The phrase "vibe coding" surged into developer conversations in late 2025. At its simplest, it means describing what you want in natural language and letting AI write the code.
The core idea
The workflow moves away from typing every line by hand. You describe the goal, the AI produces code, and if the result is off, you correct it in plain language. The role feels less like writing code line by line and more like directing how the software should behave.
Cursor's Composer mode, Claude Code's agent mode and GitHub Copilot's coding agent all point in this direction.
Where it works
Good fits include:
- Rapid prototypes, where the goal is to move from idea to runnable demo quickly.
- Repetitive CRUD code.
- First explorations of an unfamiliar framework or language.
- Test cases and documentation.
Weak fits include:
- Low-level code where performance is critical.
- Security-sensitive modules.
- Situations that require exact control over every branch of logic.
- Large legacy codebases that already carry complex constraints.
The argument around it
Supporters see vibe coding as a way to lower the barrier to software creation, giving more people a path from idea to working tool. Critics warn that people who do not understand the code they ship will face serious trouble when they have to maintain or debug it. If you cannot read the AI-written code, what happens when it breaks?
The debate echoes the one around low-code platforms. The likely answer is similar: vibe coding can be a useful tool, but it does not remove the need to understand the underlying system.
Who is using it
The heavy users are not only beginners. Many experienced developers use the same pattern, but for a different reason. Beginners use it to get code that runs; senior engineers use it to skip the tedious parts faster.
METR's research adds a useful caution: experienced developers can sometimes become slower with AI tools, by about 19% in the study cited by the source article. Vibe coding is not a silver bullet. It works best when the person using it knows when to lean on it and when to stop.
Sources: developer community discussions, CocoLoop, METR research.