The US National Science Foundation has launched a $100 million program to expand researchers' access to computing resources, technical expertise, and training for AI-enabled science. The State and Regional Artificial Intelligence Infrastructure Hubs program will support up to 10 hubs organized as state or multistate regional consortia, with a first proposal deadline of November 4, 2026.
Participating institutions will work with partners including state and local governments, industry, and philanthropic organizations. The goal is to make computing, data, software, and other AI resources available to researchers, students, and educators across institutions of different sizes, including community and technical colleges.
NSF funds people, not hardware
Despite the program's name, NSF will not pay for hardware, data infrastructure, software, networking, storage, cloud services, or other AI systems. Participating consortia must secure those resources themselves. Instead, NSF money covers workforce development, researcher support, and faculty training.
The program prioritizes the people behind the 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," according to the agency's announcement. Other priorities include data engineering, research software engineering, model deployment, and GPU programming.
Skills around access control, secure research environments, and management of shared computing resources are also identified as workforce needs. For lab managers, this reflects the range of expertise required when AI moves from a pilot into routine scientific workflows.
Shared resources for advanced workflows
The regional hubs could support "autonomous laboratories and other AI-enabled research experiences," NSF states. These shared resources provide an alternative to every research organization independently developing the same compute capacity.
However, compute alone isn't sufficient. Labs still need experimental data that are structured, contextualized, and suitable for AI analysis. The program emphasizes that data architecture and interoperability become foundational management considerations as research environments grow more connected and autonomous.
The infrastructure workforce includes cybersecurity specialists who will manage access control, sensitive data governance, and responsibility boundaries across institutional lines. NSF says lab managers increasingly need to engage with cybersecurity governance, even when separate IT teams handle technical controls.
How the funding works
NSF expects roughly 10 awards per funding cycle, each for a five-year period, totaling $4 million to $12 million per hub. Only one hub will be funded per state or multistate region.
Typical proposals include consortia coordination, workforce development, researcher support, and faculty training. The emphasis on people over hardware matches a broader pattern: supporting advanced research increasingly requires coordination among physical labs, digital systems, data practices, and specialized expertise.
Why this matters for science and research professionals
For scientists and lab managers, the program signals a shift in how research infrastructure needs to be planned. AI-enabled science does not start with a tool purchase - it starts with an assessment of whether the whole environment supports computation, secure data flows, and technical staffing. The NSF program suggests that successful labs will coordinate more closely with IT, data science, and cybersecurity teams while building specific AI skills among their own staff.
Your membership also unlocks:
