Part-3: Building Machines That Build Software: The New Team Playbook

Now that you have the foundations in place, how do you actually build software when individual AI operations fail 30% of the time? The answer isn't better prompts or smarter models - we can borrow some principles that make distributed systems reliable despite unreliable components: composition, redundancy, and error correction. I'll reveal the "error-squashing workflows" that achieve 99.9% reliability from 70% accurate AI, why you need developers deliberately working at two different speeds, and which human skills have become 10x more valuable while others have become completely worthless. Most importantly, I'll share the exact 5-phase roadmap we use to transform traditional teams into AI-native machines that compound capabilities instead of complexity. Spoiler: the teams winning with AI aren't those with the best models - they're those who've learned to build machines that build software.

Part 2: Building the AI-Native Foundation

You might think the solution is to simply adopt AI tools and move faster. It turns out that when you accelerate development by 10x, your technical debt doesn't grow linearly - it compounds exponentially, and wrong foundations become fatal within weeks, not years. In Part 2, I'll show you why platform engineering must be frontloaded before any AI acceleration, the non-negotiable foundations that separate successful AI-native teams from those drowning in AI-generated chaos, and how we achieved 300% faster development with 50% fewer defects by building what I call "The Compounding Machine." The counterintuitive truth: in AI-native development, going slow at the start is the only way to sustainably go fast.

Part 1: The Paradigm Shift

"Why Everything You Know About Software Building and Software Teams are Now Changed"

I watched a junior developer on my team ship more production code in two hours than I wrote in my entire first year as an engineer. You'd think I'd be thrilled. Instead, I was staring at our velocity metrics, trying to understand why they were getting worse.

That's when it hit me: We've been optimizing the wrong bottleneck for the last 50 years.