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CHAPTER 10Scaling AI-Native Engineering in Teams

A CTO whose bill went up and whose velocity did not. This chapter is about the part no tool fixes: shared context, standardised stacks, rational resistance, the new shape of the team, and metrics that measure capability instead of activity. The canvas comes from here.

  • 34 sections
  • 11k words
  • ~48 min
  • 4 figures
key takeaways8 points
  • 01Scaling AI-native engineering is a people-and-standards problem, not a tooling problem.
  • 02Individual gains do not add up to team gains on their own.
  • 03Use the AI-Native Team Canvas to design how your team adopts AI.
  • 04Treat resistance as rational and answer it with a better definition of the job.
  • 05Standardise the stack and sanction experimentation.
  • 06The harness is the real deliverable, and it compounds.
  • 07The team's shape changes: smaller, tighter, with product and design closer to the code.
  • 08Measure real capabilities, not vanity metrics.
the aine canvasascii

inside this chapter

  • Why Scaling to the Team Level Is a Different Problem
  • The Bottleneck Moves, It Doesn't Disappear
  • New Failure Modes That Only Show Up at Team Scale
  • AI Is a Mirror and a Multiplier
  • Practice
  • The AI-Native Team Canvas: Design How Your Team Adopts AI
  • A Business Model Canvas, but for AI Adoption
  • How to Run It
  • The Eight Boxes
  • Skill Gaps and System Gaps
  • Practice
  • Usage Is Not Readiness
  • Resistance Is Rational: Why Engineers Push Back
  • The Three Objections You Will Hear
  • Reframing the Craft
  • What This Means for Leaders
  • Practice
  • Standardize the Team Stack
  • Standardize What Matters
  • The Shared Context Is the Real Stack
  • The Harness Gets Better Over Time
  • Someone Has to Own It
  • Leave Room to Experiment
  • How an AI-Native Team Works
  • Pair on the Agent, Not Just the Code
  • Write and Review Specs as a Team
  • The Team Gets Smaller and Tighter
  • The Human Cost of Running a Fleet
  • Agents That Work Overnight
  • Product and Design Move Closer to the Code
  • Practice
  • Measuring Adoption
  • Practice
  • Summary

figures

fig 10-1
The AI-native Team Canvas: eight areas, turning "how do we adopt AI?" into a set of decisions a team can answer together.
The AI-native Team Canvas: eight areas, turning "how do we adopt AI?" into a set of decisions a team can answer together.
fig 10-2
AI rules as a tree: a spine from a shared base down through language, framework, and team/project rules, with cross-cutting concerns--testing, observability, infrastructure--branching off to the side rather than stacking on top. Everything is versioned in Git, and different projects share the base while composing their own branches.
AI rules as a tree: a spine from a shared base down through language, framework, and team/project rules, with cross-cutting concerns--testing, observability, infrastructure--branching off to the side rather than stacking on top. Everything is versioned in Git, and different projects share the base while composing their own branches.
fig 10-3
The experiment loop as a flow chart: run an experiment, and if it doesn't find something useful, discard it; if it does, the team discusses and validates it, and only if the team accepts it does it become a standard--then loop back and keep testing new things and assumptions.
The experiment loop as a flow chart: run an experiment, and if it doesn't find something useful, discard it; if it does, the team discusses and validates it, and only if the team accepts it does it become a standard--then loop back and keep testing new things and assumptions.
fig 10-4
Before: Work moved in a straight relay from Product through Design to Engineering to QA. After: A tight loop of three roles working from a shared spec, with an agent fleet handling the mechanical work.
Before: Work moved in a straight relay from Product through Design to Engineering to QA. After: A tight loop of three roles working from a shared spec, with an agent fleet handling the mechanical work.

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