Google is offering its TPUs externally for the first time as a strategic move to gain market share while it has a temporary hardware advantage over Nvidia. This classic tactic aims to build a crucial install base that can be upgraded later, even after its competitive performance edge inevitably narrows.

In a significant strategic misstep, Google sold a large volume of its custom TPU accelerators to rival Anthropic. Immediately after, demand for Google's own Gemini model surged, leaving Google compute-constrained and trying to secure more capacity from a sold-out TSMC.

While competitors pay Nvidia's ~80% gross margins for GPUs, Google's custom TPUs have an estimated ~50% margin. In the AI era, where the cost to generate tokens is a primary business driver, this structural cost advantage could make Google the low-cost provider and ultimate winner in the long run.

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.

Google training its top model, Gemini 3 Pro, on its own TPUs demonstrates a viable alternative to NVIDIA's chips. However, because Google does not sell its TPUs, NVIDIA remains the only seller for every other company, effectively maintaining monopoly pricing power over the rest of the market.

Google's cloud division (GCP), incentivized to sell compute, is allocating scarce TPU chips to external customer Anthropic. This directly constrains Google's own AI lab, Gemini, hindering its progress in the hyper-competitive AI race and revealing significant internal friction between business units with conflicting goals.

While NVIDIA CEO Jensen Huang conceptualized the 'five-layer AI cake' (apps, models, infrastructure, chips, energy), Google's Alphabet is the only company successfully operating across all five layers. This deep vertical integration, from custom TPU chips to funding its own power plants, is its key competitive advantage, allowing it to outmaneuver the very company that defined the framework.

The primary threat to NVIDIA isn't startups, but custom silicon from Google (TPU), Amazon (Trainium), and Meta. If the AI market remains concentrated among these few giants, their internal, specialized chips will increasingly displace NVIDIA's more general-purpose GPUs within their massive data centers.

This theory suggests Google's refusal to sell TPUs is a strategic move to maintain a high market price for AI inference. By allowing NVIDIA's expensive GPUs to set the benchmark, Google can profit from its own lower-cost TPU-based inference services on GCP.