As automation improves and models make fewer errors, an uncomfortable question appears. Do we still need developers reviewing AI-generated code? And if not, what exactly is the developer’s role?
Logical thinking will remain critical. The main problem with citizen developers and low-code platforms was never the tools, but the lack of structured thinking. The same shows up when business users build applications with AI. They understand workflows but often struggle with edge cases, error handling, and how systems behave as they grow. When applications become large enough, someone still has to make sense of the whole and connect business requirements with logic.
Industry knowledge will also matter more, not less. Developers with deep experience in a specific industry understand regulations, standards, and local variations that AI often misses, especially in smaller markets. That knowledge makes guiding AI far more effective and may become a key differentiator for development companies.
Understanding code will still be necessary. There will be cases where AI gets stuck, security constraints limit its use, or the technology is obscure enough that manual debugging is required. These situations may become rarer, but they will not disappear.
So what should developers do now? Learn AI-assisted development properly, not just the tools but the workflows and limitations. Double down on industry expertise if you have it. And keep your technical fundamentals sharp, because even if you never write code, you will still need to understand it.
