AI Coding Tools: An Honest Review After a Year of Daily Use

A year ago I started using AI coding tools seriously — not just playing with them, but integrating them into my actual workflow. Claude Code for complex work, Copilot for inline completions, Claude in the browser for research. Here’s where I am now.

What Genuinely Saves Me Time

Boilerplate is basically solved. Writing API endpoints, database models, test fixtures, type definitions — I describe what I need and review the output. A CRUD endpoint that used to take 20 minutes now takes 3 minutes of review and minor adjustments. This is the unambiguous win.

Codebase exploration is transformational. I joined a new project last month — a codebase I’d never seen. Instead of spending days reading code, I asked Claude Code: “How does the authentication flow work in this codebase?” and got a clear walkthrough in 30 seconds. Then: “Find all places where user permissions are checked.” Then: “What happens when a webhook payload is received?” Three queries and I understood the architecture better than I would have after a day of reading.

Debugging. When I’m stuck, describing the symptoms and sharing relevant code with Claude almost always accelerates the fix. It’s particularly good at spotting off-by-one errors, race conditions in async code, and null reference paths that I overlook because I’m too close to the code.

Tests. AI-generated tests aren’t perfect, but they’re a solid starting point. I generate the initial suite, then add edge cases manually. Gets me to decent coverage much faster.

What Doesn’t Work As Well As Advertised

Architecture decisions. AI can suggest architectures, but it doesn’t know your team, your constraints, your scale, or your organizational politics. I’ve watched junior developers accept architectural suggestions without questioning them, ending up with over-engineered solutions. Use AI for implementation, not for decisions that shape the long-term system.

The “looks right” problem. AI-generated code often looks correct at a glance — it compiles, it passes the happy path. But there’s a subtle edge case that only shows up in production. The code is plausible enough to slip through a quick review. I now review AI-generated code more carefully than code written by my team, not less.

Context windows. AI works great for focused, well-defined tasks. Dump a complex, multi-faceted problem — “fix the performance issue in our data pipeline” — and the results are mediocre. Breaking it down into specific steps is the key, but that decomposition is a skill in itself.

The Productivity Trap

Here’s the thing nobody in the AI tool marketing departments tells you: experienced developers sometimes take longer with AI tools. A METR study found that developers took 19% longer on tasks when using AI. The Stack Overflow 2025 survey found 65% of developers use AI tools weekly, but only 33% trust the output.

Why? Because the verification loop is real. You still need to read every line, understand what it does, and think about edge cases. Crafting good prompts and reviewing outputs breaks your flow state. And when you accept code you don’t fully understand, you create tech debt that bites you later.

My take: AI tools genuinely make me faster, but only because I’ve learned when to use them and when not to. I use AI for well-defined subtasks and write critical business logic myself. The discipline to choose is what makes the difference.

Practical Habits I’ve Developed

1. Be specific. “Add error handling to the upload function for file size limits and invalid MIME types” beats “improve the upload function.”
2. Provide context. Share types, interfaces, and existing patterns. AI writes much better code when it sees your project’s conventions.
3. First draft only. Generate, review, refine. Never ship AI output without reading every line.
4. Track what works. I noticed AI helps most with boilerplate and tests, least with business logic and debugging complex systems. Knowing this saves me from reaching for AI when it won’t help.
5. Learn the tools deeply. Claude Code’s ability to explore codebases, run commands, and iterate is dramatically more powerful than basic completion. The investment in learning pays off.

Bottom Line

AI coding tools are the most significant productivity shift since the IDE. But they’re tools, not magic. The developers who benefit most understand where AI excels (boilerplate, exploration, debugging) and where it doesn’t (architecture, nuanced business logic, security-critical code).

I estimate AI saves me 2-3 hours on a good day. But I’ve also wasted entire afternoons debugging AI-generated code that looked right but wasn’t. The net positive is real, but it’s not as dramatic as the hype suggests.