When AI Makes Features Cheap, Judgment Matters More

With AI code generation, simply saying yes to new feature requests is much easier, almost too easy!

Someone asks for one more option in a workflow, a slightly different path for a group of customers, or a new setting. A developer can use AI to get a plausible pull request together very quickly. While this is useful, it is also where the problem can start.

The question is no longer just, “Can we build this?” More and more, the important question is, “Should we build this at all?”

A quick feature is not a cheap feature

Before AI, a small feature request came with some built-in friction and speed bumps. Someone had to understand the request, work through the behavior, write the code, and think about what could go wrong (edge cases!). That did not guarantee a good product decision, but it did force some thought before the feature existed.

Now it is easier to skip ahead, a request can turn into seemingly working code before the team has agreed on the problem it is trying to solve.

Consider a common request: a customer wants one more option in an existing workflow. On the surface, it may sound simple: Add a setting, show another button, or route certain users through a slightly different process. AI can help create the UI, API changes, tests, and documentation faster than before.

But that is only the first cost.

Someone still needs to ask:

• Which customers actually need this option?

• Does it make the normal workflow harder to understand?

• What happens when it conflicts with another setting or an unusual customer state?

• Who supports it when a customer is confused?

• What happens when the business rule changes six months from now?

• Is this a real product capability, or a one-off workaround that should be handled another way?

• How much technical debt is this adding to our platform?

Those questions do not go away because the code was quick to generate. In some ways, they become easier to ignore. When implementation feels cheap, a feature can start to feel inevitable.

Reviewing the code is not the same as thinking through the feature

There is another issue with AI-generated code: it can be harder to review than to write.

When you write code yourself, you are usually thinking through scenarios as you go. You know which assumptions you made, which edge cases you still need to handle, and what business rule you were trying to represent.

When you review code written by someone else, you are proof-checking their reasoning. With AI, the code may look clean and complete, but there is no real reasoning to inspect. It can do exactly what was requested and still represent the wrong business rule.

A green test suite is useful. A code review is useful. Neither one, by itself, tells you whether the feature should exist or whether the business expectation was correct in the first place.

This matters most when a change affects money, eligibility, compliance, or a commitment to a customer. Those business scenarios need to be clear before the code starts moving quickly. Someone needs to own the decision, not just approve the pull request.

Part of the job is knowing when not to build

Being a good engineer or consultant is not just about finding a way to build what someone asks for. It is also about helping them decide when not to build it.

Sometimes the better answer is a simpler operational process. Sometimes an existing workflow needs to be improved instead of adding another configuration option. Sometimes the right thing to do is learn more about the customer problem before committing it to the codebase.

That is not being resistant or slowing things down. It is how you keep a product from becoming a collection of exceptions that nobody fully understands.

Every feature adds something that users need to learn, support teams need to explain, engineers need to test, and future changes need to account for. One small option may be fine. Enough small options become a system that is difficult to explain and risky to change.

That is technical debt, even if every pull request looked reasonable when it was merged.

Use the speed to make better decisions

This is not an argument to avoid AI or turn every small change into a long process. AI is great for well-understood and thought-out work. It can remove repetitive effort and help move a good idea forward much faster.

But we should not confuse a fast implementation with a good product decision.

As code gets cheaper to generate, judgment becomes more valuable. The teams that use AI well will not be the ones that add the most features. They will be the ones that use the extra speed to build the right things and are comfortable leaving the wrong things out.