The collision between the worlds of sustainability and AI is presenting a significant challenge that needs attention. AI, while groundbreaking, demands a substantial amount of computing power and energy. If not addressed, this could hinder business leaders in achieving their sustainability objectives.
One approach to confront the issue involves optimizing the available resources. Companies can make use of their spare capacity, a strategy that holds potential to reduce the environmental impact of data-intensive operations, which can contribute to as much as 40% of a company’s carbon footprint.
Another avenue to explore is repurposing vacant office buildings that have fallen out of favor due to the rise of remote work. Embracing these spaces could offer a solution to the environmental demands posed by AI and data-driven activities.
Even as generative artificial intelligence gains widespread acceptance, the creation and duplication of data continue to surge. Experts predict that the current annual growth rate of 23% will persist until 2025. The catch is that AI’s energy-intensive nature magnifies the scope of big data.
Left unattended, these challenges could obstruct the progress of businesses in achieving their sustainability goals. Thankfully, companies within the IT landscape are proactively seeking solutions to address these concerns.
Ben Golub, CEO of Storj, a decentralized cloud storage company, views AI as a substantial data challenge. Storj operates by allowing organizations to rent out their unused capacity through a shared network. In this endeavor, Storj is joined by established giants like Dell and IBM, all striving to enhance data storage efficiency for both economic and environmental sustainability.
Arthur Lewis, President and COO of Dell Technologies’ Infrastructure Solutions Group, is aiding customers in transitioning from traditional three-tier data center models to more adaptable software-defined architectures. This transition empowers customers to optimize their workloads in terms of cost, performance, user-friendliness, and efficiency.
Lewis emphasized the advantages of a software-defined architecture, stating that it offers the flexibility to acquire necessary resources as needed, thus scaling out according to capacity requirements. This adaptive approach aligns well with the challenges posed by AI and sustainability, paving the way for a more harmonious coexistence.