Generative AI systems are increasingly integrated into knowledge workflows, yet their fluency and speed can obscure a critical threat: the erosion of epistemic integrity. Users may imitate AI outputs, converge prematurely, or misinterpret confidence as understanding—producing brittle knowledge loops that collapse under scrutiny. This paper introduces a dual-friction framework to address these risks. The Memetic Design Loop models how mimetic desire and semantic drift unfold across human–AI exchanges, highlighting the cognitive vulnerabilities induced by fast, confident AI. To counteract this, we propose design interventions that embed intentional slowness, divergence, and reflective scaffolding into AI-mediated reasoning environments. Drawing from epistemic friction theory, mimetic philosophy, and regenerative design principles, we present a methodology that resists premature closure by foregrounding attentional rhythm, deliberative pause, and conceptual variation. We also offer a two-layered evaluative lens focused not on performance, but on epistemic resilience: the system’s capacity to sustain plurality, preserve semantic coherence, and foster reflective trust. AI systems do not merely process information—they shape the cognitive environments in which decisions are made, beliefs are formed, and knowledge is legitimized. As such, they must be studied not only as technical tools, but as socio-technical systems: assemblages of algorithms, interfaces, cultural norms, institutional logics, and human desires. This paper contributes to an interdisciplinary discourse at the intersection of design theory, epistemology, and social science by articulating how AI interfaces co-produce epistemic authority, and how design choices encode political, ethical, and cognitive consequences. We position friction not just as a technical affordance, but as a cultural and epistemic intervention—opening space for dissent, pluralism, and attentional agency in systems increasingly optimized for speed and conformity. In doing so, the work aligns with ongoing debates in science and technology studies (STS), critical AI ethics, and the sociology of knowledge infrastructures, offering both a critique and a design philosophy for preserving epistemic sovereignty in AI-augmented societies.