Posts

10,000 Agents, $14,000, and a Quarter of Security Research in a Weekend

There is a number that should reorganize how you think about security budgets: a couple of days, ten thousand concurrent agents turned loose on an entire monorepo, and $14,000 in tokens — and out the other end, “several quarters worth of security research.” That framing comes from a recent conversation on Naval’s AI Industrial Revolution episode, and it’s the seed for this post. But the claim is no longer a thought experiment you have to take on faith. The receipts are now public, and they’re worth reading closely — both for what they prove and for where they quietly fall apart.

100x a Year Is a Compounding Claim, Not a Benchmark

Here is the number that should make you stop and check the math: once an AI-native digital twin of a workflow is running and improving itself, the expected performance gain is 100x or more per year. Stated concretely — a task that took 100 days collapses to one. Not 100 days to 80, not a tidy doubling. Two orders of magnitude, every twelve months, from a single rebuilt process.

A Very Large Number of Very Small Teams

The intuitive read on a productivity shock is that it kills jobs: do the same work with fewer people, fire the rest. That read is half right and badly framed. Productivity does shrink the headcount needed to ship any given thing. What it does not do is hold the number of things constant. When the team-per-task collapses, the binding constraint stops being “how do I staff this?” and becomes “what else could I build?” The output of that question is not unemployment. It’s an explosion of new teams — what Naval, on a recent episode, called “a very large number of very small teams.”

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.)

AI and Crypto Won Because Math Is the Last Unregulated Domain

Here is a pattern worth sitting with. The two largest technology waves of the last fifteen years — first crypto, then AI — both grew up in the same place: the part of the world where you can do math without asking permission. Not because math is glamorous, and not because the people working on it were unusually brave. Because math, for a brief window, was the last domain a regulator hadn’t reached. You could write an algorithm, publish it, and run it before anyone had a form to fill out.

Approve a Bad Thing, Career Over; Block a Good Thing, Nobody Notices

Picture the payoff matrix a drug reviewer actually faces. Approve something that later hurts people, and the failure has a name, a face, and a hearing room. Block — or just slow-walk — something that would have saved lives, and nothing happens to you at all, because the people it would have saved never knew the drug existed. One quadrant ends careers. The other is invisible. Put a rational person in that matrix and they will, on the margin, say no. Not because they’re cowardly or captured, but because the incentives were built that way.

Build an AI-Native Digital Twin: Copy the Workflow, Don't Move It

Here is the move, stripped of ceremony: pick one workflow, fork its data feed, and stand up an AI-native copy of the process running in parallel with the original. Let the copy improve itself every loop. When it pulls ahead on the numbers that matter, deprecate the old one. The discipline that makes this safe is in the second verb — you build “an AI native digital twin,” and crucially “you don’t move it, you copy it.”

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.

Four Moats That Survive the Agentic Era

Start with the question that should keep any operator up at night: is there a high-margin line of your business that two people with off-the-shelf AI could rebuild in 60 to 90 days? If the answer is yes, the moat you thought you had is already gone — you just haven’t met the team that’s draining it. That framing comes from Peter Diamandis’s Organizational Singularity conversation (EP #258), and it’s the right place to begin, because once you accept that execution is cheap, the only interesting question left is what isn’t.