LLM based chatbots have become central interfaces in technical, educational, and analytical domains, supporting tasks such as code reasoning, problem solving, and information exploration. As these systems scale, sustainability concerns have intensified, with most assessments focusing on model architecture, hardware efficiency, and deployment infrastructure. However, existing mitigation efforts largely overlook how user interaction practices themselves shape the energy profile of LLM based systems. In this vision paper, we argue that interaction level behavior appears to be an underexamined factor shaping the environmental impact of LLM based systems, and we present this issue across four dimensions. First, extended conversational patterns increase token production and raise the computational cost of inference. Second, expectations of instant responses limit opportunities for energy aware scheduling and workload consolidation. Third, everyday user habits contribute to cumulative operational demand in ways that are rarely quantified. Fourth, the accumulation of context affects memory requirements and reduces the efficiency of long running dialogues. Addressing these challenges requires rethinking how chatbot interactions are designed and conceptualized, and adopting perspectives that recognize sustainability as partly dependent on the conversational norms through which users engage with LLM based systems.

翻译：基于大语言模型（LLM）的聊天机器人已成为技术、教育和分析领域的核心交互界面，支持代码推理、问题解决和信息探索等任务。随着这些系统的规模化应用，可持续性问题日益凸显，现有评估多集中于模型架构、硬件效率和部署基础设施。然而，当前的缓解措施大多忽视了用户交互实践本身如何影响LLM系统的能耗特征。在本愿景论文中，我们认为交互层面的行为是影响LLM系统环境足迹却未得到充分研究的因素，并从四个维度展开论述：第一，冗长的对话模式会增加令牌生成量，推高推理计算成本；第二，对即时响应的期望限制了能源感知调度与工作负载整合的机会；第三，日常用户习惯以难以量化的方式累积形成持续运行需求；第四，上下文累积效应会提升内存需求，降低长程对话效率。应对这些挑战需要重新设计并概念化聊天机器人的交互模式，并采用新视角——认识到可持续性部分取决于用户与LLM系统交互时所遵循的对话规范。