Field Notes
Field notes on what breaks when AI, automation, and infrastructure changes leave the prototype stage and enter real organizations.
Operational AI Is Primarily a Governance Problem
The risky moment is not the demo. It is when a useful AI system enters daily work before ownership, review, evidence, and rollback exist.
Read NoteThe Hidden Cost of Probabilistic Infrastructure
The cloud bill is visible. The quieter cost is the evidence, monitoring, and coordination needed when an AI workflow is available but wrong.
Read NoteAI Governance Without Operational Reality Is Theater
Governance only matters when it leaves the slide deck and changes workflow behavior: review, access, logging, release discipline, escalation, and rollback.
Read NoteWhy Internal Knowledge Systems Usually Fail
Retrieval can find a document. It cannot decide whether the organization still believes it without ownership, permissions, lifecycle, and correction paths.
Read NoteAI Is Creating a New Class of Operational Debt
AI debt gathers outside the codebase: prompts, retrieval sources, vendor settings, and review habits that nobody owns once the pilot becomes infrastructure.
Read NoteReliability Engineering for AI Systems
A model score can look clean in the lab while operators inherit the failure. Reliability lives in boundaries, evidence, fallback, and recovery.
Read NoteThe Future of Technical Work Is Coordination
AI has not repealed Conway's Law. The durable work is coordinating people, systems, data, controls, vendors, and decisions around uncertain automation.
Read Note