AI Strategy Guides
AI Systems Strategy
Writing on AI systems strategy, evaluation, governance, architecture, and the organizational tradeoffs that determine whether AI work creates leverage or just new failure modes.
This page brings together the technical and leadership sides of AI systems work: what to build, how to evaluate it, where the risk lives, and how teams should reason about adoption under real business pressure.
What You’ll Find Here
Architecture
How retrieval, agents, context, prompts, and infrastructure choices shape AI system behavior.
Evaluation and Control
How to measure whether an AI system is helping, drifting, leaking risk, or simply moving cost elsewhere.
Adoption Strategy
How teams, leaders, and organizations make better choices about where AI fits and where it does not.
Featured AI Systems Reading
Leadership
How to Measure AI Productivity When the Cost Moves Instead of Disappearing
Learn how to measure AI productivity in engineering by tracking flow, quality, learning, code health, and ROI without being fooled by throughput alone.
Technical
Your AI Architecture Isn’t Broken
AI architecture issues rarely come from the model. Learn where retrieval, data, agents, and fine-tuning break down in real systems.
Technical
Where AI Systems Drift
A companion guide to the interactive systems drift page, focused on which controls belong at which layer, when to use them, and what problem each one actually solves.
Leadership
Mental Models a Senior Engineering Leader Uses (and How to Know When You're Using the Wrong One)
A practical set of mental models for senior engineering leadership, with guidance on when each model helps and when it starts causing harm.
Technical
How to Architect Secure AI Agents Before They Architect Your Incident
Autonomous AI agents need governance, bounded authority, and clear accountability before they touch real systems.
Technical
Vector Embeddings Explained (with Hands-On Demo)
A practical explanation of vector embeddings, distance metrics, and why similar setups can behave differently in real systems, with a hands-on demo.
Technical
The RAG Atlas: A Visual Guide to Retrieval Patterns
An interactive visual reference covering ten RAG retrieval patterns — from Vanilla RAG to Graph RAG — with animated data-flow diagrams, a hoverable node inspector, and a simulator for tuning chunk size, top-k, and reranking.
Leadership
Temporal Leadership: The Skill of When, Not Just What
Strong leaders do more than choose what to do. They develop temporal judgment: the discipline to decide when to act, wait, accelerate, or pause.
Technical
Teaching AI to See your UI
How I use Puppeteer to give an AI visibility into a real UI, letting it observe failures, reason from evidence, and repair its own mistakes.
Technical
Prompt Debt
Prompt Debt is the hidden architecture problem behind inconsistent AI behavior. Learn why systems drift, how context fails, and what teams must do to fix it.
Leadership
AIOps Won’t Save You. Clean Ops Will.
AIOps won’t fix messy systems. This piece explains why clear telemetry, clean ops, and disciplined incident practices matter more than any AIOps model.
Technical
7 JavaScript APIs You Probably Aren’t Using (But Should)
Modern browsers quietly ship with APIs that can replace entire libraries — if you know they exist. This guide highlights seven underused JavaScript APIs, explaining what they do, when to use them, and the real-world benefits they unlock for developers and teams. From BroadcastChannel and PerformanceObserver to File System Access and View Transitions, you'll learn which are production-ready today and which to adopt with progressive enhancement.