Day two of CoreWeave's Fully Connected conference brings together Parallel Web Systems CEO Parag Agrawal on agent-driven web infrastructure, SemiAnalysis founder Dylan Patel on AI cloud fragmentation and security, Formula One champion Jenson Button on team culture under pressure, and CoreWeave CEO Mike Intrator on financing, risk, and building an AI cloud from a New Jersey garage.
A Signal65 TCO study, presented with CoreWeave, examining the fully loaded economics of AI training and inference. It covers hidden costs such as storage fees, support, and reliability, how performance compounds with price to set cost per million tokens, and how serverless and dedicated inference models differ on useful-token economics.
A NASDAQ executive explains how high-performance compute and agentic AI are reshaping both the physical trading infrastructure (data centers, colocation, liquid cooling, and CoreWeave partnership) and the fintech software stack (Verafin, Axiom, Calypso) through a shared intelligence layer. The talk centers on trust, resilience, governance and the bounded-exception problem that makes agentic workflows hard to scale in a regulated market.
MasterClass engineers Mandar and Priya explain how they built NCX, a multi-agent AI tutoring system, and the CoreWeave-based tracing, MCP server, and AgentOps loop that keeps it reliable and self-improving in production.
A session on CoreWeave's forward deployed engineering partnership with the Aston Martin Aramco F1 Team, covering why Formula 1 is an unforgiving environment for AI, and three projects: a unified data platform for aerodynamic and telemetry data, Delta to Map, a small transformer model that reduces the gap between wind tunnel predictions and track measurements, and Radio Insights, a fine-tuned speech-to-text and agentic system that makes sense of 40 live radio channels. The discussion extracts lessons applicable to any team moving AI from experiment to production under fixed deadlines.
nTop and CoreWeave completed 10,000 LES CFD simulations in about 32 hours, meeting NASA's 2030 CFD Vision goal four years early. The approach combines signed distance field geometry, which cannot break under parametric variation, with a GPU-native Lattice Boltzmann solver that removes the meshing step entirely. The talk also covers the 14 TB dataset generated and the path toward physics AI surrogates trained in implicit latent space.