The US National Science Foundation has launched a $100 million program aimed at expanding researchers’ access to the computing resources, technical expertise, and training needed for AI-enabled scientific research.
The State and Regional Artificial Intelligence Infrastructure Hubs program will initially support up to 10 hubs organized as state or multistate regional consortia. Participating institutions will work with partners that can include state and local governments, industry, and philanthropic organizations to make computing, data, software, and other AI resources available to researchers, students, and educators.
For laboratories pursuing AI-enabled research, access to infrastructure can be as important as selecting an AI application. Advanced workflows can require computing capacity, appropriately structured data, storage, software, cybersecurity controls, and specialized personnel to manage those systems. Preparing a laboratory for AI therefore extends beyond purchasing or adopting individual tools and includes assessing whether the lab has the broader digital infrastructure and expertise to support them.
NSF funding emphasizes people and access
Despite the program's focus on AI infrastructure, NSF funding will not pay for computing hardware, data infrastructure, software, networking, storage, cloud services, or other AI systems. Instead, participating state and regional consortia must secure those resources through institutions, governments, industry, philanthropy, or other sources. NSF will fund consortium coordination, workforce development, researcher support, and faculty training.
That structure puts significant emphasis on the people required to operate advanced research infrastructure. NSF plans to fund professionals including systems administrators, system and storage architects, cybersecurity specialists, network and software engineers, and training and user-support experts. The program also identifies skills such as data engineering and curation, research software engineering, model deployment, GPU programming, access control, and management of secure research environments as workforce priorities.
For lab managers, those priorities reflect the range of expertise increasingly involved when AI moves from a pilot project into routine scientific workflows. Laboratories may need to coordinate more closely with IT, data science, cybersecurity, and research computing teams while also determining which skills should be developed among laboratory staff. Training strategies for AI and automation can therefore become part of broader workforce planning rather than a one-time component of technology implementation.
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Shared resources could support advanced lab workflows
NSF says the regional hubs could support applications including autonomous laboratories and other AI-enabled research experiences. The program is intended to broaden access across institutions of different sizes, including smaller institutions and community and technical colleges that may have less independent access to advanced computing infrastructure.
For laboratories, shared infrastructure could provide another route to advanced computing without requiring every research organization to develop the same capabilities independently. But access to compute alone does not make a laboratory AI-ready. Labs still need experimental data that are structured, contextualized, accessible, and suitable for analysis. As laboratories move toward more connected and potentially autonomous workflows, data architecture and interoperability become foundational management considerations.
The program also puts cybersecurity within the infrastructure workforce it plans to support. For research organizations connecting laboratory data and workflows to shared or cloud-based computing environments, that introduces questions about access control, sensitive research data, system governance, and responsibility across institutional boundaries. Lab managers increasingly have a role in cybersecurity governance and risk awareness, even when dedicated IT teams retain responsibility for technical security controls.
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NSF anticipates approximately 10 awards per funding cycle, with typical five-year proposals requesting between $4 million and $12 million. Only one hub will be funded per state or multistate region. The first full proposal deadline is November 4, 2026.
The initiative comes as research organizations assess how to build the infrastructure, workforce, and governance needed to support AI-enabled science. Similar priorities around computing capacity, secure data systems, and workforce development have also emerged in national discussions about AI-ready research laboratories. For lab managers, the NSF program highlights a broader shift in laboratory infrastructure planning: supporting advanced research increasingly requires coordination among physical laboratory systems, digital resources, data practices, and specialized technical expertise.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
