Delta, United, and American are surfaced by all five engines, on nearly every prompt. Below them the picture splinters — Alaska, JetBlue, and Southwest trade places depending on which AI you ask. Airlines is a frozen category: the answer has already set.
Each row is not a ranking. It is observed surfacing — how often a company entered the AI-mediated consideration set across a bounded battery of buyer questions. The heatmap maps citation territory: for each question the engines repeatedly surface a small set of companies, and those companies currently hold the answer layer for that question. The question is not "who is best?" — it is "who appears when the buyer asks?"
Observed surfacing, not endorsement. These pages sit inside the same Content / Retrieval / Trust architecture as the rest of our working papers on AI-mediated buyer discovery.
The engines have converged on a small canonical set, reinforced by broad corroboration, accumulated entity clarity, and a large shared retrieval surface. On-page changes alone are unlikely to displace the top tier; the strategic question shifts to defending edge cases and emerging buyer questions.
The answer has not set. Engines disagree, adjacent or foreign firms may enter the answer set, and retrieval-surface quality matters more. Companies can still claim citation territory by publishing answer-shaped, entity-clear, machine-retrievable content.
Airlines is the calibration example of a frozen category. Industrial machinery is the molten one.
Hover or tap any engine cell above to see the real prompts behind that number.
Scope — US market. The roster (Fortune 100) and the prompts are US-specific; foreign carriers surface only on the international-business-class prompt. A global/regional edition would be a separate cut with its own roster and prompts.
This is not a market where a mid-tier carrier publishes one better page and displaces Delta, United, or American from the canonical answer. The top tier appears frozen by accumulated market prominence and corroboration — on-page changes alone are unlikely to move it.
The opportunity is at the edges: loyalty mechanics, route-specific questions, business-travel subsegments, premium-cabin comparisons, corporate-travel-policy content. In a frozen category, citation opportunity tends to live in the long-tail buyer questions the incumbents haven't yet answered cleanly — and even the leaders have to keep claiming that new territory before a challenger does.
Underneath, surfacing appears more sensitive to a few things, in rough order: corroboration density (how redundantly the web names you), entity clarity (whether your identity resolves cleanly — "Delta" vs "SkyMiles" vs "Delta Air Lines" can fragment it), retrieval surface (whether an AI crawler can reach and parse you), and answer-shaped content (whether the liftable answer exists). At the frozen top, corroboration dominates; in the contested tier and at the edges, the lower three start to decide who gets named. Hover any cell above to see the prompts behind each number — the retrieval surface, made legible.
Research publication based on sampled AI outputs collected on 2026-05-27. Findings reflect observed outputs in this sample and are not statements of company quality, ranking factors, or business performance.