Materials Acceleration Platforms (MAPs) aim to leverage modern artificial intelligence (AI) algorithms to accelerate the discovery of molecular and solid state materials. For solid state materials, automation and integration with computation 2 has been historically achieved via combinatorial methods and recently implemented using autonomous or self-driving labs. The pace of advancement in experiment automation is staggering and motivates rethinking of how instruments are made and controlled. Initial efforts in AI-guided automated workflows have naturally focused on achieving super-human efficiency of data acquisition. Here, automated experiment selection alleviates reliance on human researchers but does not supplant human governance of the scientific line of inquiry and the strategy for its exploration. Recent developments in physics and chemistry-aware models as well as hypothesis learning algorithms exemplify the everincreasing sophistication of automated experiment design. While such algorithms do not yet rival the decision making of human experts, the trajectory of AI algorithms clearly indicates that the frontier of experiment automation is not the automation of india Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; * E-mail: guevarra@caltech.edu, gregoire@caltech.edu b Liquid Sunlight Alliance, California Institute of Technology, Pasadena, CA, USA c TUM School of Natural Sciences, Department of Chemistry, and Munich Data Science Institute, Technical University of Munich, Munich, Germany + present address: Good Terms LLC, CO, USA ‡ present address: deepXscan GmbH, Dresden, Germany vidual experiments but rather entire workflows and their ensembles.