The field of materials science stands at a critical inflection point. While laboratory innovations continue to emerge at an unprecedented pace, the traditional timeline from discovery to market in 10-20 years has become an unacceptable bottleneck in addressing urgent technological challenges. We argue that self-driving laboratories (SDLs) represent not merely another step in automation, but a fundamental reimagining of the materials development pipeline. By integrating manufacturing constraints and scalability considerations from the earliest stages of discovery, SDLs can collapse the laboratory-to-factory timeline while improving reproducibility and success rates. This requires abandoning the traditional sequential approach of materials screening, device optimization and manufacturing scale-up; in favor of concurrent cross-scale development. Here, we critically examine current SDL implementations, challenge prevailing assumptions about automation in materials science, and propose a roadmap for truly integrated materials development platforms that could revolutionize how we translate laboratory discoveries into commercial products.