What is the first hurdle that needs to be overcome? What's the smallest goal we can meet while making some sort of difference?
All of the goals below can be done independently in ways that will have an impact (I think), the easiest is by creating a compendium of resources for individuals to follow the same path. The next would be to make an open source design for a specific goal (this would be the super expandable super clusters). From previous conversation, it sounds like the current bottleneck is affordable high speed network technology, so we can start by understanding that and then tackling other avenues.

What does it take to run popular machine learning models (huggingface) on risc-v hardware.

What makes a super computer? Memory Bandwidth? CPU Clock? Instructions per clock? 200,000 nodes?

Can we use the same hardware to scale a super computer from $100 to $100 million?

Can we produce a risc-v processor that uses DDR5, PCIE5, 10Gbe network, and runs C/python without paying license fees? Do we need NVME?

What is the fastest way to connect nodes in a cluster that is "plug and play" (just buy more nodes, not more connectors, or bigger nodes)

Is it possible to run nvidia CUDA software on risc-v? If not what is a realistic alternative to CUDA that is actually competative. Is there something close to a drop in replacement? What does that look like? Does VULKAN+SPIR-V fit in here?

Can Kubernetes run on a risc-v cluster?

Can FreeNAS run on a risc-v cluster?

A plurality of CPUs are mounted to a "blade" that allows an interface into a rack mounted motherboard, more than likely through a PCIe bus. These motherboards can be stacked and expanded through high speed ethernet interfaces that link to high speed network switches in order to expand a supercomputer cluster further.