Generative AI (GenAI) is being celebrated as a game-changing tool for software development, opening up exciting possibilities for development teams. But while AI can assist in many ways, it is crucial to remember that AI acts as a junior developer rather than a seasoned engineer.
The belief that AI will replace developers overlooks the essence of the job. Developers and engineers are not merely code writers — they solve problems by designing systems that meet specific constraints and requirements. While AI can generate code, it lacks the human ability to ensure the code aligns with the broader goals of the software.
In the AI era, the developer’s role is evolving, not disappearing. Human creativity, contextual understanding, and strategic thinking will remain central to shaping effective software solutions.
The limitations of AI in software development
Over the years, developers have relied on tools like code templates and auto-complete to speed up programming. GenAI pushes this further by generating full code blocks from simple natural language prompts. However, AI struggles with logic and lacks the contextual understanding to tackle complex business challenges effectively.
For instance, an AI tool could generate code to calculate total sales revenue. But without additional context, it may overlook essential details such as factoring in returns, rebates, or specific formatting needs for reporting. While the code might technically work, it often fails to solve the underlying problem fully.
Moreover, GenAI frequently produces poor-quality code. Its training data includes high- and low-quality examples, and the AI cannot distinguish between them. Research from Bilkent University highlights this issue, showing that ChatGPT generates correct code only 65% of the time, with GitHub Copilot and Amazon CodeWhisperer performing even worse.
AI-generated code can also compromise security. Without following established protocols, AI often introduces vulnerabilities. Developers’ misplaced confidence in AI exacerbates this problem. A Stanford University study revealed that programmers using AI tools were likelier to believe their code was secure, even when it wasn’t. This false confidence poses significant risks, especially since over 90% of security leaders have expressed concerns about AI in coding. Alarmingly, less than half of organisations have implemented policies to manage these risks.
These challenges underscore the importance of experienced human developers in overseeing and refining AI-generated work.
The evolving role of developers
The future of development will involve greater collaboration between humans and AI. By 2028, Gartner predicts that 90% of enterprise software engineers will use AI code assistants. However, developers’ core responsibilities will remain vital to maintaining code quality, adapting systems to new demands, and addressing project—specific needs.
In this new era, developers are expected to act more as architects, defining high-level requirements and constraints while AI handles routine coding tasks. This shift will enable developers to concentrate on understanding business needs, designing robust system architectures, and ensuring performance and security.
AI’s role in development could mirror pair programming, where it acts as a less experienced partner. While AI manages simpler tasks, developers will focus on reviewing and improving the output.
This partnership may also transform traditional development workflows. Developers might integrate code reviews, testing, and compliance checks earlier to catch AI-generated errors before they escalate. By doing so, teams can enhance code quality, minimise risks, and maintain consistency.
However, the widespread use of AI presents a dilemma for career growth. If AI fills the junior developer role, fewer entry-level opportunities may be available for humans, reducing the talent pool of future senior developers. This issue will need careful consideration as the industry embraces AI tools.
The enduring value of developer skills
Despite GenAI’s rise, the skills that define a good developer remain unchanged. Success in this field depends on critical thinking, problem-solving, and adaptability—qualities AI cannot replicate. With AI taking over routine tasks, developers will spend less time typing code and more time directing AI and refining its output.
Ultimately, a developer’s value lies in their ability to understand code’s broader purpose and structure. While AI can assist in coding, it cannot replace the creativity and strategic thinking that human developers bring to the table. As AI tools become more prevalent, mastering the art of working with AI will be key to building innovative, secure, and effective software solutions.
