An in-depth look at TPUv7 Ironwood, and how the latest Google TPU generation positions Google as the most threatening challenger to Nvidia's AI chip dominance
Fascinating article. They argue that the reason for NVIDIA's circular investment deals is to intertwine their own fate with that of the big labs, to keep themselves on top — OpenAI saved 30% on their NVIDIA GPUs merely by buying TPUs — open.substack.com/pub/semianal...
Context & Ripple Effects
The TPU discussion sits against a recent comparison in which Nvidia hardware retained a reported tokens-per-dollar lead over Google’s prior TPU generation, underscoring the economic bar Ironwood must clear. It also follows a longer software-stack story in which alternatives to CUDA have been gaining traction, even as Nvidia’s ecosystem remains central.
The strategic significance is not simply a new accelerator: related coverage frames Google’s eventual move from internal TPU use toward external customers as a test of whether a cloud provider can turn proprietary infrastructure into a credible merchant-chip alternative.
First-order effects
- Google gains a stronger basis to pitch TPUs as a cost and supply alternative for large AI workloads, while Nvidia faces a more credible challenge at the point where accelerator spending is concentrated.
- The article’s claim that OpenAI reduced Nvidia GPU costs by buying TPUs suggests that even partial multi-sourcing can improve a major buyer’s negotiating position and reduce dependence on a single accelerator vendor.
Second-order effects
- Nvidia’s response is likely to lean on the advantages highlighted in the earlier TPU v6e versus Nvidia comparison: performance economics, software maturity, and tightly coupled systems—not chip specifications alone.
- Google must pair hardware progress with accessible capacity and customer support. That requirement is reflected in later reporting on Google funding TPU capacity for Anthropic, which applies a platform-financing playbook to chip adoption.
Third-order effects
- If large labs increasingly run mixed accelerator fleets, AI-chip competition shifts from a single-vendor hardware market toward competition among integrated stacks: silicon, compilers, cloud capacity, financing, and model-serving economics.
- The outcome remains uncertain because Nvidia’s installed software base and Google’s need to serve external customers are both durable constraints; the key structural change is that buyer leverage can grow before Nvidia’s leadership necessarily erodes.
The trend: AI accelerator competition is broadening from peak-chip performance into a contest over integrated infrastructure stacks and the buyer economics of multi-sourcing.