Prompt - copy - paste - works. Until it doesn’t.

One of the most appealing AI workflows in development looks like this:

1. Write a prompt.
2. Get the code.
3. Copy it.
4. Paste it.
5. It works.

The problem is that "it works" is not the same as "it is correct."

AI-generated code can pass the first test, solve the obvious problem, and look good enough to commit.

But it is often only later that we discover what had actually been introduced into the system earlier: a wrong assumption, a hidden dependency, a bad edge case, a security issue, or a solution that does not fit the existing architecture.

Keep in mind - AI is extremely smart. AI is particularly good at producing results that look convincing.

That is useful - but also dangerous.

That very confidence AI projects - that it wrote the code easily and flawlessly - is dangerous.

The easier the result is to obtain, the greater the temptation to skip the part of the process where we try to understand what we actually got.

That is why AI-enabled development should not stop at "works."

You need to verify:

• why it works,
• under what conditions it works,
• what happens when those conditions no longer apply,
• how it fits into the rest of the system,
• and who will maintain it six months from now.

The copy-paste approach is not a problem in itself. Developers have always reused code, libraries, examples, and existing solutions.

The problem starts when copying replaces understanding.

That is true. AI really can significantly shorten the path to the first functional result.

But that first result is not the end of the engineering process.

It is only the beginning of the work required to determine whether what works now can continue to work reliably.