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Six months ago, I was skeptical. Really skeptical.

I’d been coding for years, and suddenly everyone was telling me that AI would write my code for me. The demos looked impressive, sure. But I’d seen enough overhyped tools come and go. Remember when we were all supposed to be building apps with voice commands by now?

So I decided to actually test these AI coding tools. Not for a weekend. Not for a quick project. For six months of real, daily development work.

Here’s what I learned—the good, the frustrating, and the stuff nobody seems to talk about.

The Honest Truth About AI Coding Tools

Let me start with something that might surprise you: AI coding tools didn’t make me 10x faster. Not even close.

But they did make me about 30-40% more productive. And honestly? That’s huge. That’s an extra day of output every week. Over a year, that adds up to months of time saved.

The catch is that the productivity boost isn’t automatic. It took me almost two months to figure out how to actually use these tools effectively. The first few weeks, I was probably slower than before because I kept fighting with AI suggestions that didn’t quite fit what I needed.

Here’s what changed everything for me: I stopped treating AI as a replacement for thinking and started treating it as a really fast, really patient collaborator.

What AI Coding Tools Actually Do Well

After six months, I’ve got a clear picture of where AI genuinely shines:

Boilerplate Code is Basically Free Now

Setting up a new React component with TypeScript types, props, and basic structure? Used to take me 5-10 minutes of tedious typing. Now it’s about 30 seconds. Same with API endpoints, database queries, and test files.

This doesn’t sound exciting, but it removes so much friction from starting new things. I used to procrastinate on creating new files because of the setup overhead. Now I just… do it.

Debugging Got Way Less Frustrating

This surprised me the most. When I hit a weird error, I paste it into Claude along with my code, and probably 70% of the time, it either spots the issue immediately or points me in the right direction.

Last week I spent two hours on a bug that turned out to be a race condition. When I finally thought to ask Claude, it identified it in about 30 seconds. I felt dumb, but also grateful.

Learning New Things is Faster

I recently needed to implement WebSockets for the first time. In the old days, I’d spend an hour reading documentation, another hour looking at examples, and then stumble through my first implementation.

With AI assistance, I described what I wanted, got a working example tailored to my specific setup, and asked follow-up questions about the parts I didn’t understand. The whole process took maybe 20 minutes, and I actually understood what was happening.

Where AI Falls Flat (And Nobody Wants to Admit It)

Okay, time for the uncomfortable stuff. Here’s what AI coding tools are genuinely bad at:

Complex Architecture Decisions

AI can implement whatever architecture you describe. But should you use microservices or a monolith? Event-driven or request-response? These decisions require understanding your specific context—your team size, your growth trajectory, your operational capabilities.

When I ask AI for architecture advice, I get reasonable-sounding answers that miss crucial context. It’s like asking a very smart person who just joined your company yesterday—they don’t know what they don’t know about your situation.

Code That Needs to Be “Just Right”

Performance-critical code. Security-sensitive operations. Anything where subtle bugs have serious consequences. AI can get you 80% of the way there, but that last 20% often matters most.

I’ve caught AI suggestions that would have introduced security vulnerabilities, performance bottlenecks, and edge cases that would have caused production bugs. The code looked fine at a glance. That’s what made it dangerous.

Understanding Your Existing Codebase

This is getting better (tools like Cursor are making progress here), but AI still struggles with understanding how your specific codebase works. It doesn’t know about that weird legacy system you have to integrate with, or the naming conventions your team uses, or why you made certain decisions.

The AI Coding Tools I Actually Use Daily

After trying basically everything, here’s what stuck in my workflow:

Claude for complex conversations. When I need to think through a problem, debug something tricky, or learn a new concept, Claude is my go-to. The reasoning is noticeably better than alternatives, and it doesn’t just give me code—it explains the why.

Cursor for daily coding. It’s VS Code with AI baked in. The inline suggestions are helpful, but the real magic is being able to select code and ask questions about it directly in my editor. No context-switching to a browser tab.

GitHub Copilot when I’m in flow. The autocomplete suggestions are less powerful than full conversations, but they don’t break my concentration. When I’m cranking through implementation, that matters.

You will find a fully exhaustive analysis of Claude, Cursor, and GitHub Copilot in my post Cursor vs GitHub Copilot vs Claude: Which AI Coding Tool Should You Use?

How to Actually Get Faster With AI (A Realistic Approach)

If you’re just getting started, here’s what I wish someone had told me:

Start with debugging, not writing code. This is the safest way to learn. Paste an error message and your code, see what the AI says. You’ll quickly develop a sense for when it’s helpful and when it’s guessing.

Be specific about what you want. “Write me a function” gets generic results. “Write a TypeScript function that takes an array of user objects and returns only users who have logged in within the last 30 days, using functional programming patterns” gets useful results.

Always review before committing. I have a rule: no AI-generated code goes to production without me understanding every line. This has saved me multiple times.

Use it to explain, not just generate. “Explain this code” and “Why would someone write it this way?” are often more valuable than “Write code that does X.”

Six Months Later: Would I Go Back?

Not a chance.

Look, AI coding tools aren’t magic. They won’t turn you into a 10x developer overnight, and they definitely won’t replace the need to understand what you’re building. The YouTube thumbnails promising you’ll “never write code again” are lying.

But used well, these tools are genuinely useful. They handle the tedious parts so you can focus on the interesting problems. They help when you’re stuck. They make learning faster.

That 30-40% productivity boost I mentioned? It compounds. Every project ships a little faster. Every bug gets fixed a little sooner. Every new technology is a little less intimidating to learn.

The developers who figure out how to work effectively with AI are going to have a real advantage over the next few years. Not because AI replaces thinking—but because it amplifies it.

Ready to compare specific tools? In my next post, I break down exactly how Cursor, Copilot, and Claude stack up against each other for different types of work.