The story follows one fictional organization, stitched together from things I lived through, across three eras of its life: the early monolith, the reorg into "autonomous teams" that nobody thought all the way through, and the arrival of LLM assistants. The whole arc plays out on a single network graph of people, teams, services, and code that evolves on screen as the company changes, so you can watch the architecture respond to the conversations, and stop responding when the conversations stop.

Along the way: how well-intentioned autonomy can manufacture a distributed monolith; what actually happens to a codebase when every developer's closest collaborator becomes a model; what the industry's own data says about feeling faster versus being faster; and why AI makes Conway's Law bite harder, not softer. And because this isn't a doom talk, the way out is here too, grounded in Diana Montalion's Learning Systems Thinking.

You'll leave with a new lens to view your own codebase, a sharper question to ask about every system your team ships, two gut checks for your team's AI adoption, and a handful of practices you can pilot without waiting for anyone to approve a reorg.