I support heads of Enterprise I&O and cover the general topic of “AI compute optimization” across multiple AI infrastructure layers. At the very bottom is the Facility and Energy layer including power and cooling; above that is the Compute Hardware layer including GPUs, custom ASICs, High Bandwidth Memory (HBM), high-speed interconnects, and CPUs; then is the System Software layer including compilers, libraries, memory management, etc., and the Service Orchestration layer including schedulers, inference engines, training frameworks. I do not generally cover topics above these layers regarding data and LLM models, devops, or AI services and applications.
I help clients to address questions like:
What are the key considerations to decide the best deployment environment for enterprise AI workloads?
How to optimize existing on-premises data centers to accommodate AI workloads?
What are the infrastructural planning and investment strategies to accomodate continuous AI stack optimizations and model improvement?
Strategic planning on data center competitive and collaboration strategy, custom silicon/hardware roadmap, software and application commercialization, product-market fit, and Go-To-Market / pricing strategy.
Market sensing on enterprise IT spending trend, workload deployment patterns, AI security software adoption and utilization behaviors, technology competitive landscape, ecosystem development/partnership trend, and AI software developer requirements and preferences.
Intel Corporation, Director of Strategy, Office of the CTO, 5 years
GLG, Vice President, Strategic Solutions, 4 years
Lumanity, Senior Vice President, 2 years
APLUSA, Senior Director, 5 years
Boston Consulting Group, Management Consultant, 2 years
AI, Agents and Analytics
I&O Value and Cost Optimization
Emerging Technology Adoption in I&O
Enterprise AI Strategy
University of Pennsylvania, PhD, Communication
University of Connecticut, MA, Communication
Communication University of China, BA, Economics
What are the key considerations to decide the best deployment environment for enterprise AI workloads?
How to optimize existing on-premises data centers to accommodate Agentic AI workloads?
What are the infrastructural planning and investment strategies to accommodate continuous AI stack optimizations and model improvement?
How to control LLM costs / optimize tokenomics across enterprise workloads?
How can AI infra software be used to squeeze the "most value" out of existing AI infrastructure?