Google develops Frozen v2 chip to embed Gemini architecture into silicon

The shift from general-purpose accelerators to model-specific silicon signals a vertical integration pivot to stabilize the unit economics of AI inference. By etching model logic into metal, Google aims to bypass the power-intensive data movement inherent in existing architectures, a move necessary to justify its nearly $190 billion annual infrastructure spend. This strategy forces a trade-off between extreme efficiency and future flexibility, potentially locking Google into current model architectures for years. Watch for whether external cloud customers can access these specialized chips or if they remain exclusive to internal Gemini workloads to resolve internal supply bottlenecks.

The development of Frozen v2 arrives as high-scale AI labs move to mitigate the rising costs of LLM inference. Per OpenAI in June 2026, the company partnered with Broadcom to tape out its own inference processor, Jalapeño, in just nine months. Unlike Google’s 2028 timeline, OpenAI expects Jalapeño to arrive in data centers by late 2026. Simultaneously, Anthropic began exploratory talks with Samsung in July 2026 to manufacture custom 2-nanometer chips, following its massive Series H funding round where Samsung participated as an infrastructure partner. This shared industry roadmap highlights a collective intent to reduce total reliance on Nvidia’s general-purpose H-series and B-series chips.

Google’s own internal pressure has reportedly reached a critical threshold. Per The Information and subsequent reports in July 2026, Google Cloud has been forced to reject some external enterprise orders due to a internal capacity crunch, even reportedly agreeing to a $1 billion monthly compute lease with SpaceX to bridge the gap. While Google previously launched the ARM-based Axion CPU in April 2025 to handle general-purpose cloud tasks like YouTube ads and BigQuery, Frozen v2 represents a deeper technical commitment to optimizing the 'full stack' specifically for generative AI workloads. This hardware-level specialization is intended to protect operating margins as model sizes grow and electricity becomes the primary cost bottleneck.