I’m looking at OpenLedger’s Proof of Attribution layer and how it tries to connect data, models, and agents into a reward system based on contribution. What makes me pause is how attribution is actually measured when multiple models remix the same data. Who decides the boundary between original input and derived output? Can rewards stay fair when agents continuously retrain on overlapping signals, or does attribution blur at scale? And if OPEN becomes the settlement layer, how resistant is it to incentive gaming or subtle reward farming? For me, the real test is whether ownership attribution stays meaningful under continuous reuse.

@OpenLedger #openledger $OPEN