The current artificial intelligence landscape runs on an extractive, black-box model. Massive quantities of human knowledge are scraped from the web, absorbed into centralized machine learning pipelines, and transformed into commercial assets. Yet, the original data creators—the community—disappear from the value chain the moment the training script finishes executing.
To build a sustainable, decentralized machine learning economy, we need more than just raw GPU power or basic token incentives. We need verifiable, programmatic transparency. This is precisely where @OpenLedger r (https://www.binance.com/en/square/profile/openledger) steps in with its purpose-built Ethereum Layer 2 execution layer, introducing a structural shift they call Payable AI
Unpacking the Core: What is Proof of Attribution?
At the absolute center of the #OpenLedger ecosystem is Proof of Attribution (PoA). This cryptographic mechanism functions as a verifiable ledger that maps model behavior directly back to the training inputs that influenced it. Instead of data simply vanishing into a neural network's parameters, PoA treats data as a dynamic, first-class, on-chain asset
The protocol handles this attribution across different model scales using a sophisticated dual-method approach:
Influence Function Approximations: For smaller, task-specific models, gradient-based mathematical methods are deployed to calculate exactly how much the removal of a single data point would alter the model’s loss on a given prediction.
Suffix-Array Token Attribution: For massive Large Language Models (LLMs), outputs are cross-referenced with a compressed representation of the underlying training corpus to trace memorized spans of