U.S. National Science Foundation State and Regional Artificial Intelligence Infrastructure Hubs: | 26 513

Frequently Asked Questions (FAQs)

1) What is the NSF State and Regional AI Infrastructure Hubs opportunity?

This opportunity supports the creation of state- or region-based Artificial Intelligence (AI) Infrastructure "Hubs" that broaden access to advanced computing and data resources needed for AI-enabled scientific discovery. The Hubs are intended to help scientists and engineers use powerful compute (on-premises systems and/or cloud services) and modern AI tools to accelerate research across many disciplines.

2) What problem is NSF trying to solve with this program?

The program responds to a growing need for scalable computing, data, and AI tooling that researchers increasingly require for modern scientific work. The goal is to make advanced resources easier to access and use, while also building the training and workforce capacity needed to apply AI methods effectively in the scientific enterprise.

3) What is an "AI Infrastructure Hub" in practical terms?

An AI Infrastructure Hub is meant to function as the connective tissue across a state or region: aligning shared infrastructure goals, coordinating access to compute and data resources, and creating pathways for researchers to adopt AI at scale. The Hub model emphasizes coordination and usability so that infrastructure investments translate into broad research and workforce outcomes.

4) Who is expected to participate in a Hub?

Each Hub is expected to be organized as a consortium (a flexible partnership). Based on the description, that consortium can include state and local governments, universities and other research institutions, private-sector companies, and philanthropic organizations.

5) Is this program primarily about buying computers and cloud credits?

The program is about expanding access to compute and data resources for AI-enabled research, but the funding model is split: the consortium is responsible for securing and paying for the actual new or expanded computing, data, and AI resources. NSF funding focuses on the coordination and human-capital components that make the infrastructure usable and impactful.

6) What types of infrastructure and resources are envisioned?

The description points to advanced computing and data resources, including powerful compute that could be provided through on-premises systems and/or commercial cloud services, plus modern AI tools that help accelerate scientific research.

7) What will NSF funds support under this opportunity?

NSF funding is focused on enabling the Hub to function and have lasting impact through people and coordination. Specifically, NSF will support consortium coordination activities, workforce development tied to AI infrastructure, and faculty training plus coursework development aimed at helping instructors and researchers leverage AI for scientific work.

8) What costs are the consortium expected to cover?

The consortium is responsible for securing and paying for the actual new or expanded computing, data, and AI resources. This includes major infrastructure components whether they are procured and operated locally (on-premises) or accessed via commercial cloud platforms.

9) Does the opportunity require cost sharing?

The information provided indicates that infrastructure hardware and large-scale resource expansion are expected to be cost-shared or fully supported by Hub partners, while NSF funds the organizational and human-capital elements. Specific cost-sharing requirements (if any) are not detailed in the provided text.

10) Who is eligible to submit a proposal?

Eligibility is limited to two main categories of U.S.-based organizations:

• Non-profit, non-academic organizations located in the United States that are directly connected to education or research activities (examples given include independent museums, observatories, research laboratories, and professional societies).
• Institutions of higher education (two-year and four-year colleges and universities, including community colleges) that are accredited, have a campus located in the U.S., and submit on behalf of their faculty.

11) Are community colleges eligible?

Yes. The eligibility description explicitly includes two-year institutions and notes that community colleges are included, provided they are accredited, have a U.S. campus, and submit on behalf of faculty.

12) Are for-profit companies eligible to apply as the submitting organization?

The provided information describes eligible submitters as (1) non-profit, non-academic organizations and (2) accredited U.S.-based institutions of higher education. Private-sector companies are mentioned as potential consortium partners, but the text does not list for-profit companies as eligible lead submitting organizations.

13) Can state or local government agencies be involved?

Yes. State and local governments are explicitly mentioned as entities that can be part of the Hub consortium partnership model.

14) Can philanthropic organizations participate?

Yes. Philanthropic organizations are explicitly mentioned as potential consortium partners, reflecting an emphasis on broader participation in supporting R&D ecosystems.

15) What is expected regarding education, training, and workforce development?

The program places heavy emphasis on improving education, training, and workforce readiness. NSF support is described as covering workforce development tied to AI infrastructure and faculty training plus coursework development to help instructors and researchers leverage AI for scientific work.

16) Does the program focus on a single scientific discipline?

No. The description emphasizes accelerating research across many disciplines, using AI-enabled approaches supported by advanced compute and data resources.

17) How does the opportunity relate to national AI priorities?

The opportunity is described as reflecting NSF and broader federal priorities tied to "AI for science" as a national mission, renewing U.S. R&D for an era where AI is central to discovery, and expanding the scale and flexibility of access to advanced compute and data infrastructure.

18) What funding instrument does NSF use for this opportunity?

The opportunity uses a cooperative agreement funding instrument. The description notes that this typically signals substantial NSF involvement during the project period compared to a standard grant.

19) What is the official title and funding opportunity number?

The title is "U.S. National Science Foundation State and Regional Artificial Intelligence Infrastructure Hubs." The Funding Opportunity Number is 26 513.

20) What is the closing date and posting date provided?

The original closing date listed is 2026-11-04. The posting creation date is 2026-08-06.

21) Are the award ceiling and number of awards known?

They are not specified in the provided listing. The description notes that applicants typically must rely on the full solicitation for budget guidance, scale expectations, and any limits or target award sizes.

22) What are the CFDA numbers associated with this opportunity?

The listing includes CFDA numbers 47.070, 47.076, and 47.084.

23) What if a proposal involves an international branch campus of a U.S. institution?

If any funding would flow to an international branch campus (including through subawards or consultants), the proposal must clearly explain why work at that location benefits the project and why the activities cannot be carried out at the U.S. campus instead.

24) Can a Hub use cloud services instead of building on-prem infrastructure?

Yes. The description explicitly mentions that compute may be on-premises or accessed via commercial cloud platforms. In either case, the consortium is responsible for securing and paying for the actual new or expanded resources.

25) What is the program ultimately trying to build?

The opportunity aims to create durable regional ecosystems where AI-focused compute and data access can be scaled through broad partnerships, while NSF invests in coordination, training, and curriculum development so that infrastructure translates into widespread scientific productivity and a stronger AI-capable workforce.