Synthetic Realness describes a contemporary perceptual dynamic in which polished, curated, or artificially generated signals can appear more coherent or credible than unfiltered lived experience. Distinct from hyperreality (Baudrillard’s focus on simulation) or performance theory (Goffman’s analysis of social roles), Synthetic Realness focuses on how distinctions between authentic and artificial signals become less salient in everyday mediated environments. This paper, part of the Reality Drift project, examines how coherence bias and algorithmic optimization can privilege fluency and surface consistency over contextual grounding. Drawing on the Meaning Equation (Meaning = Context × Coherence), it considers how high coherence combined with reduced context may shift perceptions of credibility across AI systems, social media feeds, and institutional dashboards. Rather than treating authenticity as a fixed property, this work approaches it as a perceptual judgment shaped by optimization pressures and representational conditions in algorithmic systems. Part of Reality Drift framework (2023-2026) by A. Jacobs.