As AI increasingly takes over routine tasks, human strengths like problem-solving and innovation will become even more valuable.

Artificial Intelligence (AI) has become the defining buzzword of the 21st century. It’s all around us, integrating into our lives more rapidly than we could have imagined just a few years ago. While AI’s advancement is set to transform the way we work, concerns about job displacement are understandably widespread. However, a closer look at these trends suggests that, in the long run, the rise of AI may produce counter-intuitive effects on the labour market.
AI pioneers envision a future where AI plays a central role across nearly all sectors of the economy. Although we may not be there just yet, the IT sector’s vulnerability to rapid AI advancements is already becoming apparent. AI systems today are becoming increasingly capable of writing and debugging computer code. Anecdotal evidence confirms that many professionals in the IT industry are already leveraging large language models (LLMs) to assist with coding. Although human oversight remains necessary, the growing sophistication of these tools have sparked legitimate concerns. Fears are ripe that with time, simpler, & repetitive coding jobs will be the first to be replaced by AI. Looking ahead, people argue that continued advancements may eventually lead to the automation of even more complex software development roles. In such a scenario, the remaining jobs will only be for AI/ML engineering and maintaining AI systems. While there is considerable support for this viewpoint, a closer examination of how AI works, suggests that coding jobs are not going to completely disappear.

We know that AI tools can generate code in various programming languages mainly because they have been trained on vast amounts of publicly available code from sites like Stack Overflow (a popular forum where developers share ideas, ask questions, and exchange knowledge). However, these tools don’t “understand” code in the way humans do; instead, they generate plausible solutions based on patterns learned from existing examples. A crucial point to recognize is that AI relies on existing code to solve problems. In other words, it struggles with problems that haven't already been addressed in its training data. If the trend of relying on AI for coding continues, we may eventually reach a point where the internet’s repositories of new and original code are updated more slowly. That is because fewer people may retain the skills or motivation to write and share novel solutions independently. This growing reliance on AI tools could erode essential problem-solving and debugging skills, particularly among newer developers. In his blog, The Pragmatic Engineer, Gergely Orosz highlighted a sharp decline in the number of questions posted on Stack Overflow, suggesting that the site is becoming increasingly irrelevant as more developers turn to AI for coding help. This shift suggests a potential weakening of hands-on coding abilities and raises concerns that online code repositories may not become obsolete but could see slower updates as fewer developers actively share new solutions. This poses a significant limitation for AI: since these tools depend on existing code to generate solutions, they struggle to address novel challenges. If the pool of skilled coders continues to shrink, AI will lack the fresh examples it needs to stay effective.
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