I Think $OPEN Might Be Pricing AI Disagreement, Not Attribution

I used to think attribution was the valuable part of AI infrastructure. Track the source, verify the contributor, record the model interaction, and the system becomes trustworthy. But the more I watch AI evolve, the more I think attribution is only the surface layer people can easily understand.

What actually matters starts later — when systems disagree.

I keep thinking about what happens after an AI output creates consequences. A model recommends something, another agent acts on it, money moves, rankings shift, visibility changes, and then suddenly the outcome gets challenged. At that point, attribution alone does not solve anything. A record is just evidence. Someone still has to decide which version of events becomes authoritative enough to act on.

That is where I think $OPEN becomes interesting.

Maybe the real demand is not proving who contributed to an output. Maybe it is creating infrastructure for replay, validation, challenge resolution, and settlement once AI-generated decisions start colliding with each other.

Because AI systems will not become simpler as they scale. They will become denser, more layered, and more dependent on outputs from other uncertain systems.

And unresolved ambiguity becomes expensive fast.

That is why I no longer think the biggest market is memory.

I think it might be disagreement.