Did you hear that generative AI tools use a huge amount of water to function?

This concern is one of many that most leaders don’t consider when they invest in a new AI agent or other AI technology. The environmental impact of AI is essential as it is a carbon-intensive technology and can therefore affect the green pledges of any organization if they don’t manage it responsibly.

Sustainable AI development allows companies to get the technological innovation they need without costing the Earth and allows them to proudly hold the badge of responsible AI user, benefitting the Earth and their brand.

This article explores why AI’s carbon footprint is a growing concern, principles of low-energy AI design, greener infrastructure, smarter resource use, and sustainable digital advertising.

Why AI’s Carbon Footprint Is a Growing Concern

Energy use is high when AI uses it in GPT-style models like ChatGPT to generate images or text. This is because it has to scan through vast amounts of information quickly and then generate its response. Part of the reason this process is so energy-hungry is that it is different every time, especially if the user types a detailed request.

The biggest environmental concerns around AI use are water usage and emissions. These are the biggest concerns because cooling and powering the equipment that generative AI runs on require high amounts of cooling and power. These are two areas to focus on when developers want to create greener AI tools.

The challenge of building green AI tools is that the demand is high for powerful, fast tools, meaning it is challenging to build AI tools that are sustainable for the environment as well as commercially viable.

Principles of Low-Energy AI Design

Certain software practices are emerging that aim to lower energy usage.

Some of the most common examples are:

  • Algorithm optimization: Improves processing efficiency by reducing unnecessary steps, leading to faster computations and less energy use without sacrificing accuracy.
  • Sparsity techniques: Remove or skip over unimportant data points in models, reducing computational load and memory usage for lower energy consumption.
  • Model pruning: Eliminates redundant or less important connections in neural networks, shrinking model size and improving speed while saving energy during operation.
  • Transfer learning: Reuses parts of pre-trained models for new tasks, avoiding full retraining and significantly reducing time, data, and energy requirements.

So, how do devs achieve high performance with low energy consumption? Efficiency. During the training and inference phases, it’s essential to prioritise the most efficient processes to ensure the energy needed is low while performing at the required level.

The benefits of higher efficiency are lower costs, faster deployment, and lower emissions.

Green Infrastructure and Smarter Resource Use

All these efforts to achieve greener AI tool development are only possible due to renewable-powered data centers. These are centers that process data using renewable power sources like wind, solar, or hydro energy.

So what exactly is green infrastructure, and how does it use resources in smarter ways? Green infrastructure means using technology in ways that save energy and protect the environment, like measuring how much power data centers use every day.

New computer parts like low-power chips and custom tools help computers work faster without wasting electricity. Cooling systems and building in cooler places also save a lot of energy, which makes the process of using computers to run AI tools a lot greener.

Sustainable Digital Advertising: The Role of Connected TV Ads

AI is used in everything these days, including digital advertising. It optimizes ad delivery and targeting accurately and efficiently.

These days, there is a growing shift toward connected TV ads, which make it easier for ad companies to target their ads to reach more relevant people with less waste than traditional linear TV advertising methods.

Many advertising brands are reviewing the amount of carbon they produce as part of their regular campaign performance metrics, signaling a change toward sustainable ad tech that is seeking eco-conscious innovation.

Conclusion

It is a challenge, but it’s possible to balance AI innovation with environmental responsibility. Renewable-energy data centers and focusing on efficient processes for training AI models. These renewable data centers are not optional in sustainable AI development: They are essential.

Prioritizing sustainability can feel like an afterthought in the rush to secure the fastest, most powerful AI innovations. However, it is an essential shared responsibility for developers, businesses, and policymakers. It’s the only approach if organizations want to help secure a future for themselves and their customers.