<p>Self-driving laboratories (SDLs) merge autonomous experimentation, advanced reactor engineering, robotics and artificial intelligence to accelerate scientific knowledge creation. Over the last decade, SDLs have progressed from narrowly focused automation tools to multipurpose discovery platforms in which algorithms propose, execute and interpret experiments with limited human intervention. This Review traces the evolution of SDLs and examines the structural asymmetries that limit their maturation into shared scientific infrastructure. We frame the next phase of the field around three interdependent requirements: scalability, generalizability and provenance-complete experimentation. Realizing collective scientific superintelligence will require SDLs that reliably scale throughput, transfer workflows and learned models across laboratories and scientific domains and capture end-to-end experimental data and metadata from precursor preparation through synthesis, characterization and performance evaluation. Achieving this transition will depend on interoperable data and metadata standards, modular and integrable experimental hardware, and trustworthy artificial intelligence agents that reason under uncertainty within rigorous safety and ethical boundaries.</p><p></p>