Datasets built with past federal money make up over a quarter of the $1.8 billion biology data pledge

Biohub describes the $1.8 billion as a mix of funding, data, computation and new measurement technology ^[1]. The four contributions it lists reach that total only when NIH's datasets are counted at the more than $500 million of earlier federal investment that produced them ^[11][13]. On that basis, work the government has already paid for makes up about 28% of the figure ^[14]. DOE and the three companies together bring $800 million, or about 44% ^[16].

Public money is moving toward shared biological data, at a slower rate than the total suggests. Federal agencies account for more than $1 billion of the $1.8 billion, and half of that is past spending ^[17]. The new federal cash is DOE's: more than $500 million over five years, or roughly $100 million a year ^[15]. DOE will spend it through the Genesis Mission on its own National Laboratory system ^[6]. That system includes exascale computers, the Joint Genome Institute, the Environmental Molecular Sciences Laboratory and autonomous laboratories ^[6].

The people paying say data generation is the part no single organisation can do alone. "Generating the data to solve predictive systems biology requires scaling past the limits of what any single organization can produce today," said Max Jaderberg, President of Isomorphic Labs ^[8]. Alex Rives, Biohub's head of science, said building a virtual cell "will require coordinated data generation efforts at a national and international scale, which is why these partners are coming together" ^[7]. We take those words, and the $300 million from Google DeepMind, Isomorphic Labs and Meta ^[12], as good evidence of where the funders think the constraint sits.

One objection to the total is that it prices old datasets at what they cost to build. We think that is fair for the headline figure and weaker for the work. Biohub will standardise NIH's datasets for AI model training ^[11], and that conversion is new labour on old data.

The two releases quote DOE's Under Secretary for Science, Dario Gil, in different words. In Biohub's release he said, "This partnership represents a critical step forward in leveraging artificial intelligence for public benefit" ^[9]. In DOE's he said, "Advancing Super Intelligence in biology represents a critical step forward in leveraging new technologies for public benefit" ^[10]. DOE frames the whole memorandum around "Super Intelligence" and presents it as delivering on President Trump's executive order launching the Genesis Mission ^[5][6].

For anyone deciding this quarter, the schedule matters more than the headline. DOE's money runs over five years ^[2]. Biohub's $400 million goes to measurement technologies such as cryo-electron tomography and microscopy of millions to billions of cells ^[4], and the initiative's plan includes building and validating tools of that kind ^[20]. The releases do not give a date for the first open datasets. A company choosing now whether to fund its own cell data is weighing a resource Biohub says will be open to the research community ^[19], built over five years, in a format Biohub and NIH will set ^[11].