AI Training Crisis: 60% of Data Unverifiable, Teams Spend 80% of Time Cleaning
AI training faces a quiet crisis as data quality deteriorates despite scaling compute and funding. Over **60% of training data** is duplicated, weakly labeled, or unverifiable, while teams spend **80% of development time** cleaning data rather than training models.
The core issues:
- **No provenance**: Data lineage remains unclear
- **No incentives**: Contributors aren't rewarded for quality
- **No verification**: Trust is assumed, not proven
Synthetic data loops cause model collapse, where AI trains on other AI outputs, leading to higher hallucination rates and declining reliability.
KGeN's VeriFi protocol addresses this by introducing:
- Human-verified data contributions
- On-chain proofs of authenticity and ownership
- Reputation systems rewarding accuracy
The shift: data should arrive verified, attributable, and reputation-weighted rather than scraped blindly. The next phase of AI will be defined by who controls the quality of truth flowing into machines, not who trains the largest model.
