AI's GPU obsession blinds us to a cheaper, smarter solution

GPUs have become the default hardware for many AI workloads, but this thinking has created a blind spot that's holding us back. GPUs are incredible at crunching massive numbers in parallel, perfect for training large language models or running high-speed AI inference. However, CPUs are still very capable and are being forgotten, which could be costing us time, money, and opportunity. CPUs aren't outdated and can be used for AI tasks, efficiently and affordably, if only we'd give them a chance. AI tasks include running smaller models, interpreting data, managing logic chains, making decisions, fetching documents, and responding to questions, which require flexible thinking and logic. CPUs are impressive at what they were designed for: flexible, logic-based operations. Autonomous agents, which use AI to complete tasks, can run on CPUs, and even inference can be done on CPUs, especially with smaller, optimized models. Decentralized compute networks, like DePINs, allow people to contribute their unused computing power, creating a global network that others can tap into, making it cheaper, scalable, and bringing computing closer to the edge. By shifting our thinking and using decentralized networks to route AI workloads to the correct processor type, we can unlock scale, efficiency, and resilience. It's time to stop treating CPUs like second-class citizens in the AI world and rethink how we scale AI infrastructure.