2026 / Free online book · The AI-Native Canon
Building an AI-Native Team
Hiring for judgment, not throughput
Access
Free
Chapters
8
Read time
40 min
Alpesh Nakrani
CRO at Devlyn · former CTO & COO
The skills that made a great engineer in 2018 are not the ones that matter now. Who you hire when the machine writes the code.
This edition is free to read onsite. Each chapter has its own URL, so readers can bookmark, share, and return to the exact section they need.
Table of contents
FM Front Matter: Building an AI-Native Team Hiring for judgment, not throughput 3 min 01 The Throughput Team Breaks The team looked productive until the launch slipped. 6 min 02 What Work Becomes When Drafting Is Cheap The first week after rollout, everyone was delighted by drafts. The legal team drafted contract clauses faster. 5 min 03 The JOLT Framework The founder said the company needed "AI people." The VP of Engineering asked what that meant. The answer came back as a list of tool names. 4 min 04 Hiring for Judgment You Can Observe The candidate's portfolio looked spectacular. Every example used the latest tools. 4 min 05 Seniority, Review Load, and Apprenticeship The senior engineers were drowning. Not in implementation, but in review. 4 min 06 Roles, Boundaries, and the AI-Native RACI The incident review was awkward because everyone had done their job. Product approved the AI assistant's scope. 4 min 07 Operating Cadences and Performance Management The manager wanted an AI adoption scorecard. The first draft had tool usage, prompt count, generated words, generated lines of code, and number of employees trained. 4 min 08 Scaling Without Making Humans the Bottleneck The company had one successful AI workflow: support reply drafting. It saved time, improved consistency, and made new agents productive faster. 4 min END Conclusion: Closing Note The AI-native team operating model is not a smaller old team with better tools; it is a system redesigned around judgment scarcity. 2 min