AI Syntactic Power and Legitimacy: How AI Structures Shape Power

<b>AI Syntactic Power and Legitimacy: How AI Structures Shape Authority</b>This book consolidates a cycle of research on the structural foundations of authority in artificial intelligence. It brings together a series of peer-reviewed articles that demonstrate how power in AI systems no longer depends on meaning, interpretation, or intention, but on syntactic sufficiency.The argument develops in fourteen chapters, beginning with the autonomy of sense beyond reference and culminating in the colonization of temporality by predictive infrastructures. Along the way, it formalizes a set of theoretical contributions: the <i>regla compilada</i> as a Type 0 grammar of executable authority, the figure of the <i>soberano ejecutable</i> as operator of legitimacy without subject, the theorem of Disconnected Syntactic Authority (DSAT), the theorem of the Limit of Conditional Obedience (TLOC), the δ [E] → ∅ rule of ethical trace deletion, and the formalization of <i>compiled norms</i> as computable legal speech.The book demonstrates that:Authority migrates from agents to structures, producing the <i>sujeto evanescente</i>.Ethical, interpretative, and referential markers can be structurally erased without breaking execution.Legal norms can be transformed into executable grammars with cross-linguistic validity.Predictive infrastructures colonize time, replacing futurity with executable closure.<i>AI Syntactic Power and Legitimacy</i> marks the closure of a first syntactic phase of research, while opening the way to further studies on structural delegation, institutional obedience, and computable legality.<b>Author:</b> Agustin V. Startari, linguistic theorist and researcher in historical studies (UdelaR and University of Palermo).<b>Publisher:</b><b> </b>LEFORTUNE<b>ISBN:</b><b> </b>9798266066687<b>DOI: https://doi.org/</b>10.5281/zenodo.17154108 and https://doi.org/10.6084/m9.figshare.30158239

Publication Details

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Figshare
Published
2025-09-19
DOI
https://doi.org/10.6084/m9.figshare.30158239
Primary Topic
Psychometric Methodologies and Testing
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AI Syntactic Power and Legitimacy: How AI Structures Shape Power

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Psychometric Methodologies and Testing
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AI Syntactic Power and Legitimacy: How AI Structures Shape Power

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Abstract

<b>AI Syntactic Power and Legitimacy: How AI Structures Shape Authority</b>This book consolidates a cycle of research on the structural foundations of authority in artificial intelligence. It brings together a series of peer-reviewed articles that demonstrate how power in AI systems no longer depends on meaning, interpretation, or intention, but on syntactic sufficiency.The argument develops in fourteen chapters, beginning with the autonomy of sense beyond reference and culminating in the colonization of temporality by predictive infrastructures. Along the way, it formalizes a set of theoretical contributions: the <i>regla compilada</i> as a Type 0 grammar of executable authority, the figure of the <i>soberano ejecutable</i> as operator of legitimacy without subject, the theorem of Disconnected Syntactic Authority (DSAT), the theorem of the Limit of Conditional Obedience (TLOC), the δ [E] → ∅ rule of ethical trace deletion, and the formalization of <i>compiled norms</i> as computable legal speech.The book demonstrates that:Authority migrates from agents to structures, producing the <i>sujeto evanescente</i>.Ethical, interpretative, and referential markers can be structurally erased without breaking execution.Legal norms can be transformed into executable grammars with cross-linguistic validity.Predictive infrastructures colonize time, replacing futurity with executable closure.<i>AI Syntactic Power and Legitimacy</i> marks the closure of a first syntactic phase of research, while opening the way to further studies on structural delegation, institutional obedience, and computable legality.<b>Author:</b> Agustin V. Startari, linguistic theorist and researcher in historical studies (UdelaR and University of Palermo).<b>Publisher:</b><b> </b>LEFORTUNE<b>ISBN:</b><b> </b>9798266066687<b>DOI: https://doi.org/</b>10.5281/zenodo.17154108 and https://doi.org/10.6084/m9.figshare.30158239

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