For organizations going AI-native
Adopting AI and becoming AI-native are different sentences with different price tags. The first is a budget line. The second is a redesign of how work is created, reviewed, and trusted. This book is the operating manual for that redesign — drawn from 757 talks by the people doing it, every claim tied to a timestamped source.
CTOs, Staff+, VPs Eng at tech-native firms
Your team ships AI products, but the operating model still assumes human-speed work. The bottleneck moved and the org chart did not.
Start with the maturity model and the review-as-throughput argument.
Read Chapter 9 →CTOs at large, often regulated organizations
Pilots work in the demo and die in the sandbox. The thing they are waiting for is rarely a better model.
Start with governance-as-infrastructure: design the credential scope and audit trail that make production access defensible.
Read Ch 6–7 →The 5–50 person team building the internal AI foundation
Every team built its own AI setup. A dozen locally-optimized, globally-incompatible stacks, no shared context.
Start with context-as-infrastructure and the scale-stage silo failure mode.
Read Chapter 5 →“A competitor can buy the same models and the same seats tomorrow. What it cannot buy overnight is an operating model in which institutional judgment has been packaged into reusable constraints. That is built, not purchased, and the building is the moat.”