I let AI write 100% of my code for 30 days. Here’s what broke

Description and use

AI rapidly scaffolds and boilerplates code, yet fails to maintain system coherence, can produce plausible‑but‑wrong logic, and neglects edge cases. Human review and role separation are essential for production readiness.

Problem

Can an AI generate all application code without human coding, and what failures arise when relying solely on AI for production code?

Outcome

AI can rapidly scaffold and boilerplate code, but struggles with system‑wide coherence, subtle logic errors, debugging, and edge‑case handling. Human review and separate roles are essential to catch plausible‑but‑wrong code and maintain architecture integrity.

Workflow

• 1. Set rules: no hand‑coding, AI writes, reviewer approves, breaks logged after >1h stuck
• 2. Prompt AI for scaffolding (Next.js, Postgres, Drizzle, auth)
• 3. Generate boilerplate (CRUD, Zod, table component)
• 4. Review AI output, reject or re‑prompt as needed
• 5. Log breaks when AI fails to hold system coherence, writes wrong logic, or over‑adds features
• 6. Fix breaks manually after AI fails to resolve
• 7. Iterate until product is production‑ready
• 8. Implement guardrails: separate author, reviewer, and merge gate

Evidence

The AI optimizes locally. Coherence across a system is still a human job.

Source

The billing webhook handler it wrote acknowledged Stripe events before persisting them.

Source

When something broke that the AI couldn't see, asking it to fix it produced changes, not fixes.

Source