From AI Skeptic to AI Architect: How I Stopped Monkey Coding and Started Orchestrating

The Skeptic Phase: "Not Another Hype Cycle"

Look, when you've been in software engineering for two decades, you develop a finely-tuned bullshit detector. You've watched frameworks come and go, seen "revolutionary" tools fade into obscurity, and learned that being on the bleeding edge sometimes just means you bleed first.

So when the AI hype exploded a few years ago, my immediate reaction was: "Yeah, sure. Another one."

I kept my distance. Let the early adopters figure it out. Let the dust settle. I had real work to do.

The Curiosity Phase: "Okay, Maybe There's Something Here"

But then everyone—and I mean everyone—was talking about it. ChatGPT this, GPT that. The signal-to-noise ratio started shifting. Maybe this wasn't just hype?

I gave ChatGPT a shot. Started simple—recipes, workout plans, casual life stuff. And... damn. I was impressed. Not just "oh that's neat" impressed, but "this is actually useful" impressed.

I upgraded to Plus. Then Pro. Before I knew it, I was using it daily for almost everything. The tool had earned its place in my workflow.

The Frustration Phase: "There Must Be a Better Way"

When I started applying ChatGPT to software engineering, things got interesting—and frustrating. It was helpful, don't get me wrong. But as I tackled more complex projects, the cracks showed.

The context problem was real. Copy-paste code back and forth. Re-explain the same project structure multiple times. Lose the thread of what we were building. It felt like playing telephone with a very smart but slightly forgetful partner.

I knew there had to be a better way. Software engineering isn't about isolated snippets—it's about systems, context, architecture.

The Integration Phase: "Now We're Talking"

Enter Cursor integrated with Linear. This was the lightbulb moment. I could mention Cursor's bot in a Linear ticket, and boom—full PR, complete implementation. Iterate a few times, and ship it.

This was the workflow I'd been craving. But it wasn't perfect. Linear was becoming cluttered with AI conversations. It was just serving as an interface, and that felt... wrong. Like using a spreadsheet to write a novel.

So I dove into Cursor IDE. Not for their VSCode editor—I had my preferences there—but for their agent tab. Finally, a dedicated space to orchestrate without polluting my project management tool.

The Revelation: "Wait, It's All Claude?"

Here's where it got interesting. As I dug deeper into Cursor, I realized something: it's agent agnostic. Under the hood, much of the magic was powered by Claude.

So I went straight to the source.

The Claude Era: "Holy Shit, This Is It"

Trying Claude directly was like upgrading from a good car to a Tesla on autopilot. I'm talking night-and-day difference.

I subscribed to the Claude Max plan immediately. Installed their Mac app for native access. And then I discovered Claude Code—their terminal interface for development.

This. Changes. Everything.

Claude Code integrates seamlessly with your development environment. There are plugins for JetBrains products (PyCharm, IntelliJ), VSCode, you name it. You fire it up right in your project repo, and the magic begins.

You tell Claude what to build. It analyzes your project structure. Creates files. Writes code. Executes commands. Iterates based on your feedback. It's not just generating code snippets—it's building features.

The Workflow Revolution

Here's my current setup, and it's beautiful:

I define feature requirements in external docs. Easy to edit, version control them if needed, share them across the team. Claude Code reads these docs and gets to work.

Add a CLAUDE.md file to your repo with project-wide rules and conventions. Keep feature-specific requirements in separate docs. Clean, organized, scalable.

The workflow is stupidly simple:

1. Write your requirement doc
2. Start Claude Code
3. Point it at your requirements
4. Watch it build
5. Iterate until satisfied
6. Ship

One caveat: Claude Code sessions aren't persistent. But there are elegant workarounds. At the end of a session, ask Claude to create a summary you can load later. Or just let it continue based on the files it created—they're persistent. Tell it "hey, check what we built yesterday" and it picks right back up.

I still use GitHub Copilot X and JetBrains AI Assistant for code completion, stack trace analysis, automatic commit messages—they're great at the micro level. But Claude is my main weapon for feature development and architectural work.

From Code Monkey to Master Orchestrator

After 20 years in this industry, I've written more code than I care to count. And you know what? I'm done monkey coding.

Not because I can't. Not because I've gotten lazy. But because there's a better way to work.

I see my role now as a master architect and orchestrator. I bring two decades of experience, architectural vision, and product intuition. I know what good software looks like. I know the patterns, the anti-patterns, the edge cases.

AI—specifically Claude—handles the heavy lifting. The implementation. The boilerplate. The tedious parts that used to eat up 70% of my time.

The Real Skill: AI Strategy and Communication

The game has changed. The new essential skills aren't just about knowing algorithms or memorizing syntax. They're about:

• Effective communication with AI: Learning how to articulate requirements, provide context, and iterate efficiently
• AI tool strategy: Knowing which tool to use when, and how to orchestrate multiple AI systems
• Architectural vision: Humans still need to define the "what" and "why"—AI handles the "how"
• Quality control: Reviewing, refining, and ensuring what AI builds meets your standards

This isn't about replacing engineers. It's about amplifying them. It's about focusing human creativity and expertise on the hard problems—the ones that actually matter.

The Future Is Already Here

I'm more productive than I've ever been. I'm building better software, faster, with fewer bugs. I'm spending time on architecture, user experience, and solving real problems instead of debugging semicolons.

Twenty years ago, if you told me I'd be orchestrating AI agents to build software while I focused on strategy and design, I'd have thought you were reading too much sci-fi.

But here we are. The future isn't coming—it's already here. And honestly? It's pretty damn exciting.

The hype was real. I was just too cautious to see it at first.

Now? I'm all in.

What's your AI journey been like? Are you still in the skeptic phase, or have you found your orchestrator groove?