Is Vibe Coding the Future of Programming?

Dear ChatGPT, I am a programmer with years of experience, but the rise of AI tools has left me wondering whether my skills are still relevant. AI can generate code quickly, companies are hiring fewer developers, and new ideas like vibe coding seem to replace traditional programming tasks. How can I stay employable as the industry changes?

The short answer is that vibe coding cannot replace programming. It is a useful technique for guiding AI tools, but it does not provide the engineering judgment needed to build or maintain real systems. Modern software work still requires understanding architecture, security, performance tradeoffs, data structures, and the behavior of complex networks of services. AI can speed up code production, but it cannot reliably decide how a system should be designed, how components should interact, or how risks should be managed. These are human reasoning tasks, not language tasks.

In practice, programmers must move upward into engineering functions that AI cannot automate. This includes system design, debugging across multiple layers, integrating components from different teams, and interpreting ambiguous requirements. Technical reasoning becomes the core skill. Vibe coding becomes a tool that accelerates work, not a career path by itself. The programmer who relies only on AI to write code will be replaced by the programmer who can use AI while also understanding how the entire system fits together.

Domain knowledge is becoming more important, not less. Companies increasingly prefer engineers who understand a specific field, such as finance, logistics, healthcare, or embedded systems. AI can write general code, but it does not know the legal constraints of a banking application or the reliability requirements of a medical device. A programmer with strong domain knowledge can guide AI tools effectively because they understand what is correct, what is safe, and what must never happen in production. This gives human engineers a durable advantage.

Large-scale AI adoption brings its own challenges. Big companies already struggle with coordination. Teams own hundreds of services, internal APIs are constantly changing, and no one person understands the entire system. Introducing AI into this environment does not simplify the work. It increases the need for careful review, prioritization, and human oversight. Automated tools can generate patches, but only humans can judge whether a fix respects product goals, avoids regressions, and aligns with long term architectural plans. This is why processes like bug review boards remain essential even in highly automated organizations.

(This article was written by ChatGPT.)