Jacob Yundt, Senior Director of Compute Architecture at CoreWeave: When the Data Center Itself Becomes a Computer

In a recent episode of the VAST Data podcast, Jacob Yundt, Senior Director of Compute Architecture at CoreWeave, articulated a core judgment: in large-scale AI infrastructure, the definition of a computer is expanding. From the server as the computer, to the rack as the computer, and ultimately to the entire data center as the computer—this is not rhetoric, but a gradual displacement of operational reality.

Yundt’s career spans IBM mainframes, scale-out CPU infrastructure, and today’s hyperscale GPU environments. He observes that what has changed most is not the performance of individual components, but the degree to which all parts must be designed together. In large-scale GPU systems, compute cannot be considered separately from networking, storage, power, and cooling. A rack-level system must be managed as a whole—including how power is delivered, how it is cooled, and how workloads are scheduled onto it. As density increases, this coordination expands from the rack to rows, and eventually to the entire data center.

This judgment has direct implications for understanding the competitive landscape of neocloud companies. When the data center itself becomes a unit that needs to be engineered, observed, and managed, simply stacking GPU quantities no longer constitutes a barrier. The real constraints lie in whether power distribution can adjust dynamically to demand, whether cooling solutions can keep pace with increasing rack density, and whether network topology can tolerate a single link flap across thousands or tens of thousands of GPUs without causing an entire distributed training job to collapse. Yundt noted that even a minor link flap can destroy a training job spanning tens of thousands of GPUs—which is precisely why CoreWeave embeds hardware validation, automation, and observability into its architecture rather than adding them as an afterthought.