Sid Nag, Tekonyx & Corey Quinn, Duckbill
In this interview from theCUBE + NYSE Wired: AI Factories - Data Centers of the Future, Sid Nag, chief research officer of Tekonyx, joins Corey Quinn, chief cloud economist of Duckbill, to talk with theCUBE's Dave Vellante about the gap between AI factory hype and the economic and operational realities enterprises must navigate to build scalable intelligence. Nag reframes the AI factory concept not as a $500 billion data center build but as a logical entity combining curated data, multimodal LLMs, orchestration engines and compute fabric into what he calls "scalable intelligence" — actionable outcomes a CIO can deliver across business functions. He also introduces the "hybrid processing unit," arguing that AI workloads require a mix of CPUs, GPUs, NPUs and TPUs rather than any single dominant chip, reflecting a fundamentally more distributed and heterogeneous compute reality. The conversation also explores the precarious position of neo clouds, with Quinn warning that pure GPU rental businesses face commoditization the moment hyperscaler capacity constraints ease — unless they move up the stack with platform services, much as AWS did layering value atop EC2. Nag draws a parallel to exchange businesses that commoditized rapidly and urges neo clouds to elevate their value proposition beyond compute. The panel then weighs AWS's ambitions with Amazon Q, which both guests find credible as an emerging enterprise work surface — but critically undermined by internal fragmentation, with Agent Core, Connect and other services still operating as disconnected products. Looking ahead, Quinn emphasizes that companies are finally demanding proof of where AI actually generates value rather than spending without accountability, while Nag flags a widening chasm between vendor silicon investment and enterprise readiness to pay. From the commoditization of inference and the survival pressures facing neo clouds to the internal fragmentation haunting even the most ambitious hyperscaler platforms, the discussion provides a practical lens for separating durable AI infrastructure value from supercycle noise.