The text challenges the notion held by some venture capitalists that customer acquisition cost (CAC) inevitably increases as companies scale, arguing instead that CAC can be reduced through the strategic use of machine learning (ML) technologies. The author, drawing from experience with over 3,000 companies, highlights that many firms fail to effectively implement ML due to outdated data processing methods and a lack of integration into production environments. The text provides an example of a billion-dollar startup that successfully reduced CAC by 52% through a comprehensive approach involving advanced ML experiments, leveraging customer and behavioral data, and integrating these insights across various business functions. It emphasizes the importance of collaboration, data-driven decision-making, and patience in deploying ML technologies to optimize CAC and improve overall business performance.