One gigawatt equals a nuclear reactor, 750,000 homes, billions in capital. The world needs 220 by 2030. A primer for boards and operators on the unit that now defines AI infrastructure scale.
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 comparison of US enterprise AI training: MIT xPRO, HBS Online, Wharton, MIT Sloan, Section, Reforge, Korn Ferry, BetterUp, AI Guru. When to pick which.
Third-Party AI Risk: Most organizations don't build AI — they buy it, embed it, or use it as a service.
ISO 42001 Explained: The first certifiable AI Management System (AIMS) standard.
AI Data Governance: Consent, legality, and ethical considerations in data collection.
AI Decommissioning: Regulatory changes that render the system non-compliant.
The EU AI Act Explained: Social scoring, manipulative AI, untargeted facial scraping, real-time biometric identification.
AI Training and Awareness: Policies without understanding produce compliance theater, not governance.
Model Cards, Datasheets, and AI Documentation: What they contain: intended use, limitations, performance metrics, ethical considerations.
Agentic AI Governance: The shift from recommendation to action — and why it changes everything.
AI and Consumer Protection Law: Section 5 unfair or deceptive practices applied to AI.
Building an AI Governance Program: AI governance officer or chief AI ethics officer.
AI Testing and Validation: A model can be 95% accurate overall and 60% accurate for a specific demographic.
What Is AI Governance: AI systems are fundamentally different from traditional software — they are probabilistic, opaque, autonomous, and data-dependent.
NIST AI RMF vs. ISO 42001 vs. EU AI Act: OECD = principles, NIST = voluntary framework, ISO 42001 = certifiable standard, EU AI Act = law.
NIST AI Risk Management Framework: Core functions, categories, and subcategories.
Evaluating AI for Deployment: Business objectives and performance requirements.
How Data Privacy Laws Apply to AI: Notice requirements for AI-processed data.
AI Developers vs. Deployers vs. Providers: Developer: builds the model or AI system.
AI Vendor Contracts: Data ownership and data handling provisions.
Continuous AI Monitoring: Data drift: input distributions change over time.
The OECD AI Principles: Inclusive growth and sustainable development.
AI Lifecycle Policies: Use case assessment and approval: when AI is (and isn't) the right solution.
AI Governance for Financial Services: SR 11-7: Federal Reserve model risk management guidance applied to AI.
Transparency Obligations for AI: Transparency by risk tier: prohibited, high-risk, limited risk, minimal risk.
AI Explainability: Explainability vs. interpretability vs. transparency — three distinct concepts.
AI and Product Liability: Design defects: flawed training, biased data, inadequate testing.
AI Incident Management: Brittleness, opacity, and cascading effects distinguish AI incidents from IT incidents.
AI Impact Assessments: Privacy Impact Assessment (PIA/DPIA).
AI Risks and Harms: Discrimination in automated decisions (hiring, lending, insurance).
Governing AI System Design: Business need, feasibility, and risk/benefit analysis.
AI Release Readiness: Model performance validated on representative test data.
AI and Intellectual Property Law: Can copyrighted material be used for AI training? Current legal landscape.
AI Governance Maturity: Level 1 — Ad hoc: AI experiments without oversight or policy.
Responsible AI Principles: Bias testing using demographic parity, equalized odds, and disparate impact analysis.
Secondary Risks and Unintended Uses: Function creep: when AI is used beyond its intended purpose.
External Communication Plans for AI: What stakeholders need to know about your AI — before anything goes wrong.
Cross-Functional AI Governance: AI impacts cannot be understood by examining technology alone.
EU AI Act: Risk management system: continuous, iterative, throughout lifecycle.
General-Purpose AI Under the EU AI Act: What qualifies as a general-purpose AI model under the Act.
The AIGP Certification: IAPP's AI Governance Professional certification.
Ongoing AI Governance: Internal audit vs. external audit vs. algorithmic audit.
AI and Non-Discrimination Law: Title VII and AI in hiring, promotion, termination.
Deploying AI Responsibly: Translating organizational policies to the deployment context.
Existing laws already apply to AI — civil rights, HIPAA, GLBA, and new state rules. Here is what matters for your work.
A detailed comparison of enterprise AI training providers in India — AI Guru, Great Learning, UpGrad, Simplilearn, and NASSCOM FutureSkills — covering curriculum depth, trainer credentials, delivery models, and enterprise readiness.
How AI is showing up in pharmacies, clinics, and billing offices — practical applications, real results, and why human oversight matters.
Automation bias, rubber-stamping, and the most dangerous assumption in AI governance
The skills that matter most in an AI-augmented workplace and a three-month framework for staying current.
Shadow tools, agentic risk, and the governance gap in AI-assisted development
Four ethical principles and a five-step decision framework for when workplace AI rules do not give you a clear answer.
When your AI agent holds the keys - the new governance gap for agentic assistants.
When your AI agent holds the keys
Recovery, containment, authority
Ten minutes before the pre-read goes out, the general counsel forwards an email thread marked "URGENT."
Ritesh VajariyaCEO, AI Guru | 50K+ Users, 4 Live Products | Former AWS, Cerebras, Bloomberg
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