Why Device-Centric Computing Architectures Become Non-Competitive Under Context-Aware Adaptive Computation

Traditional computing architectures are fundamentally device-centric, relying on explicit user interfaces, discrete applications, and persistent operating system layers to mediate interaction. These systems assume that computation is invoked through deliberate user action—such as queries, taps, or navigational input—executed within predefined software boundaries. This paper introduces Presence-Centric Computation (PCC) as an architectural successor enabled by Adaptive Computing (AC). In PCC, computation is no longer initiated by user interaction with a device, but by continuous inference of contextual, environmental, temporal, and biometric signals. Functionality is dynamically synthesized and executed across distributed compute substrates without reliance on static applications, persistent interfaces, or centralized operating systems. Through comparative architectural analysis, this paper demonstrates that device-centric models incur increasing latency, inefficiency, and cognitive overhead as contextual complexity rises. Conversely, presence-centric systems scale proportionally with context richness, rendering interface-bound interaction models structurally non-competitive. The transition is not presented as a behavioral preference shift, but as an inevitable outcome of system constraints, execution efficiency, and interaction economics.