Incentive Framework
veREPPO is the governance and incentive alignment mechanism that enables researchers and AI/ML teams to capture strength of preferences in training data—backed by real economic stake. It's the first of its kind extension of the traditional vote-escrowed tokenomics (veTokenomics) model which is primarily used to enable efficient allocation of capital to generate AI training data.
How It Works
Locking REPPO → veREPPO
Voters, who are also data annotators, lock $REPPO tokens for a chosen length.
In return, they receive veREPPO i.e voting power.
The amount of veREPPO is proportional to both the quantity of tokens locked and the lock duration. Longer locks grant disproportionately higher voting power (non-linear function).
Voting & Emissions
veREPPO holders vote each epoch to predict which collabs (AI content published by creators) will get the highest votes. We use a commit-reveal function to ensure privacy in the voting process.
At the end of each epoch, net new $REPPO token emissions are distributed and split 50-50 between content creators who got votes and everyone who voted for them.
This creates a dynamic prediction market for AI content while crowdsourcing AI training data.
Epoch-Based Flexibility
Voting allocations can be adjusted each epoch by the voters, but the locked REPPO remains illiquid for the chosen duration.
This balances long-term capital stability with short-term governance flexibility.
Key Properties
Alignment of Incentives: Token holders are rewarded to surface the highest quality content and publishers in the system while getting rewarded for doing so
Sticky Economics: VeREPPO create persistent relationships between voters and creators, reducing short-term churn.
Anti-Farming Mechanisms: The commit-reveal adds a delay in how and when emissions are distributed, delivering real economic value to creators and voters.
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