The Provenance Substrate. Why Personal AI, Safety Systems, and AI Governance Cannot Function Without Trace.
Year: 2025
Type: article
Abstract: This whitepaper sets out the structural requirements for civilisation in the post-AI era. As generative AI systems increasingly produce text, images, reasoning chains, and decisions indistinguishable from human work, societies face a foundational crisis: the disappearance of verifiable authorship. Without proof of human origin, legal systems cannot adjudicate responsibility; regulators cannot enforce compliance; organisations cannot protect IP; education cannot validate learning; and AI cannot safely ingest or align to human meaning. The Planetary Provenance Substrate offers the necessary remedy. Built on the Trace Economy and Proof of Cognitive Work (PoCW) protocol, it anchors human-origin cognition at the moment of creation, preserves chain-of-custody, supports legally admissible lineage, and enables planetary AI to operate only on verified inputs. Once cognitive contributions become authenticated assets, they can be recombined by AI into solutions, discoveries, and products whose lineage and royalty obligations remain transparent. The substrate becomes not merely protective but transformative. It unlocks distributed innovation across eight billion people, integrates neurodivergent and polymath cognitive strengths, activates previously invisible Global South knowledge, stabilises sovereign currencies through continuous cognitive inflows, expands national tax bases without raising tax rates, and creates intergenerational symbolic estates. Because provenance is anchored in human declaration, timestamping, and decentralised lineage, it cannot be absorbed or replicated by platforms, LLMs, or states. The paper concludes that provenance is not optional—it is the operating system on which all legal, economic, educational, and AI governance frameworks must depend in the emerging civilisation.
Author: Coupland, Stephen John
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7115947094