AI and the Climate

Balancing innovation with environmental impact

Artificial intelligence (AI) is revolutionising industries, shaping technologies, and transforming how we live and work. AI's potential is vast and unprecedented, from powering voice assistants to enabling medical breakthroughs. However, beneath the promise of innovation lies an uncomfortable truth: the environmental cost of AI. With growing concerns about climate change, it’s crucial to assess whether the benefits of AI justify its significant ecological footprint.

AI, particularly deep learning AI, is energy-intensive. Training large machine learning models requires enormous computational power, translating into significant electricity consumption. Data centres, the backbone of AI operations, consume vast amounts of electricity. These facilities are filled with energy-demanding servers and require extensive cooling systems to prevent overheating. While strides are being made to make data centres more energy-efficient, their energy consumption continues to rise as the demand for AI applications grows. 

A related concern is the source of the energy used. In regions where electricity grids rely heavily on fossil fuels, the carbon footprint of AI becomes significantly more prominent. While some tech giants like Google are committing to renewable energy and carbon neutrality by purchasing small nuclear reactors to power their AI operations, the global picture remains mixed. 

Despite these negatives, AI also can mitigate many of the issues we face in tackling climate change. AI can help us optimise our energy grids, ensuring that our systems are efficient and minimise waste. It can effectively anticipate the demand for electricity and adjust the supply dynamically. In agriculture, AI can be used to improve precision, allowing farmers to utilise their resources to bring in more bountiful yields at lower environmental costs. All this is to say that AI, despite its potential for a large carbon footprint, can also be a powerful tool in mitigating climate change. 

With both of these understandings in mind, it is vital to think about trade-offs when it comes to AI use. First, a broad effort should be made to reduce the environmental impact of AI by making programs less computationally intensive while maintaining performance. Secondly, power sources for AI should be renewable. Many AI programs that are available today also do not need to exist: chatbots and generative image/video creators may be fun to mess around with, but they offer little benefits and demand many resources to run. Finally, AI’s power to fight climate change must be fully utilised; if the benefits are worthwhile, they may compensate for some of AI’s impacts. 

Governments and regulators have a role in ensuring AI development and use align with climate goals. Policies that incentivise what has been mentioned previously are crucial. At smaller scales, AI developers and researchers can help advocate for responsible practices. Raising awareness of the environmental impacts of AI is a start, as many who utilise AI need to fully understand the effects of their use. The question of whether the climate impact of AI is ‘worth it’ doesn’t have a simple answer — it depends on how we choose to harness the technology. If AI can be a powerful tool in fighting climate change, it must be nurtured and expanded in this role when needed. It depends on our ability to minimise AI’s impacts and maximise its benefits for all. The choice we must make is simple: using AI responsibly and sustainably to ensure that its legacy makes progress, not pollution.

Image from Wikipedia Commons