Blogs

AI-native is not a scale of how much of the old org you have automated — it is a redesign of how people, agents, information, and decisions relate. From four mornings to why a digital employee can charge $20K–200K a year.

Everyone already knows you just need to supervise AI instead of executing everything yourself; but here's what you don't know: you haven't hired anyone, yet you're already running a company. The AI-Native Organization, Part 1: The individual and the small team.

A department is a context-bounded token partition, a company is a federation of amoebas, and every rule is inherited from software engineering.
The AI-Native Organization, Part 2: Organizations and scale.

Why SaaS is hitting its ceiling and how the "results economy" grows out of the cracks: the business logic of agents selling results instead of tools, and the stages their commercialization will pass through. The second half is a labeled fantasy that pushes this logic decades further out.

Why does AI give you mediocre answers? Because it knows everything, any specific piece of knowledge gets diluted. The solution is simple: tell it which mental models you have mastered. This article gives you a list of frameworks you can use right away.

I've tried most of the prompt-engineering tricks, and they wear off like superstition. What actually changed how my AI works for me is duller: every session resets clean, like a brilliant new hire who forgets everything overnight. So I wrote it an onboarding doc, and this is the work discipline and task-handing habits I actually use.

How does a small American town that seems to produce nothing actually stay alive? A layer-by-layer dissection of what really keeps a local economy running.

Why does the same haircut carry a wildly different price tag from one country to the next? Set the old three-sector model aside and look through a new lens: tradable versus non-tradable.

New York and Shanghai look more alike on the surface every year. Put them under the tradable/non-tradable X-ray, though, and their economic DNA turns out to be nothing alike.

The economy is running toward two extremes: maximum efficiency or maximum experience. The stuck-in-the-middle ground between them is quietly disappearing.

The storm of the Great Mismatch is four layers of mismatch resonating at once. You can't cross it until you see exactly how it's built.

Complex products, AI-native output, and fast iteration naturally split team truth. The SSOT Nine-Layer Pyramid lets AI agents, product, operations, sales, support, and engineering share one current decision snapshot.

A reusable method for constructing a team SSOT that aligns AI agents, product, operations, sales, support, and engineering across mission, strategy, promise, Flow, contract, implementation, and evidence.

Once AI is deeply involved in development, release workflows need to be redesigned too. True one-click release is not about clicking faster. It is about removing human attention from release details.

How doubling your codebase creates completely different engineering challenges, and why most teams aren't ready for the transitions.