Thinking on AI organizations.
How AI labor, operational governance, and enterprise AI infrastructure are changing the way companies work.
Featured Essay
Why AI-native companies will manage goals, not headcount
The assumption that getting work done requires hiring people is breaking down. Here is what comes next — and why the management layer matters more than the model.
ReadLatest Thinking
The difference between an AI agent and an AI worker
Agents are impressive in demos. Workers are accountable in production. Understanding the distinction is the first step to running AI labor safely.
What a work trail actually needs to contain
Audit trails in AI systems are not optional. But most implementations capture what happened — not why it happened, who authorized it, or what data was used.
Human-in-the-loop is a feature, not a limitation
The goal is not to remove humans from AI operations. The goal is to put humans exactly where their judgment adds value — and keep them out of the rest.
Deterministic workflows and AI reasoning: when to use each
Not every step in an operation needs AI reasoning. Knowing when to use a structured workflow vs. an AI worker is one of the most important design decisions in AI operations.
Use Case
Running mortgage prequalification with Miji
A walkthrough of how Miji coordinates intake, credit data, calculations, compliance checks, and human approval — with a complete work trail from start to finish.
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