The big question that determines AI deals: How much money can OpenAI raise this round?

# Source: Wall Street Insights

By Yang Chen

SoftBank and Intel have joined forces to bet on next-generation AI memory technology ZAM. Originating from a U.S. government project, the technology focuses on low power consumption and cost-effectiveness, directly addressing AI's energy bottlenecks and supply chain constraints. A prototype is expected to be launched in 2028. Stimulated by this news, shares of both SoftBank and Intel surged over 5%.

Teaming up to wager on next-generation AI memory technology, SoftBank and Intel aim to carve out a new path in the market dominated by High-Bandwidth Memory (HBM). Their jointly developed "Z-Angle Memory" (ZAM) project focuses on reducing power consumption and costs, directly targeting the energy bottlenecks and supply chain shortages currently plaguing the AI industry.

Saimemory, a subsidiary of SoftBank, announced on Tuesday the signing of a cooperation agreement with Intel to jointly advance the commercialization of this next-generation memory technology for artificial intelligence and high-performance computing. The technology seeks to improve the architecture of traditional Dynamic Random Access Memory (DRAM) to meet the growing performance demands of AI applications.

According to a press release issued by SoftBank, the prototype product of the ZAM project is expected to be completed within the fiscal year ending March 31, 2028, with commercialization targeted for fiscal year 2029.

Dr. Joshua Fryman, Chief Technology Officer of Intel's Government Technology Division and Intel Fellow, stated in a declaration: "Standard memory architectures cannot meet AI requirements. The new architecture and assembly methods developed by Intel enhance DRAM performance while reducing power consumption and costs."

Following the announcement, SoftBank's stock rose 5.13% in Tokyo trading, while Intel's stock climbed 5%.

## Technology Originates from a U.S. Government Project

Founded in December 2024, Saimemory will leverage Intel's expertise in memory technology, particularly the technological achievements developed by Intel as a participant in the U.S. Department of Energy's Advanced Memory Technology Project.

This Department of Energy project focuses on developing core technologies for advanced memory, with Intel responsible for improving the performance and energy efficiency of next-generation DRAM used in computers and servers.

According to a Nikkei Asia report last year, Fujitsu, a Japanese multinational IT equipment and services company, is also participating in this project.

## AI Demand Triggers Supply Chain Tensions

The industrial backdrop for this cooperation is the surging demand for memory driven by AI-related applications, where demand growth far outpaces supply capacity, leading to shortages across the entire memory supply chain.

Current AI chips extensively adopt high-performance memory solutions such as HBM, but these products are difficult to manufacture, costly, and highly concentrated in supply.

The ZAM project aims to provide an alternative path by improving traditional DRAM architecture, reducing manufacturing complexity and costs while ensuring performance. This could offer more options for the AI hardware supply chain.

## Energy Efficiency Becomes a Core Consideration

The ZAM project's emphasis on energy efficiency reflects the industry's growing concern about the massive energy consumption of AI computing. Joshua Fryman stated that the new memory architecture and assembly methods developed by Intel could be widely adopted within the next decade, positioning the technology to significantly reduce power consumption while enhancing performance.

As AI model scales continue to expand, the demands for memory bandwidth and capacity during training and inference processes keep rising, and the corresponding energy consumption has become one of the bottlenecks restricting AI development.

If more energy-efficient memory technology can be successfully commercialized, it will directly impact data center operating costs and the economic feasibility of AI applications.

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