Vamsi Talks Tech

This is Part 1 of a four-part series on architecting security for LLM and agentic AI systems in the enterprise.

In my recent piece on sovereign AI and the geopolitics of compute, I covered export controls, national chip programs, and the fracturing of the global AI hardware stack.

Every GPU procurement conversation in AI infrastructure focuses on acquisition — allocation, lead times, pricing on the newest generation.

Agentic AI adoption has outpaced the legal and governance frameworks that determine who bears responsibility when an autonomous workflow makes a costly mistake.

The single-model enterprise AI strategy — standardize on one provider, integrate deeply, move on — is quietly giving way to something more fragmented and, for most CIOs, more sensible: a routing layer sitting between applications and multiple underlying m…

Telecom operators sit on an asset hyperscalers have spent a decade and tens of billions of dollars trying to replicate: physical proximity to the end user.

Agentic AI has moved fast in customer support, software engineering, back-office document processing.

A new category of cloud provider has emerged specifically to rent GPU capacity: CoreWeave, Lambda, Crusoe, Together AI, and a growing list of “neoclouds” that do one thing — acquire GPUs at scale and rent them out — better and cheaper than the hyperscalers

Power gets the headlines.

The AI capacity conversation has spent two years fixated on chip allocation — who can get NVIDIA’s next generation first, who’s secured supply from TSMC.