AI will flatten parts of the organization, but it won't remove accountability. The real shift is that more of the company's operating logic becomes encoded, observable, and continuously adjustable, and someone still has to own it.
Every enterprise is arguing about the right per-employee AI spend cap. Tomer Elias and I compared notes on why that number is unanswerable until you can attribute the spend to something.
Spec-driven development gave you a fast software lifecycle. It stops at released. The product lifecycle does not, and the gap between the two is where AI adoption quietly dies.
Most of your scaling apparatus is now optional. Your first principles are not. A companion for leaders running SAFe while adopting AI-native, spec-driven development, and trying to tell the difference.
Is Scrum still relevant when agents write the code? Yes, and more than before, provided you are willing to customize it. A companion to the Scrum Guide walking the accountabilities, events, artifacts, and values for a team running an AI-native, spec-driven lifecycle.
A companion to the Kanban Guide for Scrum Teams for AI-native product teams. What changes in your Definition of Workflow, WIP limits, flow metrics, and Scrum events once agents do the building, and what happens to scaling when the old dependencies start dissolving.