The future of AI-Powered coding: Why code generation is not enough

The dawn of the digital age brought forth a range of technological advancements, reshaping industries and redefining norms. In the realm of software engineering, generative AI coding assistants, including tools like GitHub Copilot and Tabnine, epitomise this wave. Drawing from the impact of foundational models like OpenAI’s GPT and Anthopic’s Claude, these tools interpret natural language inputs to suggest and generate code snippets, amplifying developer productivity. Notably, GitHub Copilot now underpins a staggering 46% of coding tasks, enhancing coding speed by an impressive 55%.

A study from McKinsey emphasised that software development stands as one of the best ways to achieve organisational efficiency with generative AI. Yet, the overarching question remains: How can generative AI go beyond mere code generation to elevate the software development life cycle?

Code better, not just faster

According to a recent survey from Stack Overflow, 70% of developers are either harnessing AI tools or gearing up to integrate them in the imminent future. Yet, while tools like GitHub Copilot and Replit’s Ghostwriter are predominantly centred on development and testing, there are several ways that generative AI could be used to enhance developer’s workflows.

Among the various stages of the Software Development Life Cycle, code optimisation is one that is often overlooked. Yet, when embedded within the Continuous Integration and Continuous Deployment processes, it becomes the point wherein code is developed to peak performance. It’s the point at which code isn’t just moulded to function but to excel, to minimise latency and to amplify user experiences.

However, the benchmarks for code performance are continuously being changed, particularly in a landscape dominated by AI. But what exactly is driving this?

Cost of compute and profitability: Software is eating the world. Even the allure of modern vehicles often lies in digital features like parking assistance and IoT connectivity. Yet, the attraction of generative AI coding assistants comes at a price. A16Z’s report underscores this, with cloud spending often taking 75-80% of revenue for software firms. Clearly, efficient code is not merely a technical goal but a financial necessity, as it can significantly cut cloud costs and boost profit margins for organisations.

Speed, Scale and Customer Experience: In the business world where milliseconds matter, code optimisation is the linchpin. From high-frequency trading to autonomous vehicle decision-making, performance is king. However, the advent of Generative AI and LLMs brings a new dimension to the speed challenge. Despite their benefits, the extensive processing times associated with LLMs can pose a significant hurdle for real-time and edge applications, particularly as the number of users and applications continues to grow.

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