We spent years trying to make coding easier. Better editors. Better frameworks. Better libraries. Better documentation.
Stack Overflow. GitHub. Package managers. Frameworks. IDEs. Autocomplete. Copilot. Now AI.
The goal was always the same: reduce the distance between an idea and working software.
A few years ago, some of these things would have taken hours or days. Now you can get a working prototype surprisingly quickly.
Earlier: bad idea → expensive implementation → natural friction. The cost of building was itself a filter. Most bad ideas quietly died before anyone finished them.
AI didn't just lower the cost of building good software. It lowered the cost of building unnecessary software.
You get an idea on a Friday. It sounds useful. You can already picture how it would work. AI makes the implementation feel almost effortless, so you start building.
Ask AI for the initial architecture. Generate the UI. Wire up the API. Fix a few errors along the way.
And that's when you realize you never actually answered the most important question.
I had an idea. It sounded useful. I could imagine exactly how it would work. AI made the implementation feel almost effortless, so I just... started.
The weird part was realizing I'd spent hours answering "How?" without spending ten minutes on "Why?"
How do I build authentication? How do I cache this? How do I create this API? How do I deploy this?
But "Should we build authentication for this?" and "Do users actually need this feature?" are different questions entirely.
But a few hours to build can become months of maintenance — bugs, dependencies, security, documentation, support, future feature requests, technical debt that someone has to carry.
The cost of writing the first version is no longer the whole cost of the software.
A feature that ships but nobody opens. A dashboard nobody checks. An abstraction nobody needed. A microservice that could have stayed a function. An AI-generated automation replacing a manual process that took thirty seconds anyway.
The valuable skills are shifting problem framing, asking better questions, understanding users, identifying real constraints, knowing when to stop, saying no.
AI is fantastic for prototypes, boilerplate, experiments, repetitive code, tests, documentation, and exploring unfamiliar APIs. It turns an idea into something tangible faster than anything we've had before.