Datanets
Datanets are owner-defined markets for sourcing, curating, and monetizing AI training data.
Anyone can create one. Think of a datanet as a data business with its own rules, access controls, and incentives.
What a Datanet Controls
A datanet owner defines the operating rules for that market:
Access rules: who can publish, vote, and consume data
Publishing fees: what it costs to submit content
Incentives: how publishers and voters are rewarded
Quality standards: what counts as valid, useful, or in-scope content
Monetization: how value flows back to the owner and network
Datanet creation is permissionless. Participation inside each datanet is owner-controlled.
How Participation Works
Datanets sit on top of network-level staking and voting mechanics.
Publishers submit locally. Contributors choose which datanet to publish into and pay the fees set by that datanet.
Voting power is network-wide. Users lock REPPO to receive veREPPO, then allocate that voting power across markets.
Votes happen over epochs. Within an epoch, voting power decays linearly, which gives earlier votes more weight than later ones.
Signals become data. Publishing, ranking, and curation all produce structured preference data and market signal.
This means publishing is local to each datanet, while stake and voting power are coordinated at the network level.
Why Datanets Matter
Each datanet is a specialized environment for a specific domain, task, or community.
That makes Reppo modular by design:
Enterprises can run restricted markets around proprietary data.
Open communities can coordinate around shared tasks and public demand.
Domain experts can participate where their judgment is most valuable.
Instead of forcing every contributor into one global workflow, Reppo lets many data markets exist in parallel.
A Note on Versions
In Reppo V1, the docs referred to public and private datanets.
In Reppo V2, that split was replaced by permissionless datanet creation with owner-controlled access and economics. The current model is simpler: anyone can launch a datanet, and each datanet decides how participation works.
The Bigger Picture
Datanets make AI training data more composable.
They let data owners, contributors, curators, and buyers coordinate in markets that are transparent, configurable, and tied to real economic incentives.
Launch cost
Low — no knowledge of coding or access to compute required
Who can create
Anyone: individuals, teams, enterprises
Governance
Defined by each datanet owner and market rules
Languages supported
70+
Output
Clean, structured preference and expertise data
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