Digitized natural history records, now numbering in the billions (1), span widely across the tree of life and provide the foundation for numerous recent advances in biodiversity research (2, 3). Mechanistic insights are emerging for old questions, including how diversity has expanded and contracted through Earth’s history (4), how species have come to occupy the wide range of ecological roles observed on land and sea alike (5, 6), and how the millions of species on Earth will respond to a rapidly changing climate in the future (7). Fundamentally, such studies require an understanding of both how individual organisms are classified to species and how species are related in their evolutionary history. In deep time, where fossils provide scattered snapshots of historical diversity, taxonomic resolution is particularly elusive. Molecular data are entirely absent, the fossil record contains numerous gaps in time and space, and fossil preservation presents a host of challenges for evaluating the shape and structure of diagnostic anatomical traits. Numerous fossil specimens remain poorly resolved, particularly in their evolutionary relationships to modern taxa, clouding the temporal and geographic resolution of biodiversity in deep time. For widespread ecologically important clades like plants, this limits our ability to reconstruct the dynamics of ancient ecosystems (8). In PNAS, Romero et al. (9) present a deep-learning–based approach for classifying some of the fossil record’s most widely documented yet vexing historical material—fossil pollen (8, 10). Paired with an ecological and climatic understanding of the distributions of plant groups today, taxonomically resolved pollen studies provide an important lens for paleobotanical diversity and data for paleoclimatic inference (8, 10). Romero et al. (9) show how this record can be further … [↵][1]1Email: whiteae{at}si.edu. [1]: #xref-corresp-1-1