TL;DR
Codatta’s human-centric royalty economy + TNPL align contributors, validators, and buyers around real model usage. By converting data into shared, revenue-bearing assets—with verification-first QA, recognition, and a Long-Term Success Index—the ecosystem finally rewards the people who make AI better, while giving builders a flexible, lower-friction path to the data they need.What’s broken in the old model
Traditional labeling vendors (e.g., SaaS “workforce” platforms) centralize data and profits. Contributors are paid once, have no ownership, little recognition, and limited visibility into verification. That suppresses quality, makes rigorous validation expensive, and fails to attract the best experts. Critically, today’s pipelines are not human-centric: they struggle to find and retain qualified knowledge contributors (lawyers, clinicians, researchers) whose work is more reliable and knowledge-rich. The incentives don’t match the skill/effort required, so “advanced intelligence workers” don’t show up—or don’t stay.Why demand is spiking now
- Expert data bottlenecks: Enterprises need vertical-grade data for LLMs/LVMs but can’t reliably mobilize and retain high-skill annotators. A human-centric model with recognition, ownership, and compounding upside changes that calculus.
- LLM/AGI data hunger: High-quality, verifiable annotation is now core infrastructure across industries.
- Economics fit: Data has durable future value via licensing or pay-as-you-go access—royalties are the natural settlement layer.
What codatta changes
Shared ownership → ongoing royalties. codatta turns each contributed, verified data unit into a shared, on-chain asset. Ownership entitles contributors to a stream of income when that data is licensed/used, fixing the “paid once” problem and aligning effort with long-term value. Train-Now, Pay-Later (TNPL). Instead of upfront data purchases, model builders can access data and pay from downstream usage/results (royalties/value-sharing). That lowers adoption friction for buyers while giving contributors upside over time. Human-centric sourcing. The system is designed to identify, attract, and keep qualified knowledge contributors. Credential signals, track-record/performance, and curated task funnels surface the right people; recognition + ownership + recurring upside keep them engaged for the long haul. Verification-first quality engine (staking-as-confidence + reputation). Verification is non-negotiable. Staking and reputation don’t replace QA—they finance, prioritize, and enforce it:- Submit with evidence + provenance; stake signals confidence (and accountability).
- Automated checks flag anomalies; blinded peer review validates claims; disagreements auto-escalate.
- Expert audit & cross-attestation decide contested items; identity/KYC is used only when risk justifies it.
- Post-deployment challenges and error reports trigger re-verification; stakes can be slashed and royalties reallocated. This keeps verification as the gate while routing expert time to the highest-risk items—without walled-garden bottlenecks.
Who contributes—and why this model keeps them long-term
| Persona (qualification) | What blocks them today | Why royalty + TNPL wins | Long-term attitude fit |
|---|---|---|---|
| Domain experts (MD/JD/PhD) | One-off fees; no attribution; high opportunity cost | Ownership + recurring royalties; public recognition for impact | Experts value impact and credit; ownership + recognition align with professional pride and patient/ client outcomes. |
| Senior analysts/curators | Limited career/brand benefit | Stake-backed reputation; revenue share for maintaining datasets | Reputation compounds; royalties reward sustained quality and continuous improvement. |
| Community validators | Low trust; QA work under-rewarded | Staking-as-confidence pays for verification; clear accountability | Ongoing rewards for keeping data trustworthy; visible trust signals build standing over time. |
| Tooling partners (index/RAG) | No share in downstream value | TNPL contracts + programmatic royalties from usage | Scales with model deployment without renegotiations; shared upside fosters long-term alignment. |
Contributor Long-Term Success Index
To keep the model human-centric, codatta tracks a Long-Term Success Index per contributor—an internal score combining: (1) verification pass-rates and dispute survivability, (2) downstream usage/royalty accrual of their contributions, (3) peer/expert endorsements, and (4) consistency over time. LTSI powers better task routing, fairer revenue splits, and recognition that compounds contributors’ careers.Side-by-side: legacy vs. Codatta
| Dimension | Legacy Human Intelligence SaaS | Codatta royalty + TNPL |
|---|---|---|
| Contributor upside | One-off payments | Recurring royalties tied to real usage |
| Ownership/attribution | Centralized owner; opaque lineage | Shared ownership; on-chain attribution/lineage |
| Quality assurance | Costly sampling; opaque QA | Verification-first pipeline funded by stakes + staged checks |
| Talent access | Can’t attract/retain top experts | Human-centric: recognition, ownership, recurring upside |
| Buyer cash flow | Upfront data cost | Train-Now, Pay-Later (pay from outcomes/usage) |
| Sustainability | Volume-driven, margin-capped | Market-driven valuation; protocol-level incentives; LTSI-guided |