One of the biggest questions surrounding OpenLedger is whether Proof of Attribution (PoA) can truly accurately measure the contribution of real world AI systems.
AI models rely on massive amounts of data fine tuning human feedback prompts and agent interactions. OpenLedger aims to create an attribution layer that tracks how those inputs contribute to outputs and value creation.
But measuring contribution in AI is far more complicated than simply recording transactions on a blockchain.
For example:
How much did a single dataset improve a model?
Which contributor had the greatest impact on performance?
How do you measure indirect influence across multiple training stages?
What happens when thousands of small contributions combine into one output?
PoA appears to approach this by combining usage tracking provenance records interaction history and contribution mapping across models datasets and agents. Instead of treating AI creation as a black box the system attempts to build transparent records around how intelligence is generated and used.
