Real-Time Immersive Telepresence as a Precursor to Adaptive Computing

Adaptive computing is often discussed as a future paradigm enabled by advanced inference, continuous context modeling, and autonomous system behavior. However, transitional systems already demonstrate the insufficiency of static content models without requiring full adaptive computation. This paper argues that high-fidelity, real-time immersive telepresence systems constitute a functional precursor to adaptive computing by exposing the limitations of static, pre-recorded, and non-responsive media. Through analysis of real-time immersion, network feasibility, and interaction latency, we show that static content models are already becoming an evolutionary dead-end. Telepresence systems do not yet implement adaptive computing, but they validate its trajectory by demonstrating that computation must increasingly respond to presence, timing, and state rather than fixed content delivery.