The open source AI conversation measures itself in the wrong units. Post engagement, GitHub stars, and release announcements are easy to count; the actual population of practitioners who have cleared the hardware and configuration threshold to run frontier-scale models locally is not. The result is a persistent overestimation of how broad the movement is. The gap between perceived and actual open source AI adoption is not a bug in the ecosystem's messaging — it is the primary reason the conversation carries so much commercial energy for labs that release nothing.