AI Infrastructure Research
Research & Insights
Technical perspectives, industry research, and systems thinking around AI workload execution, infrastructure reliability, distributed compute, and the evolving AI infrastructure stack.
Why AI Workloads Need Job-Completion Reliability
Why infrastructure availability alone doesn't determine workload success—and why execution reliability, durable state, recovery behavior, and completion visibility become increasingly important as AI workloads grow longer and more infrastructure-intensive.
Understanding the Economics of Workload Interruption
A systems-oriented exploration of how infrastructure interruptions, lost progress, recovery time, and operational effort can affect the true cost of completing compute-intensive AI workloads.
Rethinking AI TCO: Why Cost per Token Is the Only Metric That Matters
NVIDIA argues that AI infrastructure economics should be evaluated around delivered output rather than input metrics such as GPU-hour cost alone, using cost per token as the primary measure for inference workloads.
We're in 1905: Why Electricity (Not Dot-Com) Is the Right AI Analogy
Joe Reis explores the analogy between today's AI infrastructure transition and the early development of electrical infrastructure, offering a useful framework for thinking about standardization, infrastructure abstraction, and how technology ecosystems mature.
Cloud Waste and AI: Why GPU Workloads Are Exposing the Next Technical Debt Crisis
An industry perspective on how GPU-intensive AI workloads are changing cloud-cost and infrastructure-management requirements, with implications for utilization, provisioning, and operational efficiency.
Third-party research and perspectives are included because we find them relevant to the evolution of AI infrastructure. Inclusion does not imply endorsement of Vector Fabric or its technology by the authors or organizations referenced.