This work investigates the potential of the waste hierarchy from the EU's Waste Framework Directive, which suggests five different measures for how to manage waste: prevention, reuse, recycling, recovery, and disposal, and calls to attention that prevention of unnecessary use of LLMs carry huge potential for lowering the climate impact of the models.
Abstract
Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy efficiency of these models have not translated into reduced consumption due to rebound effects such as Jevons Paradox - that increased efficiency drives increased use. There is therefore a need for additional measures to solve this problem. We suggest that one possible way forward is to use life cycle thinking, and view LLMs as products that can become waste. With this perspective, we investigate the potential of the waste hierarchy from the EU's Waste Framework Directive, which suggests five different measures for how to manage waste: prevention, reuse, recycling, recovery, and disposal. We examine how these measures can inform and motivate new types of thinking and approaches to reducing LLM waste and their environmental impact in general. Applying the waste hierarchy to LLMs highlights that preventing waste is essential for reducing the models'environmental impact, mainly because it reduces the need for training new models. Prevention can be achieved through many existing methods for reusing,"recycling", and"recovering"LLMs. Additionally, disposal can be important both for saving energy and for keeping a considerate attitude to the resources being spent on training LLMs. We also call to attention that prevention of unnecessary use of LLMs carry huge potential for lowering the climate impact of the models.
Large language models (LLMs) are rapidly reshaping software development, but their impact across the full software development lifecycle is underexplored. Existing work tends to focus on isolated activities such as code generation or testing, leaving open questions about how LLMs affect developers, processes, products, and the broader software ecosystem. We address this gap through semi-structured interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023. We treat these interviews as early empirical evidence and compare participants’ accounts with recent work on LLMs in software engineering, noting which early patterns persist or shift. Using thematic analysis, we organize our findings around four dimensions: people, process, product, and society. Developers reported substantial productivity gains from reducing mundane tasks, streamlining search, and accelerating debugging, but also described a productivity-quality paradox: they frequently discarded generated code and shifted effort from writing code to critically evaluating and integrating it. LLM use was highly phase-dependent, with strong uptake in implementation and debugging but limited influence on requirements gathering and collaborative work. Participants developed new competencies to use LLMs effectively, including prompt engineering strategies, multi-layered verification, and security-conscious integration to protect proprietary data. They also anticipated changes in hiring expectations, team practices, and computing education, while emphasizing that human judgment and foundational software engineering skills remain essential. Our findings, consistent with evidence from large-scale studies, offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
Benyamin T. Tabarsi, Heidi Reichert, Sam Gilson et al.· Empirical Software Engineeri...· 0 citations
: The efficient adoption of Large Language Models (LLMs) in enterprises requires formalizing and systematically implementing LLM Operations (LLMOps), as it could reduce costs and ensure the reproducibility of LLM solutions. One of the critical phases of LLMOps is selecting a suitable LLM, as it can significantly affect a system’s performance, feasibility, and maintainability during the operational phase. As a result, uninformed decisions about model selection that do not align with business, functional, and non-functional requirements may severely impact subsequent stages of the LLMOps life cycle. To investigate the model selection phase, we conducted an extensive Systematic Literature Review (SLR) and synthesized its findings to derive a set of activities fundamental to this phase. These activities are presented in an end-to-end UML activity diagram. The proposed model aims to support the formalization of model selection processes, improve decision-making, and enhance the traceability and transparency of LLMOps practices. This work forms part of a broader research effort toward the formalization of the entire LLMOps life cycle, with the goal of facilitating scalable and reliable integration of LLMs in enterprise environments.
Maria Chernigovskaya, A. Nahhas, Christian Haertel et al.· Proceedings of the 21st Inte...· 0 citations
The use of large language models (LLMs) is being introduced into requirements, code generation, testing, maintenance, and documentation processes, but most IT organizations have yet to establish a practical and evidence-based methodology regarding when these tools are value added, when they become risky, and how to regulate their usage. The article is a synthesis of recent empirical research, surveys of developers, and guidance on the use of LLMs in software engineering and translates that information into a playbook of guidance that can be applied by practitioners. The primary contribution of the article is a staged adoption framework, which includes explore, pilot, and scale, supported with lightweight survey templates, small-task assessment designs, and accept/edit/reject logging practices that organizations can adopt to produce their own context-specific evidence. The objective is to facilitate disciplined, open-minded adoption of LLMs in actual software engineering environments.
: Software is used in our modern world to increase automation in almost all areas: From simple calculations to complex tasks, such as self-driving vehicles or coding agents based on artificial intelligence. While this automation increases efficiency and convenience for the users, it has an often overlooked negative impact on the environment, primarily caused by the severe energy and water consumption. Various approaches to reduce this impact and to develop and operate software more sustainably were developed in recent years. However, the wide adoption of these approaches is currently missing due to a lack of knowledge about green software development, operations, and usage. Hence, a common knowledge base must be established that facilitates applying green computing solutions. Patterns are a well-known concept to document and share knowledge about proven solutions to commonly recurring problems in an abstract manner. Therefore, they are also promising for documenting green computing solutions and best practices and to use them for reference or educational purposes. However, there exists no pattern language documenting and connecting relevant knowledge about green software development, operation, and usage. In this paper, we envision a pattern language for green computing, covering the various phases of the software lifecycle in which green computing solutions can be applied, as well as important application areas, such as artificial intelligence or cloud computing.
Martin Beisel, Benjamin Weder· Proceedings of the 21st Inte...· 0 citations
AI model lifecycles are commonly understood as a series of technical and organizational processes. Yet once a model enters sustained use, subsequent changes can also affect established user practices and user value. Using the Keep4o movement around GPT-4o as a case, this study examines post-deployment AI model lifecycle issues from the user side. We collected 61,846 public original posts on X from August 2025 to March 2026 and, using a systematically developed coding framework and LLM-assisted content analysis, analyzed discussion themes, users'reasons for wanting to keep GPT-4o, and the specific claims they made. Findings show that the Keep4o discussion extended well beyond continued access to the model itself. It covered concrete experiences of use, model behavioral characteristics and how they changed, and management issues across different stages of the model lifecycle. Reasons for keeping GPT-4o reflected interactional and relational value formed through long-term use, as well as judgments about the adequacy of replacement and the reasonableness of related decisions. The corresponding claims further reflected users'specific expectations for model lifecycle arrangements and governance. Overall, the call to"keep GPT-4o"brought together different judgments about user value and governance concerns. These findings suggest that technical version succession does not necessarily amount to effective replacement on the user side. Post-deployment AI model lifecycle management therefore needs to consider whether established user value can be carried forward and how model changes affect actual use. This study thus provides user-side empirical evidence for AI model lifecycle management. It further shows that user experience can provide important information for identifying post-deployment impacts and should be incorporated into lifecycle evaluation and decision-making.
Systems engineering has historically provided an effective framework for managing large-scale acquisitions. Yet, as systems grow in size, complexity, and timeframe, traditional approaches are becoming less effective. Accelerating rates of change and high levels of uncertainty reduce the long-term value of acquired systems, while the scale of modern programs increases costs, coordination demands, and timelines — heightening the risk of obsolescence before delivery. These pressures raise fundamental questions about the continued viability of conventional acquisition practices. In response, tools such as Model-Based Systems Engineering (MBSE), Agile Systems Engineering (AgileSE), and digital engineering have been introduced. While these provide incremental performance gains, they do not resolve the core challenge: how to manage acquisition in environments where predictability and uncertainty coexist. Doing the same things faster is not enough; a shift in mindset and method is required. This paper argues that ontology-driven approaches provide the foundation for that shift. Ontologies — machine-readable representations of concepts, relationships, and constraints — create a semantic backbone that integrates models, data, and stakeholder perspectives. By embedding ontologies into acquisition systems, organizations can simultaneously bridge predictive and adaptive paradigms, enhance traceability and interoperability across technical, financial, and operational domains, and enable automated reasoning to expose dependencies, conflicts, and opportunities. The result is an acquisition ecosystem that is not only more coherent and collaborative but also adaptive to the dynamic conditions of the 21st century. Many of the elements needed for this transformation already exist; the critical step forward is adopting ontologies as the integrative layer that unites them.
P. de Haan, Mahmoud Efatmaeshnik, Ady James· Proceedings of the INCOSE AO...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
