But, writing code is only one part of the work.
Before building a solution, someone has to understand the problem and who it is for. Then come the decisions: how the system should be structured, how data will move through it, which services need to communicate, how information should be protected, and what happens when something fails. There is also testing, fixing issues, monitoring the system in production, and keeping it working as the product grows.
AI can help with many of these steps. It can suggest approaches, explain concepts, generate code, point out possible bugs, and speed up repetitive tasks. That is genuinely useful. Still, an answer can sound convincing and be wrong, incomplete, or disconnected from the project’s context. Code that works in a small example can cause problems when it meets real data, real users, and the systems around it.
That is why knowing how to write a prompt helps, but it does not solve everything. You still need to evaluate what the tool produced, ask better questions, test assumptions, and notice when the answer does not address the actual problem. That kind of judgment depends on context and experience, as well as technical knowledge.
This does not mean only people with the job title “developer” can build things with AI. In fact, these tools are bringing more people closer to programming and making ideas easier to try. Someone with no software background can create a useful prototype, automate a task, or get an idea off the ground. That is a meaningful change.
The difference shows up when we need to turn that first result into something reliable, secure, and maintainable. That takes decisions a single prompt cannot make for us: understanding the needs of the people who will use it, considering risks, making technical trade-offs, and taking responsibility for the outcome.
I think AI is changing software development, but it is not removing the need to understand what we are building. It can reduce the time spent writing code and make room for more experimentation. At the same time, it makes it even more valuable to ask the right questions, see the system as a whole, and recognize when something needs another look.
Maybe the question is not whether anyone can build software with AI. More people can, yes. The question is what it takes to make sure what we build works well, solves a real problem, and still makes sense after the first prototype.