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We analyze the growth of dataset sizes used in machine learning for naturallanguage processing and computer vision, and extrapolate these using twomethods; using the historical growth rate and estimating the compute-optimaldataset size for future predicted compute budgets. We investigate the growth indata usage by estimating the total stock of unlabeled data available on theinternet over the coming decades. Our analysis indicates that the stock ofhigh-quality language data will be exhausted soon; likely before 2026. Bycontrast, the stock of low-quality language data and image data will beexhausted only much later; between 2030 and 2050 (for low-quality language) andbetween 2030 and 2060 (for images). Our work suggests that the current trend ofever-growing ML models that rely on enormous datasets might slow down if dataefficiency is not drastically improved or new sources of data become available.

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning