The dominant architecture of contemporary AI supply chains is structurally extractive: centralized platforms aggregate data labor, agent orchestration, and computational substrate into vertically-integrated stacks owned by a small number of corporations. The hidden labor pool that makes these systems "safe" — annotators, moderators, fine-tuners — is treated as expendable input rather than co-owner. The downstream consequences are predictable: data-integrity failures (the May 2026 GitHub squash-merge incident silently dropped commits from 2,092 pull requests), platform decay (Mitchell Hashimoto, founder of HashiCorp, publicly walked from GitHub the day this abstract is being written), and a population-scale "AI clone hustle" market that depletes the contact-economy commons faster than its operators can recoup their costs.

This paper proposes a unified architectural alternative: the substrate-IS-the-primitive inversion, instantiated jointly across five operational scales — memory, render, conversation, dispatch, and physical/game-mechanic — under a single substrate schema with cross-vendor symmetric peer operation. The substrate is the primary unit of identity, state, provenance, and temporal progression. Applications and agents become thin computations that traverse the substrate. Data labor is structurally co-owning rather than wage-extracted: a binding Pay-It-Forward 300 mechanism distributes 20% of patent revenue and 5% of cooperative-currency surplus to the first three hundred contributors, with the recursive commitment that every recruit receives the same deal back, eliminating decay across recruit generations.

We present empirical receipts: two USPTO provisional applications totaling 199 pages filed on the day of submission (Apps 64/060,080 and 64/060,093 under Cooperative Defensive Patent Pledge #2260); a 7-of-7 prediction-validation result on a high-tier-versus-low-tier model inference comparison conducted as a single-Founder, four-AI cooperative session producing approximately 112,000 words of counsel-grade output; and the AGPL-3.0 release of the consumer-facing application surface ("LB Frame Hearth") under a twelve-brand Nine-Pin survival architecture explicitly designed to prevent single-platform capture.

The framework offers an alternative research agenda for the conference's central question: how to design AI supply chains that are scientifically optimized, economically viable, and grounded in international labor standards. The cooperative-substrate architecture is presented not as a moral preference but as an architectural one: every measurable dimension of data integrity, fault tolerance, scaling cost, and governance resilience is structurally superior under cooperative ownership. The moral consequences fall out of the architecture, not the other way around.