AI-generated code: what it can and can't do (2026)
Where AI code is production-ready, where a human is required, and how not to ship something that breaks on the first real user.
AI writes code that looks correct. Sometimes it is. Sometimes it looks correct — and that's exactly why it's dangerous. Here's where the line is.
Where AI code is enough
- Isolated, well-defined pieces. A function that formats a date, parses a CSV, validates a form. Clear input, clear output, easy to test.
- Scaffolding and repetition. CRUD screens, API routes, types, migrations.
- Prototypes to test an idea. If the goal is "let's see if this makes sense", not "let's ship it to customers".
- Help with an unfamiliar library. AI reads the docs and gives a working example faster than searching.
Where a human is required
- Architecture. How the parts connect, where data lives, what happens on failure — that's a decision, not autocomplete.
- Security. AI routinely misses: permission checks, rate limits, server-side validation, data leaking into logs.
- Performance at scale. Code that works with 10 records can kill the database at 100,000.
- Business logic. AI doesn't know your rules for discounts, stock, taxes. It will invent them confidently.
- Understanding what's written. If nobody on the team can explain what a file does, it's a time bomb.
A practical rule
AI writes the first draft; a human is responsible for it. Every line that goes to production has to be read, understood and maintainable by someone. "It works, I don't know how" is not acceptable for something a business stands on.
Vibe coding — when it's fine
Fast generation without deep understanding is great for: a personal tool, an internal script, a demo prototype, something that doesn't hurt if it breaks. It's not fine for: payments, personal data, something 1,000 people use daily.
Have an AI-generated project you want to ship for real? Write to us — we review it, fix the gaps and take it to production, instead of rewriting everything from scratch.