NEARLY $2B NEWLY COMMITTED TO AI + BIOLOGY Biohub, the U.S. Department of Energy, and the NIH announced a $1.8B international push to generate the foundational data behind AI models that can predict — and eventually treat — human disease. It's the largest commitment to AI-ready biological data to date. Who's putting up what: @biohub, $500M: the founding commitment. $400M for new measurement tech (cryo-electron tomography at near-atomic detail, microscopy imaging millions-to-billions of cells in living tissue, tools to build and perturb biology at every level), $100M for research outside Biohub. @ENERGY, $500M+ over five years: through its Genesis Mission: cell measurement, AI analytics, modeling, and exascale supercomputing across the National Labs. @NIH, coordinating datasets: built through $500M+ in prior federal investment, and working with Biohub to standardize them for AI model training (the Bio Genesis Mission). @GoogleDeepMind, @IsomorphicLabs, @Meta, $300M collectively: into the Virtual Biology Initiative for the technologies and multi-modal datasets needed to build predictive models of life. Plus: the @AllenInstitute, @broadinstitute, @GladstoneInst, Human Cell Atlas, Human Protein Atlas, and Wellcome Sanger Institute aligning the science; @nvidia on accelerated computing; @RenPhilanthropy expanding data-generation funding. What it's all for: an open data commons to train a "virtual cell" letting researchers ask, predict, and answer biological questions *digitally* instead of running lab experiments first. In Biohub head of science @alexrives's words: a predictive model of biology could "dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally." I sat down with him to talk about what Biohub is building & what the current roadblocks are. That interview is coming soon.