I recently built a production-ready application entirely with AI assistance, 100% written by the AI - not as a proof-of-concept, but as a functional tool designed to solve complex workflow problems.
It wasn’t magic, it was work.
Update: The application code is now over 5,600 lines of code, plus 16,000 lines of code in 883 tests giving 100% coverage including branches.
There is a lot of discussion about using AI for development. Much of it is over-stated hype, either for or against.
“AI only turns out garbage” is what some people will tell you.
“AI will replace developers” is what others will say.
Like most things, the truth is somewhere in-between.
AI-assisted development can help create code faster, but pitfalls like amplified design flaws and cascading errors demand careful handling
This article cuts through the hype by showing how unchecked AI use mirrors real-world failures like Knight Capital’s $500M loss (caused by human process gaps, not technology).
Even skilled developers like “Alice, Bob, and Charlie” introduce subtle mistakes when inheriting code under time pressure, and AI accelerates these risks by acting like a new developer every time you hit “enter” - forgetting context, skipping safeguards, and magnifying poor documentation or rushed decisions.
It is important that you need to enforce defensive practices (like file operation safeguards and explicit error checks) while juggling roles as architect, reviewer, and quality gatekeeper to prevent small oversights from exploding at machine speed.