The artificial intelligence (AI) race is no longer just about models. It is now deeply about infrastructure, cost, and control. Over the past few years, as enterprises rushed to build and deploy AI systems, one company quietly dominated the backbone of this revolution: Nvidia. Its graphics processing units (GPUs) became the default engine powering everything from large language models to enterprise AI workloads. Until now, most AI chips, including earlier TPUs, were designed to handle both training and inference. Training is the process of building AI models, while inference is about running those models in real-world applications. As AI adoption scales, these two workloads are diverging in both complexity and cost. Google’s decision to create dedicated processors for each reflects this shift. Will Google really close the gap with Nvidia?

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