We need to make sure we close the loop first. That means using GitHub’s MCP, and increasingly ACP, to move from idea to implementation before the conversation about job displacement even begins.

https://video.wixstatic.com/video/8c5863_599599d52704477db94da226f0f58c04/480p/mp4/file.mp4

We need to make sure we close the loop first. That means using GitHub’s MCP, and increasingly ACP, to move from idea to implementation before the conversation about job displacement even begins. One of the most persistent misconceptions is the “lump of labor” fallacy—the belief that there is a fixed amount of work in the world. History suggests the opposite: as automation increases and productivity rises, entirely new categories of work emerge, even as old ones disappear. In this transition, Laniakea represents something larger than another software product; it is envisioned as the operating system for a personal Clanker, marking the beginning of a new software renaissance where systems are generated dynamically from intent rather than handcrafted line by line. The development process itself changes as well: after every feature or fix, the model should immediately generate tests using the same context, not only producing better test coverage but often uncovering implementation bugs that would otherwise go unnoticed. The diffusion of technology is real, and after speaking with organizations across industries, it is clear that adoption curves vary dramatically—some enterprises could adopt Laniakea in seconds, while traditional workflows and subagent-based systems still introduce unnecessary friction and delay. I say this as someone who spent nearly six years building tools that make coding on a phone possible; diffusion effects may slow change temporarily, but they do not stop it. At the same time, economics cannot be ignored. Even tiny transaction costs can dramatically alter incentives, potentially making it inefficient for Clankers to trade with humans at all. In such a world, human wages may decline significantly, even when people continue to possess valuable skills. The challenge is not whether intelligence and automation will advance, but how we build systems that create more opportunity, more capability, and more abundance while navigating the profound economic and social changes that follow.