Date of Publication December 22 2025

Tracing the historical shift from symbolic AI and information filtering systems to large-scale gener
ative transformers, we argue that [ LLMs are not epistemic agents but stochastic pattern-completion
systems, formally describable as walks on high-dimensional graphs of linguistic transitions rather
than as systems that form beliefs or models of the world ]. By systematically mapping human and ar
tificial epistemic pipelines, we identify seven epistemic fault lines, divergences in grounding, parsing,
experience, motivation, causal reasoning, metacognition, and value. We call the resulting condition
Epistemia: a structural situation in which linguistic plausibility substitutes for epistemic evalua
tion, producing the feeling of knowing without the labor of judgment. We conclude by outlining
consequences for evaluation, governance, and epistemic literacy in societies increasingly organized
around generative AI.