Mr Curry of Digital Realty said that the three data centres it operates run on 100 per cent renewable energy coverage as of last month, with some of its power coming from solar facilities installed on-site in 2023 and 2024.
It has also taken on other initiatives such as a collaboration with national water agency PUB to pilot a cooling tower that can reduce the amount of water discharged from its cooling systems monthly by 60 per cent, or about 650,000 litres.
Over at Alibaba Cloud Intelligence Singapore, its deputy country manager Hon Keat Choong said that the company has pledged to use 100 per cent clean energy by 2030.
In the fiscal year 2023-2024, the company’s self-built data centres improved their power usage efficiency and 56 per cent of electricity consumption came from clean sources, he added.
He also said that the company has developed Energy Expert, a management tool that allows enterprises to measure and analyse their carbon emissions and energy consumption using AI. This has been used by 3,000 organisations globally, including in Singapore.
MAKING GENERATIVE AI GREENER
Even though there are people who believe that the future efficiencies of generative AI will outweigh the current environmental costs, Mr Kumar of WWF-Singapore warned that this assumption is problematic.
“It shifts our focus away from making sustainable AI a priority. We become so focused on what AI can do and its promised efficiencies that we stop asking how to make the technology itself more sustainable.
“While AI can be beneficial, we still need to make sure that its infrastructure and full life cycle have as little environmental impact as possible,” he said.
However, there are several hurdles in making generative AI models environmentally sustainable, one of which is the design and use of energy-efficient hardware, Mr Somani of KPMG said.
For example, developing cutting-edge AI chips that are more sustainable would require substantial investments in research and development. This also means that it would be costly and harder for small firms to obtain and use newer and greener technology.
“Compounding this problem is the rapid pace of technological innovation, which drives frequent hardware upgrades. This not only increases costs but also contributes to the rise of e-waste, diluting any long-term environmental benefits,” he added.
Getting access to renewable energy can pose a challenge due to limited availability and high costs. This makes it difficult for businesses and data centres to move away from traditional energy sources such as fuel, Mr Somani said.
Sustainability can also get in the way of development.
“While smaller, energy-efficient AI models can reduce environmental impact, they sometimes trade off performance or computational power. For applications requiring high precision, this trade-off makes them less practical,” he explained.
“Additionally, many companies prioritise rapid market entry and profitability over sustainability, slowing the adoption of energy-efficient practices.”
So, is there a way to unlock the full potential of generative AI while minimising its harmful impact on the environment?
Mr Oostveen from data storage firm Pure Storage said that companies developing AI should look at improving the efficiency of their models and thereby reducing the computing power needed.
He pointed to DeepSeek, an AI model from China that is on par with advanced models from OpenAI and Meta in the United States, but developed at a fraction of their costs.
“DeepSeek’s approach is rooted in a mixture-of-experts model, where smaller, highly trained models work together in tandem. This sophisticated method selects the most appropriate expert model, optimising for both performance and efficiency.”
