### The Economics of AI Training Data: A First Principles Analysis ### 1. Current Data Landscape (2024) ```` Global Data Creation: - Daily: 328.77M terabytes - Annual: ~149 zettabytes - Per user: 1.7MB/second High-Quality Text Data: - Available: 100-200T tokens - Currently used by top models: 13-15T tokens - Annual growth rate: ~22% (based on bandwidth increase) ```` ### 2. Model Training Requirements **Current Generation Models** - GPT-4: ~13T tokens, multiple epochs - Llama 3: ~15T tokens - Claude: Undisclosed, but similar scale **Training Costs** - GPT-3 (175B params): $4.6M - Llama 2 (70B): $3.8M - Infrastructure: 1.72M GPU hours (A100-80GB) ### 3. Data Quality Metrics **Measurable Indicators** 1. Perplexity scores 2. Token repetition rates 3. Information density 4. Citation frequency 5. Expert validation rates **Source Hierarchy (by information density)** ```` Academic papers: ~2.1 bits/token Technical docs: ~1.8 bits/token Wikipedia: ~1.6 bits/token Social media: ~0.8 bits/token ```` ### 4. Growth Projections **Data Creation (2025)** - Total: 181 zettabytes - Cloud storage: ~50% - High-quality text: Est. 220-250T tokens **Model Requirements** - Scaling laws suggest 10x params need ~5x data - Projected 2025 model needs: 30-50T tokens ### 5. Economic Analysis **Cost Structure** ```` Training costs: - GPU hours: $2.21/hour (A100) - Storage: $0.02/GB/month - Bandwidth: $0.08/GB Data acquisition: - Public: Free but limited - Private: Variable licensing - Custom: $0.1-1/labeled data point ```` ### 6. Alternative Data Sources **Screen Recording** - Data generation: ~100GB/user/month - Information types: - Visual context - Text (OCR) - Audio - Metadata - Unique value: Behavioral patterns **Synthetic Data** - Generation cost: ~10% of real data - Quality metrics: 80-90% of human data - Scalability: Exponential ### 7. Future Scenarios (2025-2030) **Base Case** ```` Data Mix: - Public: 30% ↓ - Private: 40% ⬆️ - Synthetic: 30% ⬆️ Cost Trajectory: - Training: -15%/year - Storage: -20%/year - Compute: -30%/year ```` **Breakthrough Case** - New training methods reduce data needs by 50% - Synthetic data achieves parity with human data - Privacy-preserving techniques enable broader data access ### 8. Key Uncertainties 1. **Technical** - Efficiency improvements in training - Quality of synthetic data - New architectures' data requirements 2. **Economic** - Data marketplace development - Computing cost trajectory - Storage cost evolution 3. **Regulatory** - Privacy legislation - Data ownership rules - Cross-border data flows ### Conclusion Unlike the fossil fuel analogy, AI training data isn't fundamentally scarce. The challenge is in efficiently collecting, processing, and utilizing high-quality data while managing economic and technical constraints. The future likely belongs to those who can best optimize these tradeoffs rather than those who simply accumulate the most data. #ai #data-science #economics #machine-learning #data-analysis #artificial-intelligence