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

A paper posted to arXiv on 22 October 2026 by Amirreza Zeinali introduces "Joules-to-GDP," a framework measuring Net Value per Joule (NVJ) and Time Saving per Joule (TSJ) for AI systems, applied to 220 GDPval tasks across 44 occupations. Among five tested models, GLM 5.2 delivered the most net value at $72.27 per task on roughly four hours of active laptop energy, while Gemma 4 31B reached 56% of that value on 23% of the energy, for 2.5x the NVJ and 2.8x the TSJ. The paper also found eight H100s use 3.2x the energy of one GPU to cut completion time by only 22% at similar net value, and that GLM 5.2's NVJ falls 70% and TSJ 65% when measured against work by experts who use AI.

Computer Science Machine Learning Submitted on 22 Oct 2026 v1 https://arxiv.org/abs/2610.30858v1 , last revised 2 Nov 2026 this version, v2 Title:Joules-to-GDP: How Much Economic Value Do AI Systems Produce per Unit of Energy? View PDF https://arxiv.org/pdf/2610.30858 HTML experimental https://arxiv.org/html/2610.30858v2 Abstract:AI systems are increasingly used to complete economically valuable tasks in various professions, such as software engineering and finance. This motivates the question: how much economic value does an AI system produce per unit of energy? To answer it, we introduce Joules-to-GDP, a framework that measures output dollar value minus AI execution cost per unit of energy, which we call Net Value per Joule NVJ , and expert time saved per unit of energy, or Time Saving per Joule TSJ . We then apply the framework to 220 GDPval tasks across 44 occupations. Four findings stand out. First, the most valuable work is not always the most efficient. For example, engineering work such as CAD design yields about 10% more net value per task than software development such as building websites, yet the latter yields about 27% more per joule. Second, defaulting to the most capable model can mean less total value from the same energy budget. Of the five tested models, GLM 5.2 delivers the most net value, $72.27 per task on average, using the energy of about four hours of active laptop use, while Gemma 4 31B achieves 56% of that with 23% of the energy, for 2.5x the NVJ and 2.8x the TSJ. Third, for the same model, NVJ and TSJ vary widely across different accelerators, and adding GPUs can raise energy use for modest gains: eight H100s use 3.2x the energy of one to cut completion time by only 22%, at similar net value. More capable models and more hardware must therefore earn their extra energy with enough gains in output value. Finally, the work of experts who use AI is harder for AI to match than that of experts working alone. Measured against it, GLM 5.2's NVJ falls by 70% and its TSJ by 65%. Taken together, these findings justify economic return per joule as an objective for selecting professional tasks and improving language models and accelerators, and as a basis for examining AI's effect on the economy Submission history From: Amirreza Zeinali view email https://arxiv.org/show-email/c359684a/2610.30858 Thu, 22 Oct 2026 07:35:57 UTC 8,268 KB \ v1\ https://arxiv.org/abs/2610.30858v1 v2 Mon, 2 Nov 2026 07:56:20 UTC 5,964 KB Additional Features Current browse context: cs.LG References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .