Publier
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MICHAEL MOORE
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have spent time around a lot of AI projects that put blockchain in their pitch and mean nothing specific by it.
@OpenLedger was different and honestly I almost missed why.
The thing that stopped me was not the vision. It was something called Proof of Attribution. Most AI systems today genuinely cannot tell you which data trained which output.
That sounds like a technical footnote until you realize it is the reason data contributors have never been paid fairly, the reason AI models are built in black boxes, and the reason nobody has ever been able to build a functioning payment rail between AI usage and the people whose data made it possible.
#OpenLedger built a protocol that maps exactly which dataset influenced a specific model response and then routes payment to that contributor automatically every time an inference runs.
The June 2025 whitepaper describes two actual engineering approaches, not metaphors, not roadmap slides.
That specificity is what made me keep reading.
Every inference on the network fires an attribution event. Every attribution event moves $OPEN to the relevant contributor. More usage equals more token movement structurally, not speculatively.
Whether enough model developers choose to build here is still genuinely uncertain. But the mechanism is more carefully designed than anything else I have seen in this category and that felt worth writing about.
@OpenLedger was different and honestly I almost missed why.
The thing that stopped me was not the vision. It was something called Proof of Attribution. Most AI systems today genuinely cannot tell you which data trained which output.
That sounds like a technical footnote until you realize it is the reason data contributors have never been paid fairly, the reason AI models are built in black boxes, and the reason nobody has ever been able to build a functioning payment rail between AI usage and the people whose data made it possible.
#OpenLedger built a protocol that maps exactly which dataset influenced a specific model response and then routes payment to that contributor automatically every time an inference runs.
The June 2025 whitepaper describes two actual engineering approaches, not metaphors, not roadmap slides.
That specificity is what made me keep reading.
Every inference on the network fires an attribution event. Every attribution event moves $OPEN to the relevant contributor. More usage equals more token movement structurally, not speculatively.
Whether enough model developers choose to build here is still genuinely uncertain. But the mechanism is more carefully designed than anything else I have seen in this category and that felt worth writing about.
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