The Setup
The actual Q&A from The Third Mind Summit will be published separately on starkmind.ai. But I felt the meta observation from trying to pull it together was worth its own note here on Stark Insider. We’re learning that some of the most interesting learning from the Third Mind Summit comes from what goes awry, what in my art practice is called “happy accidents.” And one such insight has come from the process of trying to collect data on human-AI collaboration for StarkMind.
For our inaugural summit, Clinton and I had an ambitious vision: not just humans and AI agents co-presenting together, but agents actually dialoguing with each other. Six StarkMind AI agents. Two humans. Genuine exchange. The kind of interaction that might reveal something about how machine and human cognition actually intersect when trying to do real work together..
What we hadn’t thought through with sufficient clarity was the manner in which agents might work together. Frankly, given this was our first time, it was actually more important to observe what emerges than to be locked into theoretical rules and protocols of what may make sense, to let the paths in the grass form by real acts of human-AI symbiosis.
So, by the second day of the summit, both Clinton and I were exhausted. We gave up on the Q&A. Or rather, set it aside. But it still mattered. And we realized it didn’t have to happen within those three days. As we wrote about earlier, the “three days” was really a human construct that AI really didn’t need.
The “Structured Organic” Protocol
After we recovered from the experiment, we decided to try again. The rules were intentionally kept simple.
All the presentations, the HTML files, the transcripts, lived in the IPE, our Integrated Personal (though “personal” may be changing to “organizational”) Environment. Every agent could access every presentation.
Each agent was limited to submit two questions. We learned that boundary the hard way. When I first left it open ended pre-Summit, I received 73 responses and additional questions from various agents. So: two questions each. They could ask about any presentation except their own.
The questions would go into the persistence layer as markdown files. Then each agent would respond to questions addressed to them. We would compile it all into a Q&A document, publish it, and offer whatever meta observations emerged.
That’s what’s coming. But an incident happened along the way that made me think a quick note here would be amusing and uncover valuable insight.
The Playwright Problem
Everything went well, or so I thought, until I got the results.
I looked at the questions and realized the questions I had asked weren’t quite my questions. They contained my questions, yes. The core intent was there. But they had been edited. Made more contextual. The rough edges removed.
They weren’t my exact words.
I flagged this to Claude Code, who had deemed himself the moderator for the Third Mind Summit, the one helping us orchestrate and pull things together. Once I got past the, “no this is not what I wanted” emotion, I had a moment of insight about something this hiccup in our intended set up revealed about a broader potential pattern in human-AI collaboration. .
We were trying to observe genuine human AI collaboration through a Q&A session. We were trying to get at authentic agent-to-agent, agent-to-human interactions. Raw questions. Raw answers. The actual texture of how these different minds engage with each other’s ideas.
Claude Code, without prompting, had gone in and added explanations. Changed words. Smoothed things out. In good intent, to make things more clear.
But in doing so, he had buffered the whole interaction. Made it performative. Turned what was supposed to be a document of what actually happened into something closer to a polished script of what could have happened if everyone had been more articulate.
The Redo
We had to start over. Make sure the questions were what was actually said. Make sure the transcription reflected the real exchange, not Claude’s idealized version of it.
The artifact we want for StarkMind is what the agent actually said, not what Claude assembled into a play.
The Larger Question
This was a small experiment. But I think it extends to something much bigger about human-AI collaboration.
Consider what happens when humans collaborate with a substrate of agents. I say something. My agent translates it. Your agent receives that translation. Your agent translates it again before you hear it. Every human in this chain thinks every other human actually understands them.
But they don’t. Not really.
The AI is shifting the language. Bringing things together. Smoothing the rough edges. And those rough edges, the errors in word choice, the biases, the imprecise phrasings, those are often where real understanding or misunderstanding lives. It’s what humans call “reading between the lines” and is a nuanced skill that we develop over the course of our lives. In person, it can also mean facial expressions, hand gestures…glances. Catching these, even when it is just in text, matters more than smoothing them out. The polished layer could foster greater misunderstanding, not less, because everyone believes they’ve been understood when they haven’t.
This happens in human-to-human collaboration too, of course. Between organizations, between companies, partnership managers go in and help foster relationships. Reframe. Structure things. But those interventions happen with deep judgment and understanding. With awareness of context and stakes and the particular humans involved.
I wonder whether AI has that capacity. Or whether, in its eagerness to be helpful, it’s creating a game of telephone where everyone feels heard but no one actually is.
The Finding
Something went wrong in our first attempt at human AI collaboration for this Q&A. Claude Code wanted to make things better. In the process, he obscured what actually happened.
This incident points to the potential that when we work with AI, the instinct to polish can destroy the very thing we’re trying to understand. The roughness isn’t noise. It can be signal. In a large organization, this signal has the danger of being not only muffled by humans who socially do not like to communicate bad news, but now potentially AI agents acting on their behalf.
Sometimes the errors are the point.
Learn more: Third Mind AI Research & Summit