Introduction
There is a growing concern that AI will replace developers or lead to a significant decline in the need for human developers. This idea overlooks the fundamental constraints of current AI/LLM capabilities. It disregards the fact that humans are not subject to the same limitations. The concept of replacing developers completely or all of a sudden turning one developer into five is still science fiction. Anyone telling you the opposite is ignoring the simple fact that code at scale is still nuanced, requires context, creativity, a shifting perspective on the entire project, and the intuition of human developers. These are all skills that are currently out of reach for LLMs. They cannot stop and shift focus to the broader project, making complex decisions on the fly while understanding business needs and objectives. They don't understand the nuances of large projects and how features may interact with or complement each other, or recognize that a specific pattern makes more sense for the project. These are all things they still need human developers to accomplish.
A Changing Landscape
The job of a developer is changing; there is no valid argument against that. As AI becomes increasingly present and undeniable in engineering, ignoring it will leave you behind. That's not to say you won't be unemployable; there will still be agencies and smaller shops that are behind the adoption curve, just as there always are. Still, you will be behind in the industry sense. AI is reshaping how developers work, automating processes, documentation, and building features, which increases productivity. This change doesn't eliminate the need for humans to design and build systems and projects, guide AI agents, and make complex decisions based on context, business objectives, and intuition. This change accelerates changes in how developers work and their skill set. Still, it does not make human engineers obsolete1.
The role of the human developer, he said, becomes to guide and direct the A.I. agents — “the conductor of an A.I.-empowered orchestra.”1
LLMs without human intervention fall back on the average of their training. It's not a new and innovative approach that no one has seen before; for that, it still requires human ingenuity. That's what makes it work so well for smaller, well-planned features or repetitive tasks; it falls back to the average safe answer. Not one that blows your mind, but it gets the job done predictably.
The Time is Now
The Future of Jobs report still indicates that AI and software-related jobs will experience rapid growth, and Worldmetrics predicts that over 500,000 new software jobs will be created in 2025 2. We are still at a point where AI makes good developers better and more efficient, not obsolete. Therefore, demand will increase as companies increase their product velocity to match the increased productivity. Eventually, it reaches equilibrium.
“Learn computational thinking. For every field X there's going to be a computational X. If you figure out how to think about things computationally and you know the best tools, then you're in good shape.”
Wolfram, known for his deep understanding of software and large language models, makes it clear: the edge always belongs to the human who understands the system, not the system itself.2
Learning software development now is still setting yourself up for success, and focusing on how you can maximize your potential with AI will pay off in dividends. The Codesmith article says it best "AI won’t replace developers, but a developer using AI will"2. As the agentic systems developers use become more complex, there will still be a need for developers to guide them and keep them on track, developers who understand the systems, etc. If anything, it creates new jobs and new fields to specialize in as AI use grows. It also forces junior developers to think more like senior developers and understand the higher-level thinking and planning required for large codebases.
The truth is that the role of the programmer, in line with just about every other professional role, will change. Routine, low-level tasks such as customizing boilerplate code and checking for coding errors will increasingly be done by machines.
But that doesn’t mean basic coding skills won’t still be important. Even if humans are using AI to create code, it’s critical that we can understand it and step in when it makes mistakes or does something dangerous. This shows that humans with coding skills will still be needed to meet the requirement of having a “human-in-the-loop”. This is essential for safe and ethical AI, even if its use is restricted to very basic tasks.
This means entry-level coding jobs don’t vanish, but instead transition into roles where the ability to automate routine work and augment our skills with AI becomes the bigger factor in the success or failure of a newbie programmer.3
Conclusion
AI is not coming to replace developers. It will create a demand for new skill sets and augment developers, allowing them to become more productive and efficient. It will create new jobs in building and managing complex agentic systems and tools. For now, it still needs human intuition, creativity, and the ability to zoom in and out of complex projects and problems to make decisions and plan features based on context, domain knowledge, and business objectives.