The Thesis: The Landauer Wall
Intelligence is an energy-intensive process. The human brain operates at roughly 20 Watts. A cluster of H100s training a frontier model consumes megawatts. As we scale agentic systems, we approach a Thermodynamic Wall.
The Efficiency Equation
We model the Energy Cost per Token Et as a function of parameter count N and hardware efficiency η:
The BitNet Revolution
The shift from FP16 (Floating Point 16-bit) to INT1 (1-bit integer) weights has been the defining hardware shift of 2025. By eliminating multiplication in matrix operations and replacing it with addition, we reduce energy consumption by ~70%.
The Jevons Paradox of Inference
Efficiency does not lead to reduced consumption; it leads to increased usage. This is the Jevons Paradox. As inference costs drop towards zero (via BitNet and specialized ASICs), we will not see a reduction in energy use. Instead, we will see an explosion in "Agentic Density".
We will move from one-shot answers to agents that "think" for hours—generating millions of internal tokens to verify a single output. The grid demand will shift from Training Clusters (bursty) to Inference Swarms (continuous baseload).