PyTorch Conference Europe 2026

PyTorch was built to track how models learn, but not whether they should have. As AI systems increasingly operate on regulated, jurisdiction bound, and sovereign data, lineage and policy can no longer live outside the runtime. This talk explores data sovereignty as a first class constraint and argues that lineage is the missing primitive in modern ML frameworks. Building on PyTorch’s dynamic graphs and autograd system, we outline how tensors could carry origin, consent, and policy metadata through training and inference. The goal is not compliance tooling, but a lineage aware PyTorch that enables trustworthy, auditable, and deployable AI across edge, federated, and European AI ecosystems.