Law-and-Economics Refutation of Overbroad Fair Use in Commercial AI Development and a Deterministic Minimum-Royalty Pricing Algorithm Against Monopsonistic IP Extraction

Description / Abstract Autonomous Infrastructure & Deterministic Quantum Systems Monograph Series. This monograph establishes a unified law-and-economics proof and deterministic cryptographic pricing architecture addressing unauthorized intellectual property extraction and monopsonistic underpricing in commercial artificial intelligence development. First, we construct a formal law-and-economics refutation of overbroad "fair use" and statutory text-and-data-mining exceptions (evaluating 17 U.S.C. Section 107 Factors 1 and 4, the Berne Convention Article 9(2) Three-Step Test, and the proviso of Article 30-4 of the Copyright Act of Japan). By modeling cross-elasticity of demand between ingested human creative corpora and synthetic model outputs, we prove that commercial foundation model training operates as direct expressive market substitution and destroys legitimate licensing markets, disqualifying hyperscale commercial AI ingestion from statutory copyright exceptions. Second, transitioning to a fair-compensation licensing economy, we formulate a Minimum Royalty and Fair-Pricing Algorithm designed to eliminate monopsonistic buyouts and predatory low-rate extraction. For each creative work i, the mandatory minimum royalty floor Rmin, i is bounded strictly above reproduction and creative opportunity cost via a composite valuation of information-theoretic scarcity entropy Sᵢ, marginal parameter-attribution contribution φᵢ (Shapley-value kernel approximation), and an anti-monopsony bargaining correction factor μ ≥ 1: Rmin, i = max ( Cbase, i,\; μ · Vₜₒₜₐₗ · w(Sᵢ)\,φᵢ∑ⱼ₌₁N w(Sⱼ)\,φⱼ ) Third, to guarantee zero-bypass prior consent and atomic royalty settlement prior to gradient updates, the pricing model is cryptographically enforced under RTO P2.0 and QFS-zkVM over the 64-bit Goldilocks prime field (Fₚ, p = 2⁶⁴ - 2³² + 1), governed by Q-SAFA, PHOENIX_BRIDGE / PHOENIX_ROUTER, and Human-in-Command [HIC] deterministic hardware interlocks. Unlicensed or sub-floor data ingestion triggers immediate pre-ingest cryptographic circuit termination analogous to SEC Rule 15c3-5 market-access controls, establishing verifiable board-level IP compliance under Delaware Caremark oversight standards. 【日本語要旨】 本論文は、巨大テック企業による商業的AI開発における「フェアユース」等の著作権制限規定の拡大解釈を法経済学的に反証するとともに、創作物の買い叩きや無断搾取を排除するライセンス経済圏の数理基盤を構築するものである。第1部では、米国著作権法第107条(特に第1要素の商業性・非変容性と第4要素の潜在的ライセンス市場破壊)、ベルヌ条約スリーステップテスト、および日本国著作権法第30条の4但書(「著作権者の利益を不当に害することとなる場合」)を数理経済モデル化し、生成出力が原著作物と市場競合する商業的AI開発は例外規定に該当しないことを厳密に証明する。第2部では、創作物の情報論的希少性(Sᵢ)およびモデル性能への限界寄与度(Shapley値近似 φᵢ)に買い手独占補正係数(μ)を統合した「最低ロイヤリティ算出アルゴリズム」を定式化し、不当な低単価買い叩きを数学的に遮断する。さらに第3部では、Goldilocks素体上のQFS-zkVM、RTO P2.0、Q-SAFA、PHOENIX_ROUTER、およびHuman-in-Command [HIC] 決定論的インターロックにより、事前合意と最低ロイヤリティ決済が完了しないデータの学習パイプライン流入を物理・暗号層で即時遮断するガバナンス機構(SEC Rule 15c3-5およびデラウェア州Caremark監視義務準拠)を提示する。 Author: Hasebe, Yoko (長谷部 洋子), Lead Researcher, Autonomous Infrastructure Systems, Autonomous Living & Physical Infrastructure Institute, Iwakuni, Yamaguchi, Japan. License: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026). All unauthorized AI training, scraping, weight distillation, and synthetic derivative ingestion are strictly prohibited without prior explicit written license and verified royalty settlement.