TL;DR Most enterprise network architectures begin with established design patterns. Engineers choose proven topologies, routing protocols, redundancy models, and traffic-engineering methods because those approaches have understandable failure behavior and operational

Continue ReadingCan AI Discover Better Network Architectures? Designing a Controlled Experiment

TL;DR Artificial intelligence can generate unconventional engineering ideas, but generating ideas is not the same as discovering valid solutions. A credible AI discovery engine needs an architecture that separates candidate

Continue ReadingThe AI Discovery Engine: Generators, Evaluators, and Verification

TL;DR Much of artificial intelligence is evaluated against human performance. We ask whether a model can pass examinations, write convincing explanations, produce expert-level code, solve mathematical problems, or reason through

Continue ReadingBeyond Human Benchmarks: Why AI’s Greatest Value May Be Discovery, Not Imitation

TL;DR Enterprise AI can become more useful when organizations preserve engineering experience, learn from unsuccessful experiments, and systematically compare predictions with observed outcomes. But collecting more real-world evidence creates a

Continue ReadingThe Learning Enterprise: Governing Reality Based AI Evidence

TL;DR Artificial intelligence can generate hypotheses and recommend solutions using recorded knowledge. Its usefulness can improve when those hypotheses are tested against independently observed outcomes. The opportunity is to connect

Continue ReadingFrom Hypothesis to Measurement: How AI Can Experiment Safely