Alphabet, the parent company of Google, is developing a new AI server chip codenamed "Frozen v2" to improve the operational efficiency of its self-developed Gemini model. According to The Information, citing anonymous sources, the chip is expected to be launched in 2028, and its efficiency in generating tokens per unit of energy could be 6 to 10 times that of Google's current AI chips.

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In response to the report, Google told TechCrunch that the company continues to explore and test innovative technologies to enhance system performance and efficiency, but not all R&D projects will eventually reach mass production. Google emphasized that the "full-stack development" approach, which combines hardware and software design, can create more integrated computing systems optimized for real-world AI workloads.

In recent years, as generative AI applications have rapidly expanded, global AI compute demand has continued to grow, prompting tech companies to increase investment in custom chips to improve model performance and reduce reliance on external suppliers. NVIDIA has long dominated the AI chip market with its GPU technology, prompting AI companies like OpenAI and Anthropic to explore their own chip routes. In June this year, OpenAI released its first custom inference chip, "Jalapeño"; recently, Anthropic was also reported to be negotiating chip manufacturing cooperation with Samsung.

Alphabet previously announced that it plans to invest between $18 billion and $19 billion to advance its artificial intelligence strategy. As the scale of AI infrastructure investments grows, chip efficiency has become an important metric for evaluating return on investment. After the release of information about Frozen v2, market confidence increased, and Alphabet's stock rose by about 3% in the early trading session on the day of the report, with investors focusing on whether its AI investments can translate into long-term competitive advantages.

As AI model sizes continue to grow, custom chips have become an important strategic direction for tech companies to build compute ecosystems and enhance cost control capabilities. Google's latest move also reflects that competition in the AI industry is expanding from model capabilities to chip, infrastructure, and full-stack technology system competition.