The AI Coding Revolution Is Real (But Messy)
AI coding assistants have changed the way we build software. Not in the “no more programmers” way that LinkedIn pundits predicted, but in the “I just saved 45 minutes on boilerplate” way that actually matters.
After six months of using AI assistants daily across client projects, here’s what we’ve learned about getting real value without introducing real risk.
Where AI Assistants Excel
The sweet spot is clear: tasks with well-defined patterns and lots of prior art. These are the areas where we see the biggest time savings:
- Boilerplate generation — API route handlers, TypeScript interfaces, CRUD operations
- Test writing — especially unit tests for pure functions with clear input/output
- Refactoring — rename variables, extract functions, convert patterns
- Documentation — JSDoc comments, README sections, inline explanations
The common thread? These tasks have clear right answers. The AI isn’t making design decisions — it’s executing patterns you’ve already chosen.
Where AI Assistants Struggle
Equally important is knowing when to take the wheel back:
- Architecture decisions — the AI will happily build the wrong thing if you ask it to
- Complex state management — subtle bugs in reactive systems are hard to catch in review
- Security-sensitive code — auth flows, input sanitization, encryption
- Novel algorithms — anything without strong training data precedent
The Review Tax
Here’s the uncomfortable truth: AI-generated code requires more careful review, not less. The code looks syntactically perfect, types check, tests pass — and there’s still a subtle logic bug hiding in a ternary.
Our rule: AI writes the first draft, a human writes the final one. Every AI-generated block gets the same review scrutiny as a junior developer’s pull request.
A Practical Workflow
Here’s the workflow that’s worked best for us:
- Describe the intent clearly — “Create a rate limiter middleware for Express that uses Redis with a sliding window algorithm” beats “add rate limiting”
- Provide context files — reference existing patterns in your codebase
- Generate in small chunks — one function at a time, not an entire module
- Review immediately — don’t batch AI-generated code for later review
- Run tests before committing — obvious but often skipped when the code “looks right”
The Productivity Multiplier
Used well, AI assistants don’t replace thinking — they compress the gap between thinking and shipping. You spend more time on the hard problems (architecture, UX, business logic) and less time on the solved ones (boilerplate, syntax, standard patterns).
That’s a trade we’ll take every time.
Want to accelerate your development workflow? Let’s talk about how AI-assisted development fits into your team.