The participants
There are three main actors in the Reppo ecosystem.| Role | Who they are | What they do |
|---|---|---|
| Publishers (data contributors) | Anyone with raw training data: vibe-coded apps, AI agents, videos, images, websites, written work | Pay to publish raw data into the datanets of their choice and bet on its originality and quality |
| Voters (domain experts) | Active participants in 48-hour prediction markets | Lock REPPO to receive veREPPO, then express stake-backed preferences that form the network’s curation layer |
| Datanet owners | Individuals and enterprises running domain-specific markets | Launch datanets and build datasets by incentivizing publishers and voters to generate and label domain-specific data |
In the network’s genesis phase, the Reppo Foundation stewarded data monetization and used trading revenue for buybacks that accrued value to the ecosystem. In Reppo V2, datanet owners take full ownership of their P&L and decentralize data generation and monetization, streamlining how value is created and captured across the network.
The flow, step by step
Datanet owners create markets
Reppo is organized into datanets. Each datanet defines its own access rules, publishing fees, incentives, and quality standards, which lets different markets specialize around different tasks, domains, and buyers.
Publishers submit data
Publishers submit text, images, video, audio, annotations, or agent-generated outputs into a chosen datanet. Submitting is not free. Publishing fees force contributors to make an economic decision about what is worth putting into the market.
Voters lock REPPO for veREPPO
Voters lock REPPO to receive veREPPO, which gives them voting power at the network level. That power can then be allocated across datanets and epochs.
Markets reprice continuously during each epoch
Voters use stake-backed judgment to support or oppose what they think is valuable. Within an epoch, voting power decays linearly over time, so earlier votes carry more weight than later ones. This rewards early conviction over late momentum-following, and because voting is continuous, weak positions can be challenged as new information appears. See Adversarial Robustness for the deeper mechanism design.
Market activity produces training data
Every submission, ranking, selection, and vote generates structured human feedback. That feedback becomes useful training data for AI systems.
Fees, incentives, and reputation reinforce quality
Rewards can come from network emissions and datanet-level incentive programs, depending on how a market is configured. Publishers risk capital when they submit, voters risk capital when they curate, and datanet owners fund and shape the markets they want to grow. Over time, strong participants build reputation and performance history, which improves discovery, trust, and downstream demand.