I know there are countless LinkedIn posts about achieving 10x productivity gains with AI. This isn't one of those. This is the story of how I got there—the frustrations, the false starts, and the breakthroughs that finally unlocked AI-native development for our team.
The Frustration of Falling Short
As a startup founder, I was growing increasingly frustrated. We had adopted every tool—Claude, ChatGPT, Cursor, Windsurf. We kept upgrading. And yet, the productivity gains weren't materializing.
Sure, we saw some improvement. Maybe 20-30%. But we were also seeing quality problems. PRs were getting rejected because the code was garbage. We'd Slack each other prompts trying to debug what went wrong. Meanwhile, I kept hearing stories—credible stories from experienced engineers—about 2x, 5x, even 100x improvements.
One that stuck with me: an Anthropic engineer mentioned on a podcast that their velocity had become so fast it overwhelmed their ability to review PRs. They had literally broken their PR process with speed. And yet their quality was high. I use their products daily; I don't run into bugs.
Another came from a casual mention by the Base44 founder after their acquisition. He said something offhand about how he'd architected his application to support AI development, and for three months hadn't written a single line of code himself. It was a throwaway comment. Most people missed it. But I was out tending to my peppers and plants, and that one line stayed with me.
How were these teams achieving both velocity and quality? That question started my journey.
The Greenfield Breakthrough
The turning point came from an unlikely place: a fitness app for my daughter.
She was starting a program called 75 Hard, and I'd had this idea for a personal fitness tracker sitting on the back burner for years. I decided to build it—not to ship it, but to learn. I wanted to understand what AI-native development actually looked like when I wasn't constrained by our existing codebase.
I made a deliberate choice: I didn't want to vibe code it. I didn't want to give the AI a high-level goal and let it generate garbage. I wanted to learn how to do AI-assisted software engineering properly, which later evolved into AI-native development.
What I found immediately was that I kept running into the same architectural problems. The AI wouldn't stay on track. I'd prompt it one way, it would drift. Prompt it again, it would drift differently.
The first breakthrough was realizing I needed to give the AI strict guidelines on architecture. Not suggestions—constraints.
Architecture for AI
Here's what I learned about architecting for AI:
Extreme modularity isn't optional. This is already a software engineering best practice, but as humans we relax it. Four functions in one file? No big deal—you just scroll to where you need to go. But for an AI, that's context rot. You're overloading the model with unnecessary information, and quality degrades.
We architected for extreme modularity so the AI could operate with less context. Smaller files. Narrow responsibilities. Clear boundaries. The result: less context per change, fewer accidental regressions, more precise edits.
Testability is how AI validates its work. Anthropic's engineers kept making comments about AI needing to validate and verify. That means tooling and tests. We realized our code wasn't testable at the level AI needed.
We restructured everything. Domain logic stayed pure. Data objects didn't leak upward. Mapping happened at the edges. Pure domain logic is easy to test. Tests matter because they're how AI verifies its own work. Without tests, the agent guesses. With tests, it converges.
Rigidity helps AI more than humans. We documented architecture exhaustively. No ambiguity. No contradictions. Explicit rules. For humans, this level of rigidity would feel constraining. For AI, it was stabilizing.
I wrote architecture documents for everything—thousands of lines covering Next.js, FastAPI, our UI component library, Rust services, Python agents. The key was making them absolutely consistent with no room for interpretation.
The Monorepo Unlock
The next breakthrough came when I consolidated everything into a monorepo.
Before, I was effectively acting as a bridge between two agents—one with frontend context, one with backend context. Even with good tools, synchronization slowed everything down.
I slacked one of our admins late at night: "Hey, I created this new repo. Don't worry about it—just a side project, junk basically."
Within two days, something clicked. Between the architectural decisions, the testability, and the monorepo, I was seeing an unlock for the first time. I pinged the admin again: "What would it take to move everything under this new structure? I want to try brownfield now."
With a monorepo, a single agent could operate across the full stack. No translation layer. No context switching. The agent could see end-to-end intent, and both quality and iteration speed improved.
The "Just Rewrite It" Moment
Migrating our brownfield code was brutal.
One of the big questions was deciding what was worth migrating versus recreating. It sounds crazy—we had six months of work by five people. Thirty human-months of labor.
I was having lunch with someone very senior, explaining what we were doing. As he and his wife were leaving, he made an offhand remark: "Just rewrite it."
I spent two days thinking about that. It was insane. But the more I thought about it, the more I realized we needed to get to an AI-native architecture as fast as possible.
We didn't gut the whole thing—that would be genuinely psychotic. But we rewrote section by section. Each piece that came under the new architecture with comprehensive test coverage showed immediate acceleration. And we started seeing the uplift we'd been hoping for.
It worked for frontend. Then we expanded to our backend API code. Each time we had doubts—will this work for systems code? Platform work?—the answer proved to be yes.
Plans Beat Prompts
Then we hit a new wall.
We were still seeing occasional quality problems. Engineers would submit PRs and we'd look at them thinking, "This is not good." We'd ask for their prompts, try to figure out what went wrong.
Two issues became obvious: prompts were ephemeral, and they were underspecified.
So we did something that felt obvious in retrospect—we put our prompts on disk. We called them plans. We pulled our PRDs into them. Then our technical design documents. Everything went into Git.
This collapsed our product work and engineering work into the same versioned repository. That was a massive unlock.
We added frontmatter to track status—draft, pending, in progress, completed. We wrote scripts to parse it. Basic task management in thirty minutes.
But here's the interesting part: we started using AI to review the plans themselves. There would be thousands of edits in an afternoon. The AI could speed-run the maturation of a technical design, catching inconsistencies, architectural flaws, and anti-patterns before a human did final review.
For anyone who's been through enterprise technical design reviews—the brutal ones where Infosec, DevOps, platform teams, and principal engineers tear apart your work—we had essentially automated that process.
Why Traditional Tools Don't Work
At this point, we needed coordination. We're a team. The plans were working, but how do we keep everyone in sync?
The natural thought was to use existing tools—Jira, Linear, whatever. But here's the hard truth: if you want to achieve 2x, 5x, 10x gains, existing tooling simply doesn't work.
The problem is AI-native workflows involve thousands of micro-iterations in short periods. We were doing thousands of reviews of a single document in a few hours. MCP calls take 15-20 seconds—authenticate, hit the API, bring something back. You cannot do thousands of operations with multi-second delays. It just doesn't work.
So we built our own task management. Files in Git for versioning. Remote synchronization with conflict resolution so everyone knows what everyone's working on. Status updates that happen automatically as artifacts of work.
The old way: leave your IDE, log into another app, find the thing, click a dropdown, update status, save. The AI-native way: status is derived from work. No one updates anything manually anymore.
AI Is a Mirror
We discovered something uncomfortable along the way.
AI replicates what it sees. If your code has bugs, AI will replicate bugs at 10x speed. If you have inconsistent architecture, AI will produce inconsistent code. If you're sloppy with your thinking, AI gives you sloppy output.
We ran the same plan through different engineers—same codebase, same agent, same tooling, same architecture. Some got great results. Others got garbage. The difference was environmental discipline. Some had let things degrade.
AI is a mirror. When we saw slop, it was usually because we'd allowed slop first. That was a bitter pill.
This led to our zero-tolerance policy on tech debt. In traditional development, you cut corners to ship. One small error is manageable. But with AI, that one error becomes a hundred by day two. And on day three, AI looks at those hundred errors and replicates each of them again.
Tech debt became a killer at AI speed. You have to do it right the first time.
The Honest Answer on Brownfield
People say AI works for greenfield but not brownfield. We don't think that's true anymore.
The real issue is that brownfield code often reflects reasonable trade-offs that were historically correct. Engineers were explicitly told to cut corners to ship. Those weren't bad decisions—they were the right decisions at the time. They helped the business progress.
But those decisions are now legacy. They need to be undone if you want AI-native velocity.
Our approach:
- Accept that you need to redo your architecture. If you want 10x gains, you're going to have to redo some work.
- Use natural boundaries. Microservices give you clean seams. Monoliths can be tackled module by module.
- Rewrite section by section. Don't gut everything at once.
Where We Are Now
Right now, we're running 9 AM to midnight, seven days a week. That's how we're coping with the velocity. But the velocity is there.
The gains didn't come from a single insight. They came from a system:
- Monorepo for full-stack context
- Modular architecture to reduce context requirements
- Testable design for AI verification
- Strict tooling for fast feedback
- Plans instead of prompts for durable, reviewable intent
- Architecture enforced as code via custom linting tools
- Task management derived from work, not manual updates
The Real Takeaway
Most teams chase AI for speed. The real unlock is control.
Velocity is a side effect of clarity, constraint, and rigor. When quality becomes inevitable, speed follows—and compounds.
The future isn't faster developers. It's systems that make doing the right thing the default.
If you're on a similar journey, I'd love to hear what's working for you. The path from AI-assisted to AI-native isn't obvious, but I believe it's where software development is heading.