Ultrashort pulse laser processing has become an indispensable technology across a wide range of industries, enabling high-precision structuring and material modification with minimal thermal damage. Recent advances in laser source technologies, such as GHz burst operation and other spatiotemporal control schemes, have dramatically expanded the accessible parameter space for laser?matter interaction. While this progress provides unprecedented flexibility, it also presents significant challenges in identifying optimal conditions within the vast multidimensional landscape of processing parameters. To address this, the concept of a Digital Twin for precision laser processing is gaining momentum. By integrating fully automated experimental platforms with deep learning methods, it becomes possible to construct a virtual replica of the laser process that evolves in parallel with experimental data. This digital twin not only accelerates the discovery of optimal parameters but also enables predictive control, which is essential for exploiting the full degrees of freedom offered by evolving light sources. In this talk, we discuss the foundations of such digital twins, the enabling technologies of automation and artificial intelligence, and the outlook for mastering evolving light sources in precision laser manufacturing.