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This paper investigates the energy efficiency of AI assistants in software development by analyzing a dataset of user prompts. It finds that nearly half of the queries are unnecessary relative to their expected benefit, with factoid-style information retrieval being the largest contributor. The authors suggest that a significant portion of AI usage could be replaced with lower-cost alternatives, highlighting the importance of user behavior in AI sustainability.
Turns out, almost half of AI assistant queries in software development are unnecessary, suggesting we're over-relying on these tools for tasks better suited to simpler solutions.
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.
