Sustainable practices through copilots and AI

2024 is shaping up to be a transformative year, and the world of web development is no exception. A significant trend is the rise of generative AI, which dominates the tech news space. Like any emerging technology, it brings numerous opportunities, and business leaders are prioritizing it for the coming years.

While the application of AI as a great enabler to solve customer needs and increase productivity is well-known, its role in tackling the ‘environmental sustainability’ challenge is less discussed.

With the emergence of generative AI, copilots have become increasingly useful in a software engineer’s daily work. These coding assistants work closely with software engineers to streamline and improve their workflow, freeing them from the complexities of non-functional code and allowing them to focus on the core functionality and business logic of applications. A good application of this can be in good development practices and enhance code quality, security, and guidance for more reliable applications. Furthermore, these tools combined with Low-Code/No-Code (LCNC) capabilities make a match made in technology heaven, unlocking a new level of accessibility and efficiency in addition to empowering a wide range of engineers, including the most junior ones, to create much more robust solutions.

“Machine learning and generative AI enable personalized web content based on user behavior, delivering more relevant content, reducing unnecessary pageviews, and optimizing data transfers.”

In light of this, how can technologies be a powerful tool for minimizing the carbon footprint produced during web development processes? Let’s explore two key areas: code optimization and personalization.

Code Optimization

Software engineers can optimize code and resource usage by generating more efficient code, focusing on core functionality and business rules, minimizing redundancies, eliminating unnecessary code blocks, enhancing image and video formats, and simplifying data transfer. These practices help engineers choose better options that lead to smaller code and file sizes, reducing server load and power consumption.

Personalization

Machine learning and generative AI enable personalized web content based on user behavior, delivering more relevant content, reducing unnecessary pageviews, and optimizing data transfers.

Additionally, AI can predict what content a loyal and recurring user of the application is likely to access, allowing the pre-cache of the content, eliminating the need for additional server requests, and lowering server load and power consumption.

Optimizing server load is vital in combating climate change. Data centers, with their immense processing power, are among the largest energy consumers globally, consuming hundreds of TWh (terawatt-hours) with a rising trend. As major tech companies increase AI model training, which demands significant resources and consistent energy, employing these strategies ensures that energy savings from optimizations surpass the their usage in training models. Knowing this, engineers can and should take advantage of it to create greener applications, contributing to a sustainable digital future.