Essays for the early innings.
Four long-form field notes extending the book’s argument into operating systems, adoption signals and weekly action.
The AI-Native One-Person Company Operating System
Design an AI-native operating system for thinking, building, selling and learning without creating a pile of disconnected tools.
02Why AI Feels Late but Is Still Early
Why attention and rapid capability growth can coexist with shallow institutional deployment.
03The AI Era Early Signals Most People Are Still Missing
Durable signals, practical workflows and mistakes to avoid while operational adoption remains early.
04Personal AI Compounding Advantage: What to Build Weekly
A weekly system for turning small experiments into skills, proof, distribution and reusable assets.
Editorial method
How to use these field notes.
Each essay starts from a specific claim about AI adoption, individual leverage or operating practice. Read it as a working model, not as a prediction that every company or profession will move at the same speed. The useful question is whether the described signal is visible in your own work and whether a small experiment can test it.
Separate capability from deployment. A model may demonstrate a task before an organization has the data, process, incentives or review needed to use it reliably. The essays examine that gap and favor repeatable workflows over tool announcements. When an example depends on a changing product, price or benchmark, verify the current source before making a costly decision.
A weekly reading loop
- Choose one essay connected to a real bottleneck.
- Write down the claim you can test this week.
- Build the smallest reusable workflow or artifact.
- Record what improved, what failed and what remains human work.
- Keep the result only if it compounds into the next week.
The archive grows by extending these arguments with evidence, counterexamples and operating details. Existing URLs are updated when a claim or workflow materially changes.