Maxime Vermeir, Senior Director of AI Strategy, ABBYY
It has the potential to do incredible things, but Generative Artificial Intelligence (AI) is having considerable impacts on the environment, with global AI energy demand projected to exceed the annual electricity consumption of a country the size of Belgium by 2026.
Many companies still aim to be carbon-neutral by 2030, yet recent investments in AI infrastructure have significantly raised their emissions. The global tech giants in particular are forging ahead with new technologies and in 2023 Meta’s Scope 3 emissions increased by over 65%, while Google’s emissions were almost 50% higher in 2023 than in 2019.
The key drawback of generative AI is that companies must navigate massive stores of data which use huge amounts of energy. Not only does this raise risks in the way of ethics, accuracy, and privacy, but it also exacerbates the amount of energy required to use the tools.
Many of these companies currently engage in carbon offsetting but this will likely only work in the short- to medium-term, as the industry searches for lasting solutions to reduce AI’s dependency on fossil fuels. So, what needs to be done?
Improve regulation to keep businesses accountable
So far, there is only a very brief legal framework for AI and legislation has largely failed to reign in the ecological implications, focusing instead on privacy and other ethical areas.
The EU’s AI Act, for example, aims to regulate the use of AI systems based on their risk levels. It requires AI systems to prioritise transparency and safety, which could encourage responsible AI development and usage. However, how effective it ends up being will depend on how well it is implemented.
More clearly defined national, regional and international frameworks are needed on energy consumption, particularly given the role of the energy sector in the global economy and its importance for climate goals.
Regulation is important for responsible AI use, but there is also a role for businesses to both make sure they follow the rules. They must keep themselves accountable for the environmental impacts of their use of AI beyond the regulations.
It’s up to business leaders to take the initiative and ensure transparency and accountability for sustainability credentials are upheld when implementing AI.
Businesses can invest in purpose-built AI to keep emissions down
There is a balance to be struck to continue hitting developing sustainability targets, whilst continuing to innovate in AI. One way to reduce the environmental impact is to pivot to narrower purpose-built AI, specialized for specific tasks and goals, rather than relying on general AI tools.
There are situations where the AI models used are oversized compared to the task at hand, and are therefore very energy-intensive. By adopting more efficient AI models, it is possible to significantly reduce energy consumption.
Switching to purpose-built AI such as small language models (SLMs) can significantly reduce energy consumption. These solutions are built for specific tasks and tailored to improve accuracy in real-world scenarios. For example, ABBYY trains its machine learning and natural language processing (NLP) models to 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, organizations can save trees by eliminating the use of paper while also reducing the amount of carbon emitted through cumbersome document management processes.
AI is actually starting to reshape sustainability programs. It can optimise the operation of wind turbines, solar panels, electric vehicles, and batteries – and it can even help improve energy efficiency. By minimizing unnecessary processes, it is possible to reduce overall consumption.
It’s vital that businesses address the environmental implications of AI now. Companies willing to embrace the challenge of managing their AI emissions effectively can not only gain substantial economic advantages but also establish a new standard for sustainable innovation.
