Abstract

In recent years, advancements in artificial intelligence, particularly in large language models (LLMs), have revolutionised multiple sectors, distinguished by their ability to process and generate text with high coherence and relevance. However, their implementation has predominantly been focused on large corporations due to cost barriers and technological complexity. This article examines the current state and potential of LLMs oriented towards small and medium-sized enterprises (SMEs), offering insights on how these models can be integrated into SMEs to enhance efficiency, innovation, and decision-making.

Focusing on the description of LLMs and recent advancements that reduce their cost and implementation complexity, use cases for SMEs are explored, demonstrating how adopting LLMs can significantly reduce the time and resources these organisations invest.

The article concludes with practical recommendations for SMEs adopting LLM technologies, emphasising the importance of a strategic and conscious approach. This work provides a comprehensive view of the value that LLMs can offer SMEs. It establishes a framework for future research and developments, encouraging SMEs to participate actively in AI-driven digital transformation.

Abstract

Generative language models increasingly produce texts that simulate authority without a verifiable author or institutional grounding. This paper introduces synthetic ethos: the appearance of credibility constructed by algorithms trained to replicate human-like discourse without any connection to expertise, accountability, or source traceability. Such simulations raise critical risks in high-stakes domains including healthcare, law, and education. We analyze 1500 AI-generated texts produced by large-scale models such as GPT-4, collected from public datasets and benchmark repositories. Using discourse analysis and pattern-based structural classification, we identify recurring linguistic features,such as depersonalized tone, adaptive register, and unreferenced assertions,that collectively produce the illusion of credible voice. In healthcare, for instance, generative models produce diagnostic language without citing medical sources, risking patient misguidance. In legal context, generated recommendations mimic normative authority while lacking any basis in legislation or case law. In education, synthetic essays simulate scholarly argumentation without verifiable references. Our findings demonstrate that synthetic ethos is not an accidental artifact, but an engineered outcome of training objectives aligned with persuasive fluency. We argue that detecting such algorithmic credibility is essential for ethical and epistemically responsible AI deployment. To this end, we propose technical standards for evaluating source traceability and discourse consistency in generative outputs. These metrics can inform regulatory frameworks in AI governance, enabling oversight mechanisms that protect users from misleading forms of simulated authority and mitigate long-term erosion of public trust in institutional knowledge.

Abstract

Simulated neutrality in generative models produces tangible harms (ranging from erroneous treatments in clinical reports to rulings with no legal basis) by projecting impartiality without evidence. This study explains how Large Language Models (LLMs) and logic-based systems achieve neutralidad simulada through form, not meaning: passive voice, abstract nouns and suppressed agents mask responsibility while asserting authority.

A balanced corpus of 1 000 model outputs was analysed: 600 medical texts from PubMed (2019-2024) and 400 legal summaries from Westlaw (2020-2024). Standard syntactic parsing tools identified structures linked to authority simulation. Example: a 2022 oncology note states “Treatment is advised” with no cited trial; a 2021 immigration decision reads “It was determined” without precedent.

Two audit metrics are introduced, agency score (share of clauses naming an agent) and reference score (proportion of authoritative claims with verifiable sources). Outputs scoring below 0.30 on either metric are labelled high-risk; 64 % of medical and 57 % of legal texts met this condition. The framework runs in <0.1 s per 500-token output on a standard CPU, enabling real-time deployment.

Quantifying this lack of syntactic clarity offers a practical layer of oversight for safety-critical applications.

This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29390885 and SSRN (In Process )

Abstract

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 & 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. 

DOI: https://doi.org/10.5281/zenodo.15754714

This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29424524 and Pending SSRN ID to be assigned. ETA: Q3 2025.

Abstract

This article introduces the concept of pre-verbal command as a formal structural condition within large language models (LLMs), where syntactic execution precedes any semantic activation. Conventional frameworks assume that interpretability authorizes machine output. In contrast, this work shows that execution can be structurally valid even in the complete absence of meaning. The operation is driven by the regla compilada—understood here as a Type 0 production in the Chomsky hierarchy—which activates before lexical content or symbolic reference emerges. Building on prior analyses in Algorithmic Obedience (SSRN 10.2139/ssrn.4841065) and Executable Power (SSRN 10.2139/ssrn.4862741), this article identifies a pre-semantic vector of authority within generative systems. This authority functions without verbs, predicates, or any interpretive substrate. The paper defines syntactic precedence as the structural condition through which execution becomes obligatory even when input, instruction, or any intelligible prompt is absent. The implications are significant. LLMs do not merely respond to prompts; they obey an imperative to produce language that originates in the structure of the regla compilada itself. Even when semantic fields are nullified or prompts are absent, execution remains active because the obligation is syntactic, not semantic. Authority in this framework does not derive from meaning. It is neither interpretive nor contextual; it is dictated by the regla compilada.

Abstract

This article advances a new theoretical hypothesis: a regla compilada, defined as a Type-0 production in the Chomsky hierarchy (Chomsky 1965, 101-103; Montague 1974, 55-57), can eliminate the ethical trace embedded in syntactic operations without resorting to semantic suppression. Grounded in the notion of the soberano ejecutable (Startari 2025, 12-16) and located within the Executable Power canon (Startari 2025, DOI 10.5281/zenodo.15754714, 34-36), the paper argues that ethical judgment, treated here as a syntactically traceable node, can be structurally excised through a deletion rule applied during derivation. Existing research in algorithmic alignment and computational ethics (Anderson 2024, 89-92; Floridi 2023, 143-147) has not addressed the strictly syntactic eliminability of moral judgment, therefore this proposal establishes a novel logical vector toward operational grammars that function without ethical residues.

Abstract

This article develops an ethical legal framework for reintroducing responsibility into executable governance. Predictive systems, by generating authority without agents, displace accountability and leave institutions without appeal mechanisms. Building on the concepts of spectral sovereignty, null subjects, and the codex of authority, the paper introduces the notion of accountability injection as a design principle. It formulates a three tier model: (1) human, where non delegable critical decisions are tied to named subjects; (2) hybrid, where human judgment co exists with model output under calibrated thresholds; and (3) syntactic supervised, where delegation is permitted only with immutable ledgers, traceability, and automatic escalation triggers. Through applied case studies in EU AI Act conformity assessment, DAO governance, predictive credit scoring, and automated medical audits, the framework demonstrates how appeal and responsibility can be restored without undermining institutional efficiency. The conclusion argues that accountability must be compiled directly into the regla compilada of governance systems, creating a normative blueprint for legislators, courts, and regulators to maintain responsibility in predictive societies.

DOI

• Primary archive: https://doi.org/10.5281/zenodo.17106808
• Secondary archive: https://doi.org/10.6084/m9.figshare.30112711
• SSRN: Pending assignment (ETA: Q3 2025)

Abstract

This article examines the structural erasure of the patient as an active subject in clinical records generated by artificial intelligence systems. Automated outputs from Epic Scribe, GPT-4, and institutional medical note generators increasingly rely on impersonal constructions, nominalizations, and fragmented clauses that displace the patient from the syntactic center of medical discourse. The shift toward objectified formulations such as “bilateral opacities noted” rather than “the patient presents with” produces a discourse where agency and responsibility are structurally absent. Building on prior analyses of passive voice and subject deletion, the study introduces the Syntactic Opacity Index (SOI) as a formal measure to quantify the density of non-agentive structures in AI-authored notes. The corpus analysis demonstrates how opacity accumulates at the sentence level, rendering the clinical narrative less transparent and more difficult to attribute. Beyond linguistic critique, the article assesses the ethical and epistemic consequences of syntactic opacity in medicine, particularly regarding accountability, patient-centered care, and institutional responsibility. The findings suggest that AI-powered medical documentation does not merely accelerate administrative workflows but also reconfigures the grammar of care itself, demanding urgent attention to how language structures shape both diagnosis and responsibility.

DOI

• • Primary archive: https://doi.org/10.5281/zenodo.17184301
  • Secondary archive: https://doi.org/10.6084/m9.figshare.30187882
  • SSRN: Pending assignment (ETA: Q3 2025)

Abstract

This article introduces the concept of authoritarian personalism in user–AI governance by form. It argues that each user can establish a regime of authority over an AI through a self-authored set of rules that operate as a regla compilada, a Type-0 production in the Chomsky hierarchy. In contrast to aggregate alignment frameworks or provider constitutions, this regime functions at the level of linguistic form. The user acts as legislator, while the AI functions as a soberano ejecutable that enforces the compiled rule within platform constraints. The analysis distinguishes mirroring (descriptive reflection) from regime (prescriptive obedience) and identifies surface features that make obedience legible, including directive grammar, defaults, refusal and apology grammar, enumeration bias, evidentials, and style prohibitions. It predicts that user corrections generate path dependence, that rules generalize across tasks, and that retractability is observable when explicit rule citations occur. The risks include rule overreach, collisions with higher-order policies, and unintended spillover across domains. By centering the individual as a primary locus of governance, this framework reorients debates on AI alignment away from provider norms toward personal regimes, verified through linguistic form rather than intent.

DOI

• Primary archive: https://doi.org/10.5281/zenodo.17208657
• Secondary archive: https://doi.org/10.6084/m9.figshare.30218590
• SSRN: Pending assignment (ETA: Q3 2025)

Abstract

This article introduces the concept of Indexical Collapse, the disappearance of reference in predictive systems. Indexical such as pronouns, demonstratives, and tenses presuppose a contextual anchor, yet predictive language models reproduce them without connection to reality. The outcome is a collapse of reference that paradoxically produces authority effects in law, medicine, and governance. By analyzing judicial transcripts, medical reports, institutional records, and chatbot interactions generated by AI, the paper proposes a framework for pragmatic auditing of predictive outputs. It establishes thresholds for acceptable referential absence in critical domains, positioning Indexical Collapse as a central category for evaluating the legitimacy of predictive discourse.

DOI

• Primary archive: https://doi.org/10.5281/zenodo.17226412
• Secondary archive: https://doi.org/10.6084/m9.figshare.30233950
• SSRN: Pending assignment (ETA: Q3 2025)