AI Guru® Insights
Practical insights, governance frameworks, and career guidance for professionals navigating the AI-first era.
One gigawatt is a billion watts - the unit now defining AI data centers from Meta to OpenAI's Stargate, and why the world may need over 200 of them by 2030.
A primer on disaggregated inference: how separating prefill and decode reshapes LLM serving, why the KV cache matters, and when this architecture pays off.
Honest 2026 guide to US enterprise AI training across academic incumbents, skills platforms, and practitioners - how to choose by fit, not just brand prestige.
Most AI risk lives in vendor and SaaS tools you didn't build - manage it through procurement, vendor assessment, contracts, and ongoing monitoring controls.
From Flash Attention to RAG — the definitive dictionary for AI, ML, and Governance terminology.
ISO 42001 is the first AI management standard you can certify against - how auditing works, and how it complements NIST AI RMF and EU AI Act compliance.
Governing AI training data in practice - consent and legality, quality dimensions, sources of bias, and cross-border challenges, grounded in NIST and EU rules.
When to retire an AI system - regulatory shifts, performance decay, stakeholder opposition - plus a checklist for shutdown, transition, and notification.
A practitioner's walkthrough of the EU AI Act's four risk tiers, GPAI rules, and penalties up to 7% of global turnover - plus what the timeline means.
Policies without understanding produce compliance theater - how tiered AI literacy training, from board to staff, makes governance culture truly take root.
What model cards and dataset datasheets should document - intended use, limitations, metrics, ethics - and how to match documentation rigor to system risk.
When AI acts instead of advising, a governance failure becomes a bad outcome. Covers authority delegation, scope limits, oversight, and incident response.
How FTC Act Section 5 applies to AI - what makes a practice unfair or deceptive, enforcement actions, and emerging risks like AI-personalized dark patterns.
AI governance is an operating model, not a document - the roles, teams, and structure to scale oversight from 5 models to 500 without becoming a bottleneck.
A model can be 95% accurate overall yet 60% for one demographic - why testing needs the TEVV framework, disaggregated evaluation, and fairness analysis.
What Is AI Governance: AI systems are fundamentally different from traditional software — they are probabilistic, opaque, autonomous, and data-dependent.
How NIST AI RMF, ISO 42001, and the EU AI Act differ - voluntary framework, certifiable standard, and law - and why they complement rather than compete.