This paper proposes a novel conceptual framework for an early warning system based on deliberately exploiting relativistic time dilation between a space-based sensor platform and Earth's surface. Unlike existing prediction systems which collect present-moment data and extrapolate forward mathematically, this framework proposes engineering a machine whose proper time runs measurably slower than Earth's surface time through combined velocity-based and gravitational time dilation effects. This platform continuously receives numeric sensor data broadcast from Earth, accumulating a relativistically offset data buffer. Upon retrieval, this buffer combined with AI pattern recognition provides predictive state information from signals Earth has not yet fully processed. The system targets early warning of natural disasters, cosmic events, solar storms, and climate shifts with an explicit humanitarian motivation and ethical governance framework. ACKNOWLEDGEMENTS: The core conceptual idea in this paper — using relativistic time dilation as a deliberate data collection mechanism for humanitarian early warning — originated with the author through independent inquiry. The mathematical framework, scientific terminology, literature references, and written content were developed with substantial assistance from Claude, an AI assistant made by Anthropic (claude.ai, 2026). AI assistance in this work was used as a tool to formalise and express the author's original concept — similar to how an author might work with an editor or translator. The humanitarian motivation, ethical framework, and core intellectual direction remain solely the author's contribution.
Saurabh Mishra (Fri,) studied this question.