One of the easiest mistakes to make in AI-assisted development is assuming the target stays fixed just because the feature list does.
At first, the moving target may look like the specification itself. You think you know what the project needs, then new requirements show up, assumptions change, and the design has to adapt. That happens all the time.
But there is another kind of moving target that shows up later, and in some ways it matters even more.
It is the moment when a project stops being judged by whether the pieces work and starts being judged by whether the whole thing is actually ready to release.
That is where AI can make things tricky.
AI is very good at helping you get to working. It can help build features, scaffold systems, fill in repetitive code, and move a project forward at a speed that still feels a little wild sometimes. But the closer you get to the finish line, the more dangerous it becomes to confuse visible progress with actual readiness.
A feature can work and still be part of a product that is not ready.
A screen can behave correctly and still contribute to an application that feels slow, tangled, or heavier than it should.
A long list of individually acceptable compromises can quietly add up to something that is no longer acceptable as a whole.
That's the kind of moving target that matters near release.
I’ve been reminded of that recently on a project that was getting very close to version one. The feature set had grown in a good way. The product had become capable. It could do a lot of useful things, and on the surface that felt encouraging. But as we worked through the final stretch and started walking through the application as a complete experience instead of a series of isolated tasks, it became obvious that something was off.
There were places where the performance had started to slip below what I would consider acceptable for a normal user.
What made this tricky is that none of those issues had looked catastrophic when they first appeared. While we were focused on solving a particular bug, refining a particular feature, or getting a particular workflow in place, the slowdowns and overlaps did not always seem like the most important thing in the room. Each one could be explained away. Each one could be tolerated for the moment. But once the system was viewed as a whole, it became clear that some of the core pieces were starting to overlap, interfere, and step on each other in ways that were dragging down the overall experience.
At that point, the right move was not to push ahead and call it good enough for version one.
The right move was to stop.
Not forever. Not dramatically. Just honestly.
Stop the release push. Dig in. Map the moving parts. Trace what each piece is doing. Evaluate the behavior at a lower level than is comfortable when all you want is to cross the finish line. That kind of pause can feel like lost momentum, but it is often the difference between shipping something that merely functions and shipping something that deserves to exist in front of users.
This is where one of the most popular sayings in software starts to need a little more precision.
Done is better than perfect.
That is good advice as far as it goes. It keeps people from polishing forever and never shipping anything. It keeps you from getting stuck in vanity work and endless refinement.
But done is not the same as ready.
Done is better than perfect, but ready to release is what counts.
Let me repeat that again because it deserves to be said.
Done is better than perfect, but ready to release is what counts.
That's especially true when AI is involved, because AI can help you get to done much faster than it can help you judge whether done is actually good enough. It can help produce code, fill in gaps, and keep momentum going. What it can't reliably do is make the final call on whether the whole thing feels coherent, performs well enough, and meets the standard you actually want attached to your name.
AI may tell you that something is fine for an MVP. Sometimes it may even be right. But “good enough for a first release” is also one of those phrases that can become a hiding place for problems you already know are real. If the overall experience is slower, messier, or less coherent than it should be, then the question is not whether AI can justify shipping it. The question is whether you, as the programmer, believe it should go out the door that way.
That's not a coding question. It's a programming question.
Programming means understanding when the interactions between parts matter more than the existence of the parts themselves. It means caring about the total behavior of the system, not just whether individual items on a checklist can be marked complete. And sometimes it means having the discipline to stop near the finish line, go down to the micro level, and figure out which small pieces are fighting each other before you let the product leave the shop.
That's the difference between AI-assisted development and AI slop.
AI slop says the code works, the screens are there, the demo succeeds, and version one can always be cleaned up later.
AI-assisted development, done well, says something else. It says that speed is useful, but only if someone is still paying attention to quality, performance, interaction, and fit. It says that release readiness is not measured only by feature count. It is measured by whether the whole thing behaves the way it ought to behave.
The moving target is not just the spec.
Sometimes the moving target is your understanding of what “done” is supposed to mean.
And if that target moves, the smartest thing you can do is admit it before you ship.
Related field note
For the lived version of this lesson, see When “Done” Wasn’t Ready to Release.