The conversation around integrating large language models (LLMs) into software development is heating up. It’s not just a matter of whether these tools are useful or flawed—it’s about how they are reshaping our profession, sometimes challenging the very foundations of traditional software engineering.
I remember a time when learning to code was seen as a reliable ticket to a stable and well-paid career. Coding bootcamps flourished, promising a swift path to essential skills. Yet, like many other industries, the market eventually felt the pinch when oversupply, particularly of junior engineers, began to create real challenges.
The tech boom of the mid-2010s, driven by venture capital, propelled rapid hiring. But post-pandemic, layoffs have exposed an unsustainable growth model. Today’s landscape shows a clear shift: while there remains a high demand for experienced engineers with specialised skills, many newcomers struggle to break in without the vital hands-on experience they need.
For those with years under their belt, tools like GitHub Copilot (an example of LLM-based assistance) can help with routine coding tasks. However, these tools produce probabilistic outputs, which may lead to missteps. Experienced engineers are better equipped to spot and correct such errors—a skill that junior developers are still in the process of acquiring. Over-reliance on these tools by less experienced coders can inadvertently fuel a cycle of mistakes.
Critics have raised valid concerns about what some call ‘vibe coding’, where the allure of quick fixes might eclipse the essential learning curve of making and correcting mistakes. If you’ve ever struggled with debugging a tricky piece of code, you know that these challenges often lead to growth and expertise. The risk is that prioritising speed over learning could eventually erode the quality and reliability of our codebases.
The real challenge lies in finding a balance. Embracing the productivity boosts of LLM tools is important, but so is nurturing new talent through hands-on experience and mentorship. It’s about safeguarding the future of our industry by ensuring that the next generation of engineers is well-prepared to tackle complex problems with both knowledge and caution.
