Does Every Piece of Software Need the Same Code Quality in the AI Era?

We have discussed how clean code, software architecture and engineering principles become more important in the AI era. But there is another side to this conversation: disposable software.

With AI, we can now build software for a specific need in a few hours. Sometimes we may use it for a single task or a single day, then throw it away.

Examples include a personal automation script, a one-time data analysis tool or an application for a small event.

Does that software need the same engineering effort as a banking system that will be maintained for years?

There are four main factors to consider:

Consequences of failure — How much harm could the software cause if it fails?
Dependencies — How many people and other systems depend on it?
Cost of replacement — How easy would it be to rebuild if necessary?
Cost of maintenance — How much effort would it take to maintain over time?

If all four are very low, we can treat it as disposable software. In those cases, meeting the immediate need quickly may matter more than adding engineering complexity.

For durable software, where failure can cause serious harm, many people depend on it, and it must be maintained for years, code quality and architecture become more important.

This is not a binary distinction. The degree to which software is disposable should guide the engineering rigor we apply. And calling software disposable never means we can ignore security or safety.

The important skill in the AI era is deciding how much engineering effort each piece of software needs.

When we develop software with AI agents, AI harnesses and harness engineering also play an important role in maintaining code quality. We will explore that in the next article.