Lesson Identifying AI use cases in business environments. Learning Objectives Prerequisites No coding is required to understand this lesson. Helpful background: AI use cases…
Category: AI
All things AI from my professional perspective.
AI Use Cases: 7 Smart Rules for Business
LLM Understanding: 7 Critical Lessons for Business
Many companies deploy AI as if fluent output proves real understanding. Current research suggests a more useful mental model: LLMs are powerful probabilistic tools with limited grounding, which means better results come from constraints, retrieval, validation, and careful workflow design.
LLM Scaling: 7 Hard Lessons for Business
MIT/FutureTech research is being cited as evidence that conventional LLM scaling may be nearing diminishing returns. The stronger takeaway is narrower and more useful: brute-force compute may buy less strategic advantage over time, shifting value toward efficiency, integration, and commercial execution.
Amazon AI Incidents: 7 Hard Lessons for Business
Amazon AI incidents remain some of the clearest case studies in enterprise AI failure. From biased hiring models to privacy enforcement around Alexa and Ring, the known facts point to practical lessons about governance, deployment risk, data quality, and operational control.
Claude Code Leak: 7 Critical Lessons for Business
The Claude Code source leak was not a model-weights disaster. It was a revealing look at how real AI products work in production. For business leaders, the most important lessons are about permissions, telemetry, retention, governance, cost control, and operational maturity.
AI Token Costs: The Hidden Incentive Problem
AI token costs are not just a technical metric. They sit at the center of a real incentive mismatch. Most inference providers get paid when applications send and generate more tokens, while users usually benefit from fewer tokens, faster responses, and lower bills. This guide explains where that mismatch shows up and how to control it.
LLMs for AGI: The Useful but Uncomfortable Truth
Large language models may be extremely useful without being a clear path to artificial general intelligence. This article explains what today’s systems do well, where they still break down, why benchmark gains can mislead, and how to think about AI strategy without buying into AGI hype.
LLM Integration: 7 Best Python Patterns
LLM integration is one of the most important fundamentals in modern AI development. Before you build retrieval, agents, workflows, or polished product features, you…
AI Tokens: The Essential Guide to Lower Cost
AI tokens are the operating units behind modern language models. They affect context windows, pricing, latency, multilingual behavior, embeddings, training, inference, and the practical design tradeoffs behind real AI products.
How LLMs Work: The Definitive, Surprising Truth
This article explains how LLMs work without hype. It traces the path from early probabilistic language models and n-grams to embeddings, tokenization, transformers, scaling laws, and post-training, showing how next-token prediction became the foundation of modern AI systems.