WhatA platform that aggregates, verifies, and licenses high-quality curated datasets with provenance scoring, so AI companies can train on trusted sources rather than raw internet scrapes.
SignalMultiple commenters identify the core problem: LLMs trained on the open internet are ingesting fiction, spam, and opinion as fact, and there is no scalable way to separate high-quality factual sources from low-quality noise during training.
Why NowThe first wave of models trained heavily on synthetic and low-quality web data is now producing visibly degraded outputs, and AI labs are actively seeking differentiation on factual accuracy as a competitive axis.
MarketAI labs and enterprise AI teams spending billions annually on training data; Scale AI and Appen operate adjacently but focus on labeling, not source-level quality curation. TAM is $10B+ as training data becomes the key differentiator.
MoatNetwork effects from publisher relationships and a growing trust graph of verified sources that becomes harder to replicate as more authoritative publishers sign exclusives.
Google’s AI is being manipulated. The search giant is quietly fighting backView discussion ↗ · Article ↗ · 327 pts · May 20, 2026
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