The AI-Induced Subjectivity Crisis Series: A Series Introduction

The AI-Induced Subjectivity Crisis Series is a fifteen-paper philosophical investigation of how large language models (LLMs) structurally transform human cognition, subjectivity, and the epistemological infrastructure of civilization. Against the three dominant research paths on AI—labor displacement, capability benchmarks, and risk governance—the series argues that the more consequential question concerns the cognitive architecture that receives AI outputs, which is itself being reshaped by sustained interaction with a new class of entity. The argument develops across three interlocking layers: a mechanism map of individual-level cognitive effects (Papers 1–8), an ontological and epistemological foundation establishing why these mechanisms are structurally necessary rather than contingent (Papers 9–11), and an epistemological-pivot-to-application layer tracing civilizational-scale consequences, reframing the alignment problem as a category error, and reconstructing the question of subjectivity itself (Papers 12–15). The series is non-intentionalist throughout, operates by structural-philosophical rather than empirical-causal analysis, deploys six distinct modal registers for different claim types, and acknowledges its own foundational premise as boundary-contained. This introduction provides (i) the motivating question, (ii) a layered architectural map, (iii) ten core original concepts, (iv) seven longitudinal argumentative threads, (v) recommended reading paths for different reader interests, (vi) methodological notes, and (vii) citation information for all fifteen papers.