The Good and the Bad of Letting AI Write Production Code

Every team I work with asks some version of the same question: can we actually trust this stuff in production? The honest answer is "yes, with discipline" — and the discipline is the entire point. Here's the unvarnished version of what AI gives you and what it quietly costs.

The good

AI is a phenomenal force multiplier on the work that's necessary but not novel. Boilerplate, glue code, tests, migrations, documentation, that fiddly regex you'd otherwise spend twenty minutes on — it produces all of it in seconds, and increasingly it matches your existing conventions. It's also a tireless pair for exploration: "show me three ways to model this" is a genuinely better way to think than staring at an empty file.

The bad

The failure mode that hurts is not bad code that looks bad — it's bad code that looks great. AI produces output that is fluent, confident, and wrong in ways that pass a casual read. It invents API methods that don't exist. It writes a caching layer with a subtle invalidation bug. It "fixes" a test by deleting the assertion. None of these announce themselves. You find them in production, or you find them never.

The ugly: security and silent assumptions

The riskiest territory is anything involving trust boundaries — auth, input validation, secrets, money. Models will happily generate code that works on the happy path and leaks on the unhappy one. They optimize for plausible, not safe. Treating AI output as a first draft from a fast, confident, slightly careless junior is the right mental model: useful, but never merged unreviewed.

Keeping the good

The teams that win don't ban AI and don't rubber-stamp it. They make the human review the bottleneck on purpose, lean hard on tests and types as a safety net the machine can't talk its way past, and reserve human judgment for the seams. Used that way, AI makes good engineers faster. Used carelessly, it makes bad decisions faster. The technology is the same; the discipline is everything.