AI has produced an extraordinary paradox. The tools available to individual workers have never been better - AI agents write and test code, create documents, and parse through data at speeds that seemed impossible before. Every employee in an organization can now be a 10x professional with the help of AI. And yet, organizations aren't getting 10x faster. The speed at which AI executes is causing organizations to get more complex, more fragmented, and harder to keep track of. Before AI, execution was the main organizational bottleneck: executives needed to prioritize what got done, by which teams, to achieve their goals on time and remain competitive. With agents that execute in the blink of an eye, the real bottleneck becomes visible: context coordination. In the US alone, companies spend $1.2tn in meetings where context - what, why and how should we do something - gets transferred and discussed among knowledge workers. An engineering manager spends 234 hours a year - nearly six full weeks - just answering context questions. AI agents make 75% more business logic errors because the organizational knowledge that governs how a system should behave is either written down outside of code or not written down at all - it lives in people's heads, in DMs, in documents, and in decisions made in meetings that were never documented. And when people leave, that context walks out the door with them, forcing teams to rediscover the same hard lessons every time. The scar tissue, the knowledge that comes from having tried and failed, the "this is how things are done here" - none of this is easily made available to the very agents that execute at lightspeed. All of this would be a hard enough problem if everybody on a team always agreed on facts and goals, but if you've ever worked with other people you know context reconciliation is a big part of any job.