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The study of the [infrastructure] that supports AI research and deployment: datasets, computing clusters, software frameworks, model zoos, annotation pipelines, and the energy grids that power them. AI infrascience examines how infrastructure choices shape what AI can do—who gets to train large models, whose data is included, how carbon footprints are measured, and how open source vs. proprietary tools affect innovation. It reveals that AI is not just algorithms; it’s a material system dependent on rare earth minerals, cloud contracts, and precarious labeling labor. [Understanding] AI requires [understanding] its infrascience.
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