstable: rewording a question slightly, or asking it again tomorrow, changes the sources and the names returned. It introduces three metrics — Visibility Probability, Share of Voice, and Discovery Depth — and the Barkhausen Ladder, the field's maturity map, pointing to the conventions for formal definitions.
WhitepaperBA-W-2026-012026
A brand's visibility in AI assistants is routinely 'verified' with a single screenshot or one daily query. This paper argues such verification is not measurement. Answer engines are stochastic and their retrieval changes continuously: lightly rewording a query while holding its intent fixed cut the overlap of the brands an assistant recommended to a Jaccard similarity near 0.3 — far below the 0.50–0.61 overlap of a plain re-run — and an identical prompt re-issued a day later overlapped only 34–42% in cited sources and 45–59% in mentioned brands. A single observation of a moving distribution estimates nothing. The paper enumerates what voids a visibility claim — no sample size, no interval, no window, no engine version, one fixed phrasing, uncontrolled personalization, discarded refusals — and shows a three-sigma jump can be pure drift. It then states what valid measurement requires — repeated sampling to a declared precision, bounded intervals near the extremes, a phrasing distribution, partial pooling, explicit windows and versions, change-point monitoring, recorded refusals — specified in BA-C-2 and BA-C-3.