Building an always low-compute intelligence system. I’m building Buddy AI around a simple constraint: it always runs on low compute, regardless of task complexity. And so far language output is fully grounded. Instead of scaling models up for harder problems, the system is designed to stay lightweight and operate within strict compute limits at all times. The idea is that intelligence doesn’t have to mean heavier inference: it can mean better structure, routing, and efficiency under constraint. This matters because AI training and inference costs are exploding. Reports across the industry show frontier models costing tens to hundreds of millions to train, with inference becoming the dominant long-term expense at scale. Buddy AI explores the opposite direction: not bigger models, but always-low-compute systems that stay efficient by design. What are your thoughts on strict low-compute AI systems? submitted by /u/AguaTrading
Originally posted by u/AguaTrading on r/ArtificialInteligence
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