The paper introduces A-LEMS, a framework that measures AI energy consumption at the goal level rather than per model invocation, defining Energy per Successful Goal (EpG) as total workflow energy divided by successfully completed goals. It also proposes the Orchestration Overhead Index (OOI) to quantify the extra energy cost of agentic systems compared to linear execution, revealing significant overheads across various hardware platforms and task families. The study demonstrates that traditional per-inference metrics underestimate the true energy cost of completing user goals in agentic AI workflows.
By Deepak Panigrahy, Aakash Tyagi, Stephen Abkin, David Kebo Houngninou, Rabi Mahapatra
arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.
By Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e
arXiv:2610.06824v2 Announce Type: replace
Abstract: We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick...
By Oliver Jaffe, Dane Sherburn
The paper proposes a token‑based method for including purchased AI inference in corporate Scope 3 greenhouse gas inventories, offering a structured four‑term factor that accounts for serving electricity, embodied hardware, data‑centre construction, and disclosed training runs. It provides an open dataset with cradle‑to‑gate boundaries, four tiers of activity data, and a rule to map model identifiers and regions to specific factors, revealing up to twelve‑fold variation between model classes and nine‑fold variation between regions. Example calculations show annual emissions of 8.5 kg CO₂e for a 200‑person firm and 2.1–8.3 t CO₂e for developers using mid‑class or frontier‑class models, respectively.
By Guillermo Llopis-Garcia (SOMA AI, Barcelona)
arXiv:2610.14389v1 Announce Type: new
Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86...
By Zeyu Yang, Wes Armour
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
By Jie Wu, Ming Gong, Feixiang Cheng, Qinqin Zhao
The paper presents a systematic study of token consumption in AI agents performing coding tasks. It finds that agentic tasks are far more expensive—about 1000 times more tokens than code reasoning or chat—primarily due to input tokens, and that token usage varies wildly, with accuracy peaking at moderate costs. Models differ significantly in efficiency, and current frontier models cannot reliably predict their own token usage, often underestimating it.
By Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei
The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
By Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem Ullah
arXiv:2607. 06906v1 Announce Type: new Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value.
By Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally, Artem Yavorskyi, Chris Nickerson, Daniel Rica, Emily DuGranrut, Felix Leung, Garrett Prince, Grace Barnett, Heath Robinson, Hosain Al Ahmad, Jesse Resnick, Juan Carlos Farah, Jyothi Swaroop Meruga, Leonid Kuznetsov, Luke Gorham, Marie Schmoll, Michael Paciullo, Saumya Das, Sharath Sheripally, Tommy Griscom, Mykyta Osadchyi, Neha Mantri, Nick Westrum, Olivia Benowitz, Parikshith Kulkarni, Radik Chernyshov, Rakshith Vasudev, Rohith Nadimpally, Vikas Gangadevi, Waseem AlShikh
arXiv:2510. 22052v2 Announce Type: replace Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies.
By Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen, Thomas L. Griffiths, Hongjing Lu, Diana Marculescu, Ashiqur Rasul, Wenrui Xu, Keshab K. Parhi
Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway.
arXiv:2608. 05172v1 Announce Type: cross Abstract: The task-based framework in economics models occupations as bundles of tasks.
By Stephane Hatgis-Kessell, Tom\'as Aguirre, Alexander Wan, Rishi Bommasani