Position: AI Efficiency Gains Must Be Quantitatively Evaluated Against Rebound Effects

Efficiency gains in artificial intelligence are often interpreted as environmental progress, but this view is incomplete because AI usage may exhibit rebound effects: lower unit costs can induce larger models, more training runs, stricter latency requirements, wider deployment, and higher inference volume. Although rebound effects in AI have begun to receive attention, they are still studied mostly through qualitative or scenario-based analyses. This paper takes the position that AI efficiency gains must be quantitatively and mathematically analyzed against rebound effects. We support this position with a simple game-theoretic model showing that an efficiency improvement can inadvertently increase the total energy consumption. The model further shows that rebound can appear even in a simple setting and in forms not captured by demand-reduction arguments alone: the amount of requested work may decrease while stricter deadlines make the computation more energy intensive.