AI assistants are adopted for speed, and their energy is invisible to the team using them. For the programming tasks studied, LLM-assisted generation had nearly 33 times the carbon footprint of the manual approach, and the gap widened as tasks got harder.
The fix you can use today
Use assistants where they clearly pay back, not by reflex.
Break hard tasks into simpler steps with a person in the loop before generating.
Include assistant usage in your team's energy and cost reporting.
GreenCode researchers found that AI-assisted code had nearly 33 times the carbon of hand-written code.
Tools that help
EnergyBench (GreenCode researchers): an MIT-licensed benchmark that scores LLM-generated code for correctness and for the energy it actually uses
GreenCode Constitution skill (GreenCode researchers): open-source energy principles and a catalogue of 37 energy anti-patterns that an AI coding agent can follow to find and fix waste like this
CodeCarbon: a Python package that estimates the energy and CO2 of a run from hardware power and regional grid intensity
How GreenCode helps
GreenCode helps tackle this by setting its own AI footprint against the saving it makes. It measures reductions as real totals, so a saving can be banked rather than spent. More on this.
