AI Data Principles
The immutable truths. Models change. Architectures evolve. These don't.
The Five Principles
| # | Principle | Why Immutable | Implication |
|---|---|---|---|
| 1 | Data compounds | More data = better models = more valuable data | First to the flywheel wins exponentially |
| 2 | Collection is physical | Real-world data requires real-world sensors | Someone must deploy hardware |
| 3 | Quality beats quantity | Model performance scales with data quality, not volume | Verified, labeled data commands 10-100x premium |
| 4 | Ownership creates alignment | Those who generate data should capture value | Token incentives beat surveillance extraction |
| 5 | Compute follows data | Processing gravitates to data sources | Edge and distributed compute > centralized cloud |
1. Data Compounds
Data has the strongest compounding dynamic in technology. Each data point makes existing data more valuable.
The math: AI model performance follows power laws. Double the quality training data, step-change the model capability. This isn't linear improvement — it's exponential.
The implication: Whoever builds the data flywheel first creates an accelerating moat. Latecomers don't just trail — they face an exponentially widening gap.
DePIN advantage: Token incentives bootstrap the flywheel faster than corporate budgets. GEODNET reached 20,000+ reference stations across 148 countries in 3 years. A centralized competitor would need billions in capex.
2. Collection is Physical
AI models need real-world data. Real-world data comes from physical sensors in physical locations.
The constraint: Satellites need orbits. Weather stations need weather. RTK receivers need line-of-sight to sky. No amount of software changes the physics.
Traditional approach: Companies deploy proprietary sensor networks. Expensive, slow, geographically limited.
DePIN approach: Communities deploy devices for token rewards. Infrastructure cost distributed across thousands of operators.
The shift: From "we collect your data" to "you collect, you own, you earn."
3. Quality Beats Quantity
The era of "more data is always better" is over. Models choke on noise. Verified, curated, domain-specific data wins.
Scale AI's insight: Alexander Wang built a $29B company on one principle — human-verified labels are worth orders of magnitude more than raw data. The labeling layer captures disproportionate value.
The quality spectrum:
| Data Type | Relative Value | Example |
|---|---|---|
| Raw sensor readings | 1x | Unverified GPS coordinates |
| Cleaned and formatted | 5x | Deduplicated, standardized data |
| Labeled and annotated | 50x | Human-verified training sets |
| Domain-specific verified | 100x+ | RTK-corrected centimeter precision |
DePIN quality layer: On-chain attestations create verifiable data provenance. Every data point carries proof of when, where, and how it was collected. This is the quality moat.
4. Ownership Creates Alignment
The surveillance economy extracts data without compensating creators. This model is fragile — regulation, privacy tools, and user awareness are eroding it.
The current model: Users create data → Platforms extract value → Shareholders capture returns.
The DePIN model: Operators deploy devices → Devices collect data → Protocols distribute revenue → Operators earn proportional to contribution.
Why alignment matters: When data creators earn from their contribution, they maintain devices, improve data quality, and expand coverage. Misaligned incentives produce gaming, degradation, and abandonment.
The parallel: Same transformation as telecom — from shareholder extraction to community participation.
5. Compute Follows Data
Processing moves toward data sources. The centralized cloud model — upload everything, process centrally — is hitting physical limits.
The bottleneck: Training a frontier model requires petabytes. Moving petabytes to a data center costs time, money, and bandwidth.
Edge compute thesis: Process near the sensor. Aggregate at the edge. Send only the valuable signal, not the raw noise.
Distributed GPU: io.net, Render, and Akash create GPU marketplaces. Anyone with a GPU earns by processing data. This follows the same DePIN pattern as sensor networks.
| Processing Model | Latency | Cost | Privacy |
|---|---|---|---|
| Centralized cloud | High | Scaling up | Data leaves device |
| Edge compute | Low | Distributed | Data stays local |
| Distributed GPU | Medium | Market-priced | Encrypted processing |
The Test
Before any AI data investment or build:
| Question | Yes = Proceed | No = Reconsider |
|---|---|---|
| Does this compound data value? | More data = better models | Static dataset, no loop |
| Does this require physical presence? | Real-world deployment needed | Pure software play |
| Does this verify quality? | Provenance and attestation | Garbage in accepted |
| Does this align ownership? | Creators earn from contribution | Extraction model |
| Does this distribute compute? | Processing near the edge | Cloud dependency |
Minimum: Yes to 3 of 5.
Principles to Performance
These principles determine what to measure:
| Principle | Performance Metric |
|---|---|
| Data compounds | Dataset size, model accuracy improvement rate |
| Collection is physical | Device count, geographic coverage |
| Quality beats quantity | Verification rate, data premium over commodity |
| Ownership creates alignment | Operator retention, revenue per operator |
| Compute follows data | Edge processing %, latency reduction |
See Performance for the full metrics framework.
Context
- AI Data Overview — The transformation thesis
- Knowledge Stack — How principles become platforms
- DePIN — Physical infrastructure patterns
- First Principles — Broader principles framework