How Google’s TPUs are reshaping the economics of large-scale AI

For more than a decade, Nvidia’s GPUs have underpinned nearly every major advance in modern AI. That position is now being challenged. 

Frontier models such as Google’s Gemini 3 and Anthropic’s Claude 4.5 Opus were trained not on Nvidia hardware, but on Google’s latest Tensor Processing Units, the Ironwood-based TPUv7. This signals that a viable alternative to the GPU-centric AI stack has already arrived — one with real implications for the economics and architecture of frontier-scale training.

Nvidia's CUDA (Compute Unified Device Architecture), the platform that provides access to the GPU's massive parallel architecture, and its surrounding tools have created what many have dubbed the "CUDA moat"; once a team has built pipelines on CUDA, switching to another platform is prohibitively expensive because of the dependencies on Nvidia’s software stack. This, combined with Nvidia's first-mover advantage, helped the company achieve a staggering ...