As artificial intelligence applications grow in complexity, the infrastructure supporting them must evolve beyond conventional cloud computing models, according to CoreWeave's Corey Sanders. In a discussion on the Practical AI podcast, Sanders outlined why AI-native infrastructure has become essential for organizations training and deploying large language models and other advanced AI systems. The distinction centers on GPU optimization, specialized handling of training versus inference workloads, and architectural approaches built specifically for machine learning rather than general-purpose computing. The conversation explores emerging patterns in how AI development is reshaping software architecture, particularly around agentic AI — systems that act autonomously on behalf of users. Sanders contends that the future of software will pivot away from traditional website and app models toward AI-first experiences, requiring infrastructure that can support these new paradigms efficiently. Key considerations include optimizing GPU performance, managing research workflows, and handling the dramatically different computational requirements of AI workloads compared to legacy enterprise applications. CoreWeave's positioning reflects broader market trends as enterprises race to build robust AI capabilities. The infrastructure provider's emphasis on AI-native design suggests a maturing market where specialized solutions outperform generalized cloud offerings for machine learning workloads — a shift with significant implications for how organizations architect their AI systems going forward.