In an interview with News Medical, Dustin Tseng, Product Manager for the Edge Server Group at Advantech, and Ben Busby, Global Alliances Manager for Omics at NVIDIA, discussed how computing infrastructure is enabling AI-driven life science research. Tseng focuses on developing high-performance server platforms for AI, edge computing, and life science applications, working with equipment manufacturers and ecosystem partners like NVIDIA. Busby works with sequencing companies, research organizations, cloud providers, and hardware partners to accelerate genomics and AI workflows. Modern laboratory instruments generate enormous volumes of data from genome sequencing, medical imaging, or diagnostic systems, and organizations are rapidly adopting AI to extract insights. Much of this data is unstructured, so computing infrastructure is essential for collecting, processing, and analyzing it efficiently. AI is moving beyond pilot projects into everyday laboratory workflows, making high-performance computing a fundamental part of scientific research. A major challenge is the data-to-discovery gap, where vast datasets often need immediate analysis for clinical decisions or ongoing experiments. Moving terabytes of information to the cloud introduces latency, increases costs, and raises data privacy concerns, so laboratories increasingly need computing resources close to where data is generated. Success is measured by how efficiently infrastructure converts data into discovery. The interview was based on a recent Advantech webinar on computing for life sciences.