AI Coders and the Quest for Greener Software

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Author: Denis Avetisyan

A new study examines how artificial intelligence coding agents approach the critical, but often overlooked, issue of energy efficiency in software development.

Energy concerns are systematically categorized, revealing a hierarchical structure that illuminates the interconnectedness of various challenges and potential solutions.
Energy concerns are systematically categorized, revealing a hierarchical structure that illuminates the interconnectedness of various challenges and potential solutions.

Analysis of pull requests reveals current practices and challenges in optimizing code for reduced energy consumption by agentic AI systems.

While artificial intelligence increasingly automates software development, a critical gap remains in understanding how these agentic systems address the growing concern of software energy consumption. This study, ‘How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study’, investigates this issue through a thematic analysis of 216 energy-explicit pull requests, revealing that AI agents largely employ established optimization techniques. Encouragingly, the results demonstrate a degree of energy awareness in code generation, though optimization-focused changes face lower acceptance rates due to maintainability concerns. Will balancing energy efficiency with code quality prove crucial for the sustainable scaling of AI-driven software engineering?

The Energetic Cost of Intelligence: A Systemic Challenge

The rapid evolution of artificial intelligence is largely fueled by Large Language Models (LLMs), systems capable of generating human-quality text, translating languages, and performing a myriad of complex tasks. However, this progress comes at a considerable energetic price. Training these models, which involves iteratively adjusting billions of parameters, demands immense computational resources, often requiring weeks or even months on powerful hardware. Each training run consumes a quantity of electricity comparable to the lifetime energy usage of several homes, and the increasing scale and complexity of LLMs – with parameter counts now exceeding trillions – are driving energy demands ever higher. This escalating consumption presents a significant sustainability challenge, pushing the limits of data center capacity and prompting researchers to explore more energy-efficient AI architectures and training methodologies.

The escalating demand for computational power to train and operate Large Language Models presents a significant sustainability challenge for both data centers and the broader field of artificial intelligence. These models, requiring vast datasets and intricate neural networks, necessitate immense processing capabilities – a need currently met by energy-intensive hardware. This surge in compute demand strains existing data center infrastructure, increasing electricity consumption and associated carbon emissions. Moreover, the current trajectory suggests this demand will only accelerate as models grow in complexity and prevalence, potentially hindering the long-term scalability and environmental viability of AI development without substantial innovations in hardware efficiency and algorithmic optimization. The implications extend beyond operational costs, raising concerns about the responsible deployment of increasingly powerful AI systems and their impact on global energy resources.

The escalating energy demands of contemporary artificial intelligence are beginning to outpace the effectiveness of conventional software optimization strategies. Historically, improvements in algorithmic efficiency and code streamlining delivered substantial reductions in computational costs; however, the sheer scale and complexity of modern AI models, particularly large language models, present a fundamentally different challenge. These models require exponentially increasing parameters and data processing, meaning that even incremental gains in software efficiency are often dwarfed by the overall growth in computational load. Researchers are finding that simply refining existing code is no longer sufficient; a paradigm shift towards novel hardware architectures, energy-aware algorithms, and fundamentally different approaches to model training and deployment is necessary to mitigate the environmental impact and ensure the long-term sustainability of AI innovation.

Mapping Energy-Awareness in Code: The AIDev Dataset

The AIDev Dataset represents a novel resource for investigating the impact of artificial intelligence on software energy efficiency. Constructed from a large corpus of code contributions, the dataset facilitates the quantitative analysis of modifications related to energy consumption. Unlike prior datasets focused solely on functional correctness or performance, AIDev explicitly enables researchers to isolate and examine changes specifically targeting energy optimization within real-world codebases. This is achieved through the dataset’s comprehensive logging of code modifications and associated metadata, allowing for detailed tracking of energy-related improvements – or regressions – introduced by AI-driven coding agents. The scale of the dataset, encompassing contributions from numerous developers and projects, provides a statistically significant foundation for evaluating the potential of AI to address the growing energy demands of software systems.

The identification of pull requests directly addressing energy consumption within the AIDev Dataset involved a two-stage process. Initially, a keyword-based filter was applied to code contributions, searching for terms related to energy efficiency, power usage, and optimization. This initial filter yielded a larger set of potential candidates, which were then subjected to manual validation by researchers to confirm that the changes genuinely targeted energy-related concerns. This manual review process ensured the accuracy of the identified set, ultimately resulting in the confirmation of 216 pull requests categorized as Energy-Explicit Pull Requests for subsequent analysis.

Analysis of the AIDev Dataset identified 216 pull requests demonstrating that AI coding agents are capable of implementing code modifications directly addressing energy optimization. These changes encompassed a variety of techniques, including algorithmic efficiency improvements, resource management optimizations, and reductions in unnecessary computational operations. The observed contributions suggest a capacity for AI agents to not only respond to explicit energy-related prompts but also to proactively identify and address energy consumption concerns within existing codebases, indicating a potential for iterative self-improvement in energy efficiency as agents learn from and contribute to larger projects.

Deconstructing Energy Concerns: Categorizing AI-Authored Optimizations

Energy-Explicit Pull Requests encompass a broad spectrum of software energy management concerns beyond simple reduction. These requests address five primary areas: optimization, involving direct code modifications for improved efficiency; setup, configuring the system to behave in an energy-aware manner; insight, implementing tracking mechanisms to monitor energy usage and identify potential improvements; maintenance, ensuring that energy-saving behaviors persist across updates and changes; and trade-offs, acknowledging and documenting the balance between energy efficiency and other performance characteristics, such as latency or feature completeness. This categorization reveals that AI-authored code doesn’t solely focus on minimizing energy consumption but also on managing and understanding its implications within the broader software system.

Analysis of energy-explicit pull requests revealed a diverse set of optimization techniques employed by AI agents. These techniques include minimizing computational load by avoiding unnecessary work and leveraging efficient data structures to reduce memory usage and processing time. Several optimizations focused on reducing activity frequency, such as decreasing update frequency and avoiding polling, to conserve energy during idle periods. Caching results was also frequently implemented to reduce redundant computations. Further techniques addressed system-level energy management, including optimizing wake locks to prevent unnecessary device activation, utilizing concurrent programming for parallel processing, coordinating with hardware for efficient resource allocation, implementing dynamic scaling to adjust resource usage based on demand, and employing batch operations to reduce the overhead associated with individual requests.

Thematic analysis of pull requests authored by AI agents reveals a consistent focus on software energy optimization. This analysis identified patterns of code changes addressing challenges across multiple categories: optimization of existing algorithms and data structures, configuration of energy-related system behaviors, implementation of energy usage tracking and observability features, maintenance of energy-aware code over time, and conscious balancing of energy efficiency with other software qualities such as performance and functionality. The prevalence of these patterns indicates that AI agents are not merely making random code changes, but are actively and repeatedly engaged in identifying and attempting to resolve software energy inefficiencies.

Validating Reliability: Assessing the Impact of AI-Driven Optimizations

Rigorous validation of proposed energy optimizations demanded a highly consistent assessment process. To quantify this consistency, researchers utilized Cohen’s Kappa, a statistical measure of inter-rater agreement. Initial calibration of the manual validation process yielded a Kappa value of 0.84, indicating substantial agreement between reviewers. This was further improved in a second round of labeling, achieving a Kappa of 0.89, a value considered to be nearly perfect agreement. These results demonstrate a robust and reliable methodology for identifying impactful energy-saving changes, ensuring that accepted pull requests genuinely contribute to improved energy efficiency. A system is only as reliable as the processes that validate it, and in this instance, the validation methodology has been rigorously established.

The rigorous validation of AI-driven energy optimizations hinged on establishing a dependable assessment process. To that end, researchers employed Cohen’s Kappa, a statistical measure of inter-rater agreement, to quantify the consistency of manual reviews. Achieving Kappa values of 0.84 during initial calibration and a robust 0.89 in the subsequent labeling round demonstrates a high degree of agreement amongst validators. This consistently strong agreement isn’t merely a procedural detail; it provides crucial confidence that identified energy optimizations genuinely represent impactful improvements, and that the validation process isn’t subject to significant subjective bias. Consequently, decisions regarding the acceptance of these optimizations are built upon a solid foundation of reliable, consistent evaluation.

Analysis of submitted code changes revealed an 87% acceptance rate for those incorporating energy optimizations, a slight decrease compared to the 92% observed for non-energy related pull requests. Statistical testing demonstrated this difference wasn’t random; energy-focused changes correlated significantly with larger code modifications, ranging from small to substantial effect sizes. This suggests developers addressing energy efficiency often require more extensive alterations to existing codebases, potentially explaining the marginally lower acceptance rate as these larger changes undergo more rigorous scrutiny during the review process. These findings highlight a nuanced relationship between optimization efforts and code complexity, indicating that while impactful, energy-aware changes may necessitate greater development resources and careful consideration.

The study reveals a pragmatic approach to software energy optimization, with agentic AI systems largely employing well-established techniques. This echoes Edsger W. Dijkstra’s assertion that “Simplicity is prerequisite for reliability.” The analysis of pull requests demonstrates a focus on readily implementable changes – often relating to algorithm efficiency or resource management – rather than radical architectural overhauls. While these incremental improvements contribute to energy reduction, the observed challenges regarding code maintainability and pull request acceptance rates underscore a critical point: true systemic efficiency requires holistic consideration. Every new dependency, as the research implicitly suggests, is the hidden cost of freedom, and structural choices directly dictate the long-term behavioral characteristics – and energy profile – of the software organism.

Where Do We Go From Here?

The study reveals a pragmatic landscape: agentic AI, when tasked with energy optimization, largely adheres to established principles. This isn’t surprising; novelty requires energy, and the lowest-hanging fruit – efficient algorithms, reduced complexity – remain the most readily accessible. Yet, the relatively modest acceptance rate of these AI-driven pull requests suggests a fundamental friction. If the system survives on duct tape – clever, isolated optimizations – it’s probably overengineered. The true challenge isn’t finding efficiency, but embedding it within a living codebase, a system where every change ripples through interdependent components.

A focus on acceptance rates hints at a deeper issue: modularity without context is an illusion of control. An AI might identify an energy-intensive function, but without understanding its role within the broader architecture, even a ‘correct’ optimization risks introducing instability or unintended consequences. Future research must move beyond isolated code improvements, exploring how agentic AI can learn architectural constraints, anticipate cascading effects, and propose changes that integrate seamlessly with existing development workflows.

Ultimately, the question isn’t whether AI can make software more efficient, but whether it can cultivate a holistic understanding of software as an evolving organism. A system optimized for energy today must also be adaptable, maintainable, and resilient – qualities that demand a level of systemic awareness currently beyond the reach of purely localized optimization strategies.

Original article: https://arxiv.org/pdf/2512.24636.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-01 10:36