Ai

AI Already Killed the Modern Company. The Org Chart Just Hasn't Noticed Yet

The comet has already hit. The dinosaurs are still walking around.

That’s the uncomfortable shape of what’s happening to incumbent companies right now. The cause of death has arrived — cheap reasoning, agents that coordinate, APIs that glue any two systems together for pennies — but the visible collapse lags by years. Headcount looks fine. The org chart looks fine. Q3 looks fine. And then it doesn’t, all at once, and everyone acts surprised even though the impact landed eighteen months ago. (The framing — “AI has killed the modern company,” and “the dinosaurs didn’t go overnight” — comes from Peter Diamandis’s Organizational Singularity episode, which is the seed this post is arguing with.)

Coase's Law Breaks: When Coordination Costs More Than Building

This piece was sparked by The New Era of Jobs: Organizational Singularity with Peter Diamandis and Salim Ismail. Ismail’s framing was blunt: in an AI-native world, “Coase’s law no longer applies,” because you can now find yourself “building the feature cheaper than having the meeting about the feature.”

Compliance Was a Tax on Iteration. AI Just Cut the Rate.

The expensive part of regulatory compliance was never the document. It was everything you didn’t do because of it.

Picture a hardware team that wants to revise a part. The change is good. But it triggers a re-evaluation against a few hundred pages of standards, and that re-evaluation costs weeks of specialist time. So the change doesn’t happen. The design freezes — not because freezing is correct, but because thawing is too expensive. Multiply that across every decision in a regulated product and you get the real cost of compliance: a slow, invisible tax on iteration. Teams stop changing things that should change.

Humans Are Becoming Verifiers — and That Job Is Harder Than It Sounds

This piece was sparked by The AI Industrial Revolution with Naval Ravikant. One line from the conversation has stuck with me: “humans are becoming verifiers.” As models take over generation, the argument goes, the old functions of engineers, lawyers, and operators “move to verifying the stack” — saying yes, this is roughly right, and I’ll stand behind it.

Waste Tokens, Save Time — When Brute Force Is the Rational Choice

This piece was sparked by The AI Industrial Revolution with Naval Ravikant, where one of the builders at the table offered a deceptively simple rule: “just waste tokens, save time.” Don’t optimize the inputs or the outputs, he argued — optimize your own time and the final result, because “they’re still way cheaper than a human.”

You Always Want the Smartest Model

The cheaper model’s mistakes are the ones you never see. That is the whole argument, and it is worth sitting with before you reach for the dropdown and pick the model that costs a fifth as much.

Your Coding Agent Stopped Being a Junior and Started Pushing Back

There is a specific moment, somewhere in the last eighteen months, when the coding agent stopped doing what you told it and started telling you what it would do instead. You write a one-line prompt. Instead of immediately editing six files, it pauses, reads the codebase, and comes back with: here are three ways to do this, here’s what each one costs you later, which do you want. That pause is the whole story. It is the difference between an engineer who runs with the first interpretation of a ticket and one who has been burned enough times to ask the question behind the question.

Your Job Is Building the Factory, Not Shipping the Part

For most of software’s history, the engineer was the part. You were measured by what came out of your hands: the pull requests, the features, the lines that shipped. That measurement is breaking. The unit of engineering value has moved up a level — from the output you produce directly to the system that produces output for you. The question is no longer “how much did you build?” but, as the framing goes, are you producing the factory that would produce multiplicative outputs?