OpenMined Proposes Network Sourcing for AI to Access Private Data

OpenMined founder Andrew Trask argues AI models should access private data repositories in an orderly 'network sourcing' model, citing research that over 30% of search-augmented LLM answers lack source attribution, and a proposal suggesting a million times more data could be unlocked for AI training.

2026-09-14 ~ 2026-09-14 · 3 related posts

• IFP Essay: An ARPANET-Style Program Could Unlock a Million Times More Data for AI — iamtrask · 2026-09-14
• Over 30% of Search-Enabled LLM Responses Give No Attribution, CC Reports — iamtrask · 2026-09-14
• OpenMined's 'network sourced' AI: models as orderly clients of private repositories — iamtrask · 2026-09-14