24 Companies Join U.S. Genesis Mission to Apply AI in Science, Energy, and Security
Twenty-four organizations have joined or expressed interest in the Genesis Mission, a national effort announced in November to use AI across science, energy, and national security. The Department of Energy (DOE) said participants include Anthropic, Amazon Web Services, Google, Microsoft, Nvidia, OpenAI, and xAI.
The initiative stems from an executive order signed by President Donald Trump directing the DOE to build a unified AI platform that connects federal scientific data, national laboratory supercomputers, and private cloud capacity. The order says the Genesis Mission will "accelerate scientific discovery, strengthen national security, secure energy dominance and multiply the return on taxpayer investment in research and development."
What this means for researchers
The mission's stated goals map directly to core R&D workflows: automate experiment design, run faster simulations, and build predictive models that shorten the path from idea to result. Michael Kratsios of the White House Office of Science and Technology Policy said the research partnerships announced so far are only the start, with plans to involve companies, universities, non-profits, and federal agencies.
For labs, this points to broader access to advanced models, scaled compute, and shared datasets-tied to domain problems in energy, manufacturing, and drug discovery.
Who's in-and what they're offering
- Anthropic: "By combining DOE's unmatched scientific assets with our frontier AI capabilities, we'll support American energy dominance as well as advance and accelerate scientific productivity."
- Amazon Web Services: Said it is contributing infrastructure that turns the program's vision into operational systems today.
- Google DeepMind: "Google DeepMind will provide an accelerated access program for scientists at all 17 DOE National Laboratories to our frontier AI for Science models and agentic tools, starting today with AI co-scientist on Google Cloud."
- OpenAI: "This MOU builds on OpenAI's existing work with DOE's national laboratories, where we've already deployed frontier models in real research environments and worked directly with scientists on high-impact problems."
- Nvidia: "Nvidia will offer its services to the [Department of Energy] to integrate a discovery platform that unites the U.S. government, industry and academia."
Access and infrastructure
Early signals suggest national labs will get priority access to certain AI-for-science models and tools, along with pathways to integrate them into existing HPC environments. Private cloud capacity from participating companies will complement federal compute and datasets.
Universities and industry R&D teams should watch for DOE announcements on pilot programs, data-sharing frameworks, and calls for proposals. Keep an eye on the DOE and OSTP pages for updates: DOE National Laboratories and White House OSTP.
What to do now (practical steps for your lab)
- Prioritize high-leverage workflows: Hypothesis generation, design-of-experiments, surrogate models for multi-physics simulations, scientific code assistants, and lab automation agents.
- Get your data house in order: Standardize metadata, ensure FAIR practices, and document provenance. Flag sensitive content (ITAR, export control, PHI) and define policies before model integration.
- Plan your compute strategy: Expect hybrid setups that mix HPC and cloud. Containerize, adopt reproducible pipelines, and set up observability for model runs and data movement.
- Protect IP and results: Pre-negotiate NDAs and publication processes. Decide what stays internal, what can be open-sourced, and how you'll attribute model contributions to findings.
- Upskill your team: Train scientists and research engineers on agentic workflows, prompt design, and safety practices. If you need structured options, see AI courses by leading companies.
Governance, safety, and reproducibility
Given the mission's scope, expect requirements around model evaluation, dataset auditing, and experiment traceability. Labs should prepare for audit-ready logs, versioned datasets, and documented prompts/configurations to support reproducible results and external review.
Outlook
The DOE and participating companies frame these agreements as the opening move. More partnerships and access programs are likely as the unified AI platform matures and the agency coordinates across labs, academia, and industry.
If your team can line up the right datasets, workflows, and guardrails now, you'll be ready to plug into pilots as they open-and convert AI capabilities into publishable results and deployable methods faster.
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