Source: SiliconANGLE
The economics of AI adoption hinge on moving inference—the computational work of running trained models—from expensive, centralized infrastructure to cheap, distributed systems. If inference remains costly and siloed, AI stays a luxury good for large enterprises. Commoditization forces broader adoption and enables use cases where real-time, on-device, or edge inference becomes economically viable. Whoever owns the infrastructure layer that achieves commodity-grade inference efficiency—through specialized chips, optimized software, or novel architectures—captures enormous leverage over which AI applications reach market.