One of the easiest mistakes in technology is assuming the most valuable asset is the most visible one. For the past three years, that asset looked like GPUs. Every headline tracked NVIDIA shipments. All funding rounds celebrated larger clusters. Every single discussion about AI infrastructure turned into a count of compute. But visibility is notContinue reading “The Slowest-Scaling Constraint”
Author Archives: Vijay Vijayasankar
Forward Deployed Engineers Aren’t the Moat. The Learning Loop Is.
Usual caveat: These are strictly my personal opinions and have nothing to do with my past or present employers. Almost every discussion about the slow adoption of enterprise GenAI eventually becomes a discussion about deployment. The narrative is familiar: today’s models are remarkably capable, but they start struggling when they collide with fragmented enterprise data,Continue reading “Forward Deployed Engineers Aren’t the Moat. The Learning Loop Is.”
It Was Never Jensen vs. the Hyperscalers. It Was a Balance Sheet Problem – And Power Is Next.
Every discussion about AI infrastructure eventually turns into a story about Jensen Huang outsmarting the hyperscalers. The narrative goes something like this: NVIDIA deliberately routed scarce GPUs to upstarts like CoreWeave and Crusoe, creating a new class of AI cloud providers that would prevent Microsoft, Amazon, and Google from monopolizing AI infrastructure. It’s a compellingContinue reading “It Was Never Jensen vs. the Hyperscalers. It Was a Balance Sheet Problem – And Power Is Next.”
And Vijay Says... 