Modern commerce platforms face the challenge of delivering personalized recommendations across billions of items to users with diverse intents, temporal dynamics, and cold-start scenarios. We present a generative foundation model for commerce built on Hierarchical Sequential Transduction Units (HSTU) that integrates Liquid Foundation Models (LFM) and custom CUDA kernels developed in collaboration with Nvidia for efficient training and online serving. Our approach demonstrates that generative methods unlock substantial gains through three key innovations: (1) large-scale contrastive learning with hard negative sampling; (2) temporal mechanisms that fuse multi-scale time signals (session, day, season) with commerce-specific features; and (3) optimized training and inference kernels. While results are promising, significant challenges remain in handling non-stationary preferences, growing product catalogue, and multi-objective optimization—we discuss our roadmap toward truly foundational commerce models that generalize across domains and market conditions.