Reality Drift Working Paper (Early Note): Asymmetric Co-Cognition in Human–AI Systems

This working paper examines early patterns of asymmetric depth in human–AI co-cognition. It describes how a subset of users engage AI systems in sustained, high-bandwidth interaction—what is termed synthetic flow—where human intuition and machine compression reinforce one another in recursive feedback loops.Rather than treating this pattern as a stable class or identity category, the paper frames it as an emergent outcome of specific environmental and interaction conditions, including task structure, feedback density, and exposure to compression-driven tools. While such co-processing can enable accelerated learning, novel synthesis, and heightened pattern recognition, it also introduces risks such as semantic drift, over-alignment with machine logics, and uneven cognitive amplification.The paper is presented as an early exploratory note within the Reality Drift research program, intended to surface structural dynamics of co-cognition rather than assert fixed social categories or normative claims.Author’s Note (2026)This paper reflects an early attempt to describe differential depth in human–AI co-cognition. Subsequent work has shifted away from identity- or class-based framings toward environmental and structural explanations of uneven cognitive outcomes. The core mechanism—recursive co-cognition under conditions of compression—remains central, but the framing used here should be read as provisional.