Forecasts through 2035 hinge on macroeconomic indicators, trade patterns, and sector-specific drivers, with a focus on memory demand, supply dynamics, risks, sensitivities, and capacity investments.

Anthropic has pledged to purchase up to 1 million TPUs from Google, signaling strong demand for Google’s AI hardware ecosystem.

Google is diversifying its supply chain and partnering with Marvell Technology to bolster fabrication capacity for TPUs and related AI hardware.

The piece includes an interactive store-table and sections like 'Key Findings' and 'Forecasts to 2035' to contextualize the US memory market alongside Nvidia-focused analysis.

Meta Platforms is renting Google’s TPUs for AI workloads, indicating growing traction of TPUs in the market beyond Nvidia’s offerings.

Google has deployed TPUs with major partners such as Anthropic and Meta, and has signed large-scale TPU supply deals that could shift AI chip dynamics.

Google’s Ironwood (Seventh generation TPU) is narrowing the performance gap with Nvidia’s Blackwell, signaling increasing competitive pressure in top-tier AI workloads.

Alphabet represents a long-term competitive threat to Nvidia, leveraging TPUs and Axion CPUs as an internally driven compute stack that could erode Nvidia’s dominance over time.

Nvidia remains the dominant AI chip supplier with about 81% market share, a position it has held for roughly three and a half years, even as Alphabet and other peers intensify competition in the expanding AI infrastructure market.

Analysts say Nvidia’s robust growth trajectory and the broader AI hardware demand—projected to reach around $1 trillion in revenue by 2030—could sustain its leadership in data-center AI chips even if Alphabet captures a meaningful share.

Alphabet’s in-house TPUs and Axion CPUs have evolved for demanding training and inference workloads, with Google’s Ironwood TPU delivering meaningful performance gains.

Google touts a fourfold performance improvement per chip for training and inference with Ironwood and positions Axion as a cost-effective alternative to Intel and AMD-based GPUs.