TL;DR: Biohub, U.S. agencies and tech companies committed $1.8 billion on Oct. 7, 2026, to build open AI-ready biology data for predictive virtual-cell models.
U.S. science entered a new large-scale data push on Oct. 7, 2026, when Biohub, the Department of Energy Office of Science and the National Institutes of Health announced an expanded cross-sector collaboration to generate AI-ready biological data, with total funding, data, computation and measurement technology valued at $1.8 billion, according to AI-ready biological data: $1.8 billion global commitment. The announcement, issued from Redwood City, California, framed the resource as an open foundation for predictive models that could help researchers understand and ultimately treat human diseases, according to that Biohub announcement.
$1.8 billion commitment announced Oct. 7
The Oct. 7, 2026 announcement described the $1.8 billion package as the largest coordinated commitment to generating AI-ready biological data to date, according to AI-ready biological data: $1.8 billion global commitment. The partners said the result will be an open resource for the research community to support understanding and treatment of human diseases, according to the same Biohub statement.
The collaboration is international and cross-sector, involving Biohub, the Department of Energy, the National Institutes of Health and new funding partners, according to the Biohub announcement. The stated purpose is to generate and make accessible the data enabling predictive AI models of biology, according to the Biohub announcement.
The effort sits under the Virtual Biology Initiative, which Biohub announced in April 2026 to coordinate data generation across institutions and disciplines, according to the Biohub announcement. Biohub’s founding $500 million commitment anchors that work, according to the Biohub announcement.
That $500 million Biohub commitment includes $400 million for new measurement technologies and $100 million for research outside Biohub, according to the Biohub announcement. The technology funding supports cryo-electron tomography for near-atomic detail inside the cell, microscopy able to image millions to billions of cells in living tissue, and engineering tools to build and perturb biology at molecular, cellular, tissue and whole-organism levels, according to the Biohub announcement.
Who is paying what
The funding structure combines federal science agencies, a philanthropic research venture and three commercial technology contributors, according to U.S. government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data. Meta, Google DeepMind and drug-discovery startup Isomorphic Labs are jointly investing $300 million, according to that CNBC report.
The Department of Energy will invest more than $500 million over five years in laboratory measurement, modeling and computation, according to the CNBC report. The National Institutes of Health will coordinate datasets and repositories built with more than $500 million in earlier federal funding, which Biohub will standardize for AI training, according to the CNBC report.
The commitments follow the $500 million that Biohub put into the project in April 2026, according to the CNBC report. Biohub is a philanthropic venture of Meta CEO Mark Zuckerberg and his wife, Dr. Priscilla Chan, according to the CNBC report.
Biohub said Google DeepMind, Isomorphic Labs and Meta are collectively investing $300 million in the Virtual Biology Initiative to create technologies and multi-modal datasets needed to build predictive models of life, according to the Biohub announcement. Biohub said it will work with NIH to standardize NIH-aligned datasets for AI model training, according to the Biohub announcement.
DOE’s $500 million Genesis Mission role
Through the Genesis Mission, a cross-agency initiative led by the Department of Energy, DOE will contribute more than $500 million over five years in fundamental cell research, data collection, AI analytics, measurement and imaging, modeling and computation, according to the Biohub announcement. The work draws on exascale supercomputing, X-ray and neutron scattering, cryo-electron microscopy and tomography, and autonomous laboratories across the National Laboratory system, according to the Biohub announcement.
The partnership delivers on the Executive Order Launching the Genesis Mission by using Super Intelligence-ready biological data, automated laboratory experiments, advanced modeling approaches and resources across DOE’s National Laboratory system, according to DOE, NIH, and Biohub Partner to Build Foundational Data for Predictive Biological Super Intelligence Models. Those resources include exascale compute power using supercomputing clusters to model multi-modal biological systems and run frontier cell simulations, according to the Department of Energy announcement.
Advanced experimental facilities in the DOE contribution include the Joint Genome Institute for genome annotation and research and the Environmental Molecular Sciences Laboratory for microbial and molecular-scale research, according to the Department of Energy announcement. Other DOE measurement and imaging capabilities include X-ray and neutron scattering and cryo-electron microscopy and cryo-electron tomography to resolve atomic, molecular and cellular structures, according to the Department of Energy announcement.
DOE also listed autonomous laboratories using Super Intelligence-driven robotic and automated experimental systems to generate and validate biological data, according to the Department of Energy announcement. Those systems operate at user facilities and through the Genesis Mission Orchestrated Platforms for Autonomous Laboratories project known as OPAL, according to the Department of Energy announcement.
Biological data systems in the DOE plan include BioDataNexus Biological and Environmental Infrastructure for Data Management and Exploration, abbreviated as BRIDGE, according to the Department of Energy announcement. The plan also includes the Genesis Mission Lakehouse-enabled Super Intelligence-ready multi-modal bioimaging data architecture known as LAMBDA, according to the Department of Energy announcement.
NIH datasets and standardization
Through its Bio Genesis Mission, NIH will bring together existing biomedical datasets, national data infrastructure and research programs to help build AI-ready resources, according to the Biohub announcement. NIH resources include national biomedical repositories catalogued by the National Library of Medicine and the National Center for Biotechnology Information, according to the Biohub announcement.
NIH Common Fund programs are already developing coordinated biological atlases, shared data standards and AI-ready biomedical datasets, according to the Biohub announcement. Biohub will work with NIH to standardize relevant datasets for AI model training, according to the Biohub announcement.
NIH will coordinate the contribution of relevant datasets, repositories and knowledge bases developed through more than $500 million in prior federal investment aligned to the initiative, according to the Biohub announcement. The Department of Energy, National Institutes of Health and Biohub signed a Memorandum of Understanding to advance Super Intelligence in biology, deepen understanding of human health and disease, and accelerate breakthroughs in medicine and biotechnology, according to the Department of Energy announcement.
The partnership brings together DOE National Laboratories’ computation and biological expertise, Biohub’s Super Intelligence technologies and biomedical research infrastructure, and NIH’s health data, imaging resources and research enterprise, according to the Department of Energy announcement. The stated technical goal is to build an open biological data foundation for predictive models of living systems and to predict how cells respond to interventions, according to the Department of Energy announcement.
What a virtual cell is supposed to do
The Virtual Biology Initiative aims to measure how cells respond to changes across far more conditions than scientists have studied so far, and to use that data to build predictive models, according to the CNBC report. Those models could compress drug-development timelines that currently take years, according to the CNBC report.
The datasets will enable researchers to ask, predict and answer biological questions digitally, according to the Biohub announcement. The initiative will expand cell-response data to interventions across more cell types and conditions than have yet been studied, according to the Biohub announcement.
The initiative will also build and validate technologies for studying cells and cellular interactions at greater scale, speed and accuracy, according to the Biohub announcement. Biohub Head of Science Alex Rives said, “An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally,” according to the Biohub announcement.
Rives also said the creation of a virtual cell is one of the most important challenges for the next era of science and will require coordinated data generation at national and international scale, according to the Biohub announcement. Biohub invited the worldwide scientific community to join the project, according to the Biohub announcement.
Scale: from millions of cells to trillions
Current cell datasets run to hundreds of millions of cells, while an accurate predictive model will require billions and eventually trillions, according to the CNBC report. Biohub’s stated goal is to close that gap, according to the CNBC report.
Rives said, “We need to capture the language of biology, we need to capture the language of the cell. And that doesn’t exist today,” according to the CNBC report. The data will come from techniques including spatial transcriptomics, which maps molecular activity inside intact tissue, according to the CNBC report.
The data will also come from screens that record how cells respond to changes in environment, according to the CNBC report. Much of that coordinated data has never been generated in a coordinated way, according to the CNBC report.
Rives said the work would normally take decades and that the partners aim to compress it into five years, with a first dataset ready in about a year, according to the CNBC report. He expects accurate predictive models within five years, according to the CNBC report.
Open data with a one-year commercial head start
The datasets will eventually be released publicly, but companies that fund them get a head start, according to the CNBC report. Biohub Head of Science Alex Rives said, “With commercial funders we have embargo periods where there’s a period of time where the groups can work on the data, and then it becomes available as a public scientific resource,” according to the CNBC report.
The arrangement is how Biohub is drawing private money into a project it describes as open science, according to the CNBC report. Rives said the government-funded work running in parallel will carry no such restrictions, according to the CNBC report.
Biohub plans to approach pharmaceutical companies and philanthropies next, according to the CNBC report. The commercial partners will have one year of exclusive access to the data they develop before it is shared publicly, according to Zuckerberg teams with Google, U.S. in push to map cells.
Rives said the temporary advantage is intended to give companies a reason to contribute money while still ensuring the resulting data becomes an open scientific resource, according to that Axios report. Rives said, “We have to have some incentive for commercial players to be a part of this, and the embargo period creates that,” according to that Axios report.
Leaders frame computing, measurement and U.S. biotechnology
DOE Under Secretary for Science Darío Gil said, “This partnership represents a critical step forward in leveraging artificial intelligence for public benefit,” according to the Biohub announcement. Gil said combining DOE exascale computing, experimental measurement and modeling assets with Biohub AI models, tool development and biological data capabilities sets a new standard for open science, according to the Biohub announcement.
Gil named the Joint Genome Institute, the Environmental Molecular Sciences Laboratory and advanced structural beamlines among the DOE assets involved, according to the Biohub announcement. He said the combination will accelerate discoveries in medicine and biotechnology, according to the Biohub announcement.
Isomorphic Labs President Max Jaderberg said, “Generating the data to solve predictive systems biology requires scaling past the limits of what any single organization can produce today,” according to the Biohub announcement. Jaderberg said Isomorphic Labs, as a founding member, is helping build a massive multimodal data foundation, according to the Biohub announcement.
Priscilla Chan said, “Biology has been just sort of a clever discovery-based science until this point,” according to the CNBC report. Chan said the collaboration is being held as a community asset so it can build upon itself over time, according to the CNBC report.
Competition in AI biology
Other AI laboratories are pursuing similar biology initiatives, according to the CNBC report. Anthropic doubled down on its biology efforts with a wet lab, according to the CNBC report.
The OpenAI Foundation started a more than $125 million grant program to fund biological and medical datasets for AI research, according to the CNBC report. Biohub’s first phase will create a broad map of cellular biology, gathering different kinds of information about cells and how they respond to changes, according to the Axios report.
Rives said, “If we can put more and more reasoning and intelligence into every single question that we actually ask in the lab, the value of those empirical results will be far greater,” according to the Axios report. Rives described biology as requiring “empirical AI,” meaning models that learn from biological evidence and can predict what happens in the physical world, according to the Axios report.