As artificial intelligence applications grow increasingly complex, the infrastructure supporting them must evolve beyond traditional cloud computing platforms. CoreWeave's Corey Sanders joined the Practical AI podcast to discuss why AI requires purpose-built infrastructure optimized for the unique demands of training and inference workloads. Conventional cloud services were designed for web applications and general computing tasks—not for the specialized requirements of modern AI systems. The conversation highlights how GPU performance optimization and agentic AI development—building systems capable of autonomous decision-making—are reshaping infrastructure requirements. Traditional cloud platforms were not architected to handle these workloads efficiently, creating a gap between what enterprises need and what general-purpose infrastructure provides. CoreWeave's vision centers on infrastructure purpose-built from the ground up to handle AI's specific computational patterns. This represents a broader shift in how software engineering and product development approach AI integration. Rather than bolting AI onto existing web and application architectures, the future prioritizes AI-first experiences, fundamentally changing how organizations design their systems. The move toward AI-native infrastructure signals an industry inflection point where specialized infrastructure and workflows will become essential for competitive AI development.