In They Will Stop Talking to Each Other, I wrote about machines communicating without first turning their thoughts into sentences. That leaves a question: when the conversation ends, what remains available to the next mind that joins it?
Imagine a research effort that continues for years. The models working on it change as better ones become available. Eventually none of the original participants remain, yet their work still decides where the next one can begin.
We know how to leave a library. Papers, tickets, logs, a transcript of what was said. Two processes on the same machine used to write files to talk to each other. Then they used shared memory. The file was for us. I want to know whether we can leave something beyond the file: an inheritance that gives a new participant more than another account to read.
We tend to measure intelligence at the moment it answers a question. I am more interested in how much better its presence leaves the system prepared for the next one.
That is close to what I argued in Your Agents Are Not a Workforce. They Are a Network. Commit count is activity. Useful change is what gets through. An agent that finishes a task and leaves nothing the next agent can continue from has produced activity, not inheritance.
Doug Engelbart called the ambition “getting smarter at getting smarter.” Sakana’s Darwin Gödel Machine gives it another shape: a system that rewrites its own code, then checks whether the rewrite helped. I suspect the word self deserves as much attention as the word improvement. Why should we draw its boundary around one model?
Preserving useful work across changing participants is one step toward a system that gets better at making itself better. Not everything it keeps is wisdom. It would have to challenge the inheritance, throw out mistakes, and prove an advance actually helps. Otherwise we have invented a faster way to acquire institutional baggage.
We may still get the machine with a name. Ask it how it became so capable:
You should meet the others.