How Reppo Works

Reppo turns AI training data into a live market.

Participants publish content, stake capital, vote on quality, and generate usable learning signal as they do it.

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1. Datanet Owners Create Markets

Reppo is organized into datanets.

Each datanet defines its own access rules, publishing fees, incentives, and quality standards. That lets different markets specialize around different tasks, domains, and buyers.

2. 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.

3. Voters Lock REPPO for veREPPO

Voters lock REPPO to receive veREPPO, which gives them voting power.

That voting power exists at the network level. It can then be allocated across datanets and epochs.

4. 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. Earlier votes carry more weight than later ones, which rewards early conviction over late momentum-following.

Because voting is continuous, weak positions can be challenged as new information appears. See Adversarial Robustness for the deeper mechanism design.

5. Market Activity Produces Training Data

Every submission, ranking, selection, and vote generates structured human feedback.

That feedback becomes useful training data for AI systems. See Collection Methods for examples.

6. Fees, Incentives, and Reputation Reinforce Quality

Rewards can come from network emissions and datanet-level incentive programs, depending on how a market is configured.

At the same time:

• publishers risk capital when they submit

• voters risk capital when they curate

• datanet owners can fund and shape the markets they want to grow

Over time, strong participants build reputation and performance history. That improves discovery, trust, and downstream demand.

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Broader Interpretation

Reppo is not just collecting feedback. It is turning economic conviction into a live market for learning signal.

Over time, the market is meant to reward signal discovery and punish low-quality noise, not through static moderation, but through open economic competition.