Google Unveils 2 New AI Chips to Take on Nvidia

This is less about Nvidia losing share and more about Google widening the economic moat of its cloud stack. The key second-order effect is margin leverage: by internalizing more inference/training workload, Alphabet can lower its own COGS, improve cloud win rates on price-sensitive accounts, and use TPU economics as a bundling weapon against hyperscaler rivals that remain structurally dependent on Nvidia-priced silicon.

For Nvidia, the immediate read-through is not unit displacement but a slowing of marginal upside in one of its highest-value customer cohorts. Hyperscalers have the strongest incentive to diversify, but their deployed AI roadmaps still require the broad software ecosystem, developer familiarity, and peak performance that keeps Nvidia entrenched; that suggests this is a share-defense event over months/years, not a demand shock over days/weeks.

The contrarian point is that specialized accelerators often expand total AI workload consumption rather than replace incumbent spend. If Google’s chips materially reduce inference cost, they can stimulate more agentic usage, which ultimately increases overall compute demand and may still leave Nvidia as the default for frontier training and premium inference workloads. In other words, TPU gains can be additive to AI capex, even if they modestly compress Nvidia’s pricing power at the margin.

The bigger hidden winner is the supply chain around memory, packaging, and networking, because every new generation of custom silicon tends to intensify demand for HBM and high-speed interconnects. The market may be underestimating how much of the savings Alphabet captures gets reinvested into more AI capacity rather than passed through to customers, which limits the near-term bear case on the broader AI capex cycle.