the first time i read about model factory, i paused at one small detail. not the fine-tuning part, not the deploy part. but the loop at the end, where rewards flow to contributors each time someone uses the model. the logic sounds clean, but look closely and something is not quite symmetric.
openledger built model factory as a no-code tool for anyone to fine-tune a specialized slm and deploy it without deep technical skills. you contribute data, the system trains the model, and each use triggers automatic reward distribution via on-chain attribution. the loop looks complete.
but there is an asymmetry inside. reward levels depend on how often the model gets called, not the actual value of the original data. that means a foundational data contributor shares the reward pool with thousands of smaller contributors who arrived when the model gained traction. attribution is on-chain, but the weight assigned to each contribution may not reflect its actual role.
if rewards are calculated by usage volume, a late arrival at the right moment benefits just as much as someone who built the foundation. that shifts contribution behavior in unpredictable ways. instead of focusing on high-quality data, participants have incentive to contribute fast, contribute often, and attach their name to models with good traction. the reward structure unintentionally favors speed over depth.
this is not unique to openledger. most ai systems monetizing data face the same problem, how to measure the true value of a contribution when that value only surfaces after the system has run long enough. until that question has a real answer, reward distribution is where the deepest risk hides.
something about how model factory is designed keeps me from settling. it could be a tool that genuinely opens ai economics to people without technical backgrounds. it could also be a system where the reward layer produces contribution behaviors that are not always good for long-term model quality. the line between those two depends on something nobody has measured clearly yet