A new lay introduction breaks down the complex mathematics of superposition in neural networks. The author translates theoretical computer science results into accessible concepts after a failed attempt to digest the original paper in one hour. This synthesis helps alignment researchers understand how models pack more features than dimensions. It clarifies a dense technical hurdle.