Source: SiliconANGLE
Microsoft's pivot away from Nvidia-only GPU stacks toward heterogeneous silicon—mixing AMD, custom accelerators, and other processors—reflects the hard economics of AI scaling at hyperscale. Nvidia's supply constraints and pricing power make single-vendor dependence unsustainable when training bills run into billions. This forces chip vendors to prove performance-per-dollar across specific workloads rather than win by default. The winner is whoever can deliver tooling, software libraries, and integration support that makes switching costs low enough for Azure's engineers to rotate between vendors mid-pipeline, not the chip with the fastest specs.