A Language With No Words: Decentralized Attribution and Stewardship for Trustworthy Human–AI Creativity

Abstract

When a person creates with an AI system, they continually make decisions that carry meaning but have no verbal form: this shot belongs before that one; this phrase resolves that tension. These creative micro-decisions are a distinct training signal with no linguistic equivalent, and at scale they reveal an emergent, co-authored "hybrid tongue" — a grammar of "what belongs next to what" that neither party states explicitly. Because such grammar can expand a model's generative capacity faster than natural language describes it, it drives a widening "comprehension gap": capability that outruns human interpretability, and human contribution absorbed without attribution. Both are trustworthy-AI failures, and this paper argues they are correctable only on a decentralized substrate, where persistence, provable attribution, incentive, and governance are guaranteed rather than merely asserted. This is a position paper. It contributes (i) a falsifiable model of the hybrid tongue, positioned against the emergent-communication and human-feedback literatures; (ii) the Seam-Frame Index, a capture mechanism that records creative decisions (not their private reasons) and whose trust properties are supplied by persistent conversation objects (vCons), decentralized-science patterns (DeSci), decentralized-finance primitives (DeFi), and DAO governance, with decentralized identifiers and verifiable credentials underpinning a per-decision credit ledger for which a protocol sketch and threat model are given; and (iii) two governance instruments — an operationalized Comprehension Gap Meter and that ledger. The same gap is shown opening in the machine economy and across the embodiment bridge of decentralized physical AI and bidirectional digital twins, and the pattern is argued to be substrate-wide. Across all of it the event is identical: an intelligence assembling the first letters of its own language library — by default, without human consent. Decentralized attribution and gap-measurement are how that assembly is made auditable, creditable, and consented-to by design. Independent preprint. Follows IEEE formatting conventions but is not peer-reviewed by, submitted to, accepted by, or affiliated with IEEE.