The CO2ST of Agentic AI in Research

Abstract

Generative AI has been rapidly adopted into multiple research practices. In this paper, we focus on the newest trend, so-called agentic AI. We calculate the carbon footprint of using agentic AI in a simulation of an academic literature review. We show that the energy consumption increases proportionately with the level of agency, resulting in a carbon footprint about three times larger when moving from low agency to a multi-agent process. The most co2stly step of this system is processing large quantities of text by the LLM, and resource consumption grows proportionally to the model size. Finally, we show the energy consumption of the CPU and GPU during processing and illustrate that the use of LLMs versus other software largely drives the power consumption difference. We demonstrate that researchers may make environmentally sustainable choices in research practices by making deliberate choices about agentic AI use.1