# Fixing AI Data Labor Incentives

Traditional AI data supply chains often rely on underpaid, invisible labor.

The people doing the ranking, labeling, and evaluation work usually have little pricing power and little upside, even when their judgment is what makes the dataset valuable.

Reppo is designed to change that.

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#### The Core Problem

In most data pipelines, workers are paid fixed rates for tasks defined by someone else.

That model has three recurring failures:

* **Low bargaining power:** contributors rarely share in the upside they help create.
* **Weak quality incentives:** payment is often tied to output volume, not judgment quality.
* **Poor transparency:** buyers cannot easily see where data came from, how it was curated, or who was rewarded.

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#### How Reppo Changes the Incentive Model

Reppo turns data work into a market with explicit incentives and stake-backed curation.

1. **Datanet owners create markets**
   * Owners launch [datanets](https://reppo-labs-xyz.gitbook.io/reppo-labs/protocol-mechanics/datanets) around specific tasks, domains, or proprietary workflows.
   * They set access rules, publishing fees, and incentive structures.
2. **Publishers and data owners bring supply**
   * Contributors submit raw data, prompts, outputs, annotations, or other task-relevant material.
   * Publishing carries cost, which discourages low-quality spam.
3. **Voters and annotators bring judgment**
   * Participants lock REPPO to receive [veREPPO](https://reppo-labs-xyz.gitbook.io/reppo-labs/protocol-mechanics/stake-assured-human-feedback) and use it to curate what they believe is valuable.
   * That turns evaluation into an economically accountable activity, not just unpaid moderation.
4. **Useful signal earns support**
   * Strong submissions can attract sustained support.
   * Weak submissions can be challenged, downranked, or priced out over time.

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#### Why This Improves Labor Outcomes

Reppo gives more weight to informed judgment.

That matters because the scarce resource in high-quality AI data is often not raw labor hours. It is domain expertise, taste, context, and the ability to identify signal early.

In this model:

* skilled contributors can self-select into markets where their knowledge matters
* curation and annotation become economically legible work
* upside is tied more directly to useful outcomes
* data buyers get clearer signals about what was valuable and why

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#### Why This Matters for AI Teams

AI teams do not just need more data. They need better filters for what data is worth using.

Reppo helps coordinate data owners, annotators, curators, and buyers in the same market, so quality is surfaced through incentives instead of hidden inside opaque vendor workflows.

See [How Reppo Works](https://reppo-labs-xyz.gitbook.io/reppo-labs/foundations/how-reppo-works) for the system-level flow and [Collection Methods](https://reppo-labs-xyz.gitbook.io/reppo-labs/protocol-mechanics/collection-methods) for how different kinds of feedback are captured.

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#### Broader Framing

Reppo does not need to be its own L1 or L2 to improve labor economics in AI data.

Its role is to act as a coordination layer where high-quality human feedback can be sourced, evaluated, and rewarded with better alignment between effort, expertise, and value creation.
