The Framing Gap: Strategic Claim Bridging and the Limits of Generative AI Interpretation in Brand Representation

Generative AI systems increasingly mediate how brands are described, compared, and recommended. While such systems can retrieve evidence, generate candidate explanations, and synthesise existing patterns, this paper argues they cannot be relied upon to produce externally accountable, entity-specific, evidence-grounded interpretive frames without external support. The paper identifies that dependency as the Framing Gap: the gap between evidence available to a generative system and the interpretive context required for the system to describe a brand confidently and distinctively. A three-level model of brand-AI communication is proposed: Scattered Proof of claims, where evidence exists but remains disconnected; Connected Proof of claims, where claims and corroboration are explicitly linked; and Framed Proof of claims, where the brand supplies a logically structured interpretation of why the evidence matters. The paper introduces Strategic Claim Bridging as the mechanism by which Framed Proof of claims is produced: the externally accountable selection of a beneficial derived conclusion from accepted facts, and the construction of a logical bridge that allows generative systems to transmit that conclusion as grounded representation. Externally Accountable Frame Authorship is identified as the accountability dimension of Strategic Claim Bridging. The central problem is not that generative systems cannot generate inferences, nor that they cannot rank candidates when supplied with criteria. They can. The problem is that AI cannot supply the external authority, accountability, or consequence-bearing role that turns one selected derived conclusion into the entity's public frame. Strategic Claim Bridging operates iteratively rather than as a single act: a brand's representational position is built from many bridges compounding over time, with each successfully transmitted conclusion becoming an anchored fact in the corpus that supports subsequent bridges. The Frame Ambition Ladder measures the Creative Leap between accepted facts and the derived conclusion the brand wants the system to transmit, with three rungs of increasing leap distance and proof requirement above a baseline state the paper calls Standing Still. The Topical Expansion Frame is positioned as the canonical worked example of authority transfer into adjacent territory, with an adjacency test formalised as the constraint that distinguishes valid topical expansion from topical laundering. The paper argues that interpretive selection pressure operating at the system layer rewards entities whose framing reduces interpretive burden, and proposes that this pressure is consistent with mechanics already established at the search and knowledge-graph layers. Five testable predictions are advanced for measuring citation frequency, description stability, frame-ambition outcomes, topical transfer, and faithful frame reproduction. AI usage disclosure: in the preparation of this paper, the author used Claude (Anthropic) for drafting iterations, structural review, terminology consistency checking, and copy-editing. All conceptual content, theoretical claims, and intellectual contributions are the author's own. The author reviewed, revised, and approved all text and takes full responsibility for the content of this publication. This paper is TKF-5-19857447 in The Kalicube Framework series by Jason Barnard (Kalicube). Canonical citation identifiers for the whole research programme are maintained in The Kalicube Framework Programme Register (TKF-0): https://doi.org/10.5281/zenodo.20645889. Cite this paper as TKF-5-19857447 (Zenodo concept DOI 10.5281/zenodo.19857447).