TLDR
- Alphabet shares climbed about 3% after reports of Google’s Frozen v2 AI chip.
- Frozen v2 is designed to run Gemini models with lower power use.
- Google engineers estimate Frozen v2 could be six to ten times more efficient than TPUs.
- The chip could launch as soon as 2028 if development continues.
- Google faces AI pressure from Moonshot AI, Alibaba and delayed Gemini releases.
Alphabet shares climbed on Monday after a report said Google is developing a new AI server chip designed to run Gemini models with better power efficiency.
Google Works on Frozen v2 AI Chip
Alphabet stock (GOOGL) rose about 3% after reports said Google is building a dedicated AI chip known internally as Frozen v2. The chip is being designed to run Gemini models faster and with lower power use.
The project would embed parts of Gemini’s architecture directly into the silicon. That design could reduce the calculations and data movement needed when the model answers user prompts.
BREAKING: Alphabet, $GOOGL, is planning on launching a new “frozen” chip to run its AI models more efficiently, per The Information.
Details include:
1. This new server chip would directly integrate the blueprint of its Gemini AI model
2. The chip would then enable Alphabet to…
— The Kobeissi Letter (@KobeissiLetter) July 20, 2026
Engineers reportedly estimate that Frozen v2 could serve six to ten times more tokens per unit of power than Google’s newest TPUs. TPUs, or tensor processing units, already power many of Google’s AI systems.
Frozen v2 would not replace Google’s existing TPU lineup. The chip would become a more specialized branch of Google’s custom hardware portfolio, focused on Gemini inference workloads.
Alphabet Targets 2028 Deployment
The chip could launch as soon as 2028, based on the reported internal timeline. Google currently views the project as a trial run and does not plan to produce it at the same scale as TPUs.
The trade-off is flexibility. Frozen v2 would work best if future Gemini models keep the same core architecture, since parts of that design would be built into the chip.
Alphabet said its teams are “constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers.” The company added that not every project moves into production.
The company also said,
“By co-designing our hardware and software from the ground up, we ensure our systems are integrated and highly optimized for real-world workloads.”
AI Compute Demand Pressures Google
Google’s chip work comes as AI companies face rising demand for computing power. Large models require more chips, power, and data center capacity as customers use them for coding, search, agents, and enterprise work.
The new chip is partly aimed at easing internal compute pressure. Reports said Google Cloud has faced capacity limits and turned away some outside business because of high demand.
Google also agreed last month to pay SpaceX nearly $1 billion a month to help meet enterprise compute commitments. That deal shows how AI infrastructure demand has moved beyond chips into power, networking, and data center capacity.
Investors are now watching Alphabet’s quarterly results for AI spending updates. They will also look for comments on whether heavy infrastructure costs can produce stronger revenue growth.
Gemini Faces Competition From New AI Models
Alphabet’s AI strategy faces pressure from faster-moving rivals. Bloomberg reported last week that Google was behind schedule in delivering Gemini 3.5 Pro, its most advanced model release.
Google has also lost several senior researchers to competitors. At the same time, Chinese AI models are gaining wider use among U.S. businesses, with reports placing their share near 45% of company token usage.
New releases from Moonshot AI and Alibaba have added to the competitive pressure. These models are narrowing performance gaps in coding, reasoning, and enterprise AI tasks.
Google DeepMind CEO Demis Hassabis is also meeting lawmakers this week to discuss AI oversight. His proposal includes a federally supervised, industry-funded watchdog that would test advanced models for national security risks before release.
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