Developer Forward · answer surface
Can you trust AI-generated code?
Trust should follow evidence, not the fact that AI produced the code or the fact that the code looks polished. The amount of evidence you require should scale with the consequence, recoverability, detectability, exposure, and likelihood of failure.
Evidence
Case 33 — Verification Scales With Consequence
- Before generative AI, Ben built a payment system and deliberately spent far more effort on logging, transaction safety, queues, duplicate handling, adversarial testing, and layered verification than the happy path required.
- Years later the system encountered intermittent outages, duplicate submissions, attempted gaming, and coupon-code sabotage. The earlier safeguards made those failures visible or containable rather than silently trusting the normal path.
- The case separates implementation difficulty from verification depth: cheap generation does not reduce the consequence of being wrong.
What changes now
AI makes convincing happy paths cheap. That increases the importance of independent evidence for consequential systems. A generated implementation can be a useful contribution while still requiring tests, observed behavior, security checks, runtime evidence, or human review appropriate to what failure would cost.
Boundary
This is not a rule to maximize testing everywhere. Low-consequence, reversible work can justify much lighter verification. The point is proportionality, not blanket distrust of AI-generated code.
Grounded in Ben Chan's canonical Case 33 historical record. The answer and AI-era application are a current synthesis of that evidence, not a claim that the historical case involved generative AI.