Japan's cloud and data center operators are racing to build AI infrastructure while grappling with mounting energy demands that could strain the nation's power grid. Sakura Internet, one of Japan's largest data center providers, is planning to increase capital expenditure to between 20 billion and 30 billion yen during the current fiscal year, far exceeding previous plans, as businesses, research institutions, and government agencies compete for GPU-powered computing capacity. The surge reflects a fundamental challenge: artificial intelligence systems consume enormous amounts of electricity, and Japan's existing power infrastructure faces significant pressure to keep pace.

Why Does AI Infrastructure Consume So Much Power?

The energy demands of artificial intelligence are substantial and growing rapidly. Generating a single AI image consumes roughly the same amount of power as charging a mobile phone, and that's just one task. When scaled to training large language models and running data centers filled with graphics processing units (GPUs), the numbers become staggering. Researchers estimate that AI servers could consume between 85 and 134 terawatts of electricity annually by 2027, equivalent to the total energy consumption of the entire Netherlands in a year.

GPUs, the specialized chips that power AI systems, are the primary culprit. According to research from OpenAI, the computing power required for AI has been doubling every 3.4 months since 2012, a trend expected to continue. Modern GPUs announced recently contain 208 billion transistors and consume 1,200 watts of power per unit, compared to GPUs from 25 years ago that contained just one million transistors and used far less power. This exponential growth in computational demand directly translates to exponential growth in electricity consumption.

What Industries Must Address AI's Power Challenge?

Reducing AI's environmental impact requires coordinated action across multiple sectors. Each industry plays a critical role in making AI infrastructure more sustainable:

• Storage Industry: All-flash storage solutions can deliver a 69 percent reduction in energy consumption compared to traditional hard disk drives (HDDs), allowing data centers to process more AI workloads without proportional increases in power draw.
• Data Center Operations: Facilities can improve efficiency through better cooling techniques, renewable energy integration, and optimization of power usage effectiveness (PUE), which measures how much electricity enters a data center versus how much is actually used for computing.
• Semiconductor Manufacturing: GPU makers must continue advancing chip design to deliver more computing performance per watt, ensuring that increased power consumption translates into proportionally greater computational capability.

PROMISE Technology is demonstrating this multi-sector approach at COMPUTEX 2026 with its GreenBoost 2.0 technology, which reduces storage power consumption and enables higher GPU utilization within existing power and cooling limits. The technology combines high-density storage architectures with energy-efficient hard drives, developed in collaboration with Toshiba.

"As enterprises scale AI, power and cooling have become defining constraints. Through deep hardware and software integration, we are enabling customers to achieve both performance and energy efficiency at scale," stated Noriaki Katakura, Division President of the Storage Products Division at Toshiba Electronics Components Taiwan Corporation.

Noriaki Katakura, Division President, Storage Products Division, Toshiba Electronics Components Taiwan Corporation

How Can Organizations Balance AI Deployment With Energy Efficiency?

Companies deploying AI infrastructure face a critical challenge: maximizing the benefits of artificial intelligence while managing energy consumption. Organizations are taking several practical steps to address this tension:

• Assess Environmental Impact Before Deployment: Organizations should carefully weigh the added value of AI against its environmental impact, understanding the full energy cost of their AI initiatives before committing resources.
• Transition to Energy-Efficient Storage: Moving to all-flash storage systems and implementing more efficient cooling infrastructure can significantly reduce power consumption without sacrificing performance.
• Optimize Data Movement Between Components: Technologies like NVMeBoost create optimized data pipelines that accelerate storage-to-GPU transfers while improving inter-GPU communication, reducing wasted energy in data movement.
• Integrate Renewable Energy Sources: Data centers can incorporate renewable energy and work with local utilities to ensure sustainable power supplies for AI workloads.

Japan's government recognizes the stakes. The country is allocating more than 1 trillion yen for high-end chip and AI-related ventures this fiscal year as part of a broader strategy to reinforce domestic technology ecosystems and reduce dependence on foreign cloud providers. This investment reflects Japan's view that AI infrastructure is now central to national competitiveness and economic security.

Sakura Internet's expansion strategy illustrates how local cloud providers are positioning themselves as alternatives to global hyperscalers like Microsoft, Amazon, and Google by emphasizing data sovereignty, local infrastructure, and specialized AI capabilities. The company is building GPU-enabled infrastructure in locations like the Ishikari data center park in Hokkaido, where cooler climates reduce cooling costs. Japan's interest in building sovereign AI infrastructure also reflects growing concerns about geopolitical and economic risks tied to relying on foreign cloud providers.

However, the energy challenge remains formidable. As AI adoption accelerates across finance, healthcare, manufacturing, and government services, the demand for computing power will continue to outpace improvements in efficiency. Industry experts predict that future growth of AI will necessitate considerable upgrades to power generation, maintenance of grid stability, and incorporation of renewable energy sources. Without coordinated action across storage, data center, and semiconductor industries, Japan's AI ambitions could be constrained by power availability.

The situation underscores a broader tension in the AI era: the technology that promises to solve complex problems and drive economic growth also demands enormous amounts of energy. Japan's approach, combining aggressive infrastructure investment with a focus on energy efficiency and renewable power, offers a model for how nations can pursue AI leadership while managing environmental and energy security concerns.