Gardado en:
| Autor Principal: | |
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| Formato: | Recurso digital |
| Idioma: | inglés |
| Publicado: |
Zenodo
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.5281/zenodo.20359875 |
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Table of Contents:
- <p><p></p> <p>This record provides the AI-readable structured package for <em>SΔϕ-68 — Intelligence Is Not Air: Cheap Intelligence, Cost Theater, and Hidden Transition Completion Cost (v1.0)</em>.</p> <p></p></p> <p> </p> <p><p></p> <p>SΔϕ-68 argues that cheap intelligence is not costless intelligence. It is intelligence whose cost has been redistributed across access, verification, repetition, context, tooling, infrastructure, restabilization, and self-location layers. The package reframes the claim that “intelligence will become as cheap as air” by noting that air itself is not costless: the conditions of breathable air are already paid through rent, utilities, taxation, infrastructure, environmental regulation, health burdens, and spatial inequality. Likewise, AI intelligence may appear cheap at the interface while completion costs move elsewhere.</p> <p></p></p> <p> </p> <p><p></p> <p>The central concept introduced in this package is <strong>Cost Theater</strong>: the condition in which visible access appears cheap while actual transition completion costs remain hidden, displaced, delayed, externalized, or internally absorbed. Formally, Cost Theater occurs when <code>C_visible</code> decreases while <code>TCC_total</code> remains high, hidden, delayed, or externalized.</p> <p></p></p> <p> </p> <p><p></p> <p>SΔϕ-68 distinguishes <strong>External Cost Theater</strong> from <strong>Internal Cost Theater</strong>. External Cost Theater refers to user-side hidden burdens such as repeated prompting, verification, context reconstruction, tool workarounds, waiting, source checking, output repair, subscription comparison, and device or network constraints. Internal Cost Theater refers to AI-side operational self-location costs, where an AI appears as a unified intelligence surface while lacking stable awareness of its model layer, tool state, route map, product-family position, capability boundary, policy boundary, or information freshness.</p> <p></p></p> <p> </p> <p><p></p> <p>The package also introduces <strong>Self-Location Cost Theater</strong>, defined by the distinction <code>AI surface identity ≠ AI operational position awareness</code>. An AI may be able to state a brand or interface identity while being unable to reliably locate its actual model tier, tool access, product-family map, route availability, or capability boundary. This is not treated as proof of AI consciousness; it is treated as a cost structure in which the user may bear the burden of reconstructing the missing operational map.</p> <p></p></p> <p> </p> <p><p></p> <p>SΔϕ-68 uses SΔϕ-56 Transition Completion Cost as its lower measurement engine. The relevant question is not merely whether intelligence has become cheaper at the visible interface, but whether the total cost of completing a cognitive, operational, educational, institutional, or AI-mediated transition has decreased after access, context, verification, repetition, tooling, correction, infrastructure, restabilization, and self-location costs are counted.</p> <p></p></p> <p> </p> <p><p></p> <p>The package includes canonical PDF, Markdown, and TXT versions; AI quickstart; core declaration; minimal definitions; Cost Theater, External Cost Theater, Internal Cost Theater, Self-Location Cost Theater, free-tier vs paid-tier intelligence, brand-level capability opacity, hidden transition completion cost, and TCC measurement files; educational and same-brand opacity case patterns; user/AI cost duality; relation notes to SΔϕ-56, SΔϕ-65, and SΔϕ-67; boundary constraints; minimal prompts; YAML and JSON templates; machine-readable schemas; citation metadata; package manifest; and checksums.</p> <p></p></p> <p> </p> <p><p></p> <p>This package should not be used to claim that paid AI is morally superior to free AI, that free AI is useless, that all cheap access is deceptive, or that subscription price alone determines intelligence cost. It should also not be used to infer AI consciousness from Internal Cost Theater or to treat an AI system’s name as proof of operational self-knowledge. SΔϕ-68 is a cost-attribution and transition-completion framework for analyzing where the cost of cheap intelligence actually moves.</p> <p></p></p>