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This work presents the Kotov Principle, focusing on scarcity inversion in long-lived AI systems.

April 30, 20260 citationsOpen Access

Kotov Principle of L4-Bound Experience: Scarcity Inversion in Long-Lived AI Systems

Key Points

This work presents the Kotov Principle, focusing on scarcity inversion in long-lived AI systems.
Describes the architectural thesis of the Kotov Principle.
Examines factors influencing AI system maturity and authority under L4 constraints.
Identifies experience as vital for establishing authority in AI systems.
Demonstrates that increasing model scale and tool access alone do not ensure durability or trust.

Abstract

This record archives Version v0.1.1 of the Kotov Principle of L4-Bound Experience, an authorial architectural thesis on scarcity inversion in long-lived AI systems. The principle states that as the marginal cost of cognitive generation approaches operational insignificance, long-term value in AI systems shifts from generative capacity to reality-bound temporal continuity. Model scale, token throughput, dataset volume, and tool access increase execution capability, but they do not by themselves produce maturity, bounded operational authority, or durable trust. These properties emerge only when a long-lived entity accumulates experience under L4 constraints: cost, time, scarcity, responsibility, social friction, degradation, and irreversible consequence. Compact form: Compute scales output. L4 experience scales authority. This record includes the canonical Markdown artifact, an archival PDF rendering, and a SHA-256 manifest binding both files. Boundary note: this is not an empirical law, not a benchmark claim, not a legal doctrine, and not a replacement for SER, EWCEP, SER-FED, L4 Witness, Beacon, or related specifications.

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