Why Non-Deterministic Enrichment is Becoming a Core Primitive for Decentralized AI
As decentralized AI systems mature, a quiet bottleneck is becoming impossible to ignore: high-quality datasets do not scale the way compute does. Inference can be parallelized, training can be distributed, but data generation, especially in open-ended domains, still struggles under one fundamental assumption: that every task must produce a single correct output. On Bittensor’s Subnet…