The article discusses the challenges and pitfalls in high-throughput inorganic materials prediction and autonomous synthesis, focusing on the recent work of Szymanski et al. [Nature 624, 86 (2023)] which reported the autonomous discovery of 43 novel materials. The authors outline basic principles of solid-state chemistry and highlight common shortfalls in the analysis of these materials, leading to the conclusion that none of the materials produced were truly new. They identify two key areas for improvement: (i) the reliability of automated Rietveld analysis of powder X-ray diffraction data, and (ii) the consideration of disorder in materials, which is often neglected in predictions. The article also emphasizes the importance of defining what constitutes a "new" material and provides detailed examples of errors in the analysis of the 43 synthetic products, categorizing them into four common types. The authors conclude by highlighting the need for future improvements in automated materials characterization and computational chemistry to advance the field of materials discovery.The article discusses the challenges and pitfalls in high-throughput inorganic materials prediction and autonomous synthesis, focusing on the recent work of Szymanski et al. [Nature 624, 86 (2023)] which reported the autonomous discovery of 43 novel materials. The authors outline basic principles of solid-state chemistry and highlight common shortfalls in the analysis of these materials, leading to the conclusion that none of the materials produced were truly new. They identify two key areas for improvement: (i) the reliability of automated Rietveld analysis of powder X-ray diffraction data, and (ii) the consideration of disorder in materials, which is often neglected in predictions. The article also emphasizes the importance of defining what constitutes a "new" material and provides detailed examples of errors in the analysis of the 43 synthetic products, categorizing them into four common types. The authors conclude by highlighting the need for future improvements in automated materials characterization and computational chemistry to advance the field of materials discovery.