A GB300 workstation is pushing $375,000 CDN.
This is not a projection.
It is real.
Your local models are not the bottleneck.
Recent warnings from Tim Cook and Elon Musk regarding chip shortages highlight a much larger and more structural shift.
We are seeing a massive reallocation of manufacturing priority where fast memory is being diverted to AI data centers, leaving the rest of the world to fight over the scraps.
We have watched the price of an RTX Pro 6000 jump by $6,000 USD in a single week (2 weeks ago).
We have seen NVIDIA DGX Sparx units double in price over eight months.
If you are treating AI as a software deployment problem, you are ignoring the most significant constraint in your boardroom.
The objection is easy. Use the cloud and avoid the hardware headache.
But the hyperscalers are the primary winners in this scarcity model.
They are securing the physical supply first and passing the premium to you through consumption rates.
You cannot solve a physical resource shortage with a software budget.
1. Stress test ROI models against a 100% increase in hardware prices.
2. Prioritize strategic access to fast memory.
3. Audit hardware lead times to find out which projects are actually feasible.
Is your AI roadmap a software strategy or a procurement fantasy?
How is hardware access impacting your sovereign AI deployment?
We are building out an entire sovereign AI (local models and harness infrastructure) for clients and the costs keep changing week to week. 😒
#AI #Hardware #SupplyChain #EnterpriseAI