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Brian is designing a keel cooler to dissipate waste heat from the electric motors on Delos 2.0, a custom aluminum catamaran under construction, and uses three AI tools to estimate the required heat-sink surface area.0:33 The motors run at roughly 90% efficiency, meaning a 25 kW motor generates 2,500 watts of waste heat that must be shed through the aluminum hull directly into seawater — no raw-water pump, just a closed coolant loop conducting heat through the hull skin.4:34 Brian queries DeepSeek, ChatGPT, and Google Gemini with identical parameters: 25 kW motor, 90% efficiency, aluminum keel cooler, seawater cooling.3:33 DeepSeek returns 0.22 square meters; Google Gemini returns 0.33 square meters; ChatGPT returns 1.25 square meters — more than five times the DeepSeek figure — apparently because ChatGPT chose a much lower heat-transfer coefficient.6:21 Brian cross-checks against published keel-cooler sizing for small diesel engines and finds the DeepSeek and Gemini figures more plausible.9:07 The motor manufacturer Combee and Electric Boat Company's Chris had confirmed a keel cooler is viable for this application.2:37 Brian concludes AI is useful for breaking down complex thermal problems and generating a starting estimate, but the assumptions embedded in each answer must be verified independently before any design decision is made.9:53

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