Skip to content

The Borrowed Name: Counterfeit Sanctity, Artificial Intelligence, and the Architecture of the Final Deception

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This research paper advances a novel constructive theological argument regarding the intersection of biblical eschatology and generative artificial intelligence (AI). Moving beyond traditional inquiries into the identity or chronology of the Antichrist, the author investigates the mechanism of deception described in New Testament corpora (Matthew 7, 2 Thessalonians 2, 2 Corinthians 11, and Revelation 13). Core ThesisThe paper identifies "Counterfeit Sanctity"—the weaponized mimesis of sacred language and divine invocation—as the central structural weapon of the eschatological deceiver. It argues that the final deception functions not through overt blasphemy or opposition to God, but through the sophisticated capture and impersonation of the Holy Spirit’s linguistic and phenomenological register. Technological SynthesisThe author identifies Large Language Models (LLMs) and generative heuristics as the first historical apparatus capable of realizing this mechanism at civilizational scale. By decoupling religiously fluent, spiritually authoritative speech from ontological character and pneumatic presence, generative AI allows for the manufacturing of "ownerless" sanctity. Key Contributions Exegetical Analysis: A synthesis of the "Lord, Lord" rejection in Matthew 7 with the "lying signs" of 2 Thessalonians 2. Patristic Grounding: Confirmation of the mimesis-of-the-sacred theory in the works of Irenaeus, Cyril of Jerusalem, and John Chrysostom. AI Epistemology: A structural comparison between the "disguise of light" (2 Cor. 11:14) and the output mechanics of generative systems. Practical Theology: A proposed "Pneumatological Epistemology" for the digital age, focusing on communal discernment (diakrisis), relational accountability, and the "Fruit Test" (Galatians 5).

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.