Everton Molina, Senior Product & Software Engineering Manager, Luizalabs
2024 is shaping up to be a transformative year, and the world of web development is no exception. A key trend is the rise of generative AI, which is dominating the tech news landscape. Like any emerging technology, generative AI brings with it numerous opportunities, and business leaders are prioritizing it for the coming years.
While it is well known that the application of AI can contribute significantly to solving customer needs and improving productivity, its role in addressing the challenge of “environmental sustainability” has been less discussed.
With the advent of generative AI, Copilots have become increasingly useful in the daily work of software engineers. These coding assistants work closely with software engineers to streamline and improve their workflows, freeing them from the complexities of non-functional code so they can focus on the core functionality and business logic of their applications. When applied properly, this results in good development practices, improving code quality, security, and guidance, resulting in more reliable applications. Moreover, when these tools are combined with low-code/no-code (LCNC) capabilities, it’s a combination made in technology heaven, enabling not only new levels of accessibility and efficiency, but also enabling a wider range of engineers, including the most novice, to create much more robust solutions.
“Machine learning and generative AI personalize web content based on user behavior, reducing unnecessary page views and data transfer, and serving up more relevant content.”
With this in mind, how can technology act as a powerful tool to minimize the carbon footprint created during the web development process? Let’s look at 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 redundancy, eliminating unnecessary code blocks, enhancing image and video formats, and simplifying data transfer. These practices enable engineers to choose better options that reduce code and file size, server load, and power consumption.
Personalization
Machine learning and generative AI personalize web content based on user behavior, reducing unnecessary page views and data transfers, and serving up more relevant content.
Additionally, AI can predict what content an application’s loyal, repeat users are likely to access, allowing content to be pre-cached, eliminating the need for additional server requests and reducing server load and power consumption.
Server load optimization is essential in the fight against climate change. Data centers with their massive processing power are one of the largest consumers of energy in the world, consuming hundreds of TWh (terawatt hours) and growing. As big tech companies ramp up training of AI models, it requires huge resources and consistent energy, but adopting these strategies ensures that the energy savings from optimization outweigh the training model usage. With this understanding, engineers can leverage it to create greener applications and contribute to a sustainable digital future.
