TL;DR
This page explains how codatta turns data into a revenue‑generating asset (the Royalty Economy), how the Train‑Now, Pay‑Later (TNPL) business model rides on top of it, and, briefly, why a blockchain foundation is the most practical way to make it work at scale. For the big‑picture narrative, see our vision: https://codatta.io/vision.Key concepts:- Royalty Economy: usage‑based, ongoing revenue sharing for data owners and validators.
- TNPL: developers train first, pay later via royalties once value is created.
- Blockchain (light rationale): programmable ownership, provable provenance, and automated payouts.
The Royalty Economy
What it is
A protocol-level mechanism that links data usage—training, fine-tuning, and evaluation consumption, plus metered inference of downstream AI models derived from licensed data—to continuous royalty flows for data owners (contributors, validators, backers) and the protocol treasury.Why it matters
- Moves AI data from one‑off sales to streaming, usage‑based earnings.
- Aligns incentives: better data → better models → more usage → more royalties.
- Makes high‑skill contributions (e.g., expert labels, evidence‑backed signals) economically viable.
How it works (conceptual)
- Contribute & fingerprint: Data is contributed and bound to a contribution fingerprint and lineage‑asset record.
- Ownership fractions: Ownership is established via contribution, verification, and staking‑as‑confidence (fractional, time‑scoped).
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Usage metering & attribution:
- Training/Fine‑tune: consumption events are recorded to establish lineage and attribution (who used what, when).
- Inference (serving): usage of models derived from licensed data is metered (e.g., requests/tokens/API calls) for royalty calculation.
- Royalty routing: Smart contracts split revenue to owners and treasury, following the current ownership fractions.
Distribution knobs
- Pay‑as‑you‑train: micro‑royalties (or attribution-only, per deal) during training/fine‑tune consumption; inference of derived models is metered for settlement.
- Performance‑linked: multipliers unlock when agreed quality thresholds are met.
- Protocol share: a small public share sustains infra, audits, and research.
Analogy: Like music streaming royalties, but for knowledge: every meaningful use of your data pays you back.
New business model: TNPL (Train‑Now, Pay‑Later)
Definition: Let developers access data without upfront purchase; if/when the trained model creates value, royalties flow back to data owners.Developer workflow
- Request access → agree to TNPL terms (royalty rate, KPI clauses, sunset conditions).
- Train/evaluate → protocol tracks lineage and usage under an escrow‑like agreement.
- Launch & monetize → model/API earns; smart contracts split proceeds per TNPL terms.
Why devs love it
- Reduces upfront cost and risk; experimentation unblocks.
- Keeps cash focused on product‑market fit, not data acquisition.
Why contributors love it
- Long‑tail income that scales with real adoption.
- Fairness: payout is tied to measured impact and verifiable use.
Why the protocol loves it
- Maximizes data utilization; attracts both indie and enterprise builders.
- Creates recurring flows that strengthen token and treasury demand.
Why blockchain (light version)
We keep this brief here and dive deep in a dedicated section.- Programmable ownership: Fractional, time‑bound ownership that updates as staking/verification evolves.
- Provable provenance: On‑chain fingerprints and lineage make who contributed what auditable.
- Automated payouts: Smart contracts route royalties instantly and globally.
- Open market access: Anyone can discover, license, and build—no walled gardens.
- Composability: Ownership fractions can be repackaged into portfolios (risk/return tuning).
- Privacy by design (hybrid): Sensitive data stays off‑chain; proofs, hashes, and policy live on‑chain.
Putting it together: These primitives form the minimum viable substrate for a functioning royalty economy. Programmable ownership expresses and updates the right to be paid; provenance binds each usage to the correct contributors; automated payouts and open market access make per-event micro-royalties economical and global; composability turns otherwise illiquid data shards into financeable, discoverable assets; and the hybrid privacy model satisfies enterprise governance and regulation. Without this full set in concert, attribution becomes unenforceable, settlement becomes unscalable, or markets remain closed—conditions under which TNPL and per-use royalties cannot thrive.
Modes inside the Royalty Economy
| Mode | When it pays | Trigger | Typical use | Notes |
|---|---|---|---|---|
| Pay‑as‑you‑train | During training/serving | Metered usage | Fine‑tunes, evals, APIs | Smooth micro‑flows |
| Performance‑linked | After KPIs met | Accuracy/latency/SLA | Safety evals, risk signals | Adds fairness + rigor |
| TNPL | After success | Monetization events | Startups, pilots | No upfront; share upside |
Data (Knowledge) Lifecycle and Royalties Flow
This flow illustrates the lifecycle of data and royalties in codatta’s Royalty Economy. Data moves through four compact phases: Sourcing and Labelling (contributors create and verify data through staking), Listing & Licensing (data is published and licensed for use), Model Build (developers train, fine-tune, and launch AI models using the licensed data), and Usage & Settlement (models are deployed, users interact, and royalties flow back to contributors). The dashed feedback loops show how ownership fractions evolve and how royalties and reputation updates continually feed the ecosystem — ensuring that contributors keep earning and that ownership stays dynamically aligned with verified participation.Example — Signals for Compliance (simplified)Crypto has massive volumes of accounts that must be labeled fast and correctly. Each annotation needs evidence, dependable reasoning and must reflect the latest status (minutes, not weeks). Codatta runs a global intelligence network—contributors, validators, and AI—to produce and update these annotations at scale. The steps below illustrate the key flow from creation to royalties.
Roles & incentives (who earns what)
| Role | Contributes | Earns |
|---|---|---|
| Contributor | Original data, labels, evidence | Ongoing royalties (fractional share) |
| Validator | QA, counter‑evidence, audits | Royalty share + validator bounties |
| Backer | Staking‑as‑confidence, discovery | Royalty share proportional to stake impact |
| Protocol | Infra, governance, security | Treasury share for sustainability |
Contracts & terms (plain language)
- Royalty rate: percentage of revenue tied to usage events.
- Attribution scope: which assets/versions the royalties cover.
- KPI clauses (optional): performance thresholds that change rates.
- Sunset/renewal: time limits, re‑negotiation triggers.
- Auditability: what data is logged, how it’s verified, and dispute windows.
Why this reduces risk for everyone
- Builders: shift from capex to opex; only pay if the model works.
- Contributors: protection from one‑off “work‑for‑hire”; upside preserved.
- Buyers: transparent line‑of‑sight from source to model impact.
FAQ (short)
Q: Is TNPL just a payment plan? A: No. It’s a contractual revenue‑share anchored by verifiable data usage and performance. Q: Can I mix upfront + royalties? A: Yes. Hybrid deals are supported (minimum guarantee + royalty tail). Q: Do I lose control of my data? A: No. Access is policy‑gated; proofssimpl/on‑chain policy enforce scope and terms. Q: What about privacy/regulation? A: Sensitive content stays off‑chain; only fingerprints, policies, and flows are on‑chain. Data rooms and access logs are auditable.See also
- Our vision: https://codatta.io/vision
- Concept glossary: Royalty Economy, Royalty Loop, TNPL (coming soon)
- Deep dive: Why blockchain for data royalties (coming soon)