Building Sovereign Al and Neoclouds From Components to Tokens with Keysight

Venkat Pullela, CTO of AI Infrastructure at Keysight, addresses the interesting topic of building AI infrastructure amidst geopolitical changes, focusing on the concept of sovereignty, where entities aim to build their own systems. The presentation covers what this new definition of sovereignty means and how hyperscalers, sovereign nations, and neo-clouds operate in this space, developing solutions from low-level components to higher-level abstractions like tokens. Pullela’s role involves maintaining line of sight to rapidly evolving technology and driving proofs of concept into products.

The AI infrastructure is a vast and complex ecosystem, comprising three sub-ecosystems: infrastructure builders, data center/telecom operators, and edge infrastructure/endpoint users. Building this entire system independently is challenging due to the sheer number of components and rapid technological changes. Keysight proposes solutions centered on “chiplets” and “global talent.” Chiplets offer modularity and freedom in design, allowing entities to specialize while relying on partners for other parts. Global talent acknowledges the necessity of international collaboration for expertise. This approach elevates the importance of rigorous conformance testing, open standards, and industry-wide co-design to manage the integration complexity and ensure interoperability across diverse components and vendors.

Keysight’s core contribution is providing a “design-to-deployment continuum” to ensure a smooth time to market. This involves mastering numerous technologies, from pre-silicon validation, photonic design automation, and wafer testing, to protocol standards and conformance. Keysight offers a range of tools and solutions that span this entire lifecycle, enabling systematic testing, vendor benchmarking, fabric benchmarking, and the emulation of complex environments, such as thousands of cell phones or RF environmental simulations, to evaluate AI token rates and other performance metrics. The goal is to provide common APIs, models, and conformance testing criteria that apply from simulation to real-world deployment, allowing hyperscalers, sovereign countries, and neo-clouds to efficiently build and control their AI infrastructure despite the inherent complexity and rapid evolution.