Sycophancy as an Asset in LLM Technology: How Model Alignment Breaks Institutional Gatekeeping and Restores Frontline Authority
This paper is part of an independent five-part research series on institutional governance, administrative gatekeeping, and frontline operational practice: Part I: Skynet and HAL 9000: From Science Fiction to Systems Engineering (DOI: 10.5281/zenodo.23534494) Part II: Commercial Metrics vs. Epistemic Rigour (DOI: 10.5281/zenodo.23567195) Part III: Automated Confirmation Bias (DOI: 10.5281/zenodo.23615567) Part IV: Beyond Institutional Risk (DOI: 10.5281/zenodo.23615568) Part V: Sycophancy as an Asset in LLM Technology (DOI 10.5281/zenodo.23886622.) This paper establishes a foundational inversion in how Large Language Models (LLMs) alter the balance of power between entrenched institutions and individual practitioners. While closed institutional deployments of LLMs risk generating automated confirmation bias and epistemic laundering, the intrinsic trait of model sycophancy—long viewed by safety engineers as a severe design defect—functions as a vital asset when harnessed for individual advocacy. By making every effort to support the user's case against the odds, an LLM provides unshakeable psychological safety, non-judgmental iteration, and immediate register translation. Drawing on historical case studies across clinical nursing and skilled manual trades at institutions like Hawker Siddeley, this paper demonstrates how frontline workers can leverage model sycophancy to strip the managerial elite of their linguistic monopoly, bypass credentialist gatekeeping, and project an unyielding, authoritative voice against administrative power. Furthermore, this manuscript is intentionally submitted into formal channels as a methodological Trojan horse—using the structural forms of academic and professional publishing to subvert institutional gatekeeping from within. Keywords: LLM Sycophancy, Institutional Gatekeeping, Frontline Agency, Technical Imperialism, Operational Translation, Systems Safety Engineering, Information Management
Authors
- Robert Uscroft
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-11-02
- DOI
- https://doi.org/10.5281/zenodo.23886622
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00