Spoiler alert: Yes, it can, and here is how.
Energy demands and the costs associated with data center usage are rising all the time. The emergence of AI, in particular, is expected to amplify global data center power demand by 160% by 2030, according to Goldman Sachs. AI and machine learning (ML) require a lot of energy, particularly during the training of large models and the continuous operation of complex algorithms.
AI has already been integrated into a variety of software, including Office, Microsoft’s Copilot, as well as Google’s Gmail and Docs and Github’s Copilot, with more to come. This trend is likely to exponentially increase the internet’s environmental footprint in a very short time. According to Gartner, by 2025, without sustainable AI practices, AI will consume more energy than human labor. This will render carbon neutrality all but unattainable.
The impact of this unprecedented pressure on the energy supply chain is twofold: Rising operational costs will hit the bottom line, while environmental concerns are influencing regulatory frameworks and environmental, social and governance (ESG) goals. As companies face these challenges, the rapidly emerging category of platform engineering could offer a viable approach to reduce energy consumption and boost operational efficiency.
Why? Well, while platform engineering tools are designed to improve developer and end-user experience through enhanced self-service capabilities and tooling, they can also help increase operational efficiency, facilitate change management, accelerate DevOps and cloud adoption and critically, reduce cloud spend and carbon footprint.
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