In this Cube Conversation, DD Dasgupta, vice president of product marketing at Equinix, joins theCUBE Research's Bob Laliberte to discuss how the distributed nature of data is reshaping enterprise AI architecture as organizations shift from training to production inferencing at the edge. Dasgupta explains why moving models and intelligence to where data lives — rather than centralizing everything — is both more economical and more effective, especially as industry-specific workloads demand specialized models tailored to financial services, healthcare and retail. He introduces the Equinix Distributed AI Hub, a framework rooted in the company's 27-year history of building neutral interconnection points, now purpose-built to connect neoclouds, hyperscalers and thousands of ecosystem partners for AI inferencing.
The conversation also explores how Equinix is addressing sovereignty across three distinct tiers — data, network and AI — giving customers granular control over where data resides, how it moves and where models are trained. Dasgupta unpacks the company's partnership with Palo Alto Networks, built on a philosophy of centralizing governance while distributing security enforcement closest to where threats originate at the edge. He highlights that with 280 data centers within 10 milliseconds of 90% of the world's population, Equinix can stand up new capacity in days rather than waiting for new builds, delivering the time-to-market advantage CIOs now demand. From the growing attack surface of distributed AI to the rise of hyper-specialized infrastructure mirroring vertical business needs, Dasgupta provides a practical roadmap for how enterprises can scale AI without sacrificing flexibility, security or speed.
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DD Dasgupta, Equinix
In this interview from the Nvidia GTC AI Conference and Expo, Rev Lebaredian, vice president of Omniverse and simulation technology at NVIDIA, joins Varun Parmar, general manager at Adobe, to talk with theCUBE's John Furrier about how 3D digital twins are bridging the gap between product design and marketing content production. The pair detail a new joint solution that converts CAD files into pixel-perfect digital twins, enabling enterprises to carry a single source of truth from engineering through campaign activation. Parmar highlights that brands expect content demand to grow 5x over the next two years, making identity preservation critical where generative AI alone risks hallucination. Lebaredian explains why simulation-based rendering of physical products has been NVIDIA's own practice for over a decade, producing keynote visuals long before chips leave the fabrication line.
The conversation also explores the dramatic compression of campaign timelines, with asset production shrinking from roughly 30 days to minutes when digital twins replace traditional photography and set design. In the automotive industry, Lebaredian notes, marketing teams can gain a year's head start by working from finalized CAD data while vehicles are still in manufacturing. Parmar unpacks how Adobe's Firefly Creative Production platform layers brand governance, content analytics and agentic orchestration on top of the digital twin pipeline, freeing creative professionals from the 60% of their time currently spent on repetitive resizing and reformatting. He also underscores the risk of a "sea of sameness" as generative models commoditize basic content, arguing that agent-assisted workflows elevate human creatives to focus on the big ideas that differentiate a brand. From the Alliance of Open USD standard unifying cross-departmental pipelines to a conversational interface that lets any marketer generate on-brand assets in plain language, the discussion provides a practical blueprint for turning physical-world products into scalable, activation-ready content at machine speed.
In this Cube Conversation, DD Dasgupta, vice president of product marketing at Equinix, joins theCUBE Research's Bob Laliberte to discuss how the distributed nature of data is reshaping enterprise AI architecture as organizations shift from training to production inferencing at the edge. Dasgupta explains why moving models and intelligence to where data lives — rather than centralizing everything — is both more economical and more effective, especially as industry-specific workloads demand specialized models tailored to financial services, healthcare and reta...Read more