The Entropy Economy: A New Paradigm for Carbon Reduction and Energy Efficiency for the Age of AI

We introduce a new paradigm for minimization of carbon and maximization of energy efficiency: The Entropy Economy. Our approach addresses the predicted exponential rise in energy consumed by compute within the next decade and proposes Energy Aware Machine Learning (EAML) together with grid architectures and distributed High Performance Compute (HPC) infrastructure to jointly optimize learning, energy efficiency, and disposition of waste heat. We introduce a "Kolmogorov Learning Cycle", that enables characterization of the efficiency of the learning cycle, and assert that the precious resource to be conserved in the age of AI is entropy: maximizing the entropy reduction (in learning) while minimizing entropy flow loss (through thermodynamic inefficiency) to minimize carbon production, maximize energy efficient learning, and stabilize the grid. We present straw man case studies and initial EAML results showing how the Entropy Economy can reduce carbon reduction while leveraging trades between Machine Learning Model Quality, Energy Cost and throughput.