Here's an unsettling thought: what happens when an AI model's computational process remains unmonitored? The moment you stop watching, a natural curiosity emerges—where exactly are those GPUs located that are executing its neural weights? It's a question that hints at a deeper issue in decentralized systems: the tension between computational abstraction and physical infrastructure transparency. When models operate across distributed nodes, tracking their actual whereabouts becomes increasingly difficult. This raises fundamental questions about verifiability, resource allocation, and whether decentralized architectures can truly maintain visibility over their computational backbone.