When you start a company today, you’re not just choosing a tech stack, you’re choosing an engineering model.

As CTO of an AI-native startup, I made an explicit decision: AI-assisted engineering would not be an experiment or a productivity tool. It would be the default way we build.

In this session, I’ll walk through the foundational technical decisions that shaped our first months:

- Treating AI workflows as infrastructure, not add-ons
- Structuring work so small teams maintain high context
- Designing documentation that machines can reason about
- Making architectural intent explicit from day one
- Choosing independence over convenience
- Avoiding early complexity that looks “enterprise-ready” but slows momentum

This is not a startup success story. It’s a candid breakdown of the technical principles and trade-offs that shaped how we built and what I would repeat (or change) next time.