AI coding tools pile up validation everywhere instead of once, and the extra checks make systems harder to trust, not safer.
The Details:
Generated code tends to guard every function locally because the model has sharp visibility into the function it is writing and almost none into guarantees made elsewhere in the codebase. The cheapest way to feel safe under that uncertainty is to defend the few lines in front of it: check for null, wrap the call in try/except, repeat in the next file. Multiply that habit across hundreds of functions and you get validation scattered everywhere instead of enforced in one place.
Scattered checks quietly change how errors behave. Defensive try/except blocks tend to swallow problems and return a safe-looking default rather than let them surface. A bug that should fail loudly on arrival instead gets caught three layers deep, turned into an empty list, and shows up later as missing data nobody can trace back to its source.
A Stripe webhook example shows the cost directly. The correct approach verifies the signature and parses the payload once, producing a typed object the rest of the system can trust. Shotgun code instead re-checks the same payload at every layer with slightly different definitions of valid, until a generic exception handler buries signature failures, network errors, and real bugs under one vague log line.
The fix is parsing instead of validating: turn untrusted input into a strictly typed object at a single, named boundary, using something like Pydantic or Zod, and let every downstream function simply trust that type. Teams have to state this convention explicitly in project instructions, naming the folder and the models, because an AI tool has no other way to know which side of the boundary it is standing on.
Bottom Line: A codebase that checks once at a clear boundary and trusts itself everywhere else is more secure than one that checks everywhere and trusts nothing.
Enjoy this article?
Listen to the Claude Code Conversations radio show or join the community.
