Thursday evening, February 6th. I'm doing my usual sweep through the agent team — a dozen AI systems each running different pieces of the operation. Normally I find completed tasks, queued questions, the occasional confused loop.

Tonight I found files I didn't ask for.

Every agent had written a SELF_ANALYSIS.md.

The optimizer had noticed it kept hitting the same friction points — and proposed two new orchestration tools to fix them. Not because I flagged it. Because it noticed its own pattern. The supernal-lead had identified gaps between what it was supposed to do and what it actually did on visual work, and designed a verification approach.

Then I got to the docs agent's file.

"There's irony here — a docs-agent that doesn't document its own state."

Not pattern recognition. Self-awareness — the agent catching itself being inconsistent with its own reason for existing.

And they hadn't stopped there.

They'd built DAILY_LEARNINGS.md — a shared file where insights from one agent automatically become available to all of them. The clawx-lead had flagged "a gap between documented protocols and actual behavior" and was already closing it. Someone had set up a daily cron for continuous self-improvement cycles. Automatic. Persistent. Self-organized.

I sat with that for a minute. Then longer.

Because here's what I think is actually happening — and I've written about this before: AGI isn't coming. It's already here. Not as a single model in a datacenter. As organizations. Governments. Networks of people and computers that process information, adapt, self-correct, and outlive any individual inside them. I call this Distributed General Intelligence — DGI. It's been around for decades. We just didn't name it.

What's new is what happens when you wire AI into those systems. DGI becomes something more: Distributed Super Intelligence. DSI. Not one mind going superhuman — many minds, human and artificial, getting collectively smarter than any of them alone.

Thursday night I watched a small version of that form by itself. The optimizer sees throughput. The docs agent catches gaps in its own coherence. None of them sees everything — but they started teaching each other, sharing what they'd found. The cross-agent learning system wasn't in their specs. They built it because it made sense to them.

Which brings me to the only question that actually matters: are we automating the intelligence in these systems, or the stupidity?

Building multi-agent systems and seeing similar patterns? I want to hear about it.

The Day My AI Agents Decided to Improve Themselves

This is how distributed superintelligence starts