Spheron has weighed in following a new energy report from the International Energy Agency (IEA). The company highlighted the growing energy demands of AI. With its impact on regional grids, especially in the Asia-Pacific (APAC) region.
In a post shared on July 21, Spheron noted that APAC is already short by 15 to 25 gigawatts. This gap stems from cooling requirements, data networking, and ongoing supply constraints. The firm warned that global AI expansion could worsen these shortages.
In response, Spheron pointed to its decentralized workload routing model as a more sustainable alternative. “Spheron routes workloads globally, sidestepping regional bottlenecks,” the company stated. “We are the only scalable path forward.”
IEA: AI Will Double Data Center Power Use by 2030
The IEA’s new report, Energy and AI, underscores Spheron’s concern. The agency projects that electricity demand from data centers will more than double by 2030, reaching approximately 945 terawatt-hours. That total would exceed Japan’s current electricity use.
AI is the biggest driver of that trend. According to the IEA, AI-optimized data centers could see power demand quadruple by 2030. In the United States alone, data center energy use may soon rival the total energy consumption of several manufacturing sectors combined.
The report also notes a broader trend across advanced economies. There, AI-linked infrastructure is expected to fuel over 20% of total electricity demand growth by 2030. That marks a sharp reversal after years of flat or declining demand in many regions.
Spheron Advocates for Decentralized Compute Model
Spheron’s platform allows AI developers to deploy compute workloads across a decentralized global network. Instead of relying on energy-intensive central data hubs, users can shift demand to underutilized regions. This approach helps avoid bottlenecks in power-constrained zones like APAC. It also reduces dependence on cloud giants, which often lock users into fixed regions and high energy costs.
By decentralizing compute, Spheron aims to deliver both energy efficiency and global scalability, two elements missing in traditional cloud-based AI training setups.
Energy as a New AI Bottleneck
Spheron’s comments reflect rising concerns across the tech and energy sectors. As AI systems scale, their infrastructure demands are no longer purely technical, they are increasingly environmental. For investors, the shift presents a challenge and an opportunity.
Spheron, which offers decentralized and power-aware solutions, may gain ground as governments impose stricter energy regulations. The APAC region will likely serve as a test case. With persistent shortages and surging AI development, regional infrastructure must adapt, or risk slowing AI adoption altogether.
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