Joules-to-GDP: How Much Economic Value Do AI Systems Produce per Unit of Energy?

An AI system can produce more economically valuable work while delivering less value for the energy it consumes. Yet existing evaluations usually assess economic value separately from the end-to-end energy required to produce it, leaving this tradeoff unclear. We therefore ask: **How much economic value does an AI system create per unit of energy consumed?** To answer this question, we formalize the economic value of an AI output on a task in two ways: (1) net dollar value, the output's dollar value minus AI-system execution cost, and (2) time saving, the expert's completion time minus the AI system's completion time. Dividing each by measured end-to-end energy then yields ***Net Value per Joule (NVJ)*** and ***Time Saving per Joule (TSJ)***, reported in USD/MJ and hours/MJ. We study 220 public GDPval tasks across 44 occupations contributing to gross domestic product (GDP), serving five open-weight models locally to measure energy directly and evaluating four accelerator types. Our analysis reveals three main findings. **First,** the occupation groups where AI systems succeed most often, deliver the greatest dollar value, or save the most time do not necessarily provide the greatest economic return per joule. For example, among the tested tasks, engineering work, such as CAD design, delivers about 10% more net dollar value per task than software development, such as building website features. Yet software development delivers about 27% more net value per joule. **Second,** GLM 5.2 delivers the highest average net dollar value among five evaluated models, at $72.27 per task using 0.430 MJ, equivalent in energy to about four hours of active laptop use. By contrast, Gemma 4 31B delivers 56% of that net dollar value using just 23% of the energy, achieving approximately 2.5 times the NVJ and 2.8 times the TSJ. **Third,** across four single-accelerator deployments, Qwen3.6-27B's NVJ varies by a factor of 4.32 and TSJ by 9.27, with B200 leading both. One and eight H100s also deliver similar net dollar value, but eight reduce completion time by 22% while using 3.24 times the energy. Together, these findings show that capability, cost, and speed alone are insufficient to identify where AI systems deliver the greatest economic return per joule. Our evaluation framework makes output value and expert time saving under an energy budget explicit objectives for improving models, accelerators, and software, while offering a new basis for assessing AI's potential economic effect on occupational work.