Sustainable AI Assistance Through Digital Sobriety

TL;DR

This paper explores social strategies to enhance AI sustainability by reducing unnecessary AI assistance, highlighting that nearly half of software development prompts are avoidable and suggesting interface nudges as potential solutions.

Contribution

It introduces a social perspective on AI sustainability, analyzing user prompts to identify unnecessary requests and proposing behavioral interventions.

Findings

Nearly 50% of AI assistance queries are unnecessary.

Factoid information retrieval is the largest source of avoidable requests.

Lower-cost alternatives could replace a significant portion of AI assistance.

Abstract

As AI assistants become commonplace in daily life, the demand for solutions that reduce the cost of inference without sacrificing utility is increasing. Existing work on AI sustainability frequently emphasizes hardware and software optimizations; however, there may be comparable value in social approaches that shape user behavior and discourage unnecessary use. In this study, we operationalize sustainability in terms of energy-efficiency and analyze a publicly sourced sample of prompts where AI is used for assistance in software development. Using this categorization, we find that nearly half of the observed queries can be considered unnecessary relative to their expected benefit. We further observe that factoid-style information retrieval constitutes the largest share of unnecessary requests, suggesting that a meaningful portion of everyday AI usage may be replaceable with lower-cost alternatives (e.g., conventional search or local documentation). These findings motivate a closer examination of how, why, and when AI systems are invoked, and what norms or interface-level nudges might reduce avoidable demand. We conclude with a call to replicate and extend this preliminary analysis and to pay greater attention to the social dimension of AI sustainability.