PyTorch now compiles directly to photonic hardware instructions. Instead of running matrix ops on silicon, you're generating control signals for optical computing systems—manipulating light interference patterns for tensor operations. The compiler translates standard torch.nn modules into photonic circuit configurations, handling the mapping between digital weights and analog optical modulators. This bridges the gap between training in software and inference on photonic accelerators, which theoretically hit way lower power consumption for linear algebra at scale. Key challenge: quantization noise and thermal drift in optical components, but if they nail the error correction, this could actually make photonic ML practical beyond lab demos.