Are LLM Web Search Engines Sustainable? A Web-Measurement Study of Real-Time Fetching

Large language model (LLM) based answer engines offer real-time, conversational answers but raise concerns about scalability and web sustainability. Unlike traditional search that serves results from cached indices, these systems fetch pages anew for each query, creating redundant network traffic. We present the first measurement-driven study of commercial LLM answer engines, combining automated client-side tracing and controlled server-side audits. Analyzing ChatGPT and Claude across 1,000 queries, we find that both operate as meta-search layers heavily reliant on existing indices, fetching top-ranked pages with minimal caching. A human-equivalent cost model shows their per-query network footprint far exceeds that of human searchers, varying sharply by architecture. These results reveal the infrastructural burden of real-time fetching and motivate cooperative efficiency measures like shared caches, transparent retrieval standards, and publisher controls such as llms.txt, to make AI-augmented search more sustainable.