Small Talk, Big Impact: The Energy Cost of Thanking AI

2026-01-29

AI Summary

This study quantifies the additional energy consumption incurred when users employ polite expressions such as “thank you” during interactions with large language models (LLMs). Leveraging real-world dialogue data and fine-grained energy monitoring, it introduces everyday politeness markers as a controllable proxy variable to systematically analyze how input/output length and model scale influence inference energy usage. The findings reveal that seemingly innocuous, brief courteous phrases can collectively generate a substantial carbon footprint at scale, thereby offering critical quantitative insights and actionable pathways for designing more energy-efficient and sustainable AI systems.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Being polite is free - or is it? In this paper, we quantify the energy cost of seemingly innocuous messages such as ``thank you''when interacting with large language models, often used by users to convey politeness. Using real-world conversation traces and fine-grained energy measurements, we quantify how input length, output length and model size affect energy use. While politeness is our motivating example, it also serves as a controlled and reproducible proxy for measuring the energy footprint of a typical LLM interaction. Our findings provide actionable insights for building more sustainable and efficient LLM applications, especially in increasingly widespread real-world contexts like chat. As user adoption grows and billions of prompts are processed daily, understanding and mitigating this cost becomes crucial - not just for efficiency, but for sustainable AI deployment.

Problem

• energy cost
• large language models
• sustainable AI
• user interaction
• politeness

Innovation

• energy consumption
• large language models
• sustainable AI
• user interaction
• carbon footprint