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This is great - it's about time someone updated the discourse on LLM energy usage to reflect that coding agents use massively more prompts than occasional questions to ChatGPT Simon estimates that a day of coding agent usage comes out close to the energy needed to run a dishwasher

I'm wondering if the end goal should not be to scale down the LLMs to the extend of them running competently on a standard dev laptop and having the inference go there. This way, you still sell a product license (cursor and the likes), model upgrades, but then defer the energy cost on the end user

The problem there is that we've seen the capability difference between the best available models (Opus 4.5, GPT-5.2) and the best ones you can realistically run on a laptop - the ~30B Qwens, GLM-4.7 Flash etc The competitive open weight models - full GLM-4.7, Kimi K2 - need >600GB of RAM

Maybe in a few years time 1TB of GPU-accessible RAM will become affordable for developer laptops, at which point we may get local models that compete with today's frontier models