A terminal window is not product-market fit

A terminal window is not product-market fit. Neither is a second brain, an agent stack, a local model setup, a dashboard, a private automation, or a weekend prototype. Those things can be useful. They can be signs of skill. They can be the beginning of something. But they are not the thing. The thing is whether you understood a real problem clearly enough to create a change in someone else’s behavior. Someone uses it. Pays for it. Changes a workflow around it. Stops doing the old thing. Trusts it with a decision. Comes back to it. That is the part that gets easy to skip right now. AI has made building cheaper. That is a big deal. But when building gets cheaper, the scarce skill moves upstream. It becomes judgment. What problem is actually painful? Who feels it sharply enough to act? What are they asking for on the surface? What do they actually want underneath? What would make the solution feel obvious, useful, and safe enough to adopt? A customer might say they want an AI chatbot. Maybe they do. Or maybe they want faster lead response, less chaos in the office, better qualification, fewer missed calls, a cleaner handoff, more trust before the sales conversation, or a system that helps the team stop dropping balls. The surface request is rarely the whole request. This is where a lot of AI building gets slippery. It can produce artifacts that look like progress while never touching demand. And the artifact can be genuinely impressive. Clean architecture. Clever workflow. Nice interface. A little swarm of agents doing agent things. Still not value until it meets reality. The hard part is not just learning how to build faster. It is learning how to aim. Because a beautifully built wrong thing is still wrong. And as AI keeps compressing the distance between idea and artifact, the edge will not simply belong to people who can make more things. It will belong to people who can tell what deserves to be made. And I don’t mean “taste” as some vague cloud of human wisdom. You can write the process down in a markdown file. I mean understanding the work well enough to aim the tools at value for someone else, instead of just feeding the part of your brain that loves complexity for its own sake.