Pavan Gajula.
Five years building enterprise systems in Java and React.
Two years shipping websites for the people who actually need them.
Lately, I keep noticing the same thing — and I want to study it seriously.
Building Spring Boot microservices at Synack by day. Volunteering for a Howard County youth sports nonprofit by night. Writing about what breaks when developers and AI write code together.
The more developers lean on AI coding tools, the less the problem is writing code, and the more it's knowing when to trust what comes back.
I write code with AI assistants every day. Whether they can produce working code stopped being the interesting question — they usually can. The harder one is when a developer should trust the output, and what happens when checking it costs more than writing it would have.
The asymmetry of developer skepticism
When I'm writing core business logic in Spring Boot, I use Claude Code like an extension of my own brain — I anticipate its suggestions, accept them instantly, and rarely get burned because the patterns are deeply ingrained in memory. Last month I had to switch contexts to a complex bash script for a deployment pipeline, where my syntax knowledge is rustier, and I caught myself passively accepting lines I couldn't instantly verify. It passed a flag that didn't exist in that version of the CLI, silently breaking the build downstream.
The realization: my trust in AI tools isn't a function of the AI's output quality — it's a function of my own confidence in the language. When my own knowledge drops, my baseline skepticism drops with it. Which is exactly when silent failures slip through. The interesting design problem isn't making the AI more accurate. It's making developers more skeptical in their weak spots, not less.