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Materials AI stands at a critical juncture where rapid advances in machine learning and data-driven discovery are reshaping materials engineering. Yet the field is severely constrained by a deep-rooted publication bias that systematically favors positive results while suppressing negative outcomes. This position paper asserts that Materials AI urgently requires widespread failure reporting and that negative results must be recognized and maintained as essential community infrastructure. In the prevailing culture, only successful predictions, high-accuracy models, and newly discovered materials reach publication, while failed models, insufficient datasets, misleading benchmarks, and unsuccessful synthesis attempts remain hidden. The consequences are profound: researchers repeatedly traverse the same unproductive paths, the scientific literature presents a distorted view of reality, meta-analysis becomes impossible, and overall progress slows dramatically. Failure reporting functions as shared infrastructure—much like open databases or standardized protocols—by mapping the boundaries of what does not work and thereby enabling more efficient exploration by the entire community. Four core types of failures demand routine documentation: model failures, data failures, benchmark failures, and synthesis failures, with additional emphasis on transfer and extrapolation failures. By treating negative results as valuable contributions rather than career liabilities, the community can accelerate discovery, enhance reproducibility, and build a more trustworthy knowledge base. This paper articulates the position, analyzes the publication bias problem, defines failure reporting as infrastructure, classifies failure types, and identifies barriers before offering concrete recommendations. Embracing negative results will transform Materials AI from a literature of selective successes into a robust, transparent, and collectively intelligent discipline capable of delivering sustainable materials innovation at scale.