There was a time when building something took months. That slowness acted as a filter: if you did not believe in the idea strongly enough, you abandoned it before you even started.
With AI, that filter is gone.
You can have a functional dashboard, a database, and a decent interface with a couple of prompts. That is fine. The problem comes afterward.
The builder's high and the zero-friction trap
Watching an app work within minutes creates an immediate high. The problem is that the same spike makes what comes next — getting the first customer, iterating on real feedback, persisting when it does not take off — feel unbearably boring. The brain already got its reward. The real work has not started yet.
The practical result: a graveyard of repositories. I have several myself.
The trap of being able to do everything
When building something was expensive, specialization limited you. If you were a backend developer, you did not consider building a mobile app overnight. That limitation was, unintentionally, a prioritization system.
Today AI tells you that you can do everything. And when you can start something new at almost no cost, any friction in the current project looks like a sign to abandon it. The next idea always sounds better than sticking with the one you have.
Outsourcing judgment
When you delegate architecture or flow-design decisions to AI, you skip the process of making mistakes, researching, and defending a decision. That uncomfortable process is what makes you better. Without it, you accumulate surface-level intuition: you know how to use the tools, but you do not understand why you chose one over another.
Building products nobody asked for
AI makes building software very cheap. It also makes it very easy to hide in that process so you do not have to talk to users. Finishing a technically correct prototype over a weekend is comfortable. Calling ten people to validate whether the problem exists is uncomfortable. So people build first and validate later, or never validate at all.
The result is a collection of polished micro-SaaS products solving problems nobody has.
How to get out of the loop
AI is useful. Boilerplate, templates, repetitive code — it is fine to let AI handle all of that. The problem is not using it; the problem is using it to avoid the uncomfortable parts.
What helped me was treating the first prototype as what it is — a mockup, not a product. The real work starts when you have to convince someone to use it.
Also: when AI suggests something and I do not understand why it is the best option, I do not accept it. The discomfort of having to understand is exactly what should not be delegated.
And most importantly, something it took me a while to learn: before building, verify that the problem exists. Not with an MVP. With a conversation.
AI builds quickly. But if you do not know where you are going, getting there quickly is useless.