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Biological research spans scales from molecules to systems to organisms, seeking to understand and design functional components across all domains of life. Creating a machine to design functions across the diversity of life would require it to learn a deep, generalist representation of biological complexity. Although this complexity surpasses straightforward human intuition, advances in artificial intelligence offer a universal framework that leverages data and compute at scale to uncover higher-order patterns 6 , 7 . We reasoned that training a model with these capabilities would require data spanning the full spectrum of biological diversity to discover emergent properties similar to those found in other fields 8 .

We previously demonstrated that machine learning models trained on prokaryotic genomic sequences can model the function of DNA, RNA and proteins, as well as their interactions that create complex molecular machines 1 , 2 .…