An Intelligent Infrastructure as a Foundation for Modern Science

Abstract

Infrastructure shapes societies and scientific discovery. Traditional scientific infrastructure, often static and fragmented, leads to issues like data silos, lack of interoperability and reproducibility, and unsustainable short-lived solutions. Our current technical inability and social reticence to connect and coordinate scientific research and engineering lead to inefficiencies and impede progress. With AI technologies changing how we interact with the world around us, there is an opportunity to transform scientific processes. Neuroscience's exponential growth of multimodal and multiscale data, together with its urgent clinical relevance, demands an adaptive infrastructure that can expose computable states, coordinate across systems, and improve through use. Using neuroscience as a stress test, this perspective argues for a paradigm shift: infrastructure must evolve into a dynamic, AI-aligned ecosystem to accelerate science. Building on several existing principles for data, collective benefit, and digital repositories, I recommend operational guidelines for implementing these principles to create this dynamic ecosystem, aiming to foster a decentralized, self-learning, and self-correcting system where humans and AI can collaborate seamlessly. Addressing the chronic underfunding of scientific infrastructure, acknowledging diverse contributions beyond publications, and coordinating global efforts are critical for this transformation. A coordinating role, even more than analysis, is where AI becomes transformative rather than merely assistive. By prioritizing an intelligent infrastructure as a central scientific instrument for knowledge generation, we can overcome current limitations, accelerate discovery, ensure reproducibility and ethical practices, and ultimately translate neuroscientific understanding into tangible societal benefits, setting a blueprint for other scientific domains.