AI infrastructure buildout reshapes the enterprise stack from silicon to systems
The AI infrastructure buildout has become the defining story of the enterprise technology cycle, pushing compute, storage, networking and data into a wholesale redesign. As organizations chase instant time to value, the economics of tokens and the pressure to modernize aging data centers are forcing a rethink of how AI gets deployed at scale.
That shift is playing out across enterprise AI infrastructure, where attention is moving beyond raw GPU horsepower toward integrated systems. Demand from the enterprise has reached a monumental level, driven by interest in silicon choice, data center modernization and the total cost of tokens, according to Derek Dicker (pictured), corporate vice president of enterprise business group at Advanced Micro Devices Inc.
“If you looked at GPU being the center of a lot of these workloads, what’s happened over time as agentic has unfolded is a realization that it’s … as much a CPU workload as it is a GPU workload,” Dicker said. “A lot of the things that AMD’s been building in its roadmap for the CPU side of the house is super well-tuned to where this is heading.”
Dicker spoke with theCUBE’s John Furrier and Dave Vellante at AMD Advancing AI 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the AI infrastructure buildout, agentic workloads and how open, rack-scale architectures are reshaping enterprise computing. (* Disclosure below.)
