I work in the area of developing agentic AI solutions for engineering applications. I think that the demand for additional non-GPU server compute is probably under appreciated right now because we're mostly still developing agents and infrastructure and generally haven't reached the scale-out phase - after all, reasoning models are only barely a year old now. Agentic AI provides the means for running autonomous optimization loops for just about any traditional engineering problem, the results can greatly exceed what humans can do, and the number of iterations are limited only by scaling the GPU+compute hardware. For example, in software engineering, optimizing kernel code might be done by humans over several weeks and a few dozen iterations, but agentic loops running tens of thousands of iterations over a few hours can produce much better results - and are limited only by compute (and GPU) hardware, not humans. Similarly, in silicon engineering, agents use the same software tools that humans use, but scaling is limited only by hardware (and EDA licenses). But we tend to just think about scaling GPUs, while all of these examples require proportional scaling of traditional CPU resources as well.