Smarter artificial intelligence (AI) technology designs, combined with green data infrastructure, can help Southeast Asia realize its digital ambitions without compromising its energy transition goals.
Southeast Asia's digital economy is booming. With rapid growth in e-commerce, fintech, and AI services, the region is seeing a surge in electricity demand – particularly from data centers.
These facilities operate 24/7 and require high-capacity cooling systems, creating a constant operational burden on the national power grid.
Globally, data centers consumed approximately 415 TWh of electricity in 2024 – more than the total consumption of Indonesia.
By 2030, the electricity consumption of these centers is projected to surpass Japan's current consumption.
Although the majority of global data center expansion is taking place in the US, China, and Europe, Southeast Asia is rapidly catching up, with demand in the region projected to more than double by 2030.
National estimates have highlighted significant challenges for the power grid. In Malaysia, electricity demand from data centers could increase sevenfold by 2030, reaching approximately 30% of the country's total consumption.
In Indonesia, demand is expected to increase nearly fourfold, while in the Philippines, it could surge more than 18 times.
The surge in demand from data centers also risks competing for electricity and water with residential areas and communities – particularly in areas with limited power grids and water supplies – raising broader concerns about social and equity issues.
If this growing demand is met primarily by power grids heavily reliant on fossil fuels, it risks slowing down—or even derailing—the region’s clean energy transition.
As of 2022, fossil fuels, led by coal, still supplied more than 70% of Southeast Asia's electricity, despite the ongoing expansion of renewable energy.
In this context, a crucial part of the solution lies in improving hardware, particularly through the development of "green data centers."
These facilities employ advanced technologies such as high-efficiency cooling systems, waste heat recycling, shifting workloads to off-peak hours, and integrating renewable energy.
With these improvements, data centers can become much more energy-efficient, and more importantly, they can act as levers to accelerate the deployment of clean energy.
Southeast Asian nations have been moving in this direction. Singapore's Green Data Center Roadmap 2024 sets leading energy efficiency standards and offers incentives for the use of renewable energy. Malaysia is preparing to launch a sustainable data center framework by the end of 2025.
In addition to hardware improvements, other powerful levers can be exploited at the software level.
One solution is to design smarter, more streamlined AI by building applications that deliver similar results but with less computational power, thereby reducing the need for both infrastructure and energy.
In practice, this can be achieved by deploying smaller, task-specific AI models instead of bulky, general-purpose models; using smaller but higher-quality datasets during model training; applying model compression techniques such as pruning and quantization to reduce computational load; and employing more efficient algorithms for both training and inference.
These measures have significant potential in improving software efficiency and reducing energy use. For example, Google says its Gemini model, which combines more efficient software architectures and algorithms with hardware improvements, consumes significantly less energy than many previous public estimates.
In addition, creating a supportive environment is also very important.
For many years, AI developers—from engineers building foundational models to app creators—were typically rewarded based on accuracy, speed, and functionality, rather than energy efficiency.
This is beginning to change as rising computational and token costs force efficiency into the discussion, but most efforts remain largely spontaneous.
Without a clear policy signal to incorporate energy efficiency into AI application development, progress could stall, and energy-intensive software could gain the upper hand if energy costs decrease or priorities shift.
This is where governments and companies can collaborate. Instead of directly regulating AI design, policymakers can create a favorable environment by promoting reporting standards on the energy usage of AI applications.
For their part, companies can collaborate by sharing data, testing lightweight applications, and presenting best practices in algorithm optimization.
Public authorities should also consider prioritizing essential societal needs over non-essential uses, ensuring the grid continues to serve the broader interests of society as AI demands increase.
Source: https://www.vietnamplus.vn/xanh-hoa-ai-nhiem-vu-cap-bach-cho-dong-nam-a-post1061088.vnp
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