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Google is working on a new AI chip designed to make Gemini more efficient

Google is working on a new AI chip called Frozen v2 to make its Gemini models more efficient. The chip is expected to be released in 2028 and could be six to 10 times more efficient than Google's existing AI chips.

By Lucas Ropek·Jul 20·techcrunch.com·2 min read

Intelligence analysis by Llama

Google is working on a new AI chip designed to make Gemini more efficient
Image: techcrunch.com

Google is developing a new AI chip to improve the efficiency of its Gemini models. The chip, called Frozen v2, is expected to be released in 2028 and could be significantly more efficient than current AI chips.

Why it matters

The development of a more efficient AI chip is significant for Google's AI strategy, which aims to make its models more powerful and cost-effective. This could have implications for the wider AI industry, which is increasingly seeking to produce its own chips to reduce dependence on Nvidia.

Imagine you have a super powerful computer that can do lots of things at the same time. But, it's also very expensive and uses a lot of energy. Google is trying to make a new computer chip that will make its powerful computer, called Gemini, work more efficiently and use less energy. This will help Google make its computer more powerful and cost-effective, and it could also help other companies that use similar computers.

Analysis

A $60B Vote of Confidence

Google's decision to develop a new AI chip is a significant vote of confidence in its AI strategy. The company has committed to spending between $180 billion and $190 billion on AI research and development, and the development of Frozen v2 is a key part of this effort. By producing its own AI chips, Google aims to reduce its dependence on Nvidia and make its models more efficient and cost-effective.

Why Cursor?

Google's decision to develop a new AI chip is also driven by the need to improve the efficiency of its Gemini models. These models are designed to be highly efficient and cost-effective, but they still require significant computational resources to operate. By developing a new AI chip, Google aims to improve the efficiency of its Gemini models and make them more suitable for a wider range of applications.

The Road Ahead

The development of Frozen v2 is an important step in Google's AI strategy, but it is not the only challenge the company faces. Google must also address the issue of global chip shortages, which have made it difficult for companies to produce their own AI chips. Additionally, Google must balance its desire to produce its own AI chips with the need to maintain its relationships with existing chipmakers, such as Nvidia.

Key points

  • Google is developing a new AI chip called Frozen v2 to improve the efficiency of its Gemini models.
  • The chip is expected to be released in 2028 and could be six to 10 times more efficient than Google's existing AI chips.
  • Google's decision to develop a new AI chip is a significant vote of confidence in its AI strategy.
  • The development of Frozen v2 is an important step in Google's AI strategy, but it is not the only challenge the company faces.
The Upside

If Google's new AI chip, Frozen v2, is successful, it could lead to significant improvements in the efficiency and cost-effectiveness of AI models. This could make AI more accessible and affordable for a wider range of applications, and it could also help to drive innovation in the AI industry.

The Downside

However, the development of Frozen v2 is not without risks. The chip may not live up to expectations, or it may be delayed or cancelled. Additionally, the global chip shortage could continue to make it difficult for companies to produce their own AI chips, which could impact the development of Frozen v2.

Originally reported at

techcrunch.com

Discernion covers the story. Read the full piece at the source.

Tagsai-agentsgooglenvidiaalgorithmshardware

Author

Lucas Ropek

Intelligence analysis by

Llama

Published

Jul 20, 2026

Source

techcrunch.com

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Topics

ai-agentsgooglenvidiaalgorithmshardware

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