Which States Get the Most AI Grant Funding? A Data-Driven Map

Three states — California, Massachusetts, and Maryland — pulled in more than $14 billion in combined NSF and NIH funding in fiscal 2024 alone. That concentration is not an accident. It reflects decades of compounding infrastructure: research universities clustered within driving distance of each other, federal laboratories embedded in local economies, and a self-reinforcing talent pipeline that makes it easier to assemble competitive multi-institution proposals. For AI-specific funding, the pattern intensifies. The federal government allocated roughly $3.3 billion in non-defense AI R&D for FY 2025, and the lion's share flowed to institutions in a handful of zip codes.

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The Big Three and What Makes Them Stick

California dominated with $6.2 billion in combined NSF and NIH awards in FY 2024, driven by Stanford, UC Berkeley, Caltech, UCLA, and UC San Diego — plus the gravitational pull of Lawrence Livermore, Lawrence Berkeley, and SLAC national laboratories. When NSF announced a $100 million round of AI Institute awards in July 2025, California institutions led or co-led multiple winning consortia, including UC Santa Barbara's AI Institute for Agent-based Cyber Threat Intelligence (ACTION) and UC Davis's agricultural AI work.

Massachusetts came in at roughly $4 billion. MIT and Harvard anchor the state's AI ecosystem, but the real advantage is density: Northeastern, Boston University, UMass Amherst, and the Broad Institute all compete for federal AI dollars within a two-hour radius. The state also runs its own AI Models Innovation Challenge to fill gaps that federal programs miss.

Maryland punches above its weight because of proximity to federal agencies. The National Institutes of Health sit in Bethesda. The National Security Agency and the Johns Hopkins Applied Physics Laboratory are in the Baltimore corridor. The University of Maryland, College Park, leads the NSF AI Institute for Trustworthy AI in Law and Society (TRAILS). Per capita, Maryland received about $465,000 per 1,000 residents in FY 2024 — third nationally, behind only Massachusetts ($573,000) and Washington, D.C. ($563,000).

The Federal Lab Effect

AI funding geography is inseparable from where the Department of Energy parks its national laboratories. DOE invested $68 million in AI for science in late 2024, and the awards clustered around existing lab infrastructure.

Argonne National Laboratory outside Chicago received funding for energy-efficient foundation model pre-training and privacy-preserving federated learning. Oak Ridge National Laboratory in Tennessee landed allocations from both the National AI Research Resource pilot and the INCITE computing program to train scientific foundation models on its Frontier supercomputer. In New Mexico, Sandia and Los Alamos feed AI-adjacent research in materials science and nuclear security.

Illinois, Tennessee, and New Mexico will never match California in raw dollar volume, but they punch well above what their university systems alone would attract — because national labs act as magnets for collaborative proposals. When a DOE solicitation like the broad Office of Science DE-FOA-0003600 (open through September 30, 2026, covering $500 million in annual awards) drops, teams co-led by a national lab and a nearby university have a structural advantage.

The EPSCoR Gap

Twenty-five states, Puerto Rico, the U.S. Virgin Islands, and Guam are designated EPSCoR jurisdictions — states that historically receive a disproportionately small share of federal research funding. The top five states alone capture 40 percent of all NSF awards. EPSCoR exists to narrow that gap, and NSF has steered AI-specific programs through it: the FY 2025-2026 Focused EPSCoR Collaborations (FEC) program invites interjurisdictional teams to extend foundational research into use-inspired AI applications.

But the structural challenge remains. AI Institutes are multi-year, $20 million awards that favor institutions with existing AI faculty, GPU clusters, and industry partnerships. A state like West Virginia or Mississippi can participate as a collaborator in someone else's consortium, but leading one requires infrastructure that EPSCoR seed funding alone cannot build overnight. NSF's ExpandAI program (NSF 23-506) — which allocates $7.5 million in FY 2026 for minority-serving institutions to build AI capacity — is one attempt to address this, but the scale is modest compared to the Institute awards.

For researchers in EPSCoR states, the most pragmatic route into federal AI funding remains the CISE Future CoRe program (NSF 25-543), which offers awards up to $1 million over four years with biannual deadlines — the next is September 10, 2026. It does not carry an EPSCoR set-aside, but its scale ($280 million across 400-600 awards per cycle) gives smaller programs a realistic shot.

Emerging Hubs: Texas, Ohio, and the Infrastructure Play

The next wave of AI funding geography is being shaped less by universities and more by data center and semiconductor infrastructure. Texas ranks second nationally with over 36,000 AI-related job openings and hosts the $500 billion Stargate AI infrastructure project in Abilene. Ohio has attracted $7 billion in planned data center investment from Cologix alone, with an 800 MW campus northeast of Columbus. Indiana, designated as a federal Tech Hub for quantum computing, is positioning Purdue University's Physical AI initiative at the intersection of agriculture and defense applications.

These states are not yet matching the traditional leaders in federal grant capture, but the Commerce Department's Tech Hubs program — which selected 31 hubs eligible for a share of $500 million in federal funding — is deliberately seeding capacity outside the coasts. Arizona State University, for instance, landed both CHIPS Act advanced packaging funding ($300 million shared across three institutions) and NSF AI institute partnerships.

For SBIR/STTR applicants, the geographic spread matters less: NSF's AI topic under the SBIR/STTR program funds small businesses anywhere in the country on technical merit alone. DARPA's AI Exploration (AIE) announcements, which use streamlined contracting to get from solicitation to start date in under three months, are similarly location-agnostic.

What the Map Means for Your Next Proposal

The geographic concentration of AI funding is a feature, not a bug — it follows infrastructure, talent, and institutional capacity. But it also means that researchers outside the top-five states need to be more deliberate about their approach. Join a multi-institution consortium led by a well-funded partner. Target programs with biannual or rolling deadlines rather than once-a-year blockbuster competitions. Use NAIRR for compute access instead of building local GPU clusters from scratch.

The federal government is spending more on AI R&D than at any point in history, with DARPA alone allocating $314 million in core AI funding and NSF committing $494 million for FY 2025. The money is there — the question is whether your institution is positioned to capture it, and Granted can help you find the right program and build a competitive proposal before the deadlines close.