AWS, Google challenge CUDA as AMD, Intel cut AI switch costs

CUDA's moat was never silicon—it was a decade of developer lock-in encoded in a million lines of code. What AWS and Google are doing now reduces "switching chips" from a ground-up rewrite to a single import statement. This is the structural inflection where AI compute shifts from hardware binding to backend decoupling. Technically, once PyTorch's abstraction layer matures, the compiler stack—not the GPU—becomes the real battleground. Trainium and TPU inference throughput already hits 85-90% of A100, previously trapped in a "usable but untrusted" zone by ecosystem barriers. Once migration friction approaches zero, HBM and CoWoS packaging orders will see genuine multi-source competition, rewriting TSMC's (Taiwan, China) pricing leverage. The compliance angle is sharper: if CUDA gets added to export control lists, TorchNeuron and TorchTPU shift from optional to compliance mandate, elevating chip diversification from cost strategy to survival strategy. Nvidia's window is twelve months. If NIM microservices fail to rebuild inference-side lock-in, the 2026 multi-backend landscape becomes irreversible. CUDA's share drops from 95% to below 70%, and the valuation anchor shifts from "AI monopolist" to "premium GPU vendor." AMD and Intel's switch-cost cuts are the final piece—once hardware price gaps shrink below 15%, software ecosystem advantages are permanently erased.