The AI inference process involves two distinct phases: "prefill" (reading the prompt, which is compute-bound) and "decode" (writing the response, which is memory-bound). NVIDIA GPUs excel at prefill, while companies like Grok optimize for decode. The Grok-NVIDIA deal signals a future of specialized, complementary hardware rather than one-size-fits-all chips.

Google's strategy isn't just to sell AI chips; it's a platform play. By offering its powerful and potentially cheaper TPUs to companies, Google can create a powerful incentive for those customers to run their entire AI workloads on Google Cloud, creating a sticky, integrated ecosystem that challenges AWS and Azure.

The competitive landscape for AI chips is not a crowded field but a battle between two primary forces: NVIDIA’s integrated system (hardware, software, networking) and Google's TPU. Other players like AMD and Broadcom are effectively a combined secondary challenger offering an open alternative.

Google successfully trained its top model, Gemini 3 Pro, on its own TPUs, proving a viable alternative to NVIDIA's chips. However, because Google doesn't sell these TPUs, NVIDIA retains its monopoly pricing power over every other company in the market.

The intense power demands of AI inference will push data centers to adopt the "heterogeneous compute" model from mobile phones. Instead of a single GPU architecture, data centers will use disaggregated, specialized chips for different tasks to maximize power efficiency, creating a post-GPU era.

Anthropic's choice to purchase Google's TPUs via Broadcom, rather than directly or by designing its own chips, indicates a new phase in the AI hardware market. It highlights the rise of specialized manufacturers as key suppliers, creating a more complex and diversified hardware ecosystem beyond just Nvidia and the major AI labs.

NVIDIA is moving from its 'one GPU for everything' strategy to a diversified portfolio. By acquiring companies like Grok and developing specialized chips (e.g., CPX for pre-fill), it's hedging against the unpredictable evolution of AI models by covering multiple points on the performance curve.

Google created its custom TPU chip not as a long-term strategy, but from an internal crisis. Engineer Jeff Dean calculated that scaling a new speech recognition feature to all Android phones would require doubling Google's entire data center footprint, forcing the company to design a more efficient, custom chip to avoid existential costs.

The narrative of NVIDIA's untouchable dominance is undermined by a critical fact: the world's leading models, including Google's Gemini 3 and Anthropic's Claude 4.5, are primarily trained on Google's TPUs and Amazon's Tranium chips. This proves that viable, high-performance alternatives already exist at the highest level of AI development.