CoreWeave's AI-native infrastructure challenge: why cloud needs reinvention

Corey Sanders joined Practical AI to discuss CoreWeave's approach to AI infrastructure, drawing on his 20-year tenure at Microsoft where he worked on Azure from its early days. Sanders framed the core problem: as AI applications grow in complexity, traditional cloud computing—designed for web-scale applications—cannot efficiently serve AI's unique demands. He identified two major streams in AI usage: training (the creation of model weights by organizations like OpenAI, Anthropic, and Meta) and inference (the deployment and use of those models in production). Training requires very specific infrastructure. It demands large, expensive GPU deployments that are deeply interconnected and working in concert. When any single GPU fails, or when storage cannot be loaded fast enough, the entire training job slows down—a costly outcome because training runs involve billions of operations across expensive hardware. Traditional cloud platforms handle this inefficiently: they were designed to deploy compute reactively as demand spikes, adding racks of servers when needed. That approach fails for AI because training clusters must be pre-deployed and tightly coordinated from the start. Sanders noted that CoreWeave identified multiple points of inefficiency in traditional cloud for AI workloads: GPU failures, storage performance bottlenecks, and job orchestration (deciding which job runs on which infrastructure). Solving these problems requires both hardware-level design—how GPUs are physically laid out and interconnected—and orchestration software that understands AI workload characteristics. CoreWeave has built differentiated services around observability and storage to address this. Sanders' realization came during his final years at Microsoft, when he was pulled into discussions on AI infrastructure optimization while nominally working on industry solutions (financial services, retail). That experience—seeing how Microsoft deployed AI infrastructure and where it fell short—crystallized his view that AI infrastructure is not a variant of general-purpose cloud but requires purpose-built platforms. He began the second part of his answer but paused to let Chris interject; that "second half of the story" was intended to address inference and application deployment, signaling CoreWeave's broader ecosystem vision beyond training alone.

Sanders' framing draws a direct parallel between cloud computing's transformation in the 2000s and the current AI infrastructure moment. Just as early Azure succeeded by designing systems around web-scale applications rather than retrofitting traditional data centers, CoreWeave positions itself around the observation that AI workloads—particularly massive training runs—have radically different hardware and orchestration requirements. The key insight is that GPU failures, storage performance, and job scheduling all have outsized impact on training economics because of the sheer cost per hour of running billions of operations across deeply interconnected hardware. Sanders' 20 years at Microsoft give him credibility to spot this parallel: he lived through the early cloud transition and recognizes that optimization at the infrastructure level (hardware interconnect design, observability, storage systems) is not a feature add-on but a foundational architectural decision. This explains why he pivoted from general-purpose cloud to an AI-native platform—the problems are structurally different, not just incremental. The mention of a "second half of the story" (inference and application deployment) signals that CoreWeave sees both training and deployment as parts of the same ecosystem, not separate vendors' problems.

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