National AI Ambitions Need a Data Governance Backbone. RDaF Can Provide It.

AI represents a critical domain for America’s science and technology research and development portfolio. Public and private investment in AI, from frontier LLM models to computer vision for clinical diagnostics to autonomous manufacturing robotics, has quickly become a key driver for economic prosperity. Recently, the National Science Foundation (NSF), the Allen Institute, and NVIDIA announced a $152 million public-private partnership to develop open-source, multimodal AI models trained on scientific data and literature called OMAI. At the same time, the NSF signaled the next phase of the National AI Research Resource (NAIRR), awarding up to $35 million for a large-scale compute center. These moves are more than program news; they are a pivot point for US AI infrastructure.

However, investment in AI infrastructure alone is insufficient to guarantee global leadership in this emerging market. If the US wants trustworthy, efficient, and secure AI, its next investments cannot focus on compute alone. All organizations in the business of developing and using AI need to govern the data that fuels these systems—how it is collected, curated, described, accessed, reused, and audited. The National Institute of Standards and Technology’s (NIST) Research Data Framework (RDaF) is a practical way to do this now, without reinventing the wheel or creating onerous new regulations.