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President Donald Trump is directing the federal government to combine efforts with tech companies and universities to convert government data into scientific discoveries, acting on his push to make artificial intelligence the engine of the nation’s economic future.
Trump unveiled the “Genesis Mission” as part of an executive order he signed Monday that directs the Department of Energy and national labs to build a digital platform to concentrate the nation’s scientific data in one place.
It solicits private sector and university partners to use their AI capability to help the government solve engineering, energy and national security problems, including streamlining the nation’s electric grid, according to White House officials who spoke to reporters on condition of anonymity to describe the order before it was signed. Officials made no specific mention of seeking medical advances as part of the project.
“The Genesis Mission will bring together our Nation’s research and development resources — combining the efforts of brilliant American scientists, including those at our national laboratories, with pioneering American businesses; world-renowned universities; and existing research infrastructure, data repositories, production plants, and national security sites — to achieve dramatic acceleration in AI development and utilization,” the executive order says.
The administration portrayed the effort as the government’s most ambitious marshaling of federal scientific resources since the Apollo space missions of the late 1960s and early 1970s, even as it had cut billions of dollars in federal funding for scientific research and thousands of scientists had lost their jobs and funding.
Trump is increasingly counting on the tech sector and the development of AI to power the U.S. economy, made clear last week as he hosted Saudi Arabia’s Crown Prince Mohammed bin Salman. The monarch has committed to investing $1 trillion, largely from the Arab nation’s oil and natural gas reserves, to pivot his nation into becoming an AI data hub.
For the U.S.’s part, funding was appropriated to the Energy Department as part of the massive tax-break and spending bill signed into law by Trump in July, White House officials said.
As AI raises concerns that its heavy use of electricity may be contributing to higher utility rates in the nearer term, which is a political risk for Trump, administration officials argued that rates will come down as the technology develops. They said the increased demand will build capacity in existing transmission lines and bring down costs per unit of electricity.
Data centers needed to fuel AI accounted for about 1.5% of the world’s electricity consumption last year, and those facilities’ energy consumption is predicted to more than double by 2030, according to the International Energy Agency. That increase could lead to burning more fossil fuels such as coal and natural gas, which release greenhouse gases that contribute to warming temperatures, sea level rise and extreme weather.
The project will rely on national labs’ supercomputers but will also use supercomputing capacity being developed in the private sector. The project’s use of public data including national security information along with private sector supercomputers prompted officials to issue assurances that there would be controls to respect protected information.
On November 24, 2025, President Trump signed an executive order launching the Genesis Mission, described as “a national effort to use artificial intelligence to transform how scientific research is conducted and accelerate the speed of scientific discovery.” The Department of Energy (DOE) and its 17 national laboratories are put in charge of building what they call “the world’s most powerful scientific platform” by wiring together supercomputers, experimental facilities, AI systems, and scientific datasets across domains like energy, materials, fusion, quantum information, and biotechnology. The official DOE site says the goal is to double the productivity and impact of U.S. research and innovation within a decade by pairing scientists with AI systems that can reason, simulate, and experiment at high speed.
So what does the executive order actually do? It tells DOE to create a shared “American Science and Security Platform” and an AI experimentation environment that connects national-lab supercomputers, secure cloud environments, and decades of federally funded scientific data. The system is supposed to train “scientific foundation models,” deploy AI agents that can propose and test hypotheses, and even drive “robotic laboratories” where experiments are planned and executed in a feedback loop between AI and physical equipment. There are hard timelines attached: inventory computing resources within 90 days, identify priority datasets within 120 days, catalog robotic labs and production facilities within 240 days, and show an initial AI-driven capability for at least one national challenge inside 270 days. On paper, it’s about turning today’s scattered government AI efforts into one coordinated platform.
That’s ambitious, but it is not Apollo- or Manhattan-scale. Apollo cost about $25.4 billion in 1960s dollars—roughly $250–280 billion in today’s money—and at its mid-1960s peak it consumed around 0.8% of U.S. GDP in a single year. The Manhattan Project cost about $2 billion by 1945 (over $30 billion in 2023 dollars) and amounted to roughly 1% of total U.S. wartime spending, employing about 130,000 people and building entire secret cities around a single objective: the first nuclear weapons. Genesis, by contrast, is an executive order that reorients existing programs and tells agencies how to use assets they already have. It’s strategically important, but it’s not a multi-hundred-billion-dollar crash program to build one world-changing artifact.
Funding is where the gap really shows. Politico reports that Genesis is backed by the broader “One Big Beautiful Bill Act,” but the executive order itself functions mainly as an internal directive; it doesn’t write a giant check on its own. DOE’s Genesis materials talk about unifying existing supercomputers and experimental facilities, not constructing many new ones from scratch. Meanwhile, private and public AI spending is exploding on a completely different scale: Gartner estimates worldwide AI spending will reach nearly $1.5 trillion in 2025, spanning software, services, and the hardware that powers modern AI data centers. Market analysts put the global AI market itself at roughly $390 billion in revenue in 2025, with projections into the multi-trillion-dollar range by the early 2030s. In other words, the truly massive capital flows are still in the private sector GPU farms, not the national-lab machines Genesis is trying to coordinate.
That said, national-lab infrastructure isn’t trivial. DOE’s 17 labs already host some of the world’s most powerful supercomputers and unique experimental facilities, from fusion and particle-physics machines to light sources and nanoscale imaging capabilities. Some of those machines were designed for large-scale simulations on traditional CPUs rather than the GPU-heavy architectures that dominate modern AI, which is part of why there’s tension between the old HPC world and the new AI-training clusters. Genesis tries to bridge that gap by plugging lab supercomputers and experimental instruments into a common platform that can generate new, high-fidelity datasets and feed them straight into AI models. From a hardware perspective, it’s less about conjuring a completely new supercomputer out of nowhere and more about networking and repurposing the ones we already have.
Where Genesis does matter is in the cultural and funding signal it sends. The fact sheet and DOE copy hammer on one phrase: “accelerate AI for scientific discovery.” In practice, when Washington plants a flag like that, it reshapes how grants are written and reviewed. A UNCTAD issues paper on AI in science notes that AI could plausibly double the pace of R&D in some sectors and that more than half of surveyed researchers already expect AI tools to be “very important or essential” to their work. We’ve seen this pattern before: after COVID-19 showed what mRNA vaccines could do, there was a worldwide surge of mRNA-related trials and R&D targeting cancer and other diseases beyond COVID itself. When a technology becomes the “method du jour,” it attracts funding—and that funding inevitably comes at the expense of other approaches. Genesis is essentially saying: if you want to work on certain national priorities, you’d better have an AI-for-science angle in your proposal.
This isn’t happening in a vacuum. Earlier in 2025, China rolled out its broader “AI Plus” strategy, which explicitly highlights “AI for Science (AI4S)” as a new paradigm for research, especially in energy, materials, and other high-tech sectors. International analyses describe a dedicated Chinese “AI for Science initiative” aimed at integrating AI with open science, building AI-driven research platforms, and using embodied AI and robotics in real-world scientific scenarios. On the European side, the EU’s Horizon Europe and Digital Europe programs are already investing over €1 billion per year in AI, with a stated goal of mobilizing around €20 billion of annual AI investment across the bloc by the end of the decade. In November 2025 the Commission also launched “Resource for AI Science in Europe” (RAISE), including new calls to fund domain-specific foundation models for areas like materials science and climate. Genesis is, in part, the U.S. answering those moves.
Some of the Genesis language about “robotic laboratories” can sound more sci-fi than it really is. Labs have been using automation for decades—from pipetting robots in PCR workflows to high-throughput screening systems in drug discovery—so the idea of AI-coordinated lab equipment isn’t a total novelty. What is new is the ambition to close the loop: connect experimental gear, simulations, and AI models so that the system can propose experiments, run them, analyze results, and iterate with minimal human intervention. UNCTAD’s survey of “robot scientists” and AI-driven autonomous experimentation shows this is technically feasible in some domains already, especially in materials and nanotechnology. Genesis essentially tries to scale that up across multiple fields using government data and infrastructure.
Genesis is not “the Apollo Program for AI,” and it’s not “a new Manhattan Project.” The historical mega-projects were single-minded, insanely expensive bets on creating one transformative capability. Genesis is a policy and coordination push: it reorganizes the scientific data we already have, aligns national-lab computing around AI-for-science use cases, and sends a loud signal to universities, grant reviewers, and companies that using AI in scientific work is now an expectation rather than a fringe idea. That might not make for dramatic headlines, but it will influence where people, compute, and money flow over the next decade—and wherever those three go together, interesting things tend to follow.
The White House has launched a plan to accelerate research in the United States, by building artificial intelligence (AI) models on the rich scientific data sets held by the country’s 17 national laboratories, as well as harnessing their enormous computing resources.
An executive order issued on 24 November instructs the US Department of Energy (DoE) to create a platform through which academic researchers and AI firms can create powerful AI models using the government’s scientific data. Framed as part of a race for global technology dominance, it lists collaborations with technology firms including Microsoft, IBM, OpenAI, Google and Anthropic, as well as quantum-computing companies such as Quantinuum. Such a vast public–private partnership would give companies unprecedented access to federal scientific data sets for AI-driven analysis.
The effort, dubbed the Genesis Mission, aims to “double the productivity and impact of American research and innovation within a decade”, in a variety of fields from fusion energy to medicine. The project expects to “unlock breakthroughs in medicine, energy, materials science and beyond”, says Michael Kratsios, the US president’s science adviser. It also aims to build AI agents — general models with the ability to harness tools such as specialized software and coding suites — that can generate hypotheses and automate research workflows.
Labs around the world are already training AI systems on scientific data, to boost their capabilities in scientific domains and attempting to use AI models to make discoveries. But some researchers remain sceptical that general AI tools are capable of making truly fresh insights, and warn that their inherent flaws make the value of agents unclear.
The new US initiative formalizes and expands ongoing AI research efforts by the administration of President Donald Trump. “The impact is that it enables many more scientists and researchers to have access to all of the infrastructure that they need to explore important scientific questions that the country cares about,” says Lynne Parker, a robotics engineer at the University of Tennessee, Knoxville, who led AI-policy initiatives for the administrations of Trump and his predecessor, Joe Biden, but was not involved in the current initiative. “That really has not been possible before.”
Trump’s team has been working to funnel money and attention to AI projects even as it tries to gut federal research spending more broadly. The White House has the power to shape the direction of research at the DoE’s network of national laboratories. It did not give an estimated price tag for the AI initiative; any extra funding beyond the laboratories’ normal budgets would have to be approved by the US Congress.
Nature looks at how the project might affect researchers and AI companies, and its promises and risks.
What are government-funded scientists being asked to do?
The scale and timeline of the plan is ambitious. In 60 days, the DoE is expected to create a list of 20 potential science and technology challenges for the project to tackle, in areas of national priorities such as nuclear fusion, quantum information science and crucial materials. The agency is supposed to create a full inventory of available federal computing resources and identify initial data assets to use, then work out how to safely include external data sets. The administration expects to demonstrate the platform’s capability for one of these research challenges in nine months.
These early steps will probably build on projects that are already under way at the national laboratories. For instance, Oak Ridge National Laboratory in Tennessee has been working on advancing AI research using a hybrid approach that uses both quantum and classical computing. Lawrence Berkeley National Laboratory in California is using AI to find ways to speed up network traffic.
The “DoE has been making a case for ‘AI for science’ for over seven years, and this executive order is the starting pistol to get on with it”, says Michael Norman, an astrophysicist at the University of California, San Diego, and former director of the San Diego Supercomputer Center. “It is an exciting direction indeed.”
What are companies being asked to do?
The project has named more than 50 collaborating companies, including some that have already been working on their own ‘AI scientists’. FutureHouse, a start-up based in San Francisco, California, for instance, launched a commercially available, AI-driven research platform earlier this month.
The precise role of these private companies in the Genesis plan remains unclear — although Trump’s executive order says the project will entail “collaboration with external partners possessing advanced AI, data, or computing capabilities or scientific domain expertise”. Such partnerships could include research agreements to jointly develop technologies, or a user-facility agreement for external researchers to conduct work in government facilities. Chip-manufacturing and computer companies such as NVIDIA, Advanced Micro Devices and Hewlett Packard Enterprise have reportedly agreed to build facilities in national labs, according to a report by The New York Times.
Some partnerships are already under way. In October, for instance, Argonne National Laboratory in Illinois announced a partnership with NVIDIA and technology firm Oracle to build two next-generation AI supercomputing systems. At least two national laboratories already have arrangements with the company OpenAI, based in San Francisco, California, to host local AI models that can process classified data on computers in the facilities. In February, the company held an ‘AI jam session’ with researchers from nine US national labs that allowed scientists to test the use of OpenAI’s reasoning models in their specific domains.
What are the opportunities?
The new Genesis Mission is meant to provide “secure access to appropriate datasets, including proprietary, federally curated, and open scientific datasets, in addition to synthetic data generated through DOE computing resources”, according to the executive order. Creating a national-scale platform that harnesses rich data sets usually housed in the walls of the national laboratories could be a boon for researchers. Although task-specific models such as the protein-folding model AlphaFold were built on open scientific data sets, general-purpose AI systems such as OpenAI’s GPT-5 are thought to be largely built on data scraped from the Internet.
On Nov 24, 2025, the US launched the Genesis Mission, a massive public-private initiative framed as a "Manhattan Project for AI."
Cost: Estimated $100+ Billion (Including $50B from AWS).
Goal: Double US science/engineering productivity in 10 years.
Tech: Integrates all 17 National Labs, 3 Exascale Supercomputers, and Quantum centers.
Why? To secure dominance in AI, Fusion Energy, and Biotech against global competitors (primarily China).
The United States just initiated one of the largest scientific reorganizations in its history. If you haven't heard of the Genesis Mission yet, you will soon. It is effectively an Apollo Program for Artificial Intelligence.
Here's some of the details of the sheer scale of this effort, how it compares to historical megaprojects, and the massive energy challenges it faces.
The Scale: How does it compare to Apollo & Manhattan?
The government isn't building a bomb or a rocket this time; they are building a platform. The goal is to connect all federal data, supercomputers, and labs into a single "closed-loop discovery engine."
Here is how Genesis stacks up against America's most famous scientific sprints:
Project Cost (Adjusted for Inflation) Duration Direct Workforce Primary Output
Manhattan Project ~$30 Billion 3 Years ~130,000 The Atomic Bomb
Apollo Program ~$257 Billion 12 Years ~400,000 Moon Landing
Genesis Mission $100+ Billion* 10 Years 40,000+ AI Science Platform
*Note: The $100B figure includes massive private sector commitments, such as a $50B infrastructure investment from AWS alone.
2. The Exascale Arsenal
The backbone of this mission isn't standard cloud servers; it's the "Exascale Arsenal"—the three fastest supercomputers in the world, all located at DOE National Labs.
El Capitan (Lawrence Livermore Lab): 1.742 ExaFLOPS (Nuclear stewardship)
Frontier (Oak Ridge Lab): 1.353 ExaFLOPS (Open science)
Aurora (Argonne Lab): 1.012 ExaFLOPS (Scientific discovery)
Combined Power: >4 ExaFLOPS. To put that in perspective, an "ExaFLOP" is a quintillion calculations per second. This is roughly the computational power of the human brain, but focused entirely on math and simulation.
3. The Energy Crisis & Infrastructure Reality
One of the biggest drivers for Genesis is the exploding energy cost of AI. The US infrastructure is hitting a physical wall, and the numbers are staggering.
The Current US Data Center Footprint:
Total Facilities: ~5,427 data centers (The US is the world's largest data center hub).
Hyperscale Centers: ~614 facilities (The US holds 54% of global hyperscale capacity).
Power Demand: 183 TWh in 2024 (Already 4% of total US electricity).
The Projected "AI Boom" Impact (2030):
Electricity Usage: Projected to hit 426 TWh (Rising to 9% of total US electricity). Some estimates (Goldman Sachs) put this even higher at >10%.
Capacity Growth: Total capacity is expected to nearly triple from ~50 GW (2024) to 134.4 GW (2030).
Genesis aims to solve this by using AI to accelerate Fusion Energy and Advanced Nuclear designs. It is a race against time: can AI invent clean energy solutions faster than AI consumes the grid?
4. Who is involved?
This is a Public-Private hybrid. The government provides the labs and the "Crown Jewel" datasets (nuclear data, material science records), while Big Tech provides the cloud and chips.
Public: 17 Department of Energy National Labs (Oak Ridge, Los Alamos, etc.)
Private: AWS, NVIDIA, Microsoft, Google, IBM, OpenAI, Anthropic.
Quantum: 5 National Quantum Information Science Research Centers.
Why now?
The executive order explicitly frames this as a strategic competition. Just as the Cold War was decided by nuclear dominance, the belief is that the 21st century will be decided by Computational Supremacy.
The objective is audacious: Double the productivity of American science. Imagine discovering new cancer drugs, battery materials, or fusion reactor designs in months rather than decades.
Do you think a centralized Manhattan Project approach works for something as broad as AI, or is this just throwing money at Big Tech?
Also:
America's GENESIS MISSION
Also:
Trump’s AI ‘Genesis Mission’: what are the risks and opportunities?
Also:
The US just launched a $100 Billion Manhattan Project for AI called Genesis. Here is the massive scope of what they are actually building. The Genesis Mission is America’s new bet to double scientific productivity with AI, Fusion, and Quantum Supremacy.
