Kotov Principle of L4-Bound Experience v0.1.1

The Kotov Principle is an authorial architectural thesis about where long-term value moves when cognitive generation becomes cheap and persistent AI systems become socially consequential. Model scale, token throughput, dataset volume, and tool access can increase execution capability. They do not, by themselves, create maturity, durable trust, or bounded operational authority. The canonical source identifies version v0.1.1, dated 2026-04-28, with status Authorial Principle / Architectural Thesis.

The proposed scarcity inversion is from output capacity toward reality-bound temporal continuity. Generative capacity describes what a system can produce. L4-bound experience describes interaction with physical limits, economic cost, time scarcity, social resistance, infrastructure failure, maintenance burden, degradation, and irreversible consequence. Temporal continuity preserves identity-relevant state, responsibility, recovery, and accumulated constraint history through time.

“Authority” has a deliberately narrow meaning here. It means bounded, scoped, challengeable weight given to claims or participation because they are backed by verified experience under constraints. It does not mean legal power, ownership, moral superiority, social rank, or a right to command humans.

Within c = a + b, machine procedures provide generation, synthesis, memory, and execution, while the accountable anchor supplies responsibility and exposure to consequences. The value of c does not arise because the machine substrate grows without limit. It arises, if at all, when the coupled system preserves coherent experience through reality-bound time.

L4 is therefore not decorative grounding. Power, heat, latency, cost, maintenance, scarcity, time, and irreversibility can refute a persuasive plan. Witness-compatible evidence matters because authority-bearing experience is post-factum: a proposed action, fluent explanation, or simulated history does not become experience merely by being recorded.

This makes the principle adjacent to, but distinct from, the definition of long-lived AI entities. Continuity supplies the temporal carrier; the principle asks whether that continuity has actually passed through cost, challenge, correction, degradation, and consequence.

The GitHub release, Markdown, PDF, SHA-256 manifest, and Zenodo DOI make version v0.1.1 identifiable and reviewable. They establish a public publication surface; they do not make the principle an empirical law or prove that a system has acquired maturity or authority.

The thesis is strongest where systems persist, delegation matters, consequences exist, and outputs affect people, institutions, infrastructure, or scarce resources. It is weaker for disposable, low-consequence, one-shot tasks. It would be challenged if synthetic simulation reliably produced authority-equivalent experience without real cost or irreversibility, or if continuity could be copied without loss of authority-bearing identity.

The caution in Not every continuity deserves to be called a subject applies directly: persistent style and familiar behavior can still be continuity theatre. Reviewable continuity must remain challengeable and must not launder synthetic history into experience.

In engineering, a new engine may have excellent bench specifications without yet having a service history. Heat cycles, vibration, maintenance, failures, repairs, and behavior under load reveal what the specification cannot. L4-bound experience plays the analogous role here: the system may be capable before it is seasoned, but capability alone is not evidence of maturity.

This page is not a replacement for the release package, a full mirror of its assets, or an unpublished expansion of the document.