Generative AI models are increasingly used across various modalities, including text, images, audio, and video. Estimating the computational cost of generating con- tent is crucial for optimizing performance and resource allocation. This paper intro- duces the Cost-Per-Byte Principle: C = T × I, a universal law that relates the cost of content generation to per-byte generation time and per-second inference cost. We derive the per-byte generation time analytically based on the model’s computational requirements (FLOPs) and the hardware’s performance (FLOPs per second). By estab- lishing mappings between bytes and different content units (characters, pixels, samples, frames), we provide a modality-agnostic framework for cost estimation. We present a rigorous proof of the principle’s validity and apply it to estimate the costs of current popular models, using publicly available evidence to verify the accuracy and usefulness of this principle.