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.”
Author Archives: Vijay Vijayasankar
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.”
Systems Over Scale: What Bridgewater Teaches Us About the Enterprise AI Plateau
I have lost count of how many client conversations this year have gone the same way. Someone tells me the model isn’t accurate enough yet for what they want to do, and the plan is to just wait for the next release. GPT whatever. Claude whatever. Gemini whatever. Someone bigger and smarter is always aroundContinue reading “Systems Over Scale: What Bridgewater Teaches Us About the Enterprise AI Plateau”
And Vijay Says... 