In this episode Nicole talks to Jeff Denworth, Co-Founder of VAST Data, about the deep architectural shifts behind the 5.4 release, delving into how a new distributed runtime, native vector database, and event-driven compute layer transform VAST from a storage platform into a fully programmable AI operating system. Jeff explains how real-time vector inserts, parallelism without inter-node communication, and disaggregated shared-everything design make it possible to reason over data as it arrives, powering applications from Smart City analytics to trillion-scale AI pipelines.

On today’s episode of the Shared Everything podcast, Nicole talks to Jason Vallery, who just joined VAST after a 13-year career at Microsoft where he helped build the Azure cloud from the ground up. Jason reflects on the early days of object storage and cloud-native computing, when scaling from petabytes to exabytes redefined what infrastructure meant, and explains how lessons from Azure’s hyperscale era now shape his vision for VAST’s role in the AI age. He talks about the convergence of file and object systems, the evolution of AI storage built for thousands of GPUs, and the industry’s pivot from “data gravity” to a world where compute follows power and data must follow compute. Together, they trace how public cloud principles birthed the AI supercomputers of today and how the next wave of disaggregated, multi-cloud “neo clouds” will demand architectures that look a lot like what VAST is building

In this episode Nicole talks to Chris Powell, Chief Scientist at SAIC, and Kartik, Chief Scientist at VAST Data, about how the foundations of supercomputing are being rewritten by quantum advances, new architectures, and the collapse of distance between data and compute. Together they explore what happens when data becomes the environment of computation itself, how proximity and randomness define the next frontier, and why the systems of the future will think exactly where the information lives.

In this episode Nicole talks to Laurent Sifre, co-founder and CTO of H Company and former DeepMind scientist behind AlphaGo, AlphaFold, and Chinchilla. They explore how his discoveries in model scaling shaped the design of H Company’s computer-use agents, including the Surfer H platform that learns to navigate software through perception and action instead of APIs. The conversation dives into data infrastructure, distributed KV caching, sovereign compute, and why the future of automation depends on smaller, specialized models that act in the real digital world.

In this episode of the Shared Everything, reasoning models take center stage. No longer just text predictors, they now loop, branch, and drag in outside data, which blows open context windows and GPU limits. Alon Horev, CTO of VAST Data, unpacks how this shift strains infrastructure, while Kevin Deierling, SVP of Networking at NVIDIA, explains how NVIDIA Dynamo moves KV caches and workloads across GPUs, networks, and storage to keep agentic workflows moving. Data platforms become an extension of memory, enabling longer chains of thought, real-time agents, and secure, observable data paths. The result is a vivid picture of the AI datacenter as the nervous system for reasoning at scale.

On this episode, Nicole sits down with Danny McGinniss, VP of Product Management for Cisco Compute, Jacob Liberman, Director of Enterprise Product at NVIDIA, and John Mao, VP of Business Development and Alliances at VAST, to pull apart what it really means when three of the biggest forces in infrastructure line up behind the Cisco Secure AI Factory with NVIDIA, an architecture that brings together Cisco’s compute and networking, NVIDIA AI Data Platform, and VAST InsightEngine. The episode walks through the reimagining the datacenter as an AI factory where security, storage, speed, and data gravity collide to make enterprise AI real.

In this episode, Nicole speaks with Aaron Chaisson and Blake Golliher of VAST Data about how the company is reframing its mission for the AI era, centering on the idea of becoming the Operating System for AI. Aaron lays out the strategy behind this shift, while Blake—drawing on his deep background in building large-scale data platforms explains how the VAST SyncEngine enables customers to move and manage massive volumes of data across sites, clouds, and AI pipelines in real time. The discussion highlights why the ability to synchronize data at scale is critical for enterprise AI adoption, and how VAST’s approach marries technical architecture with business strategy to help organizations operationalize intelligence in ways traditional storage platforms never could.

In this episode, we explore how SK Telecom, South Korea’s largest wireless carrier and now a major force in AI infrastructure, joined forces with VAST Data to tackle one of the most difficult problems in large-scale computing: building a sovereign AI cloud that doesn’t compromise on speed, security, or scalability. Facing the nation’s mandate to keep AI models, data, and infrastructure fully under domestic control, SK Telecom had to rethink GPU virtualization from the ground up. The result is a platform that delivers near-bare-metal performance, strict multi-tenancy, and instant provisioning, setting a new standard for how sovereign AI infrastructure can be designed and operated.

In this episode, Nicole talks with Ken Patchett, VP of Datacenter Infrastructure at Lambda, about how hyperscale AI and sovereign LLMs are redefining datacenter and data management strategies. Ken highlights the challenge of data gravity, emphasizing the critical role of co-locating extensive storage infrastructure alongside ultra-high-density compute to support increasingly data-hungry workloads. He outlines Lambda’s "aggregated edge" model, designed for regional deployment of inference and enterprise workloads, enabling localized data processing and compliance with global sovereignty and privacy regulations. The conversation also addresses how these changes demand adaptive multi-density infrastructure, integrating flexible compute-storage designs that accommodate shifting hardware requirements and evolving regulatory landscapes.