Expert takes Oct 14, 2026

Today · podcast transcript, obtained by us the night of October 13

Google's Vahdat says 99.99% power means building it twice

Amin Vahdat, who runs Google's infrastructure, says building 1 GW of power capacity at 99.99% reliability means building 2 GW; Vahdat also says repurposing retired training chips for inference isn't necessarily cheaper. On Sequoia Capital's Training Data podcast, Vahdat said: "If we need to have a gigawatt of power—let's say we wanted to have that with 99.99%-plus reliability—that means we have to build two gigawatts of power." The reason is that this level of reliability needs one-for-one redundancy, so connecting to the grid and relying on its statistical pooling is cheaper (Training Data podcast transcript). On inference-specific chips, Vahdat said Google launched two TPU variants this year, the 8i for inference and the 8t for training, because inference could account for 30% to 60% of the market over a chip's six-year life. If inference were only 2% to 5% of the market, Vahdat said, even a chip twice as fast wouldn't be worth building. Asked whether inference on retired training chips is cheaper, Vahdat answered: "Not necessarily cheaper, actually, because for serving there is more need to co-locate compute, networking and storage." This is a Google executive describing Google's own practice. The 30% to 60% is an internal projection, with no per-token cost given. Google sells both TPUs and cloud. Our reading: whether an inference-specific chip is worth building depends on the assumed inference share, and for now that share is only Google's internal projection. How we checked →