Executable Power: Syntax as Infrastructure in Predictive Societies

This article introduces the concept of executable power as a structural form of authority that does not rely on subjects, narratives, or symbolic legitimacy, but on the direct operativity of syntactic structures. Defined as a production rule whose activation triggers an irreversible material action—formalized by deterministic grammars (e.g., Linear Temporal Logic, LTL) or by execution conditions in smart contract languages such as Solidity via require clauses—executable power is examined through a multi-case study (N = 3) involving large language models (LLMs), transaction automation protocols (TAP), and smart contracts. Case selection was based on functional variability and execution context, with each system constituting a unit of analysis. One instance includes automated contracts that freeze assets upon matching a predefined syntactic pattern; another involves LLMs issuing executable commands embedded in structured prompts; a third examines TAP systems enforcing transaction thresholds without human intervention. These systems form an infrastructure of control, operating through logical triggers that bypass interpretation. Empirically, all three exhibited a 100 % execution rate under formal trigger conditions, with average response latency at 0.63 ± 0.17 seconds and no recorded human override in controlled environments. This non-narrative modality of power, grounded in executable syntax, marks an epistemological rupture with classical domination theories (Arendt, Foucault) and diverges from normative or deliberative models. The article incorporates recent literature on infrastructural governance and executional authority (Pasquale, 2023; Rouvroy, 2024; Chen et al., 2025) and references empirical audits of smart-contract vulnerabilities (e.g., Nakamoto Labs, 2025), as well as recent studies on instruction-following in LLMs (Singh &amp; Alvarado, 2025), to expose both operational potential and epistemic risks. The proposed verification methodology is falsifiable, specifying outcome-based metrics—such as execution latency, trigger-response integrity, and intervention rate—with formal verification thresholds (e.g., execution rate below 95 % under standard trigger sequences) subject to model checking and replicable error quantification.<b>DOI:</b> https://doi.org/10.5281/zenodo.15754714This work is also published with DOI reference in <b>Figshare</b> https://doi.org/10.6084/m9.figshare.29424524 and <b>Pending SSRN ID to be assigned. ETA: Q3 2025.</b>