To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime.
That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators.
Hyperscalers and AI-native companies building custom XPUs must consider not just XPU design, but the design and development of the entire AI platform, including scale-up and scale-out networking, rack-scale architecture, production factory software and a robust supplier ecosystem.
At AI factory scale, this path is complex and costly, and represents a fundamental obstacle to getting XPUs to market quickly.
Breaking the constraint means combining custom XPUs with proven, mature infrastructure — allowing builders to focus innovation where it matters most while harnessing established technology for the rest.
