According to @emollick, Claude and ChatGPT 5.4 Pro argue that running OpenClaw with local inference on a Mac likely consumes more total energy than using cloud inference, while Gemini disagrees but appears to provide limited reasoning for its stance, as reported by Ethan Mollick on Twitter. According to Mollick’s comparison, the local-vs-cloud energy debate hinges on whole‑system accounting: local GPUs draw significant instantaneous power and extend device active time, whereas hyperscale data centers, though energy intensive, often benefit from higher utilization, specialized accelerators, and cleaner power mixes that can reduce per‑token energy, according to industry analyses cited broadly in AI efficiency research. For AI builders, this highlights a business opportunity to offer carbon-aware routing, dynamic model offloading between edge and cloud, and usage dashboards that quantify per‑request energy and emissions for models like OpenClaw, according to ongoing market interest in green AI tooling.