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

Description and use

AI rapidly scaffolded and wrote boilerplate, yet caused architecture drift, plausible‑but‑wrong logic, and missed edge cases, necessitating human review and debugging.

Problem

Relying on an AI to write all application code without human coding leads to architecture drift, plausible‑but‑wrong code, debugging loops, and missing edge‑case handling.

Outcome

The AI could scaffold and write boilerplate quickly, but produced architecture drift, plausible‑but‑wrong logic, and missed edge cases, requiring human intervention for review, debugging, and final approval.

Workflow

• 1. Prompt AI to scaffold Next.js, Postgres, Drizzle, and auth.
• 2. Review AI output line by line, rejecting or re‑prompting as needed.
• 3. Handle configuration, secrets, and dashboard actions manually.
• 4. Log any break that takes more than an hour to resolve.
• 5. Repeat until product is production‑ready.
• 6. Deploy and monitor for failures.
• 7. Refactor and fix architecture drift and logic errors.
• 8. Add missing edge‑case handling and performance fixes.
• 9. Final review and merge with human approval.

Evidence

I shipped a real product this way — a small SaaS with auth, Stripe billing, a dashboard, and a public API. It works. It's in production.

Source

The AI is brilliant at the file in front of it and blind to the one three folders over. It happily wrote a second formatCurrency helper because it didn't know the first one existed.

Source

The billing webhook handler it wrote acknowledged Stripe events before persisting them. Works perfectly in testing. In production, a DB blip = a paid customer with no access and no record.

Source

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

Source