AI Will Happily Build the Wrong Thing Very Efficiently

One of the strangest things about building with AI is how quickly a bad idea can start looking finished.

You describe something. The interface appears. The button works. The database updates. The workflow runs.

It feels like progress.

Sometimes it is.

Sometimes you have just made the wrong thing much faster.

That distinction has become one of the most important parts of how I think about AI-assisted product work.

Traditional development had friction built into it.

Even a relatively small feature required enough time, coordination, and effort that somebody usually had to explain why it was worth building.

AI removes a lot of that friction.

That is incredible.

It is also dangerous.

When something can be created in an afternoon, the cost of asking whether it should exist starts to feel strangely high.

So you skip the question.

Build first. Think later.

The problem is that software can be technically correct and strategically useless at the same time.

A faster implementation does not rescue a weak decision.

This sounds obvious, but it shows up constantly.

Ask AI to add an invite button and it will probably add an invite button.

Ask it to build a dashboard and you will probably get a dashboard.

Ask for notifications and you will get notifications.

The model does not automatically stop and ask whether the invite journey makes sense, whether the dashboard answers a useful question, or whether anybody actually wants another notification.

It optimizes for the local instruction.

The person using the tool still owns the larger problem.

That means the quality of the result depends heavily on whether the request describes a feature or the actual system the feature belongs to.

I have run into versions of this across almost every product I have built.

A feature request sounds small.

Add sharing. Add an approval flow. Add a marketplace. Add messaging. Add a scheduler. Add a recommendation engine.

Then you use it.

Who owns the thing after it is created? What happens when it fails? What does the other person see? What state does the system remember? What happens if somebody leaves halfway through? Where does the data go? Who gets notified? Who can undo it? What happens on mobile?

The feature is often the easy part.

The surrounding state, permissions, context, recovery, and human behavior are the product.

The biggest improvement in my AI workflow has not been learning better prompt tricks.

It has been getting more disciplined about the questions that come before the prompt.

What problem are we actually solving?

Who feels the pain?

What do they do today?

What changes after this exists?

What information does the system need to remember?

What can go wrong?

What part should be automated?

What part still requires judgment?

What would success look like in behavior, not just implementation?

Those questions slow the beginning down a little.

They speed up everything after it.

One way I think about this is that AI is often excellent at local optimization.

Give it a bounded problem and it can move incredibly fast.

Make this form clearer. Refactor this component. Add a loading state. Normalize these records. Generate these variants. Connect this API. Write this migration. Test this edge case.

The danger comes when the local optimization starts standing in for the global decision.

A beautifully implemented feature can still make the overall product more confusing.

A perfectly automated workflow can still automate a process that should have been removed.

A polished dashboard can still measure the wrong thing.

A highly efficient content pipeline can still produce content nobody needs.

The faster the machine gets at the local task, the more important it becomes for someone to protect the larger system.

A lot of AI workflows describe the human role as the final reviewer.

AI does the work. Human approves it.

That can be useful, but I think it is too narrow.

The human should often be involved earlier.

Choosing the problem. Defining the constraints. Deciding what evidence matters. Setting the quality bar. Identifying the irreversible actions. Knowing when an exception is actually meaningful.

Then AI can do an enormous amount of execution inside that structure.

The loop is not AI first, human last.

It is judgment → execution → verification → judgment again.

I am increasingly skeptical of workflows that are described only by what the AI can do.

A useful workflow also needs to say what the AI should not do.

Do not publish without evidence.

Do not overwrite a human decision because the data looks unusual.

Do not invent missing facts.

Do not send the customer-facing message until the record is complete.

Do not silently resolve a conflict when two sources disagree.

Do not keep retrying forever.

Escalate the ambiguous case.

Those boundaries are not limitations to remove later.

They are part of the product design.

AI makes adding things cheap.

That means subtraction becomes more valuable.

I have had plenty of moments where the right answer was not to improve the thing AI built.

It was to remove it.

A button that created another dead-end path. A control that implied functionality the product did not really support. A workflow that duplicated something already handled somewhere else. A piece of automation that created more states to manage than time it saved.

When implementation cost collapses, feature count can explode.

Restraint becomes a design skill.

This is the part of the AI conversation I think is easiest to miss.

If execution becomes dramatically cheaper, the scarce thing moves.

The scarce thing becomes knowing what is worth executing.

Taste matters more.

Product judgment matters more.

Understanding the user matters more.

Knowing which exception matters matters more.

Knowing when the system is telling you something unexpected matters more.

Knowing when to stop matters more.

AI does not make judgment obsolete.

It increases the leverage of good judgment and the damage of bad judgment.

My preferred pattern is starting to look like this:

Inspect the current system broadly.

Define the actual outcome.

Identify the constraints and things that must not change.

Plan a meaningful batch instead of issuing a hundred disconnected instructions.

Let AI execute aggressively inside that boundary.

Verify the batch together.

Use the failures as information.

Then decide what should happen next.

That workflow feels slower than immediately typing build this.

In practice, it is much faster because it reduces the number of times we efficiently build our way into the wrong corner.

That is the opportunity and the warning.

AI will happily build whatever you ask for.

The hard part is deciding what deserves to be asked for in the first place.

As these tools get faster, I do not think the winning skill is going to be generating the most software.

I think it will be maintaining enough judgment to know which software should exist, what it should actually do, and when the fastest possible answer is still the wrong answer.