The AI coding revolution is here, and it’s… not quite what I expected.

Six months ago, I started using GitHub Copilot and other AI coding assistants. Everyone was talking about AI “replacing developers” or “10x productivity gains.” After living with these tools daily, I have a more nuanced take.

AI tools absolutely changed how I code, just not in the ways the hype suggested.

What Changed: The Unexpected Benefits

1. Faster Boilerplate, More Creative Energy

The biggest win isn’t AI writing complex algorithms for me - it’s handling the tedious stuff. Setting up Express routes, writing test fixtures, creating TypeScript interfaces… all the mechanical typing that drains mental energy.

Result: I spend more time thinking about architecture and user experience, less time fighting syntax.

2. Better Documentation Habits

Here’s something unexpected: AI tools made me a better documenter. When Copilot suggests code based on my comments, I started writing more detailed comments to get better suggestions.

Example:

// Before: function to get user data
// After: fetch user profile including avatar, preferences, and recent activity from the REST API

The more descriptive I am, the better the AI suggestions. Win-win.

3. Exploration Without Fear

AI tools lowered the barrier to trying new libraries or patterns. Instead of spending hours reading documentation, I can describe what I want and get a working example to start from.

This led me to discover several libraries I wouldn’t have tried otherwise.

What Didn’t Change: The Reality Check

1. Debugging Is Still All Me

AI can generate code, but debugging complex issues? That’s still entirely human work. Understanding why something breaks requires context, domain knowledge, and intuition that AI doesn’t have.

The ratio: AI helps with maybe 20% of my debugging time (writing test cases), but the other 80% is pure detective work.

2. Architecture Decisions Matter More Than Ever

With AI generating code faster, the quality of your architecture becomes even more critical. It’s easier to build the wrong thing quickly now.

I spend more time upfront thinking about:

  • Data flow and state management
  • Component boundaries and responsibilities
  • Testing strategies and error handling

3. Code Review Is More Important

AI-generated code isn’t always optimal. It works, but it might not follow your team’s patterns, handle edge cases, or consider performance implications.

New habit: I review AI suggestions as carefully as I’d review a junior developer’s code.

The Subtle Productivity Gains

The real productivity improvement isn’t dramatic - it’s cumulative. Lots of small time savings throughout the day:

  • 5 minutes saved writing a regex pattern
  • 10 minutes saved setting up a new React component
  • 15 minutes saved writing repetitive test cases
  • 20 minutes saved converting data between formats

Add it up over a week, and I’m saving 2-3 hours. Not revolutionary, but meaningful.

What I Learned About Learning

Ironically, AI tools made me more conscious about staying sharp on fundamentals. When a tool can generate code for you, understanding why that code works becomes more important, not less.

New learning approach:

  1. Let AI generate initial code
  2. Understand every line before using it
  3. Research the patterns and principles behind it
  4. Practice implementing variations manually

The Collaboration Angle

Working with AI feels like pair programming with a very knowledgeable but literal-minded partner. Great at implementation details, needs guidance on the bigger picture.

Best practices I developed:

  • Start with clear, detailed comments about intent
  • Break complex problems into smaller, well-defined pieces
  • Always validate AI suggestions against requirements
  • Use AI for exploration, human judgment for decisions

The Tools That Stuck

After trying various AI coding assistants:

GitHub Copilot: Best for day-to-day coding, great context awareness ChatGPT: Excellent for explaining complex concepts and debugging help Tabnine: Good for teams with specific coding patterns

The surprise winner: Simple GPT conversations for rubber duck debugging. Explaining a problem to AI often helps me solve it myself.

Looking Forward

AI coding tools aren’t replacing developers - they’re changing what we focus on. Less time on mechanical tasks, more time on creative problem-solving and user experience.

The developers thriving are those who embrace AI as a productivity multiplier while staying sharp on fundamentals and system thinking.

The ones struggling are either afraid to try new tools or expect AI to do all the thinking for them.

My Honest Recommendation

Try AI coding tools, but manage your expectations. They’re powerful assistants, not magic solutions. The fundamentals of good software development - clear thinking, solid architecture, thorough testing - matter more than ever.

And remember: the best AI tool is still the one between your ears.

What’s your experience with AI coding tools? I’m curious to hear how they’ve changed (or not changed) your development process.