How can AI Sustainably be Deployed at Scale?

With Research Showing That AI Could Soon Need as Much Electricity as an Entire Country, how can AI use Less Energy While Still Working Responsibly?

One of the hottest topics of the moment, the use and demands of AI show no intention of slowing down.

If anything, AI adoption, and the demand that is fuelling it, has seen tech firms of all sizes — from established major players to start-ups established purely in this emerging landscape — clamber to add AI-powered services to their portfolios.

A subject discussed at the recently wrapped-up World Economic Forum’s annual meeting at Davos, Switzerland — where leaders gathered to discuss key issues such as this — research emerged that suggests AI could soon need as much electricity as an entire country. This ignites the question: How can we sustainably deploy AI at scale? 

AI could soon use as much electricity as a country the size of the Netherlands

In light of recent findings, Maxime Vermeir, Senior Director of AI at intelligent data firm ABBYY, has taken time to explore how businesses can approach AI responsibly and sustainably in order to maximise its potential.

Maxime Vermeir, Senior Director of AI at ABBYY

“Many drawbacks of generative AI and LLMs stem from the massive stores of data that must be navigated to yield value,” he explained. “Not only does this raise risks in the way of ethics, accuracy and privacy, but it grossly exacerbates the amount of energy required to use the tools.
“Instead of highly general AI tools, enterprises have begun to pivot to narrower purpose-built AI, specialised for specific tasks and goals. For example, ABBYY has adopted this approach by training its machine learning (ML) and natural language processing (NLP) models to specifically read and understand documents that run through enterprise systems just like a human. With pre-trained AI skills to process highly specific document types with 95% accuracy, organisations can save trees by eliminating the use of paper while also reducing the amount of carbon emitted through cumbersome document processes.” 

Vermeir also advocates the empowerment of developers in this space, acknowledging that the future and sustainability of AI is not a burden for AI companies to shoulder alone. This comes as OpenAI announced developers can create their own ‘GPT’ platforms for specialised purposes which, in turn, allows developers and organisations alike to narrow their AI use with a high degree of customisability, ridding them of unnecessary extra features and data that amplify the intelligence’s ecological damage. 

“Companies should also take a step back from the technology itself,” he continues, “and look inside their organisation for more ways to sustainably leverage AI.” An example he poses is Microsoft, which revealed that its AI-supporting hardware runs exclusively on clean energy, absolving them of creating and omitting ‘operational emissions’.

“Moreover, companies can use AI as a tool to explore other facets of their business in which sustainability could be prioritised,” Vermeir said.

Research and advisory company Forrester has conducted its own exploration in this space, with its findings highlighting that the measurement, reporting and data visualisation capabilities of artificial intelligence can be harnessed to suggest that AI could, in fact, power a climate revolution of its own. 

Vermeir concluded: “Although objectively important, emissions aren’t the only metric used to encompass ecological impacts — studies have shown that a combination of robotics and AI have reduced herbicide use in some contexts by 90%. As companies continue to grapple with the utility and consequences of AI, they must explore the full breadth of its capability to enhance and contribute to sustainability.” 

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