The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth

TL;DR

Generative AI's ability to produce convincing synthetic content risks eroding shared trust, verification practices, and the concept of truth, leading to societal challenges in distinguishing reality from fabrication.

Contribution

This paper formalizes the concept of synthetic reality, expands a taxonomy of GenAI harms, and proposes a comprehensive mitigation framework addressing epistemic security.

Findings

GenAI enables layered synthetic realities affecting content, identity, and institutions.

Recent risk cases include fraud, election interference, and harassment.

Mitigation strategies involve provenance infrastructure and institutional redesign.

Abstract

Generative AI (GenAI) now produces text, images, audio, and video that can be perceptually convincing at scale and at negligible marginal cost. While public debate often frames the associated harms as "deepfakes" or incremental extensions of misinformation and fraud, this view misses a broader socio-technical shift: GenAI enables synthetic realities; coherent, interactive, and potentially personalized information environments in which content, identity, and social interaction are jointly manufactured and mutually reinforcing. We argue that the most consequential risk is not merely the production of isolated synthetic artifacts, but the progressive erosion of shared epistemic ground and institutional verification practices as synthetic content, synthetic identity, and synthetic interaction become easy to generate and hard to audit. This paper (i) formalizes synthetic reality as a layered stack (content, identity, interaction, institutions), (ii) expands a taxonomy of GenAI harms spanning personal, economic, informational, and socio-technical risks, (iii) articulates the qualitative shifts introduced by GenAI (cost collapse, throughput, customization, micro-segmentation, provenance gaps, and trust erosion), and (iv) synthesizes recent risk realizations (2023-2025) into a compact case bank illustrating how these mechanisms manifest in fraud, elections, harassment, documentation, and supply-chain compromise. We then propose a mitigation stack that treats provenance infrastructure, platform governance, institutional workflow redesign, and public resilience as complementary rather than substitutable, and outline a research agenda focused on measuring epistemic security. We conclude with the Generative AI Paradox: as synthetic media becomes ubiquitous, societies may rationally discount digital evidence altogether.